Indexes

Symbols

• 3FS (distributed filesystem, Distributed Filesystems

A

• aborts (transactions), Transactions, Atomicity
  • cascading, No dirty reads
  • in two-phase commit, Two-Phase Commit (2PC)
  • performance of optimistic concurrency control, Performance of serializable snapshot isolation
  • retrying aborted transactions, Handling errors and aborts
• abstraction, Layering of cloud services, Simplicity: Managing Complexity, Data Models and Query Languages, Transactions, Summary
• accidental complexity, Simplicity: Managing Complexity
• accountability, Responsibility and Accountability
• accounting (financial data), Summary, Advantages of immutable events
• Accumulo (database)
  • wide-column data model, Data locality for reads and writes, Column Compression
• ACID properties (transactions), The Meaning of ACID
  • atomicity, Atomicity, Single-Object and Multi-Object Operations
  • consistency, Consistency, Maintaining integrity in the face of software bugs
  • durability, Making B-trees reliable, Durability
  • isolation, Isolation, Single-Object and Multi-Object Operations
• acknowledgements (messaging), Acknowledgments and redelivery
• active/active replication (see multi-leader replication)
• active/passive replication (see leader-based replication)
• ActiveMQ (messaging), Message brokers, Message brokers compared to databases
  • distributed transaction support, XA transactions
• ActiveRecord (object-relational mapper), Object-relational mapping (ORM), Handling errors and aborts
• activity (workflows) (see workflow engines)
• actor model, Distributed actor frameworks
  • (see also event-driven architecture)
  • comparison to stream processing, Event-Driven Architectures and RPC
• adaptive capacity, Skewed Workloads and Relieving Hot Spots
• Advanced Message Queuing Protocol (see AMQP)
• aerospace systems, Byzantine Faults
• Aerospike (database)
  • strong consistency mode, Single-object writes
• AGE (graph database), The Cypher Query Language
• aggregation
  • data cubes and materialized views, Materialized Views and Data Cubes
  • in batch processes, Sorting Versus In-memory Aggregation
  • in stream processes, Stream analytics
• aggregation pipeline (MongoDB), Normalization, Denormalization, and Joins, Query languages for documents
• Agile, Evolvability: Making Change Easy
  • minimizing irreversibility, Batch Processing, Reprocessing data for application evolution
  • moving faster with confidence, The end-to-end argument again
• agreement, Single-value consensus, Atomic commitment as consensus
  • (see also consensus)
• AI (artificial intelligence) (see machine learning)
• AI Act (European Union), Data Systems, Law, and Society
• AirByte, Data Warehousing
• Airflow (workflow scheduler), Durable Execution and Workflows, Batch Processing, Scheduling Workflows
  • cloud data warehouse integration, Query languages
  • use for ETL, Extract--Transform--Load (ETL)
• Akamai
  • response time study, Average, Median, and Percentiles
• algorithms
  • algorithm correctness, Defining the correctness of an algorithm
  • B-trees, B-Trees-B-tree variants
  • for distributed systems, System Model and Reality
  • mergesort, Constructing and merging SSTables, Shuffling Data
  • scheduling, Resource Allocation
  • SSTables and LSM-trees, The SSTable file format-Compaction strategies
• all-to-all replication topologies, Multi-leader replication topologies
• AllegroGraph (database), Graph-Like Data Models
  • SPARQL query language, The SPARQL query language
• ALTER TABLE statement (SQL), Schema flexibility in the document model, Encoding and Evolution
• Amazon
  • Dynamo (see Dynamo (database))
  • response time study, Average, Median, and Percentiles
• Amazon Web Services (AWS)
  • Aurora (see Aurora (cloud database))
  • ClockBound (see ClockBound (time sync))
  • correctness testing, Formal Methods and Randomized Testing
  • DynamoDB (see DynamoDB (database))
  • EBS (see EBS (virtual block device))
  • Kinesis (see Kinesis (messaging))
  • Neptune (see Neptune (graph database))
  • network reliability, Network Faults in Practice
  • S3 (see S3 (object storage))
• amplification
  • of bias, Bias and Discrimination
  • of failures, Maintaining derived state
  • of tail latency, Use of Response Time Metrics, Local Secondary Indexes
  • write amplification, Write amplification
• AMQP (Advanced Message Queuing Protocol), Message brokers compared to databases
  • (see also messaging systems)
  • comparison to log-based messaging, Logs compared to traditional messaging, Replaying old messages
  • message ordering, Acknowledgments and redelivery
• analytical systems, Operational Versus Analytical Systems
  • as derived data systems, Systems of Record and Derived Data
  • ETL from operational systems, Data Warehousing
  • governance, Beyond the data lake
• analytics, Operational Versus Analytical Systems-Systems of Record and Derived Data
  • comparison to transaction processing, Characterizing Transaction Processing and Analytics
  • data normalization, Trade-offs of normalization
  • data warehousing (see data warehousing)
  • predictive (see predictive analytics)
  • relation to batch processing, Analytics-Analytics
  • schemas for, Stars and Snowflakes: Schemas for Analytics-Stars and Snowflakes: Schemas for Analytics
  • snapshot isolation for queries, Snapshot Isolation and Repeatable Read
  • stream analytics, Stream analytics
• analytics engineering, Operational Versus Analytical Systems
• anti-entropy, Catching up on missed writes
• Antithesis (deterministic simulation testing), Deterministic simulation testing
• Apache Accumulo (see Accumulo)
• Apache ActiveMQ (see ActiveMQ)
• Apache AGE (see AGE)
• Apache Arrow (see Arrow (data format))
• Apache Avro (see Avro)
• Apache Beam (see Beam)
• Apache BookKeeper (see BookKeeper)
• Apache Cassandra (see Cassandra)
• Apache Curator (see Curator)
• Apache DataFusion (see DataFusion (query engine))
• Apache Druid (see Druid (database))
• Apache Flink (see Flink (processing framework))
• Apache HBase (see HBase)
• Apache Iceberg (see Iceberg (table format))
• Apache Jena (see Jena)
• Apache Kafka (see Kafka)
• Apache Lucene (see Lucene)
• Apache Oozie (see Oozie (workflow scheduler))
• Apache ORC (see ORC (data format))
• Apache Parquet (see Parquet (data format))
• Apache Pig (query language), Query languages
• Apache Pinot (see Pinot (database))
• Apache Pulsar (see Pulsar)
• Apache Qpid (see Qpid)
• Apache Samza (see Samza)
• Apache Solr (see Solr)
• Apache Spark (see Spark) (see Spark (processing framework))
• Apache Storm (see Storm)
• Apache Superset (see Superset (data visualization software))
• Apache Thrift (see Thrift)
• Apache ZooKeeper (see ZooKeeper)
• Apama (stream analytics), Complex event processing
• append-only files (see logs)
• Application Programming Interfaces (APIs), Data Models and Query Languages
  • for change streams, API support for change streams
  • for distributed transactions, XA transactions
  • for services, Dataflow Through Services: REST and RPC-Data encoding and evolution for RPC
    • (see also services)
    • evolvability, Data encoding and evolution for RPC
    • RESTful, Web services
• application state (see state)
• approximate search (see similarity search)
• archival storage, data from databases, Archival storage
• arcs (see edges)
• ArcticDB (database), DataFrames, Matrices, and Arrays
• arithmetic mean, Average, Median, and Percentiles
• arrays
  • array databases, DataFrames, Matrices, and Arrays
  • multidimensional, DataFrames, Matrices, and Arrays
• Arrow (data format), Column-Oriented Storage, DataFrames
• artificial intelligence (see machine learning)
• ASCII text, Protocol Buffers
• ASN.1 (schema language), The Merits of Schemas
• associative table, Many-to-One and Many-to-Many Relationships, Property Graphs
• asynchronous networks, Unreliable Networks, Glossary
  • comparison to synchronous networks, Synchronous Versus Asynchronous Networks
  • system model, System Model and Reality
• asynchronous replication, Synchronous Versus Asynchronous Replication, Glossary
  • data loss on failover, Leader failure: Failover
  • reads from asynchronous follower, Problems with Replication Lag
  • with multiple leaders, Multi-Leader Replication
• Asynchronous Transfer Mode (ATM), Can we not simply make network delays predictable?
• atomic broadcast, Shared logs as consensus
• atomic clocks, Clock readings with a confidence interval, Synchronized clocks for global snapshots
  • (see also clocks)
• atomicity (concurrency), Glossary
  • atomic increment, Single-object writes
  • compare-and-set (CAS), Conditional writes (compare-and-set), What Makes a System Linearizable?
    • (see also compare-and-set (CAS))
  • denormalized data, Trade-offs of normalization
  • fetch-and-add/increment, ID Generators and Logical Clocks, Consensus, Fetch-and-add as consensus
  • write operations, Atomic write operations
• atomicity (transactions), Atomicity, Single-Object and Multi-Object Operations, Glossary
  • atomic commit
    • avoiding, Multi-shard request processing, Coordination-avoiding data systems
    • blocking and nonblocking, Three-phase commit
    • in stream processing, Exactly-once message processing, Exactly-once message processing revisited, Atomic commit revisited
    • maintaining derived data, Keeping Systems in Sync
  • distributed transactions, Distributed Transactions-Exactly-once message processing revisited
  • for multi-object transactions, Single-Object and Multi-Object Operations
  • for single-object writes, Single-object writes
  • relation to consensus, Atomic commitment as consensus
• auditability, Trust, but Verify-Tools for auditable data systems
  • designing for, Designing for auditability
  • self-auditing systems, Don't just blindly trust what they promise
  • through immutability, Advantages of immutable events
  • tools for auditable data systems, Tools for auditable data systems
• Aurora (cloud database), Cloud-Native System Architecture
• Aurora DSQL (database)
  • snapshot isolation support, Snapshot Isolation and Repeatable Read
• auto-scaling, Operations: Automatic or Manual Rebalancing
• Automerge (CRDT library), Pros and cons of sync engines
• availability, Reliability and Fault Tolerance
  • (see also fault tolerance)
  • in CAP theorem, The CAP theorem
  • in leader election, Subtleties of consensus
  • in service level agreements (SLAs), Use of Response Time Metrics
• availability zones, Tolerating hardware faults through redundancy, Reading Your Own Writes
• Avro (data format), Avro-Dynamically generated schemas
  • dynamically generated schemas, Dynamically generated schemas
  • object container files, But what is the writer's schema?, Archival storage
  • reader determining writer's schema, But what is the writer's schema?
  • schema evolution, The writer's schema and the reader's schema
  • use in batch processing, MapReduce
• awk (Unix tool), Simple Log Analysis, Simple Log Analysis, Distributed Job Orchestration
• Axon Framework, Event Sourcing and CQRS
• Azkaban (workflow scheduler), Batch Processing
• Azure Blob Storage (object storage), Layering of cloud services, Setting Up New Followers
  • conditional headers, Fencing off zombies and delayed requests
• Azure managed disks, Separation of storage and compute
• Azure SQL DB (database), Cloud-Native System Architecture
• Azure Storage, Object Stores
• Azure Synapse Analytics (database), Cloud-Native System Architecture
• Azure Virtual Machines
  • spot virtual machines, Handling Faults

B

• B-trees (indexes), B-Trees-B-tree variants
  • B+ trees, B-tree variants
  • branching factor, B-Trees
  • comparison to LSM-trees, Comparing B-Trees and LSM-Trees-Disk space usage
  • crash recovery, Making B-trees reliable
  • growing by splitting a page, B-Trees
  • immutable variants, B-tree variants, Indexes and snapshot isolation
  • similarity to shard splitting, Rebalancing key-range sharded data
  • variants, B-tree variants
• B2 (object storage), Distributed Filesystems
• Backblaze B2 (see B2 (object storage))
• backend, Trade-offs in Data Systems Architecture
• backoff, exponential, Describing Performance, Handling errors and aborts
• backpressure, Describing Performance, Read performance, Messaging Systems, Glossary
  • in batch processing, Scheduling Workflows
  • in TCP, The Limitations of TCP
• backups
  • database snapshot for replication, Setting Up New Followers
  • in multitenant systems, Sharding for Multitenancy
  • integrity of, Don't just blindly trust what they promise
  • snapshot isolation for, Snapshot Isolation and Repeatable Read
  • using object storage, Setting Up New Followers
  • versus replication, Replication
• backward compatibility, Encoding and Evolution
• BadgerDB (database)
  • serializable transactions, Serializable Snapshot Isolation (SSI)
• BASE, contrast to ACID, The Meaning of ACID
• bash shell (Unix), Storage and Indexing for OLTP
• batch processing, Batch Processing-Summary, Glossary
  • and functional programming, MapReduce
  • benefits of, Batch Processing
  • combining with stream processing, Unifying batch and stream processing
  • comparison to stream processing, Processing Streams
  • dataflow engines, Dataflow Engines-Dataflow Engines
  • fault tolerance, Handling Faults, Messaging Systems
  • for data integration, Batch and Stream Processing-Unifying batch and stream processing
  • graphs and iterative processing, Machine Learning
  • high-level APIs and languages, Query languages-Query languages
  • in cloud data warehouses, Query languages
  • in distributed systems, Batch Processing in Distributed Systems
  • join and group by, JOIN and GROUP BY-JOIN and GROUP BY
  • limitations, Batch Processing
  • log-based messaging and, Replaying old messages
  • maintaining derived state, Maintaining derived state
  • measuring performance, Batch Processing
  • models of, Batch Processing Models
  • resource allocation, Resource Allocation-Resource Allocation
  • resource managers, Distributed Job Orchestration
  • schedulers, Distributed Job Orchestration
  • serving derived data, Serving Derived Data-Serving Derived Data
  • shuffling data, Shuffling Data-Shuffling Data
  • task execution, Distributed Job Orchestration
  • use cases, Batch Use Cases-Serving Derived Data
  • using Unix tools (example), Batch Processing with Unix Tools-Sorting Versus In-memory Aggregation
• batch processing frameworks
  • comparison to operating systems, Batch Processing in Distributed Systems
• Beam (dataflow library), Unifying batch and stream processing
• BERT (language model), Vector Embeddings
• bias, Bias and Discrimination
• bidirectional replication (see multi-leader replication)
• big ball of mud, Simplicity: Managing Complexity
• big data
  • versus data minimization, Data Systems, Law, and Society, Legislation and Self-Regulation
• BigQuery (database), Cloud-Native System Architecture, Cloud Data Warehouses, Batch Processing
  • DataFrames, Query languages
  • sharding and clustering, Sharding by hash range
  • shuffling data, Shuffling Data
  • snapshot isolation support, Snapshot Isolation and Repeatable Read
• Bigtable (database)
  • sharding scheme, Sharding by Key Range
  • storage layout, Constructing and merging SSTables
  • tablets (sharding), Sharding
  • wide-column data model, Data locality for reads and writes, Column Compression
• binary data encodings, Binary encoding-The Merits of Schemas
  • Avro, Avro-Dynamically generated schemas
  • MessagePack, Binary encoding-Binary encoding
  • Protocol Buffers, Protocol Buffers-Field tags and schema evolution
• binary encoding
  • based on schemas, The Merits of Schemas
  • by network drivers, The Merits of Schemas
• binary strings, lack of support in JSON and XML, JSON, XML, and Binary Variants
• Bitcoin (cryptocurrency), Tools for auditable data systems
  • Byzantine fault tolerance, Byzantine Faults
  • concurrency bugs in exchanges, Weak Isolation Levels
• bitmap indexes, Column Compression
• BitTorrent uTP protocol, The Limitations of TCP
• Bkd-trees (indexes), Multidimensional and Full-Text Indexes
• blameless postmortems, Humans and Reliability
• Blazegraph (database), Graph-Like Data Models
  • SPARQL query language, The SPARQL query language
• blob storage (see object storage)
• block (file system), Distributed Filesystems
• block device (disk), Separation of storage and compute
• blockchains, Summary
  • Byzantine fault tolerance, Byzantine Faults, Consensus, Tools for auditable data systems
• blocking atomic commit, Three-phase commit
• Bloom filter (algorithm), Bloom filters, Read performance, Stream analytics
• BookKeeper (replicated log), Allocating work to nodes
• bounded datasets, Stream Processing, Glossary
  • (see also batch processing)
• bounded delays, Glossary
  • in networks, Synchronous Versus Asynchronous Networks
  • process pauses, Response time guarantees
• broadcast
  • total order broadcast (see shared logs)
• brokerless messaging, Direct messaging from producers to consumers
• Brubeck (metrics aggregator), Direct messaging from producers to consumers
• BTM (transaction coordinator), Two-Phase Commit (2PC)
• Buf
  • Bufstream (messaging), Setting Up New Followers
• Bufstream (messaging), Disk space usage
• build or buy, Cloud Versus Self-Hosting
• bursty network traffic patterns, Can we not simply make network delays predictable?
• business analyst, Operational Versus Analytical Systems, From data warehouse to data lake
• business data processing, Characterizing Transaction Processing and Analytics
• business intelligence, Operational Versus Analytical Systems-Data Warehousing
• Business Process Execution Language (BPEL), Durable Execution and Workflows
• Business Process Model and Notation (BPMN), Durable Execution and Workflows
  • example, Durable Execution and Workflows
• byte sequence, encoding data in, Formats for Encoding Data
• Byzantine faults, Byzantine Faults-Weak forms of lying, System Model and Reality, Glossary
  • Byzantine fault-tolerant systems, Byzantine Faults
  • Byzantine Generals Problem, Byzantine Faults
  • consensus algorithms and, Consensus, Tools for auditable data systems

C

• caches, Keeping everything in memory, Glossary
  • and materialized views, Materialized Views and Data Cubes
  • as derived data, Systems of Record and Derived Data, Composing Data Storage Technologies-Unbundled versus integrated systems
  • in CPUs, Query Execution: Compilation and Vectorization, Linearizability and network delays
  • invalidation and maintenance, Keeping Systems in Sync, Maintaining materialized views
  • linearizability, Linearizability
  • local disks in the cloud, Separation of storage and compute
• calendar sync, Sync Engines and Local-First Software, Pros and cons of sync engines
• California Consumer Privacy Act (CCPA), Data Systems, Law, and Society
• Camunda (workflow engine), Durable Execution and Workflows
• canonical version (of data), Systems of Record and Derived Data
• CAP theorem, The CAP theorem-The CAP theorem, Glossary
• capacity planning, Operations in the Cloud Era
• Cap'n Proto (data format), Formats for Encoding Data
• carbon emissions, Distributed Versus Single-Node Systems
• cascading aborts, No dirty reads
• cascading failures, Software faults, Operations: Automatic or Manual Rebalancing, Timeouts and Unbounded Delays
• Cassandra (database)
  • change data capture, Implementing change data capture, API support for change streams
  • compaction strategy, Compaction strategies
  • consistency level ANY, Single-Leader Versus Leaderless Replication Performance
  • hash-range sharding, Sharding by Hash of Key, Sharding by hash range
  • last-write-wins conflict resolution, Detecting Concurrent Writes
  • leaderless replication, Leaderless Replication
  • lightweight transactions, Single-object writes
  • linearizability, lack of, Implementing Linearizable Systems
  • log-structured storage, Constructing and merging SSTables
  • multi-region support, Multi-region operation
  • secondary indexes, Local Secondary Indexes
  • use of clocks, Limitations of Quorum Consistency, Timestamps for ordering events
  • vnodes (sharding), Sharding
• cat (Unix tool), Simple Log Analysis
• catalog, Cloud Data Warehouses
• causal context, Version vectors
  • (see also causal dependencies)
• causal dependencies, The "happens-before" relation and concurrency-Version vectors
  • capturing, Version vectors, Ordering events to capture causality, Reads are events too
    • by total ordering, The limits of total ordering
  • in transactions, Decisions based on an outdated premise
  • sending message to friends (example), Ordering events to capture causality
• causality, Glossary
  • causal ordering
    • total order consistent with, Logical Clocks
  • consistency with, Logical Clocks-Enforcing constraints using logical clocks
  • happens-before relation, The "happens-before" relation and concurrency
  • in serializable transactions, Decisions based on an outdated premise-Detecting writes that affect prior reads
  • mismatch with clocks, Timestamps for ordering events
  • ordering events to capture, Ordering events to capture causality
  • violations of, Consistent Prefix Reads, Problems with different topologies, Timestamps for ordering events
  • with synchronized clocks, Synchronized clocks for global snapshots
• cell-based architecture, Sharding for Multitenancy
• CEP (see complex event processing)
• CephFS (distributed filesystem), Batch Processing, Object Stores
• certificate transparency, Tools for auditable data systems
• cgroups, Distributed Job Orchestration
• change data capture, Logical (row-based) log replication, Change Data Capture
  • API support for change streams, API support for change streams
  • comparison to event sourcing, Change data capture versus event sourcing
  • implementing, Implementing change data capture
  • initial snapshot, Initial snapshot
  • log compaction, Log compaction
• changelogs, State, Streams, and Immutability
  • change data capture, Change Data Capture
  • for operator state, Rebuilding state after a failure
  • in stream joins, Stream-table join (stream enrichment)
  • log compaction, Log compaction
  • maintaining derived state, Databases and Streams
• chaos engineering, Fault Tolerance, Fault injection
• checkpointing
  • in high-performance computing, Cloud Computing Versus Supercomputing
  • in stream processors, Microbatching and checkpointing
• circuit breaker (limiting retries), Describing Performance
• circuit-switched networks, Synchronous Versus Asynchronous Networks
• circular buffers, Disk space usage
• circular replication topologies, Multi-leader replication topologies
• Citus (database)
  • hash sharding, Fixed number of shards
• ClickHouse (database), Characterizing Transaction Processing and Analytics, Cloud-Native System Architecture
  • incremental view maintenance, Maintaining materialized views
• clickstream data, analysis of, JOIN and GROUP BY
• clients
  • calling services, Dataflow Through Services: REST and RPC
  • offline-capable, Sync Engines and Local-First Software, Stateful, offline-capable clients
  • pushing state changes to, Pushing state changes to clients
  • request routing, Request Routing
• ClockBound (time sync), Clock readings with a confidence interval
  • use in YugabyteDB, Synchronized clocks for global snapshots
• clocks, Unreliable Clocks-Limiting the impact of garbage collection
  • atomic clocks, Clock readings with a confidence interval, Synchronized clocks for global snapshots
  • confidence interval, Clock readings with a confidence interval-Synchronized clocks for global snapshots
  • for global snapshots, Synchronized clocks for global snapshots
  • hybrid logical clocks, Hybrid logical clocks
  • logical (see logical clocks)
  • skew, Last write wins (discarding concurrent writes), Limitations of Quorum Consistency, Relying on Synchronized Clocks-Clock readings with a confidence interval, Implementing Linearizable Systems
  • slewing, Monotonic clocks
  • synchronization and accuracy, Clock Synchronization and Accuracy-Clock Synchronization and Accuracy
  • synchronization using GPS, Unreliable Clocks, Clock Synchronization and Accuracy, Clock readings with a confidence interval, Synchronized clocks for global snapshots
  • time-of-day versus monotonic clocks, Monotonic Versus Time-of-Day Clocks
  • timestamping events, Whose clock are you using, anyway?
• cloud services, Cloud Versus Self-Hosting-Cloud Computing Versus Supercomputing
  • availability zones, Tolerating hardware faults through redundancy, Reading Your Own Writes
  • data warehouses, Cloud Data Warehouses
  • need for service discovery, Service discovery
  • network glitches, Network Faults in Practice
  • pros and cons, Pros and Cons of Cloud Services-Pros and Cons of Cloud Services
  • quotas, Operations in the Cloud Era
  • regions (see regions (geographic distribution))
  • serverless, Microservices and Serverless
  • shared resources, Network congestion and queueing
  • versus supercomputing, Cloud Computing Versus Supercomputing
• cloud-native, Cloud-Native System Architecture-Operations in the Cloud Era
• Cloudflare
  • R2 (see R2 (object storage))
• clustered indexes, Storing values within the index
• clustering (record ordering), Sharding by hash range
• CockroachDB (database)
  • consensus-based replication, Single-Leader Replication
  • consistency model, What Makes a System Linearizable?
  • key-range sharding, Sharding, Sharding by Key Range
  • serializable transactions, Serializable Snapshot Isolation (SSI)
  • sharded secondary indexes, Global Secondary Indexes
  • transactions, What Exactly Is a Transaction?, Database-internal Distributed Transactions
  • use of model-checking, Model checking and specification languages
• code generation
  • for query execution, Query Execution: Compilation and Vectorization
  • with Protocol Buffers, Protocol Buffers
• collaborative editing, Real-time collaboration, offline-first, and local-first apps
• column families (Bigtable), Data locality for reads and writes, Column Compression
• column-oriented storage, Column-Oriented Storage-Query Execution: Compilation and Vectorization
  • column compression, Column Compression
  • Parquet, Column-Oriented Storage, Archival storage
  • sort order in, Sort Order in Column Storage-Sort Order in Column Storage
  • vectorized processing, Query Execution: Compilation and Vectorization
  • versus wide-column model, Column Compression
  • writing to, Writing to Column-Oriented Storage
• comma-separated values (see CSV)
• command query responsibility segregation (CQRS), Event Sourcing and CQRS-Event Sourcing and CQRS, Deriving several views from the same event log
• commands (event sourcing), Event Sourcing and CQRS
• commits (transactions), Transactions
  • atomic commit, Distributed Transactions-Exactly-once message processing revisited
    • (see also atomicity; transactions)
  • read committed isolation, Read Committed
  • three-phase commit (3PC), Three-phase commit
  • two-phase commit (2PC), Two-Phase Commit (2PC)-Coordinator failure
• commutative operations, Conflict resolution and replication
• compaction
  • of changelogs, Log compaction
    • (see also log compaction)
    • for stream operator state, Rebuilding state after a failure
  • of log-structured storage, Constructing and merging SSTables
    • issues with, Read performance
    • size-tiered and leveled approaches, Compaction strategies, Disk space usage
• compare-and-set (CAS), Conditional writes (compare-and-set), What Makes a System Linearizable?
  • implementing locks, Coordination Services
  • implementing uniqueness constraints, Constraints and uniqueness guarantees
  • on object storage, Setting Up New Followers
  • relation to consensus, Linearizability and quorums, Consensus, Compare-and-set as consensus
  • relation to fencing tokens, Fencing off zombies and delayed requests
  • relation to transactions, Single-object writes
• compatibility, Encoding and Evolution, Modes of Dataflow
  • calling services, Data encoding and evolution for RPC
  • properties of encoding formats, Summary
  • using databases, Dataflow Through Databases-Archival storage
• compensating transactions, Advantages of immutable events, Loosely interpreted constraints
• compilation, Query Execution: Compilation and Vectorization
• complex event processing (CEP), Complex event processing
• complexity
  • distilling in theoretical models, Mapping system models to the real world
  • essential and accidental, Simplicity: Managing Complexity
  • hiding using abstraction, Data Models and Query Languages
  • managing, Simplicity: Managing Complexity
• composing data systems (see unbundling databases)
• compression
  • in SSTables, The SSTable file format
• compute-intensive applications, Trade-offs in Data Systems Architecture
• computer games, Pros and cons of sync engines
• concatenated indexes, Multidimensional and Full-Text Indexes
  • in hash-sharded systems, Sharding by hash range
• concurrency
  • actor programming model, Distributed actor frameworks, Event-Driven Architectures and RPC
    • (see also event-driven architecture)
  • bugs from weak transaction isolation, Weak Isolation Levels
  • conflict resolution, Dealing with Conflicting Writes-Types of conflict
  • definition, Dealing with Conflicting Writes
  • detecting concurrent writes, Detecting Concurrent Writes-Version vectors
  • dual writes, problems with, Keeping Systems in Sync
  • happens-before relation, The "happens-before" relation and concurrency
  • in replicated systems, Problems with Replication Lag-Version vectors, Linearizability-Linearizability and network delays
  • lost updates, Preventing Lost Updates
  • multi-version concurrency control (MVCC), Multi-version concurrency control (MVCC), Synchronized clocks for global snapshots
  • optimistic concurrency control, Pessimistic versus optimistic concurrency control
  • ordering of operations, What Makes a System Linearizable?
  • reducing, through event logs, Concurrency control, Dataflow: Interplay between state changes and application code
  • time and relativity, The "happens-before" relation and concurrency
  • transaction isolation, Isolation
  • write skew (transaction isolation), Write Skew and Phantoms-Materializing conflicts
• conditional write, Conditional writes (compare-and-set)
  • in transactions, Single-object writes
  • on object storage, Setting Up New Followers
• conference management system (example), Event Sourcing and CQRS
• conflict-free replicated datatypes (CRDTs), CRDTs and Operational Transformation
  • for leaderless replication, Capturing the happens-before relationship
  • preventing lost updates, Conflict resolution and replication
• conflicts
  • avoidance, Conflict avoidance
  • causal dependencies, The "happens-before" relation and concurrency
  • conflict detection
    • in distributed transactions, Problems with XA transactions
    • in log-based systems, Uniqueness constraints require consensus
    • in serializable snapshot isolation (SSI), Detecting writes that affect prior reads
    • in two-phase commit, A system of promises
  • conflict resolution
    • by aborting transactions, Pessimistic versus optimistic concurrency control
    • by apologizing, Loosely interpreted constraints
    • last write wins (LWW), Timestamps for ordering events
    • using atomic operations, Conflict resolution and replication
  • determining what is a conflict, Types of conflict, Uniqueness in log-based messaging
  • in leaderless replication, Detecting Concurrent Writes
  • lost updates, Preventing Lost Updates-Conflict resolution and replication
  • materializing, Materializing conflicts
  • resolution, Dealing with Conflicting Writes-Types of conflict
    • automatic, Automatic conflict resolution
    • in leaderless systems, Detecting Concurrent Writes
    • last write wins (LWW), Last write wins (discarding concurrent writes)
    • using custom logic, Manual conflict resolution, Capturing the happens-before relationship
  • siblings, Manual conflict resolution, Capturing the happens-before relationship
    • merging, Capturing the happens-before relationship
  • write skew (transaction isolation), Write Skew and Phantoms-Materializing conflicts
• Confluent
  • Freight (messaging), Setting Up New Followers, Disk space usage
  • schema registry, JSON Schema, But what is the writer's schema?
• congestion (networks)
  • avoidance, The Limitations of TCP
  • limiting accuracy of clocks, Clock readings with a confidence interval
  • queueing delays, Network congestion and queueing
• consensus, Consensus-Summary, Glossary
  • algorithms, Consensus, Consensus in Practice
  • consensus numbers, Fetch-and-add as consensus
  • coordination services, Coordination Services-Service discovery
  • cost of, Pros and cons of consensus
  • impossibility of, Consensus
  • preventing split brain, From single-leader replication to consensus
  • reconfiguration, Subtleties of consensus
  • relation to atomic commitment, Atomic commitment as consensus
  • relation to compare-and-set (CAS), Linearizability and quorums, Compare-and-set as consensus
  • relation to fetch-and-add, Fetch-and-add as consensus
  • relation to replication, Using shared logs
  • relation to shared logs, Shared logs as consensus
  • relation to uniqueness constraints, Uniqueness constraints require consensus
  • safety and liveness properties, Single-value consensus
  • single-value consensus, Single-value consensus
• consent (GDPR), Consent and Freedom of Choice
• consistency, Consistency, Timeliness and Integrity
  • across different databases, Leader failure: Failover, Keeping Systems in Sync, Deriving several views from the same event log, Derived data versus distributed transactions
  • causal, Consistent Prefix Reads, Problems with different topologies, Ordering events to capture causality
  • consistent prefix reads, Consistent Prefix Reads-Consistent Prefix Reads
  • consistent snapshots, Setting Up New Followers, Snapshot Isolation and Repeatable Read-Snapshot isolation, repeatable read, and naming confusion, Synchronized clocks for global snapshots, Initial snapshot, Creating an index
    • (see also snapshots)
  • crash recovery, Making B-trees reliable
  • enforcing constraints (see constraints)
  • eventual, Problems with Replication Lag
    • (see also eventual consistency)
  • in ACID transactions, Consistency, Maintaining integrity in the face of software bugs
  • in CAP theorem, The CAP theorem
  • in leader election, Subtleties of consensus
  • in microservices, Problems with Distributed Systems
  • linearizability, Solutions for Replication Lag, Linearizability-Linearizability and network delays
  • meanings of, Consistency
  • monotonic reads, Monotonic Reads-Monotonic Reads
  • of secondary indexes, The need for multi-object transactions, Indexes and snapshot isolation, Reasoning about dataflows, Creating an index
  • read-after-write, Reading Your Own Writes-Reading Your Own Writes
    • in derived data systems, Derived data versus distributed transactions
  • strong (see linearizability)
  • timeliness and integrity, Timeliness and Integrity
  • using quorums, Limitations of Quorum Consistency, Linearizability and quorums
• consistent hashing, Consistent hashing
• consistent prefix reads, Consistent Prefix Reads
• constraints (databases), Consistency, Characterizing write skew
  • asynchronously checked, Loosely interpreted constraints
  • coordination avoidance, Coordination-avoiding data systems
  • ensuring idempotence, Uniquely identifying requests
  • in log-based systems, Enforcing Constraints-Multi-shard request processing
    • across multiple shards, Multi-shard request processing
  • in two-phase commit, Distributed Transactions, A system of promises
  • relation to consensus, Uniqueness constraints require consensus
  • requiring linearizability, Constraints and uniqueness guarantees
• Consul (coordination service), Coordination Services
  • use for service discovery, Service discovery
• consumers (message streams), Message brokers, Transmitting Event Streams
  • backpressure, Messaging Systems
  • consumer groups, Multiple consumers
  • consumer offsets in logs, Consumer offsets
  • failures, Acknowledgments and redelivery, Consumer offsets
  • fan-out, Materializing and Updating Timelines, Multiple consumers, Logs compared to traditional messaging
  • load balancing, Multiple consumers, Logs compared to traditional messaging
  • not keeping up with producers, Messaging Systems, Disk space usage, Making unbundling work
• content models (JSON Schema), JSON Schema
• contention
  • between transactions, Handling errors and aborts
  • blocking threads, Process Pauses
  • performance of optimistic concurrency control, Pessimistic versus optimistic concurrency control
  • under two-phase locking, Performance of two-phase locking
• context switches, Latency and Response Time, Process Pauses
• convergence (conflict resolution), Automatic conflict resolution-CRDTs and Operational Transformation
• coordination
  • avoidance, Coordination-avoiding data systems
  • cross-datacenter, The limits of total ordering
  • cross-region, Geographically Distributed Operation
  • cross-shard ordering, Sharding, Synchronized clocks for global snapshots, Using shared logs, Multi-shard request processing
  • routing requests to shards, Request Routing
  • services, Locking and leader election, Coordination Services-Service discovery
• coordinator (in 2PC), Two-Phase Commit (2PC)
  • failure, Coordinator failure
  • in XA transactions, XA transactions-Problems with XA transactions
  • recovery, Recovering from coordinator failure
• copy-on-write (B-trees), B-tree variants, Indexes and snapshot isolation
• CORBA (Common Object Request Broker Architecture), The problems with remote procedure calls (RPCs)
• coronal mass ejection (see solar storm)
• correctness
  • auditability, Trust, but Verify-Tools for auditable data systems
  • Byzantine fault tolerance, Byzantine Faults
  • dealing with partial failures, Faults and Partial Failures
  • in log-based systems, Enforcing Constraints-Multi-shard request processing
  • of algorithm within system model, Defining the correctness of an algorithm
  • of derived data, Designing for auditability
  • of immutable data, Advantages of immutable events
  • of personal data, Responsibility and Accountability, Privacy and Use of Data
  • of time, Problems with different topologies, Clock Synchronization and Accuracy-Synchronized clocks for global snapshots
  • of transactions, Consistency, Aiming for Correctness, Maintaining integrity in the face of software bugs
  • timeliness and integrity, Timeliness and Integrity-Coordination-avoiding data systems
• corruption of data
  • detecting, The end-to-end argument, Don't just blindly trust what they promise-Tools for auditable data systems
  • due to pathological memory access, Hardware and Software Faults
  • due to radiation, Byzantine Faults
  • due to split brain, Leader failure: Failover, Distributed Locks and Leases
  • due to weak transaction isolation, Weak Isolation Levels
  • integrity as absence of, Timeliness and Integrity
  • network packets, Weak forms of lying
  • on disks, Durability
  • preventing using write-ahead logs, Making B-trees reliable
  • recovering from, Batch Processing, Advantages of immutable events
• cosine similarity (semantic search), Vector Embeddings
• Couchbase (database)
  • document data model, Relational Model versus Document Model
  • durability, Keeping everything in memory
  • hash sharding, Fixed number of shards
  • join support, Convergence of document and relational databases
  • rebalancing, Operations: Automatic or Manual Rebalancing
  • vBuckets (sharding), Sharding
• CouchDB (database)
  • as sync engine, Pros and cons of sync engines
  • B-tree storage, Indexes and snapshot isolation
  • conflict resolution, Manual conflict resolution
• coupling (loose and tight), Evolvability: Making Change Easy
• covering indexes, Storing values within the index
• CozoDB (database), Datalog: Recursive Relational Queries
• CPUs
  • cache coherence and memory barriers, Linearizability and network delays
  • caching and pipelining, Query Execution: Compilation and Vectorization
  • computing the wrong result, Hardware and Software Faults
  • SIMD instructions, Query Execution: Compilation and Vectorization
• crash-stop and crash-recovery faults, System Model and Reality
• CRDTs (see conflict-free replicated datatypes)
• CREATE INDEX statement (SQL), Multi-Column and Secondary Indexes, Creating an index
• credit rating agencies, Responsibility and Accountability
• crypto-shredding, Event Sourcing and CQRS, Limitations of immutability
• cryptocurrencies, Summary
• cryptography
  • defense against attackers, Byzantine Faults
  • end-to-end encryption and authentication, The end-to-end argument
• CSV (comma-separated values), Storage and Indexing for OLTP, JSON, XML, and Binary Variants
• Curator (ZooKeeper recipes), Locking and leader election, Allocating work to nodes
• Cypher (query language), The Cypher Query Language
  • comparison to SPARQL, The SPARQL query language

D

• Daft (processing framework)
  • DataFrames, DataFrames
  • shuffling data, Shuffling Data
• Dagster (workflow scheduler), Durable Execution and Workflows, Batch Processing, Scheduling Workflows
  • cloud data warehouse integration, Query languages
• dashboard (business intelligence), Characterizing Transaction Processing and Analytics
• Dask (processing framework), DataFrames, Matrices, and Arrays
• data catalog, Cloud Data Warehouses
• data connectors, Data Warehousing
• data contracts, Extract--Transform--Load (ETL)
  • change data capture, Change data capture versus event sourcing
• data corruption (see corruption of data)
• data cubes, Materialized Views and Data Cubes
• data engineering, Operational Versus Analytical Systems
• data fabric, Extract--Transform--Load (ETL)
• data formats (see encoding)
• data infrastructure, Trade-offs in Data Systems Architecture
• data integration, Data Integration-Unifying batch and stream processing, Summary
  • batch and stream processing, Batch and Stream Processing-Unifying batch and stream processing
    • maintaining derived state, Maintaining derived state
    • reprocessing data, Reprocessing data for application evolution
    • unifying, Unifying batch and stream processing
  • by unbundling databases, Unbundling Databases-Multi-shard data processing
    • comparison to federated databases, The meta-database of everything
  • combining tools by deriving data, Combining Specialized Tools by Deriving Data-Ordering events to capture causality
    • derived data versus distributed transactions, Derived data versus distributed transactions
    • limits of total ordering, The limits of total ordering
    • ordering events to capture causality, Ordering events to capture causality
    • reasoning about dataflows, Reasoning about dataflows
  • need for, Systems of Record and Derived Data
  • using batch processing, Batch Processing, Extract--Transform--Load (ETL)
• data lake, From data warehouse to data lake
  • data lakehouse, Cloud Data Warehouses, Analytics
• data locality (see locality)
• data mesh, Extract--Transform--Load (ETL)
• data minimization, Data Systems, Law, and Society, Legislation and Self-Regulation
• data models, Data Models and Query Languages-Summary
  • DataFrames and arrays, DataFrames, Matrices, and Arrays
  • graph-like models, Graph-Like Data Models-GraphQL
    • Datalog language, Datalog: Recursive Relational Queries-Datalog: Recursive Relational Queries
    • property graphs, Property Graphs
    • RDF and triple-stores, Triple-Stores and SPARQL-The SPARQL query language
  • relational model versus document model, Relational Model versus Document Model-Convergence of document and relational databases
  • supporting multiple, Event Sourcing and CQRS
• data pipelines, From data warehouse to data lake, Systems of Record and Derived Data, Extract--Transform--Load (ETL)
• data products, Beyond the data lake
• data protection regulations (see GDPR)
• data residence laws, Distributed Versus Single-Node Systems, Sharding for Multitenancy
• data science, Operational Versus Analytical Systems, From data warehouse to data lake
• data silo, Data Warehousing
• data systems
  • correctness, constraints, and integrity, Aiming for Correctness-Tools for auditable data systems
  • data integration, Data Integration-Unifying batch and stream processing
  • goals for using, Trade-offs in Data Systems Architecture
  • heterogeneous, keeping in sync, Keeping Systems in Sync
  • maintainability, Maintainability-Evolvability: Making Change Easy
  • possible faults in, Transactions
  • reliability, Reliability and Fault Tolerance-Humans and Reliability
    • hardware faults, Hardware and Software Faults
    • human errors, Humans and Reliability
    • importance of, Humans and Reliability
    • software faults, Software faults
  • scalability, Scalability-Principles for Scalability
  • unbundling databases, Unbundling Databases-Multi-shard data processing
  • unreliable clocks, Unreliable Clocks-Limiting the impact of garbage collection
• data warehousing, Data Warehousing, Glossary
  • cloud-based solutions, Cloud Data Warehouses
  • ETL (extract-transform-load), Data Warehousing, Keeping Systems in Sync
  • for batch processing, Batch Processing
  • keeping data systems in sync, Keeping Systems in Sync
  • schema design, Stars and Snowflakes: Schemas for Analytics
  • sharding and clustering, Sharding by hash range
  • slowly changing dimension (SCD), Time-dependence of joins
• data-intensive applications, Trade-offs in Data Systems Architecture
• database administrator, Operations in the Cloud Era
• database-internal distributed transactions, Distributed Transactions Across Different Systems, Database-internal Distributed Transactions, Atomic commit revisited
• databases
  • archival storage, Archival storage
  • comparison of message brokers to, Message brokers compared to databases
  • dataflow through, Dataflow Through Databases
  • end-to-end argument for, The end-to-end argument-Applying end-to-end thinking in data systems
    • checking integrity, The end-to-end argument again
  • relation to event streams, Databases and Streams-Limitations of immutability
    • (see also changelogs)
    • API support for change streams, API support for change streams, Separation of application code and state
    • change data capture, Change Data Capture-API support for change streams
    • event sourcing, Change data capture versus event sourcing
    • keeping systems in sync, Keeping Systems in Sync-Keeping Systems in Sync
    • philosophy of immutable events, State, Streams, and Immutability-Limitations of immutability
  • unbundling, Unbundling Databases-Multi-shard data processing
    • composing data storage technologies, Composing Data Storage Technologies-Unbundled versus integrated systems
    • designing applications around dataflow, Designing Applications Around Dataflow-Stream processors and services
    • observing derived state, Observing Derived State-Multi-shard data processing
• datacenters
  • failures of, Hardware and Software Faults
  • geographically distributed (see regions (geographic distribution))
  • multitenancy and shared resources, Network congestion and queueing
  • network architecture, Cloud Computing Versus Supercomputing
  • network faults, Network Faults in Practice
• dataflow, Modes of Dataflow-Distributed actor frameworks, Designing Applications Around Dataflow-Stream processors and services
  • correctness of dataflow systems, Correctness of dataflow systems
  • dataflow engines, Dataflow Engines
    • comparison to stream processing, Processing Streams
    • DataFrames, DataFrames
    • support in batch processing frameworks, Batch Processing
  • event-driven, Event-Driven Architectures-Distributed actor frameworks
  • reasoning about, Reasoning about dataflows
  • through databases, Dataflow Through Databases
  • through services, Dataflow Through Services: REST and RPC-Data encoding and evolution for RPC
  • workflow engines (see workflow engines)
• DataFrames, DataFrames, Matrices, and Arrays
  • implementation, DataFrames
  • in batch processing, DataFrames
  • in notebooks, Machine Learning
  • support in batch processing frameworks, Batch Processing
• DataFusion (query engine), Cloud Data Warehouses
• Datalog (query language), Datalog: Recursive Relational Queries-Datalog: Recursive Relational Queries
• Datastream (change data capture), API support for change streams
• datatypes
  • binary strings in XML and JSON, JSON, XML, and Binary Variants
  • conflict-free, CRDTs and Operational Transformation
  • in Avro encodings, Avro
  • in Protocol Buffers, Field tags and schema evolution
  • numbers in XML and JSON, JSON, XML, and Binary Variants
• Datensparsamkeit, Data Systems, Law, and Society
• Datomic (database)
  • B-tree storage, Indexes and snapshot isolation
  • data model, Graph-Like Data Models, Triple-Stores and SPARQL
  • Datalog query language, Datalog: Recursive Relational Queries
  • excision (deleting data), Limitations of immutability
  • languages for transactions, Pros and cons of stored procedures
  • serial execution of transactions, Actual Serial Execution
• Daylight Saving Time (DST), Time-of-day clocks
• Db2 (database)
  • change data capture, Implementing change data capture
• DBA (database administrator), Operations in the Cloud Era
• deadlocks, Explicit locking
  • detection, in distributed transaction, Problems with XA transactions
  • in two-phase locking (2PL), Implementation of two-phase locking
• Debezium (change data capture), Implementing change data capture
  • Cassandra, API support for change streams
  • for data integration, Unbundled versus integrated systems
• declarative languages, Data Models and Query Languages, Glossary
  • and sync engines, Pros and cons of sync engines
  • Datalog, Datalog: Recursive Relational Queries
  • in document databases, Convergence of document and relational databases
  • recursive SQL queries, Graph Queries in SQL
  • SPARQL, The SPARQL query language
• DeepSeek
  • 3FS (see 3FS)
• delays
  • bounded network delays, Synchronous Versus Asynchronous Networks
  • bounded process pauses, Response time guarantees
  • unbounded network delays, Timeouts and Unbounded Delays
  • unbounded process pauses, Process Pauses
• deleting data, Limitations of immutability
  • in LSM storage, Disk space usage
  • legal basis, Data Systems, Law, and Society
• Delta Lake (table format), Constructing and merging SSTables, Cloud Data Warehouses
  • sharding and clustering, Sharding by hash range
• demilitarized zone (networking), Serving Derived Data
• denormalization (data representation), Normalization, Denormalization, and Joins-Many-to-One and Many-to-Many Relationships, Glossary
  • in derived data systems, Systems of Record and Derived Data
  • in event sourcing/CQRS, Event Sourcing and CQRS
  • in social network case study, Denormalization in the social networking case study
  • materialized views, Materialized Views and Data Cubes
  • updating derived data, Single-Object and Multi-Object Operations, The need for multi-object transactions, Combining Specialized Tools by Deriving Data
  • versus normalization, Deriving several views from the same event log
• derived data, Systems of Record and Derived Data, Stream Processing, Glossary
  • batch processing, Batch Processing
  • event sourcing and CQRS, Event Sourcing and CQRS
  • from change data capture, Implementing change data capture
  • maintaining derived state through logs, Databases and Streams-API support for change streams, State, Streams, and Immutability-Concurrency control
  • observing, by subscribing to streams, End-to-end event streams
  • outputs of batch and stream processing, Batch and Stream Processing
  • through application code, Application code as a derivation function
  • versus distributed transactions, Derived data versus distributed transactions
• design patterns, Simplicity: Managing Complexity
• deterministic operations, Pros and cons of stored procedures, Faults and Partial Failures, Glossary
  • and idempotence, Idempotence, Reasoning about dataflows
  • computing derived data, Maintaining derived state, Correctness of dataflow systems, Designing for auditability
  • in event sourcing, Event Sourcing and CQRS
  • in state machine replication, Using shared logs, Databases and Streams
  • in statement-based replication, Statement-based replication
  • in testing, Deterministic simulation testing
  • joins, Time-dependence of joins
  • making code deterministic, Deterministic simulation testing
  • overview, Deterministic simulation testing
• deterministic simulation testing (DST), Deterministic simulation testing
• DevOps, Operations in the Cloud Era
• dimension tables, Stars and Snowflakes: Schemas for Analytics
• dimensional modeling (see star schemas)
• directed acyclic graphs (DAG)
  • workflows, Scheduling Workflows
    • (see also workflow engines)
• dirty reads (transaction isolation), No dirty reads
• dirty writes (transaction isolation), No dirty writes
• disaggregation
  • of storage and compute, Separation of storage and compute
• Discord (group chat)
  • GraphQL example, GraphQL
• discrimination, Bias and Discrimination
• disks (see hard disks)
• distributed actor frameworks, Distributed actor frameworks
• distributed filesystems, Distributed Filesystems-Distributed Filesystems
  • comparison to object storage, Object Stores
  • use by Flink, Rebuilding state after a failure
• distributed ledgers, Summary
• distributed systems, The Trouble with Distributed Systems-Summary, Glossary
  • Byzantine faults, Byzantine Faults-Weak forms of lying
  • detecting network faults, Detecting Faults
  • faults and partial failures, Faults and Partial Failures
  • formalization of consensus, Single-value consensus
  • impossibility results, The CAP theorem, Consensus
  • issues with failover, Leader failure: Failover
  • multi-region (see regions (geographic distribution))
  • network problems, Unreliable Networks-Can we not simply make network delays predictable?
  • problems with, Problems with Distributed Systems
  • quorums, relying on, The Majority Rules
  • reasons for using, Distributed Versus Single-Node Systems, Replication
  • synchronized clocks, relying on, Relying on Synchronized Clocks-Synchronized clocks for global snapshots
  • system models, System Model and Reality-Deterministic simulation testing
  • use of clocks and time, Unreliable Clocks
• distributed transactions (see transactions)
• Django (web framework), Handling errors and aborts
• DMZ (demilitarized zone), Serving Derived Data
• DNS (Domain Name System), Request Routing, Service discovery
  • for load balancing, Load balancers, service discovery, and service meshes
• Docker (container manager), Separation of application code and state
• document data model, Relational Model versus Document Model-Convergence of document and relational databases
  • comparison to relational model, When to Use Which Model-Convergence of document and relational databases
  • multi-object transactions, need for, The need for multi-object transactions
  • sharded secondary indexes, Sharding and Secondary Indexes
  • versus relational model
    • convergence of models, Convergence of document and relational databases
    • data locality, Data locality for reads and writes
• document-partitioned indexes (see local secondary indexes)
• domain-driven design (DDD), Simplicity: Managing Complexity, Event Sourcing and CQRS
• dotted version vectors, Version vectors
• double-entry bookkeeping, Summary
• DRBD (Distributed Replicated Block Device), Single-Leader Replication
• drift (clocks), Clock Synchronization and Accuracy
• Druid (database), Characterizing Transaction Processing and Analytics, Column-Oriented Storage, Deriving several views from the same event log
  • handling writes, Writing to Column-Oriented Storage
  • pre-aggregation, Analytics
  • serving derived data, Serving Derived Data
• Dryad (dataflow engine), Dataflow Engines
• dual writes, problems with, Keeping Systems in Sync
• DuckDB (database), Problems with Distributed Systems, Compaction strategies
  • column-oriented storage, Column-Oriented Storage
  • use for ETL, Extract--Transform--Load (ETL)
• duplicates, suppression of, Duplicate suppression
  • (see also idempotence)
  • using a unique ID, Uniquely identifying requests, Multi-shard request processing
• durability (transactions), Making B-trees reliable, Durability, Glossary
• durable execution, Durable Execution and Workflows
  • reliance on determinism, Deterministic simulation testing
  • Restate (see Restate (workflow engine))
  • Temporal (see Temporal (workflow engine))
• durable functions (see workflow engines)
• duration (time), Unreliable Clocks
  • measurement with monotonic clocks, Monotonic clocks
• dynamically typed languages
  • analogy to schema-on-read, Schema flexibility in the document model
• Dynamo (database), Leaderless Replication
• Dynamo-style databases (see leaderless replication)
• DynamoDB (database)
  • auto-scaling, Operations: Automatic or Manual Rebalancing
  • hash-range sharding, Sharding by hash range
  • leader-based replication, Single-Leader Replication
  • sharded secondary indexes, Global Secondary Indexes

E

• EBS (virtual block device), Separation of storage and compute
  • compared to object storage, Setting Up New Followers
• ECC (see error-correcting codes)
• EDB Postgres Distributed (database), Geographically Distributed Operation
• edges (in graphs), Graph-Like Data Models
  • property graph model, Property Graphs
• edit distance (full-text search), Full-Text Search
• effectively-once semantics, Fault Tolerance, Exactly-once execution of an operation
  • (see also exactly-once semantics)
  • preservation of integrity, Correctness of dataflow systems
• Elastic Compute Cloud (EC2)
  • spot instances, Handling Faults
• elasticity, Distributed Versus Single-Node Systems
  • cloud data warehouses, Cloud Data Warehouses, Query languages
• Elasticsearch (search server)
  • local secondary indexes, Local Secondary Indexes
  • percolator (stream search), Search on streams
  • serving derived data, Serving Derived Data
  • shard rebalancing, Fixed number of shards
  • use of Lucene, Full-Text Search
• Elm (programming language), End-to-end event streams
• ELT (extract-load-transform), Data Warehousing
  • relation to batch processing, Extract--Transform--Load (ETL)
• embarassingly parallel (algorithms)
  • ETL (see ETL (extract-transform-load))
  • MapReduce, MapReduce
    • (see also MapReduce)
• embedded storage engines, Compaction strategies
• embedding (vector), Vector Embeddings
• encodings (data formats), Encoding and Evolution-The Merits of Schemas
  • Avro, Avro-Dynamically generated schemas
  • binary variants of JSON and XML, Binary encoding
  • compatibility, Encoding and Evolution
    • calling services, Data encoding and evolution for RPC
    • using databases, Dataflow Through Databases-Archival storage
  • defined, Formats for Encoding Data
  • JSON, XML, and CSV, JSON, XML, and Binary Variants
  • language-specific formats, Language-Specific Formats
  • merits of schemas, The Merits of Schemas
  • Protocol Buffers, Protocol Buffers-Field tags and schema evolution
  • representations of data, Formats for Encoding Data
• end-to-end argument, The end-to-end argument-Applying end-to-end thinking in data systems
  • checking integrity, The end-to-end argument again
  • publish/subscribe streams, End-to-end event streams
• enrichment (stream), Stream-table join (stream enrichment)
• Enterprise JavaBeans (EJB), The problems with remote procedure calls (RPCs)
• enterprise software, Trade-offs in Data Systems Architecture
• entities (see vertices)
• ephemeral storage, Separation of storage and compute
• epoch (consensus algorithms), From single-leader replication to consensus
• epoch (Unix timestamps), Time-of-day clocks
• erasure coding (error correction), Distributed Filesystems
• error handling
  • for network faults, Network Faults in Practice
  • in transactions, Handling errors and aborts
• error-correcting codes, Hardware and Software Faults, Distributed Filesystems
• Esper (CEP engine), Complex event processing
• essential complexity, Simplicity: Managing Complexity
• etcd (coordination service), Coordination Services-Service discovery
  • generating fencing tokens, Fencing off zombies and delayed requests, Coordination Services
  • linearizable operations, Implementing Linearizable Systems, Subtleties of consensus
  • locks and leader election, Locking and leader election
  • use for service discovery, Load balancers, service discovery, and service meshes, Service discovery
  • use for shard assignment, Request Routing
  • use of Raft algorithm, Single-Leader Replication
• Ethereum (blockchain), Tools for auditable data systems
• Ethernet (networks), Cloud Computing Versus Supercomputing, Unreliable Networks, Can we not simply make network delays predictable?
  • packet checksums, Weak forms of lying, The end-to-end argument
• ethics, Doing the Right Thing-Legislation and Self-Regulation
  • code of ethics and professional practice, Doing the Right Thing
  • legislation and self-regulation, Legislation and Self-Regulation
  • predictive analytics, Predictive Analytics-Feedback Loops
    • amplifying bias, Bias and Discrimination
    • feedback loops, Feedback Loops
  • privacy and tracking, Privacy and Tracking-Legislation and Self-Regulation
    • consent and freedom of choice, Consent and Freedom of Choice
    • data as assets and power, Data as Assets and Power
    • meaning of privacy, Privacy and Use of Data
    • surveillance, Surveillance
  • respect, dignity, and agency, Legislation and Self-Regulation
  • unintended consequences, Doing the Right Thing, Feedback Loops
• ETL (extract-transform-load), Data Warehousing, Keeping Systems in Sync, Glossary
  • relation to batch processing, Extract--Transform--Load (ETL)-Extract--Transform--Load (ETL)
  • using batch processing, Batch Processing
• Euclidean distance (semantic search), Vector Embeddings
• European Union
  • AI Act (see AI Act)
  • GDPR (see GDPR)
• event sourcing, Event Sourcing and CQRS-Event Sourcing and CQRS
  • and change data capture, Change data capture versus event sourcing
  • comparison to change data capture, Change data capture versus event sourcing
  • immutability and auditability, State, Streams, and Immutability, Designing for auditability
  • large, reliable data systems, Uniquely identifying requests, Correctness of dataflow systems
  • reliance on determinism, Deterministic simulation testing
• event streams (see streams)
• event-driven architecture, Event-Driven Architectures-Distributed actor frameworks
  • distributed actor frameworks, Distributed actor frameworks
• events, Transmitting Event Streams
  • deciding on total order of, The limits of total ordering
  • deriving views from event log, Deriving several views from the same event log
  • event time versus processing time, Event time versus processing time, Microbatching and checkpointing, Unifying batch and stream processing
  • immutable, advantages of, Advantages of immutable events, Designing for auditability
  • ordering to capture causality, Ordering events to capture causality
  • reads as, Reads are events too
  • stragglers, Handling straggler events
  • timestamp of, in stream processing, Whose clock are you using, anyway?
• EventSource (browser API), Pushing state changes to clients
• EventStoreDB (database), Event Sourcing and CQRS
• eventual consistency, Replication, Problems with Replication Lag, Safety and liveness
  • (see also conflicts)
  • and perpetual inconsistency, Timeliness and Integrity
  • strong eventual consistency, Automatic conflict resolution
• evidence
  • data used as, Humans and Reliability
• evolvability, Evolvability: Making Change Easy, Encoding and Evolution
  • calling services, Data encoding and evolution for RPC
  • event sourcing, Event Sourcing and CQRS
  • graph-structured data, Property Graphs
  • of databases, Schema flexibility in the document model, Dataflow Through Databases-Archival storage, Deriving several views from the same event log, Reprocessing data for application evolution
  • reprocessing data, Reprocessing data for application evolution, Unifying batch and stream processing
  • schema evolution in Avro, The writer's schema and the reader's schema
  • schema evolution in Protocol Buffers, Field tags and schema evolution
  • schema-on-read, Schema flexibility in the document model, Encoding and Evolution, The Merits of Schemas
• exactly-once semantics, Exactly-once message processing, Exactly-once message processing revisited, Fault Tolerance, Exactly-once execution of an operation
  • parity with batch processors, Unifying batch and stream processing
  • preservation of integrity, Correctness of dataflow systems
  • using durable execution, Durable execution
• exclusive mode (locks), Implementation of two-phase locking
• exponential backoff, Describing Performance, Handling errors and aborts
• ext4 (file system), Distributed Filesystems
• eXtended Architecture transactions (see XA transactions)
• extract-transform-load (see ETL)

F

• Facebook
  • Faiss (vector index), Vector Embeddings
  • React (user interface library), End-to-end event streams
  • social graphs, Graph-Like Data Models
• facts
  • fact table (star schema), Stars and Snowflakes: Schemas for Analytics
  • in Datalog, Datalog: Recursive Relational Queries
  • in event sourcing, Event Sourcing and CQRS
• fail-slow faults, System Model and Reality
• fail-stop model, System Model and Reality
• failover, Leader failure: Failover, Glossary
  • (see also leader-based replication)
  • in leaderless replication, absence of, Writing to the Database When a Node Is Down
  • leader election, Distributed Locks and Leases, Consensus, From single-leader replication to consensus
  • potential problems, Leader failure: Failover
• failures
  • amplification by distributed transactions, Maintaining derived state
  • failure detection, Detecting Faults
    • automatic rebalancing causing cascading failures, Operations: Automatic or Manual Rebalancing
    • timeouts and unbounded delays, Timeouts and Unbounded Delays, Network congestion and queueing
    • using a coordination service, Coordination Services
  • faults versus, Reliability and Fault Tolerance
  • partial failures, Faults and Partial Failures, Summary
• Faiss (vector index), Vector Embeddings
• false positive (Bloom filters), Bloom filters
• fan-out (messaging systems), Materializing and Updating Timelines, Multiple consumers
• fault injection, Fault Tolerance, Network Faults in Practice, Fault injection
• fault isolation, Sharding for Multitenancy
• fault tolerance, Reliability and Fault Tolerance-Humans and Reliability, Glossary
  • formalization in consensus, Single-value consensus
  • human fault tolerance, Batch Processing
  • in batch processing, Handling Faults
  • in log-based systems, Applying end-to-end thinking in data systems, Timeliness and Integrity-Correctness of dataflow systems
  • in stream processing, Fault Tolerance-Rebuilding state after a failure
    • atomic commit, Atomic commit revisited
    • idempotence, Idempotence
    • maintaining derived state, Maintaining derived state
    • microbatching and checkpointing, Microbatching and checkpointing
    • rebuilding state after a failure, Rebuilding state after a failure
  • of distributed transactions, XA transactions-Exactly-once message processing revisited
  • of leader-based and leaderless replication, Single-Leader Versus Leaderless Replication Performance
  • transaction atomicity, Atomicity, Distributed Transactions-Exactly-once message processing
• faults
  • Byzantine faults, Byzantine Faults-Weak forms of lying
  • failures versus, Reliability and Fault Tolerance
  • handled by transactions, Transactions
  • handling in supercomputers and cloud computing, Cloud Computing Versus Supercomputing
  • hardware, Hardware and Software Faults
  • in distributed systems, Faults and Partial Failures
  • introducing deliberately (see fault injection)
  • network faults, Network Faults in Practice-Detecting Faults
    • asymmetric faults, The Majority Rules
    • detecting, Detecting Faults
    • tolerance of, in multi-leader replication, Geographically Distributed Operation
  • software faults, Software faults
  • tolerating (see fault tolerance)
• feature engineering (machine learning), From data warehouse to data lake
• federated databases, The meta-database of everything
• Feldera (database)
  • incremental view maintenance, Maintaining materialized views
• fence (CPU instruction), Linearizability and network delays
• fencing (preventing split brain), Leader failure: Failover, Fencing off zombies and delayed requests-Fencing with multiple replicas
  • generating fencing tokens, Using shared logs, Coordination Services
  • properties of fencing tokens, Defining the correctness of an algorithm
  • stream processors writing to databases, Idempotence, Exactly-once execution of an operation
• fetch-and-add
  • relation to consensus, Fetch-and-add as consensus
• Fibre Channel (networks), Distributed Filesystems
• field tags (Protocol Buffers), Protocol Buffers-Field tags and schema evolution
• Figma (graphics software), Real-time collaboration, offline-first, and local-first apps
• filesystem in userspace (FUSE), Setting Up New Followers, Distributed Filesystems
  • on object storage, Object Stores
• financial data
  • accounting ledgers, Summary
  • immutability, Advantages of immutable events
  • time series data, DataFrames, Matrices, and Arrays
• Fivetran, Data Warehousing
• FizzBee (specification language), Model checking and specification languages
• flat index (vector index), Vector Embeddings
• FlatBuffers (data format), Formats for Encoding Data
• Flink (processing framework), Batch Processing, Dataflow Engines
  • cost efficiency, Query languages
  • DataFrames, DataFrames, Matrices, and Arrays, DataFrames
  • fault tolerance, Handling Faults, Microbatching and checkpointing, Rebuilding state after a failure
  • FlinkML, Machine Learning
  • for data warehouses, Cloud Data Warehouses
  • high availability using ZooKeeper, Coordination Services
  • integration of batch and stream processing, Unifying batch and stream processing
  • query optimizer, Query languages
  • shuffling data, Shuffling Data
  • stream processing, Stream analytics
  • streaming SQL support, Complex event processing
• flow control, The Limitations of TCP, Messaging Systems, Glossary
• FLP result (on consensus), Consensus
• Flyte (workflow scheduler), Machine Learning
• followers, Single-Leader Replication, Glossary
  • (see also leader-based replication)
• formal methods, Formal Methods and Randomized Testing-Deterministic simulation testing
• forward compatibility, Encoding and Evolution
• forward decay (algorithm), Use of Response Time Metrics
• Fossil (version control system), Concurrency control
  • shunning (deleting data), Limitations of immutability
• FoundationDB (database)
  • consistency model, What Makes a System Linearizable?
  • deterministic simulation testing, Deterministic simulation testing
  • key-range sharding, Sharding by Key Range
  • process-per-core model, Pros and Cons of Sharding
  • serializable transactions, Serializable Snapshot Isolation (SSI), Performance of serializable snapshot isolation
  • transactions, What Exactly Is a Transaction?, Database-internal Distributed Transactions
• fractional indexing, When to Use Which Model
• fragmentation (of B-trees), Disk space usage
• frame (computer graphics), Pros and cons of sync engines
• frontend (web development), Trade-offs in Data Systems Architecture
• FrostDB (database)
  • deterministic simulation testing (DST), Deterministic simulation testing
• fsync (system call), Making B-trees reliable, Durability
• full-text search, Full-Text Search, Glossary
  • and fuzzy indexes, Full-Text Search
  • Lucene storage engine, Full-Text Search
  • sharded indexes, Sharding and Secondary Indexes
• Function as a Service (FaaS), Microservices and Serverless
• functional programming
  • inspiration for MapReduce, MapReduce
• functional requirements, Defining Nonfunctional Requirements
• FUSE (see filesystem in userspace (FUSE))
• fuzzing, Formal Methods and Randomized Testing
• fuzzy search (see similarity search)

G

• Gallina (specification language), Model checking and specification languages
• game development, Pros and cons of sync engines
• garbage collection
  • immutability and, Limitations of immutability
  • process pauses for, Latency and Response Time, Process Pauses-Limiting the impact of garbage collection, The Majority Rules
    • (see also process pauses)
• gas stations algorithmic pricing, Feedback Loops
• GDPR (regulation), Data Systems, Law, and Society, Limitations of immutability
  • consent, Consent and Freedom of Choice
  • data minimization, Legislation and Self-Regulation
  • legitimate interest, Consent and Freedom of Choice
  • right of access, Sharding for Multitenancy
  • right to erasure, Data Systems, Law, and Society, Disk space usage, Sharding for Multitenancy
• GenBank (genome database), Summary
• General Data Protection Regulation (see GDPR (regulation))
• genome analysis, Summary
• geographic distribution (see regions (geographic distribution))
• geospatial indexes, Multidimensional and Full-Text Indexes
• Git (version control system), Concurrency control
  • local-first software, Real-time collaboration, offline-first, and local-first apps
  • merge conflicts, Manual conflict resolution
• GitHub, postmortems, Leader failure: Failover, Leader failure: Failover, Mapping system models to the real world
• global secondary indexes, Global Secondary Indexes, Summary
• globally unique identifiers (see UUIDs)
• GlusterFS (distributed filesystem), Batch Processing, Distributed Filesystems, Object Stores
• GNU Coreutils (Linux), Sorting Versus In-memory Aggregation
• Go (programming language)
  • garbage collection, Limiting the impact of garbage collection
• GoldenGate (change data capture), Implementing change data capture
  • (see also Oracle)
• Google
  • BigQuery (see BigQuery (database))
  • Bigtable (see Bigtable (database))
  • Chubby (lock service), Coordination Services
  • Cloud Storage (object storage), Setting Up New Followers, Object Stores
    • request preconditions, Fencing off zombies and delayed requests
  • Compute Engine
    • preemptible instances, Handling Faults
  • Dataflow (stream processing)
    • data warehouse integration, Cloud Data Warehouses
    • shuffling data, Shuffling Data
  • Dataflow (stream processor), Stream analytics, Atomic commit revisited, Unifying batch and stream processing
    • (see also Beam)
  • Datastream (change data capture), API support for change streams
  • Docs (collaborative editor), Real-time collaboration, offline-first, and local-first apps, CRDTs and Operational Transformation
    • operational transformation, CRDTs and Operational Transformation
  • Dremel (query engine), Column-Oriented Storage
  • Firestore (database), Pros and cons of sync engines
  • MapReduce (batch processing), Batch Processing
    • (see also MapReduce)
  • Percolator (transaction system), Implementing a linearizable ID generator
  • persistent disks (cloud service), Separation of storage and compute
  • Pub/Sub (messaging), Message brokers, Message brokers compared to databases, Using logs for message storage
  • response time study, Average, Median, and Percentiles
  • Sheets (collaborative spreadsheet), Real-time collaboration, offline-first, and local-first apps, CRDTs and Operational Transformation
  • Spanner (see Spanner (database))
  • TrueTime (clock API), Clock readings with a confidence interval
• gossip protocol, Request Routing
• governance, Beyond the data lake
• government use of data, Data as Assets and Power
• GPS (Global Positioning System)
  • use for clock synchronization, Unreliable Clocks, Clock Synchronization and Accuracy, Clock readings with a confidence interval, Synchronized clocks for global snapshots
• GPT (language model), Vector Embeddings
• GPU (graphics processing unit), Layering of cloud services, Distributed Versus Single-Node Systems
• gradual rollout (see rolling upgrades)
• GraphQL (query language), GraphQL
  • validation, Pros and cons of stored procedures
• graphs, Glossary
  • as data models, Graph-Like Data Models-GraphQL
    • property graphs, Property Graphs
    • RDF and triple-stores, Triple-Stores and SPARQL-The SPARQL query language
  • DAGs (see directed acyclic graphs)
  • processing and analysis, Machine Learning
  • query languages
    • Cypher, The Cypher Query Language
    • Datalog, Datalog: Recursive Relational Queries-Datalog: Recursive Relational Queries
    • GraphQL, GraphQL
    • Gremlin, Graph-Like Data Models
    • recursive SQL queries, Graph Queries in SQL
    • SPARQL, The SPARQL query language-The SPARQL query language
  • traversal, Property Graphs
• gray failures, System Model and Reality
  • in leaderless replication, Single-Leader Versus Leaderless Replication Performance
• Gremlin (graph query language), Graph-Like Data Models
• grep (Unix tool), Simple Log Analysis
• gRPC (service calls), Microservices and Serverless, Web services
  • forward and backward compatibility, Data encoding and evolution for RPC
• GUIDs (see UUIDs)

H

• Hadoop (data infrastructure)
  • comparison to distributed databases, Batch Processing
  • MapReduce (see MapReduce)
  • NodeManager, Distributed Job Orchestration
  • YARN (see YARN (job scheduler))
• HANA (see SAP HANA (database))
• happens-before relation, The "happens-before" relation and concurrency
• hard disks
  • access patterns, Sequential versus random writes
  • detecting corruption, The end-to-end argument, Don't just blindly trust what they promise
  • faults in, Hardware and Software Faults, Durability
  • sequential vs. random writes, Sequential versus random writes
  • sequential write throughput, Disk space usage
• hardware faults, Hardware and Software Faults
• hash function
  • in Bloom filters, Bloom filters
• hash join
  • in stream processing, Stream-table join (stream enrichment)
• hash sharding, Sharding by Hash of Key-Consistent hashing, Summary
  • consistent hashing, Consistent hashing
  • problems with hash mod N, Hash modulo number of nodes
  • range queries, Sharding by hash range
  • suitable hash functions, Sharding by Hash of Key
  • with fixed number of shards, Fixed number of shards
• hash tables, Log-Structured Storage
• Hazelcast (in-memory data grid)
  • FencedLock, Fencing off zombies and delayed requests
  • Flake ID Generator, ID Generators and Logical Clocks
• HBase (database)
  • bug due to lack of fencing, Distributed Locks and Leases
  • key-range sharding, Sharding by Key Range
  • log-structured storage, Constructing and merging SSTables
  • regions (sharding), Sharding
  • request routing, Request Routing
  • size-tiered compaction, Compaction strategies
  • wide-column data model, Data locality for reads and writes, Column Compression
• HDFS (Hadoop Distributed File System), Batch Processing, Distributed Filesystems
  • (see also distributed filesystems)
  • checking data integrity, Don't just blindly trust what they promise
  • DataNode, Distributed Filesystems
  • NameNode, Distributed Filesystems
  • use in MapReduce, MapReduce
  • workflow example, Scheduling Workflows
• HdrHistogram (numerical library), Use of Response Time Metrics
• head (Unix tool), Simple Log Analysis, Distributed Job Orchestration
• head vertex (property graphs), Property Graphs
• head-of-line blocking, Latency and Response Time
• heap files (databases), Storing values within the index
  • in multiversion concurrency control, Multi-version concurrency control (MVCC)
• heat management, Skewed Workloads and Relieving Hot Spots
• hedged requests, Single-Leader Versus Leaderless Replication Performance
• heterogeneous distributed transactions, Distributed Transactions Across Different Systems, Problems with XA transactions
• heuristic decisions (in 2PC), Recovering from coordinator failure
• Hex (notebook), Machine Learning
• hexagons
  • for geospatial indexing, Multidimensional and Full-Text Indexes
• Hibernate (object-relational mapper), Object-relational mapping (ORM)
• hierarchical model, Relational Model versus Document Model
• hierarchical navigable small world (vector index), Vector Embeddings
• hierarchical queries (see recursive common table expressions)
• high availability (see fault tolerance)
• high-frequency trading, Clock Synchronization and Accuracy
• high-performance computing (HPC), Cloud Computing Versus Supercomputing
• hinted handoff (leaderless replication), Catching up on missed writes
• histograms, Use of Response Time Metrics
• Hive (data warehouse), Cloud Data Warehouses
  • query optimizer, Query languages
• HNSW (vector index), Vector Embeddings
• hopping windows (stream processing), Types of windows
  • (see also windows)
• Hoptimator (query engine), The meta-database of everything
• Horizon scandal, Humans and Reliability
  • lack of transactions, Transactions
• horizontal scaling (see scaling out)
  • by sharding, Pros and Cons of Sharding
• HornetQ (messaging), Message brokers, Message brokers compared to databases
  • distributed transaction support, XA transactions
• hot keys, Sharding of Key-Value Data
• hot spots, Sharding of Key-Value Data
  • due to celebrities, Skewed Workloads and Relieving Hot Spots
  • for time-series data, Sharding by Key Range
  • relieving, Skewed Workloads and Relieving Hot Spots
• hot standbys (see leader-based replication)
• HTAP (see hybrid transactional/analytic processing)
• HTTP, use in APIs (see services)
• human errors, Humans and Reliability, Network Faults in Practice, Batch Processing
• hybrid logical clocks, Hybrid logical clocks
• hybrid transactional/analytic processing, Data Warehousing, Data Storage for Analytics
• hydrating IDs (join), Denormalization in the social networking case study
• hypergraph, Property Graphs
• HyperLogLog (algorithm), Stream analytics

I

• I/O operations, waiting for, Process Pauses
• IaaS (see infrastructure as a service (IaaS))
• IBM
  • Db2 (database)
    • distributed transaction support, XA transactions
    • serializable isolation, Snapshot isolation, repeatable read, and naming confusion, Implementation of two-phase locking
  • MQ (messaging), Message brokers compared to databases
    • distributed transaction support, XA transactions
  • System R (database), What Exactly Is a Transaction?
  • WebSphere (messaging), Message brokers
• Iceberg (table format), Cloud Data Warehouses
  • databases on object storage, Setting Up New Followers
  • log-based message broker storage, Disk space usage
• idempotence, The problems with remote procedure calls (RPCs), Idempotence, Glossary
  • by giving operations unique IDs, Multi-shard request processing
  • by giving requests unique IDs, Uniquely identifying requests
  • for exactly-once semantics, Exactly-once message processing revisited
  • idempotent operations, Exactly-once execution of an operation
  • in workflow engines, Durable execution
• immutability
  • advantages of, Advantages of immutable events, Designing for auditability
  • and right to erasure, Data Systems, Law, and Society, Disk space usage
  • crypto-shredding for deletion, Event Sourcing and CQRS, Limitations of immutability
  • deriving state from event log, State, Streams, and Immutability-Limitations of immutability
  • for crash recovery, Constructing and merging SSTables
  • in B-trees, B-tree variants, Indexes and snapshot isolation
  • in event sourcing, Event Sourcing and CQRS, Change data capture versus event sourcing
  • limitations of, Concurrency control
• impedance mismatch, The Object-Relational Mismatch
• in doubt (transaction status), Coordinator failure
  • holding locks, Holding locks while in doubt
  • orphaned transactions, Recovering from coordinator failure
• in-memory databases, Keeping everything in memory
  • durability, Durability
  • serial transaction execution, Actual Serial Execution
• incidents
  • accounting software bugs leading to wrongful convictions, Humans and Reliability
  • blameless postmortems, Humans and Reliability
  • crashes due to leap seconds, Clock Synchronization and Accuracy
  • data corruption and financial losses due to concurrency bugs, Weak Isolation Levels
  • data corruption on hard disks, Durability
  • data loss due to last-write-wins, Timestamps for ordering events
  • data on disks unreadable, Mapping system models to the real world
  • disclosure of sensitive data due to primary key reuse, Leader failure: Failover
  • errors in transaction serializability, Maintaining integrity in the face of software bugs
  • gigabit network interface with 1 Kb/s throughput, System Model and Reality
  • leap second crash, Software faults
  • network faults, Network Faults in Practice
  • network interface dropping only inbound packets, Network Faults in Practice
  • network partitions and whole-datacenter failures, Faults and Partial Failures
  • poor handling of network faults, Network Faults in Practice
  • sending message to ex-partner, Ordering events to capture causality
  • sharks biting undersea cables, Network Faults in Practice
  • split brain due to 1-minute packet delay, Leader failure: Failover, Network Faults in Practice
  • SSD failure after 32,768 hours, Software faults
  • thread contention bringing down a service, Process Pauses
  • vibrations in server rack, Latency and Response Time
  • violation of uniqueness constraint, Maintaining integrity in the face of software bugs
• incremental view maintenance (IVM), Maintaining materialized views
  • for data integration, Unbundled versus integrated systems
• indexes, Storage and Indexing for OLTP, Glossary
  • and snapshot isolation, Indexes and snapshot isolation
  • as derived data, Systems of Record and Derived Data, Composing Data Storage Technologies-Unbundled versus integrated systems
  • B-trees, B-Trees-B-tree variants
  • clustered, Storing values within the index
  • comparison of B-trees and LSM-trees, Comparing B-Trees and LSM-Trees-Disk space usage
  • covering (with included columns), Storing values within the index
  • creating, Creating an index
  • full-text search, Full-Text Search
  • geospatial, Multidimensional and Full-Text Indexes
  • index-range locking, Index-range locks
  • multi-column (concatenated), Multidimensional and Full-Text Indexes
  • secondary, Multi-Column and Secondary Indexes
    • (see also secondary indexes)
    • problems with dual writes, Keeping Systems in Sync, Reasoning about dataflows
  • sharding and secondary indexes, Sharding and Secondary Indexes-Global Secondary Indexes, Summary
  • sparse, The SSTable file format
  • SSTables and LSM-trees, The SSTable file format-Compaction strategies
  • updating when data changes, Keeping Systems in Sync, Maintaining materialized views
• Industrial Revolution, Remembering the Industrial Revolution
• InfiniBand (networks), Can we not simply make network delays predictable?
• InfluxDB IOx (storage engine), Column-Oriented Storage
• information retrieval (see full-text search)
• infrastructure as a service (IaaS), Cloud Versus Self-Hosting, Layering of cloud services
• InnoDB (storage engine)
  • clustered index on primary key, Storing values within the index
  • not preventing lost updates, Automatically detecting lost updates
  • preventing write skew, Characterizing write skew, Implementation of two-phase locking
  • serializable isolation, Implementation of two-phase locking
  • snapshot isolation support, Snapshot Isolation and Repeatable Read
• instance (cloud computing), Layering of cloud services
• integrating different data systems (see data integration)
• integrity, Timeliness and Integrity
  • coordination-avoiding data systems, Coordination-avoiding data systems
  • correctness of dataflow systems, Correctness of dataflow systems
  • in consensus formalization, Single-value consensus, Atomic commitment as consensus
  • integrity checks, Don't just blindly trust what they promise
    • (see also auditing)
    • end-to-end, The end-to-end argument, The end-to-end argument again
    • use of snapshot isolation, Snapshot Isolation and Repeatable Read
  • maintaining despite software bugs, Maintaining integrity in the face of software bugs
• Interface Definition Language (IDL), Protocol Buffers, Avro, Web services
• invariants, Consistency
  • (see also constraints)
• inverted file index (vector index), Vector Embeddings
• inverted index, Full-Text Search
• irreversibility, minimizing, Evolvability: Making Change Easy, Event Sourcing and CQRS, Batch Processing
• ISDN (Integrated Services Digital Network), Synchronous Versus Asynchronous Networks
• isolation (in operating systems)
  • cgroups (see cgroups)
• isolation (in transactions), Isolation, Single-Object and Multi-Object Operations, Glossary
  • correctness and, Aiming for Correctness
  • for single-object writes, Single-object writes
  • serializability, Serializability-Performance of serializable snapshot isolation
    • actual serial execution, Actual Serial Execution-Summary of serial execution
    • serializable snapshot isolation (SSI), Serializable Snapshot Isolation (SSI)-Performance of serializable snapshot isolation
    • two-phase locking (2PL), Two-Phase Locking (2PL)-Index-range locks
  • violating, Single-Object and Multi-Object Operations
  • weak isolation levels, Weak Isolation Levels-Materializing conflicts
    • preventing lost updates, Preventing Lost Updates-Conflict resolution and replication
    • read committed, Read Committed-Implementing read committed
    • snapshot isolation, Snapshot Isolation and Repeatable Read-Snapshot isolation, repeatable read, and naming confusion
• IVF (vector index), Vector Embeddings

J

• Java Database Connectivity (JDBC)
  • distributed transaction support, XA transactions
  • network drivers, The Merits of Schemas
• Java Enterprise Edition (EE), The problems with remote procedure calls (RPCs), Two-Phase Commit (2PC), XA transactions
• Java Message Service (JMS), Message brokers compared to databases
  • (see also messaging systems)
  • comparison to log-based messaging, Logs compared to traditional messaging, Replaying old messages
  • distributed transaction support, XA transactions
  • message ordering, Acknowledgments and redelivery
• Java Transaction API (JTA), Two-Phase Commit (2PC), XA transactions
• Java Virtual Machine (JVM)
  • garbage collection, Process Pauses, Limiting the impact of garbage collection
  • JIT compilation, Query Execution: Compilation and Vectorization
  • process reuse in batch processors, Dataflow Engines
• Jena (RDF framework), The RDF data model
  • SPARQL query language, The SPARQL query language
• Jepsen (fault tolerance testing), Fault injection, Aiming for Correctness
• jitter (network delay), Average, Median, and Percentiles, Network congestion and queueing
• JMESPath (query language), Query languages
• join table, Many-to-One and Many-to-Many Relationships, Property Graphs
• joins, Glossary
  • expressing as relational operators, Query languages
  • handling GraphQL query, GraphQL
  • in application code, Normalization, Denormalization, and Joins, Denormalization in the social networking case study
  • in DataFrames, DataFrames, Matrices, and Arrays
  • in relational and document databases, Normalization, Denormalization, and Joins
  • secondary indexes and, Multi-Column and Secondary Indexes
  • sort-merge joins, JOIN and GROUP BY
  • stream joins, Stream Joins-Time-dependence of joins
    • stream-stream join, Stream-stream join (window join)
    • stream-table join, Stream-table join (stream enrichment)
    • table-table join, Table-table join (materialized view maintenance)
    • time-dependence of, Time-dependence of joins
  • support in document databases, Convergence of document and relational databases
• JOTM (transaction coordinator), Two-Phase Commit (2PC)
• journaling (filesystems), Making B-trees reliable
• JSON
  • aggregation pipeline (query language), Query languages for documents
  • Avro schema representation, Avro
  • binary variants, Binary encoding
  • data locality, Data locality for reads and writes
  • document data model, Relational Model versus Document Model
  • for application data, issues with, JSON, XML, and Binary Variants
  • GraphQL response, GraphQL
  • in relational databases, Schema flexibility in the document model
  • representing a résumé (example), The document data model for one-to-many relationships
  • Schema, JSON Schema
• JSON-LD, Triple-Stores and SPARQL
• JsonPath (query language), Query languages
• JuiceFS (distributed filesystem), Distributed Filesystems, Object Stores
• Jupyter (notebook), Machine Learning
• just-in-time (JIT) compilation, Query Execution: Compilation and Vectorization

K

• Kafka (messaging), Message brokers, Using logs for message storage
  • consumer groups, Multiple consumers
  • for data integration, Unbundled versus integrated systems
  • for event sourcing, Event Sourcing and CQRS
  • Kafka Connect (database integration), Implementing change data capture, API support for change streams, Deriving several views from the same event log
  • Kafka Streams (stream processor), Stream analytics, Maintaining materialized views
    • exactly-once semantics, Exactly-once message processing revisited
    • fault tolerance, Rebuilding state after a failure
  • ksqlDB (stream database), Maintaining materialized views
  • leader-based replication, Single-Leader Replication
  • log compaction, Log compaction, Maintaining materialized views
  • message offsets, Using logs for message storage, Idempotence
  • partitions (sharding), Sharding
  • request routing, Request Routing
  • schema registry, But what is the writer's schema?
  • serving derived data, Serving Derived Data
  • tiered storage, Disk space usage
  • transactions, Database-internal Distributed Transactions, Atomic commit revisited
  • unclean leader election, Subtleties of consensus
  • use of model-checking, Model checking and specification languages
• kappa architecture, Unifying batch and stream processing
• key-value stores, Storage and Indexing for OLTP
  • comparison to object stores, Object Stores
  • in-memory, Keeping everything in memory
  • LSM storage, Log-Structured Storage-Disk space usage
  • sharding, Sharding of Key-Value Data-Skewed Workloads and Relieving Hot Spots
    • by hash of key, Sharding by Hash of Key, Summary
    • by key range, Sharding by Key Range, Summary
    • skew and hot spots, Skewed Workloads and Relieving Hot Spots
• Kinesis (messaging), Message brokers, Using logs for message storage
  • data warehouse integration, Cloud Data Warehouses
• Kryo (Java), Language-Specific Formats
• ksqlDB (stream database), Maintaining materialized views
• Kubernetes (cluster manager), Cloud Versus Self-Hosting, Microservices and Serverless, Distributed Job Orchestration, Separation of application code and state
  • Kubeflow, Machine Learning
  • kubelet, Distributed Job Orchestration
  • operators, Distributed Job Orchestration
  • use of etcd, Request Routing, Coordination Services
• KùzuDB (database), Problems with Distributed Systems, Graph-Like Data Models
  • as embedded storage engine, Compaction strategies
  • Cypher query language, The Cypher Query Language

L

• labeled property graphs (see property graphs)
• lambda architecture, Unifying batch and stream processing
• Lamport timestamps, Lamport timestamps
• Lance (data format), Cloud Data Warehouses, Column-Oriented Storage
  • (see also column-oriented storage)
• large language models (LLMs)
  • pre-processing training data, Machine Learning
• last write wins (LWW), Last write wins (discarding concurrent writes), Detecting Concurrent Writes, Implementing Linearizable Systems
  • problems with, Timestamps for ordering events
  • prone to lost updates, Conflict resolution and replication
• latency, Latency and Response Time
  • (see also response time)
  • across regions, Distributed Versus Single-Node Systems
  • instability under two-phase locking, Performance of two-phase locking
  • network latency and resource utilization, Can we not simply make network delays predictable?
  • reducing by request hedging, Single-Leader Versus Leaderless Replication Performance
  • response time versus, Latency and Response Time
  • tail latency, Average, Median, and Percentiles, Use of Response Time Metrics, Local Secondary Indexes
• law (see legal matters)
• layering (of cloud services), Layering of cloud services
• leader-based replication, Single-Leader Replication-Logical (row-based) log replication
  • (see also replication)
  • failover, Leader failure: Failover, Distributed Locks and Leases
  • handling node outages, Handling Node Outages
  • implementation of replication logs
    • change data capture, Change Data Capture-API support for change streams
      • (see also changelogs)
    • statement-based, Statement-based replication
    • write-ahead log (WAL) shipping, Write-ahead log (WAL) shipping
  • linearizability of operations, Implementing Linearizable Systems
  • locking and leader election, Locking and leader election
  • log sequence number, Setting Up New Followers, Consumer offsets
  • read-scaling architecture, Problems with Replication Lag, Single-Leader Versus Leaderless Replication Performance
  • relation to consensus, Consensus, From single-leader replication to consensus, Pros and cons of consensus
  • setting up new followers, Setting Up New Followers
  • synchronous versus asynchronous, Synchronous Versus Asynchronous Replication-Synchronous Versus Asynchronous Replication
• leaderless replication, Leaderless Replication-Version vectors
  • (see also replication)
  • catching up on missed writes, Catching up on missed writes
  • detecting concurrent writes, Detecting Concurrent Writes-Version vectors
    • version vectors, Version vectors
  • multi-region, Multi-region operation
  • quorums, Quorums for reading and writing-Multi-region operation
    • consistency limitations, Limitations of Quorum Consistency-Monitoring staleness, Linearizability and quorums
• leap seconds, Software faults, Clock Synchronization and Accuracy
  • in time-of-day clocks, Time-of-day clocks
• leases, Process Pauses
  • implementation with coordination service, Coordination Services
  • need for fencing, Distributed Locks and Leases
  • relation to consensus, Single-value consensus
• ledgers (accounting), Summary
  • immutability, Advantages of immutable events
• legacy systems, maintenance of, Maintainability
• legal matters, Data Systems, Law, and Society-Data Systems, Law, and Society
  • data deletion, Data Systems, Law, and Society, Disk space usage
  • data residence, Distributed Versus Single-Node Systems, Sharding for Multitenancy
  • privacy regulation, Data Systems, Law, and Society, Legislation and Self-Regulation
• legitimate interest (GDPR), Consent and Freedom of Choice
• leveled compaction, Compaction strategies, Disk space usage
• Levenshtein automata, Full-Text Search
• limping (partial failure), System Model and Reality
• Linear (project management software), Real-time collaboration, offline-first, and local-first apps
• linear algebra, DataFrames, Matrices, and Arrays
• linear scalability, Describing Load
• linearizability, Solutions for Replication Lag, Linearizability-Linearizability and network delays, Glossary
  • and consensus, Consensus
  • cost of, The Cost of Linearizability-Linearizability and network delays
    • CAP theorem, The CAP theorem
    • memory on multi-core CPUs, Linearizability and network delays
  • definition, What Makes a System Linearizable?-What Makes a System Linearizable?
  • ID generation, Linearizable ID Generators
  • in coordination services, Coordination Services
  • of derived data systems
    • avoiding coordination, Coordination-avoiding data systems
  • of different replication methods, Implementing Linearizable Systems-Linearizability and quorums
    • using quorums, Linearizability and quorums
  • reads in consensus systems, Subtleties of consensus
  • relying on, Relying on Linearizability-Cross-channel timing dependencies
    • constraints and uniqueness, Constraints and uniqueness guarantees
    • cross-channel timing dependencies, Cross-channel timing dependencies
    • locking and leader election, Locking and leader election
  • versus serializability, What Makes a System Linearizable?
• linked data, Triple-Stores and SPARQL
• LinkedIn
  • Espresso (database), But what is the writer's schema?
  • LIquid (database), Datalog: Recursive Relational Queries
  • profile (example), The document data model for one-to-many relationships
• Linux, leap second bug, Software faults, Clock Synchronization and Accuracy
• Litestream (backup tool), Setting Up New Followers
• liveness properties, Safety and liveness
• LLVM (compiler), Query Execution: Compilation and Vectorization
• LMDB (storage engine), Compaction strategies, B-tree variants, Indexes and snapshot isolation
• load
  • coping with, Principles for Scalability
  • describing, Describing Load
• load balancing, Describing Performance, Load balancers, service discovery, and service meshes
  • in hardware, Load balancers, service discovery, and service meshes
  • in software, Load balancers, service discovery, and service meshes
  • using message brokers, Multiple consumers
• load shedding, Describing Performance
• local secondary indexes, Local Secondary Indexes, Summary
• local-first software, Real-time collaboration, offline-first, and local-first apps
• locality (data access), The document data model for one-to-many relationships, Data locality for reads and writes, Glossary
  • in batch processing, Dataflow Engines
  • in stateful clients, Sync Engines and Local-First Software, Stateful, offline-capable clients
  • in stream processing, Stream-table join (stream enrichment), Rebuilding state after a failure, Stream processors and services, Uniqueness in log-based messaging
• location transparency, The problems with remote procedure calls (RPCs)
  • in the actor model, Distributed actor frameworks
• lock-in, Pros and Cons of Cloud Services
• locks, Glossary
  • deadlock, Explicit locking, Implementation of two-phase locking
  • distributed locking, Distributed Locks and Leases-Fencing with multiple replicas, Locking and leader election
    • fencing tokens, Fencing off zombies and delayed requests
    • implementation with coordination service, Coordination Services
    • relation to consensus, Single-value consensus
  • for transaction isolation
    • in snapshot isolation, Multi-version concurrency control (MVCC)
    • in two-phase locking (2PL), Two-Phase Locking (2PL)-Index-range locks
    • making operations atomic, Atomic write operations
    • performance, Performance of two-phase locking
    • preventing dirty writes, Implementing read committed
    • preventing phantoms with index-range locks, Index-range locks, Detecting writes that affect prior reads
    • read locks (shared mode), Implementing read committed, Implementation of two-phase locking
    • shared mode and exclusive mode, Implementation of two-phase locking
  • in distributed transactions
    • deadlock detection, Problems with XA transactions
    • in-doubt transactions holding locks, Holding locks while in doubt
  • materializing conflicts with, Materializing conflicts
  • preventing lost updates by explicit locking, Explicit locking
• log sequence number, Setting Up New Followers, Consumer offsets
• logical clocks, Timestamps for ordering events, ID Generators and Logical Clocks-Enforcing constraints using logical clocks, Ordering events to capture causality
  • for last-write-wins, Last write wins (discarding concurrent writes)
  • for read-after-write consistency, Reading Your Own Writes
  • hybrid logical clocks, Hybrid logical clocks
  • insufficiency for enforcing constraints, Enforcing constraints using logical clocks
  • Lamport timestamps, Lamport timestamps
• logical replication, Logical (row-based) log replication
  • for change data capture, Implementing change data capture
• LogicBlox (database), Datalog: Recursive Relational Queries
• logs (data structure), Storage and Indexing for OLTP, Shared logs as consensus, Glossary
  • (see also shared logs)
  • advantages of immutability, Advantages of immutable events
  • and right to erasure, Data Systems, Law, and Society, Disk space usage
  • compaction, Constructing and merging SSTables, Compaction strategies, Log compaction, State, Streams, and Immutability
    • for stream operator state, Rebuilding state after a failure
  • implementing uniqueness constraints, Uniqueness in log-based messaging
  • log-based messaging, Log-based Message Brokers-Replaying old messages
    • comparison to traditional messaging, Logs compared to traditional messaging, Replaying old messages
    • consumer offsets, Consumer offsets
    • disk space usage, Disk space usage
    • replaying old messages, Replaying old messages, Reprocessing data for application evolution, Unifying batch and stream processing
    • slow consumers, When consumers cannot keep up with producers
    • using logs for message storage, Using logs for message storage
  • log-structured storage, Storage and Indexing for OLTP-Compaction strategies
    • log-structured merge tree (see LSM-trees)
  • relation to consensus, Shared logs as consensus
  • replication, Single-Leader Replication, Implementation of Replication Logs-Logical (row-based) log replication
    • change data capture, Change Data Capture-API support for change streams
      • (see also changelogs)
    • coordination with snapshot, Setting Up New Followers
    • logical (row-based) replication, Logical (row-based) log replication
    • statement-based replication, Statement-based replication
    • write-ahead log (WAL) shipping, Write-ahead log (WAL) shipping
  • scalability limits, The limits of total ordering
• Looker (business intelligence software), Characterizing Transaction Processing and Analytics, Analytics
• loose coupling, Making unbundling work
• lost updates (see updates)
• Lotus Notes (sync engine), Pros and cons of sync engines
• LSM-trees (indexes), The SSTable file format-Compaction strategies
  • comparison to B-trees, Comparing B-Trees and LSM-Trees-Disk space usage
• Lucene (storage engine), Full-Text Search
  • similarity search, Full-Text Search
• LWW (see last write wins)

M

• machine learning
  • batch inference, Machine Learning
  • data preparation with DataFrames, DataFrames, Matrices, and Arrays
  • deleting training data, Data Systems, Law, and Society
  • deploying data products, Beyond the data lake
  • ethical considerations, Predictive Analytics
    • (see also ethics)
  • feature engineering, From data warehouse to data lake, Machine Learning
  • in analytics systems, Operational Versus Analytical Systems
  • iterative processing, Machine Learning
  • LLMs (see large language models (LLMs))
  • models derived from training data, Application code as a derivation function
  • relation to batch processing, Machine Learning-Machine Learning
  • using a data lake, From data warehouse to data lake
  • using GPUs, Layering of cloud services, Distributed Versus Single-Node Systems
  • using matrices, DataFrames, Matrices, and Arrays
• madsim (deterministic simulation testing), Deterministic simulation testing
• magic scaling sauce, Principles for Scalability
• maintainability, Maintainability-Evolvability: Making Change Easy, A Philosophy of Streaming Systems
  • evolvability (see evolvability)
  • operability, Operability: Making Life Easy for Operations
  • simplicity and managing complexity, Simplicity: Managing Complexity
• many-to-many relationships, Many-to-One and Many-to-Many Relationships
  • modeling as graphs, Graph-Like Data Models
• many-to-one relationships, Many-to-One and Many-to-Many Relationships
  • in star schema, Stars and Snowflakes: Schemas for Analytics
• MapReduce (batch processing), Batch Processing, MapReduce-MapReduce
  • analysis of user activity events (example), JOIN and GROUP BY
  • comparison to stream processing, Processing Streams
  • disadvantages and limitations of, MapReduce
  • fault tolerance, Handling Faults
  • higher-level tools, Query languages
  • mapper and reducer functions, MapReduce
  • shuffling data, Shuffling Data
  • sort-merge joins, JOIN and GROUP BY
  • workflows, Scheduling Workflows
    • (see also workflow engines)
• marshalling (see encoding)
• MartenDB (database), Event Sourcing and CQRS
• master-slave replication (obsolete term), Single-Leader Replication
• materialization, Glossary
  • aggregate values, Materialized Views and Data Cubes
  • conflicts, Materializing conflicts
  • materialized views, Materialized Views and Data Cubes
    • as derived data, Systems of Record and Derived Data, Composing Data Storage Technologies-Unbundled versus integrated systems
    • in event sourcing, Event Sourcing and CQRS
    • incremental view maintenance, Maintaining materialized views
      • (see also incremental view maintenance (IVM))
    • maintaining, using stream processing, Maintaining materialized views, Table-table join (materialized view maintenance)
  • social network timeline example, Materializing and Updating Timelines
• Materialize (database), Materialized Views and Data Cubes
  • incremental view maintenance, Maintaining materialized views
• matrices, DataFrames, Matrices, and Arrays
  • sparse, DataFrames, Matrices, and Arrays
• Maxwell (change data capture), Implementing change data capture
• mean, Average, Median, and Percentiles
• media monitoring, Search on streams
• median, Average, Median, and Percentiles
• meeting room booking (example), More examples of write skew, Predicate locks, Enforcing Constraints
• Memcached (caching server), Keeping everything in memory
• Memgraph (database), Graph-Like Data Models
  • Cypher query language, The Cypher Query Language
• memory
  • barrier (CPU instruction), Linearizability and network delays
  • corruption, Hardware and Software Faults
  • in-memory databases, Keeping everything in memory
    • durability, Durability
    • serial transaction execution, Actual Serial Execution
  • in-memory representation of data, Formats for Encoding Data
  • memtable (in LSM-trees), Constructing and merging SSTables
  • random bit-flips in, Trust, but Verify
  • use by indexes, Log-Structured Storage
• memtable (in LSM-trees), Constructing and merging SSTables
• Mercurial (version control system), Concurrency control
• merge (DataFrame operator), DataFrames, Matrices, and Arrays
• merging sorted files, Constructing and merging SSTables, Shuffling Data
• Merkle trees, Tools for auditable data systems
• Mesos (cluster manager), Separation of application code and state
• message brokers (see messaging systems)
• message-passing (see event-driven architecture)
• MessagePack (encoding format), Binary encoding
• messaging systems, Stream Processing-Replaying old messages
  • (see also streams)
  • backpressure, buffering, or dropping messages, Messaging Systems
  • brokerless messaging, Direct messaging from producers to consumers
  • event logs, Log-based Message Brokers-Replaying old messages
    • as data model, Event Sourcing and CQRS
    • comparison to traditional messaging, Logs compared to traditional messaging, Replaying old messages
    • consumer offsets, Consumer offsets
    • replaying old messages, Replaying old messages, Reprocessing data for application evolution, Unifying batch and stream processing
    • slow consumers, When consumers cannot keep up with producers
  • exactly-once semantics, Exactly-once message processing, Exactly-once message processing revisited, Fault Tolerance
  • message brokers, Message brokers-Acknowledgments and redelivery
    • acknowledgements and redelivery, Acknowledgments and redelivery
    • comparison to event logs, Logs compared to traditional messaging, Replaying old messages
    • multiple consumers of same topic, Multiple consumers
    • versus RPC, Event-Driven Architectures
  • message loss, Messaging Systems
  • reliability, Messaging Systems
  • uniqueness in log-based messaging, Uniqueness in log-based messaging
• metastable failure, Describing Performance
• metered billing
  • serverless, Microservices and Serverless
  • storage, Operations in the Cloud Era
• microbatching, Microbatching and checkpointing
• microservices, Microservices and Serverless
  • (see also services)
  • causal dependencies across services, The limits of total ordering
  • loose coupling, Making unbundling work
  • relation to batch/stream processors, Batch Processing, Stream processors and services
• Microsoft
  • Azure Blob Storage (see Azure Blob Storage)
  • Azure managed disks, Separation of storage and compute
  • Azure Service Bus (messaging), Message brokers, Message brokers compared to databases
  • Azure SQL DB (database), Cloud-Native System Architecture
  • Azure Storage, Object Stores
  • Azure Stream Analytics, Stream analytics
  • Azure Synapse Analytics (database), Cloud-Native System Architecture
  • DCOM (Distributed Component Object Model), The problems with remote procedure calls (RPCs)
  • MSDTC (transaction coordinator), Two-Phase Commit (2PC)
  • SQL Server (see SQL Server)
• Microsoft Power BI (see Power BI (business intelligence software))
• migrating (rewriting) data, Schema flexibility in the document model, Different values written at different times, Deriving several views from the same event log, Reprocessing data for application evolution
• MinIO (object storage), Distributed Filesystems
• mobile apps, Trade-offs in Data Systems Architecture
  • embedded databases, Compaction strategies
• model checking, Model checking and specification languages
• modulus operator (%), Hash modulo number of nodes
• Mojo (programming language)
  • memory management, Limiting the impact of garbage collection
• MongoDB (database)
  • aggregation pipeline, Query languages for documents
  • atomic operations, Atomic write operations
  • BSON, Data locality for reads and writes
  • document data model, Relational Model versus Document Model
  • hash-range sharding, Sharding by Hash of Key, Sharding by hash range
  • in the cloud, Cloud-Native System Architecture
  • join support, Convergence of document and relational databases
  • joins ($lookup operator), Normalization, Denormalization, and Joins
  • JSON Schema validation, JSON Schema
  • leader-based replication, Single-Leader Replication
  • ObjectIds, ID Generators and Logical Clocks
  • range-based sharding, Sharding by Key Range
  • request routing, Request Routing
  • secondary indexes, Local Secondary Indexes
  • shard splitting, Rebalancing key-range sharded data
  • stored procedures, Pros and cons of stored procedures
• monitoring, Operations in the Cloud Era, Humans and Reliability, Operability: Making Life Easy for Operations
• monotonic clocks, Monotonic clocks
• monotonic reads, Monotonic Reads
• Morel (query language), Query languages
• MSMQ (messaging), XA transactions
• multi-column indexes, Multidimensional and Full-Text Indexes
• multi-leader replication, Multi-Leader Replication-Types of conflict
  • (see also replication)
  • collaborative editing, Real-time collaboration, offline-first, and local-first apps
  • conflict detection, Types of conflict
  • conflict resolution, Dealing with Conflicting Writes
  • for multi-region replication, Geographically Distributed Operation, The Cost of Linearizability
  • linearizability, lack of, Implementing Linearizable Systems
  • offline-capable clients, Sync Engines and Local-First Software
  • replication topologies, Multi-leader replication topologies-Problems with different topologies
• multi-object transactions, Single-Object and Multi-Object Operations
  • need for, The need for multi-object transactions
• Multi-Paxos (consensus algorithm), Consensus in Practice
• multi-reader single-writer lock, Implementation of two-phase locking
• multi-table index cluster tables (Oracle), Data locality for reads and writes
• multi-version concurrency control (MVCC), Multi-version concurrency control (MVCC), Summary
  • detecting stale MVCC reads, Detecting stale MVCC reads
  • indexes and snapshot isolation, Indexes and snapshot isolation
  • using synchronized clocks, Synchronized clocks for global snapshots
• multidimensional arrays, DataFrames, Matrices, and Arrays
• multitenancy, Separation of storage and compute, Network congestion and queueing
  • by sharding, Sharding for Multitenancy
  • using embedded databases, Compaction strategies
  • versus Byzantine fault tolerance, Byzantine Faults
• mutual exclusion, Pessimistic versus optimistic concurrency control
  • (see also locks)
• MySQL (database)
  • archiving WAL to object stores, Setting Up New Followers
  • binlog coordinates, Setting Up New Followers
  • change data capture, Implementing change data capture, API support for change streams
  • circular replication topology, Multi-leader replication topologies
  • consistent snapshots, Setting Up New Followers
  • distributed transaction support, XA transactions
  • global transaction identifiers (GTIDs), Setting Up New Followers
  • in the cloud, Cloud-Native System Architecture
  • InnoDB storage engine (see InnoDB)
  • leader-based replication, Single-Leader Replication
  • multi-leader replication, Geographically Distributed Operation
  • row-based replication, Logical (row-based) log replication
  • sharding (see Vitess (database))
  • snapshot isolation support, Snapshot isolation, repeatable read, and naming confusion
    • (see also InnoDB)
  • statement-based replication, Statement-based replication

N

• N+1 query problem, Object-relational mapping (ORM)
• nanomsg (messaging library), Direct messaging from producers to consumers
• Narayana (transaction coordinator), Two-Phase Commit (2PC)
• NATS (messaging), Message brokers
• natural language processing, From data warehouse to data lake
• Neo4j (database)
  • Cypher query language, The Cypher Query Language
  • graph data model, Graph-Like Data Models
• Neon (database), Setting Up New Followers
• Nephele (dataflow engine), Dataflow Engines
• Neptune (graph database), Graph-Like Data Models
  • Cypher query language, The Cypher Query Language
  • SPARQL query language, The SPARQL query language
• netcode (game development), Pros and cons of sync engines
• Network Attached Storage (NAS), Shared-Memory, Shared-Disk, and Shared-Nothing Architecture, Distributed Filesystems
• network model (data representation), Relational Model versus Document Model
• Network Time Protocol (see NTP)
• networks
  • congestion and queueing, Network congestion and queueing
  • datacenter network topologies, Cloud Computing Versus Supercomputing
  • faults (see faults)
  • linearizability and network delays, Linearizability and network delays
  • network partitions, Network Faults in Practice
    • in CAP theorem, The Cost of Linearizability
  • timeouts and unbounded delays, Timeouts and Unbounded Delays
• NewSQL, Relational Model versus Document Model, Solutions for Replication Lag
  • transactions and, What Exactly Is a Transaction?, Database-internal Distributed Transactions
• next-key locking, Index-range locks
• NFS (network file system), Distributed Filesystems
  • on object storage, Object Stores
• Nimble (data format), Cloud Data Warehouses, Column-Oriented Storage
  • (see also column-oriented storage)
• node (in graphs) (see vertices)
• nodes (processes), Distributed Versus Single-Node Systems, Glossary
  • handling outages in leader-based replication, Handling Node Outages
  • system models for failure, System Model and Reality
• noisy neighbors, Network congestion and queueing
• nonblocking atomic commit, Three-phase commit
• nondeterministic operations, Statement-based replication
  • (see also deterministic operations)
  • in distributed systems, Deterministic simulation testing
  • in workflow engines, Durable execution
  • partial failures, Faults and Partial Failures
  • sources of nondeterminism, Deterministic simulation testing
• nonfunctional requirements, Defining Nonfunctional Requirements, Summary
• nonrepeatable reads, Snapshot Isolation and Repeatable Read
  • (see also read skew)
• normalization (data representation), Normalization, Denormalization, and Joins-Many-to-One and Many-to-Many Relationships, Glossary
  • foreign key references, The need for multi-object transactions
  • in social network case study, Denormalization in the social networking case study
  • in systems of record, Systems of Record and Derived Data
  • versus denormalization, Deriving several views from the same event log
• NoSQL, Relational Model versus Document Model, Solutions for Replication Lag, Unbundling Databases
  • transactions and, What Exactly Is a Transaction?
• Notation3 (N3), Triple-Stores and SPARQL
• NTP (Network Time Protocol), Unreliable Clocks
  • accuracy, Clock Synchronization and Accuracy, Timestamps for ordering events
  • adjustments to monotonic clocks, Monotonic clocks
  • multiple server addresses, Weak forms of lying
• numbers, in XML and JSON encodings, JSON, XML, and Binary Variants
• NumPy (Python library), DataFrames, Matrices, and Arrays, Column-Oriented Storage
• NVMe (Non-Volatile Memory Express) (see solid state drives (SSDs))

O

• object databases, Relational Model versus Document Model
• object storage, Layering of cloud services, Object Stores-Object Stores
  • Azure Blob Storage (see Azure Blob Storage)
  • comparison to distributed filesystems, Object Stores
  • comparison to key-value stores, Object Stores
  • databases backed by, Setting Up New Followers
  • for backups, Replication
  • for cloud data warehouses, Cloud Data Warehouses, Writing to Column-Oriented Storage
  • for database replication, Setting Up New Followers
  • Google Cloud Storage (see Google Cloud Storage)
  • object size, Separation of storage and compute
  • S3 (see S3 (object storage))
  • storing LSM segment files, Constructing and merging SSTables
  • support for fencing, Fencing off zombies and delayed requests
  • use in data lakes, From data warehouse to data lake
• object-relational mapping (ORM) frameworks, Object-relational mapping (ORM)
  • error handling and aborted transactions, Handling errors and aborts
  • unsafe read-modify-write cycle code, Atomic write operations
• object-relational mismatch, The Object-Relational Mismatch
• observability, Problems with Distributed Systems, Humans and Reliability, Operability: Making Life Easy for Operations
• observer pattern, Separation of application code and state
• OBT (one big table), Stars and Snowflakes: Schemas for Analytics, Stars and Snowflakes: Schemas for Analytics
• offline systems, Batch Processing
  • (see also batch processing)
• offline-first applications, Real-time collaboration, offline-first, and local-first apps, Stateful, offline-capable clients
• offsets
  • consumer offsets in sharded logs, Consumer offsets
  • messages in sharded logs, Using logs for message storage
• OLAP (online analytic processing), Characterizing Transaction Processing and Analytics, Glossary
  • data cubes, Materialized Views and Data Cubes
• OLTP (online transaction processing), Characterizing Transaction Processing and Analytics, Glossary
  • analytics queries versus, Analytics
  • data normalization, Trade-offs of normalization
  • workload characteristics, Actual Serial Execution
• on-premises deployment, Cloud Versus Self-Hosting
  • data warehouses, Cloud Data Warehouses
• one big table (data warehouse schema), Stars and Snowflakes: Schemas for Analytics, Stars and Snowflakes: Schemas for Analytics
• one-hot encoding, DataFrames, Matrices, and Arrays
• one-to-few relationships, The document data model for one-to-many relationships
• one-to-many relationships, The document data model for one-to-many relationships
  • JSON representation, The document data model for one-to-many relationships
• online systems, Batch Processing
  • (see also services)
  • versus scientific computing, Cloud Computing Versus Supercomputing
• ontologies, Triple-Stores and SPARQL
• Oozie (workflow scheduler), Batch Processing
• OpenAPI (service definition format), Microservices and Serverless, Web services, Web services
  • use of JSON Schema, JSON Schema
• openCypher (see Cypher (query language))
• OpenLink Virtuoso (see Virtuoso (database))
• OpenStack
  • Swift (object storage), Object Stores
• operability, Operability: Making Life Easy for Operations
• operating systems versus databases, Unbundling Databases
• operational systems, Operational Versus Analytical Systems
  • (see also OLTP)
  • as systems of record, Systems of Record and Derived Data
  • ETL into analytical systems, Data Warehousing
• operational transformation, CRDTs and Operational Transformation
• operations teams, Operations in the Cloud Era
• operators (query execution), Query Execution: Compilation and Vectorization
  • in stream processing, Processing Streams
• optimistic concurrency control, Pessimistic versus optimistic concurrency control
• optimistic locking, Conditional writes (compare-and-set)
• Oracle (database)
  • distributed transaction support, XA transactions
  • GoldenGate (change data capture), Implementing change data capture
  • hierarchical queries, Graph Queries in SQL, Graph Queries in SQL
  • lack of serializability, Isolation
  • leader-based replication, Single-Leader Replication
  • multi-leader replication, Geographically Distributed Operation
  • multi-table index cluster tables, Data locality for reads and writes
  • not preventing write skew, Characterizing write skew
  • PL/SQL language, Pros and cons of stored procedures
  • preventing lost updates, Automatically detecting lost updates
  • read committed isolation, Implementing read committed
  • Real Application Clusters (RAC), Locking and leader election
  • snapshot isolation support, Snapshot Isolation and Repeatable Read, Snapshot isolation, repeatable read, and naming confusion
  • TimesTen (in-memory database), Keeping everything in memory
  • WAL-based replication, Write-ahead log (WAL) shipping
• ORC (data format), Cloud Data Warehouses, Column-Oriented Storage
  • (see also column-oriented storage)
• orchestration (service deployment), Cloud Versus Self-Hosting, Microservices and Serverless
  • batch job execution, Distributed Job Orchestration-Distributed Job Orchestration
  • workflow engines, Batch Processing
• ordering
  • event logs, Event Sourcing and CQRS
  • limits of total ordering, The limits of total ordering
  • logical timestamps, Logical Clocks
  • of auto-incrementing IDs, ID Generators and Logical Clocks
  • shared logs, Consensus in Practice-Pros and cons of consensus
• Orkes (workflow engine), Durable Execution and Workflows
• orphan pages (B-trees), Making B-trees reliable
• outbox pattern, Change data capture versus event sourcing
• outliers (response time), Average, Median, and Percentiles
• outsourcing, Cloud Versus Self-Hosting
• overload, Describing Performance, Handling errors and aborts

P

• PACELC principle, The CAP theorem
• package managers, Separation of application code and state
• packet switching, Can we not simply make network delays predictable?
• packets
  • corruption of, Weak forms of lying
  • sending via UDP, Direct messaging from producers to consumers
• PageRank (algorithm), Graph-Like Data Models, Query languages, Machine Learning
• paging (see virtual memory)
• pandas (Python library), From data warehouse to data lake, DataFrames, Matrices, and Arrays, Column-Oriented Storage, DataFrames
• Parquet (data format), Cloud Data Warehouses, Column-Oriented Storage, Archival storage, Query languages
  • (see also column-oriented storage)
  • databases on object storage, Setting Up New Followers
  • document data model, Column-Oriented Storage
  • use in batch processing, MapReduce
• partial failures, Faults and Partial Failures, Summary
  • limping, System Model and Reality
• partial synchrony (system model), System Model and Reality
• partition key, Pros and Cons of Sharding, Sharding of Key-Value Data
• partitioning (see sharding)
• Paxos (consensus algorithm), Consensus, Consensus in Practice
  • ballot number, From single-leader replication to consensus
  • Multi-Paxos, Consensus in Practice
• payment card industry (PCI), Data Systems, Law, and Society
• PCI (payment card industry) compliance, Data Systems, Law, and Society
• percentiles, Average, Median, and Percentiles, Glossary
  • calculating efficiently, Use of Response Time Metrics
  • importance of high percentiles, Use of Response Time Metrics
  • use in service level agreements (SLAs), Use of Response Time Metrics
• Percolator (Google), Implementing a linearizable ID generator
• Percona XtraBackup (MySQL tool), Setting Up New Followers
• performance
  • degradation as fault, System Model and Reality
  • describing, Describing Performance
  • of distributed transactions, Distributed Transactions Across Different Systems
  • of in-memory databases, Keeping everything in memory
  • of linearizability, Linearizability and network delays
  • of multi-leader replication, Geographically Distributed Operation
• permission isolation, Sharding for Multitenancy
• perpetual inconsistency, Timeliness and Integrity
• pessimistic concurrency control, Pessimistic versus optimistic concurrency control
• pglogical (PostgreSQL extension), Geographically Distributed Operation
• pgvector (vector index), Vector Embeddings
• phantoms (transaction isolation), Phantoms causing write skew
  • materializing conflicts, Materializing conflicts
  • preventing, in serializability, Predicate locks
• physical clocks (see clocks)
• pickle (Python), Language-Specific Formats
• Pinot (database), Characterizing Transaction Processing and Analytics, Column-Oriented Storage
  • handling writes, Writing to Column-Oriented Storage
  • pre-aggregation, Analytics
  • serving derived data, Serving Derived Data, Serving Derived Data
• pipelined execution
  • in data warehouse queries, Query Execution: Compilation and Vectorization
• pivot table, DataFrames, Matrices, and Arrays
• point in time, Unreliable Clocks
• point query, Characterizing Transaction Processing and Analytics
• Polaris (data catalog), Cloud Data Warehouses
• polling, Representing Users, Posts, and Follows
• polystores, The meta-database of everything
• POSIX (portable operating system interface)
  • compliant filesystems, Setting Up New Followers, Distributed Filesystems, Object Stores
• Post Office Horizon scandal, Humans and Reliability
  • lack of transactions, Transactions
• PostgreSQL (database)
  • archiving WAL to object stores, Setting Up New Followers
  • change data capture, Implementing change data capture, API support for change streams
  • distributed transaction support, XA transactions
  • foreign data wrappers, The meta-database of everything
  • full text search support, Combining Specialized Tools by Deriving Data
  • in the cloud, Cloud-Native System Architecture
  • JSON Schema validation, JSON Schema
  • leader-based replication, Single-Leader Replication
  • log sequence number, Setting Up New Followers
  • logical decoding, Logical (row-based) log replication
  • materialized view maintenance, Maintaining materialized views
  • multi-leader replication, Geographically Distributed Operation
  • MVCC implementation, Multi-version concurrency control (MVCC), Indexes and snapshot isolation
  • partitioning vs. sharding, Sharding
  • pgvector (vector index), Vector Embeddings
  • PL/pgSQL language, Pros and cons of stored procedures
  • PostGIS geospatial indexes, Multidimensional and Full-Text Indexes
  • preventing lost updates, Automatically detecting lost updates
  • preventing write skew, Characterizing write skew, Serializable Snapshot Isolation (SSI)
  • read committed isolation, Implementing read committed
  • representing graphs, Property Graphs
  • serializable snapshot isolation (SSI), Serializable Snapshot Isolation (SSI)
  • sharding (see Citus (database))
  • snapshot isolation support, Snapshot Isolation and Repeatable Read, Snapshot isolation, repeatable read, and naming confusion
  • WAL-based replication, Write-ahead log (WAL) shipping
• postings list, Full-Text Search
  • in sharded indexes, Local Secondary Indexes
• postmortems, blameless, Humans and Reliability
• PouchDB (database), Pros and cons of sync engines
• Power BI (business intelligence software), Characterizing Transaction Processing and Analytics, Analytics
• pre-aggregation, Analytics
  • serving derived data, Serving Derived Data
• pre-splitting, Rebalancing key-range sharded data
• Precision Time Protocol (PTP), Clock Synchronization and Accuracy
• predicate locks, Predicate locks
• predictive analytics, Operational Versus Analytical Systems, Predictive Analytics-Feedback Loops
  • amplifying bias, Bias and Discrimination
  • ethics of (see ethics)
  • feedback loops, Feedback Loops
• preemption, Resource Allocation
  • in distributed schedulers, Handling Faults
  • of threads, Process Pauses
• Prefect (workflow scheduler), Durable Execution and Workflows, Batch Processing, Scheduling Workflows
  • cloud data warehouse integration, Query languages
• Presto (query engine), Cloud Data Warehouses
• primary keys, Multi-Column and Secondary Indexes, Glossary
  • auto-incrementing, ID Generators and Logical Clocks
  • versus partition key, Sharding by hash range
• primary-backup replication (see leader-based replication)
• privacy, Privacy and Tracking-Legislation and Self-Regulation
  • consent and freedom of choice, Consent and Freedom of Choice
  • data as assets and power, Data as Assets and Power
  • deleting data, Limitations of immutability
  • ethical considerations (see ethics)
  • legislation and self-regulation, Legislation and Self-Regulation
  • meaning of, Privacy and Use of Data
  • regulation, Data Systems, Law, and Society
  • surveillance, Surveillance
  • tracking behavioral data, Privacy and Tracking
• probabilistic algorithms, Use of Response Time Metrics, Stream analytics
• process pauses, Process Pauses-Limiting the impact of garbage collection
• processing time (of events), Reasoning About Time
• producers (message streams), Transmitting Event Streams
• product analytics, Characterizing Transaction Processing and Analytics
  • column-oriented storage, Column-Oriented Storage
• programming languages
  • for stored procedures, Pros and cons of stored procedures
• projections (event sourcing), Event Sourcing and CQRS
• Prolog (language), Datalog: Recursive Relational Queries
  • (see also Datalog)
• property graphs, Property Graphs
  • Cypher query language, The Cypher Query Language
  • Property Graph Query Language (PGQL), Graph Queries in SQL
• property-based testing, Humans and Reliability, Formal Methods and Randomized Testing
• Protocol Buffers (data format), Protocol Buffers-Field tags and schema evolution, Protocol Buffers
  • field tags and schema evolution, Field tags and schema evolution
• provenance of data, Designing for auditability
• publish/subscribe model, Messaging Systems
• publishers (message streams), Transmitting Event Streams
• Pulsar (streaming platform), Acknowledgments and redelivery
• PyTorch (machine learning library), Machine Learning

Q

• Qpid (messaging), Message brokers compared to databases
• quality of service (QoS), Can we not simply make network delays predictable?
• Quantcast File System (distributed filesystem), Object Stores
• query engines
  • compilation and vectorization, Query Execution: Compilation and Vectorization
  • in cloud data warehouse, Cloud Data Warehouses
  • operators, Query Execution: Compilation and Vectorization
  • optimizing declarative queries, Data Models and Query Languages
• query languages
  • Cypher, The Cypher Query Language
  • Datalog, Datalog: Recursive Relational Queries
  • GraphQL, GraphQL
  • MongoDB aggregation pipeline, Normalization, Denormalization, and Joins, Query languages for documents
  • recursive SQL queries, Graph Queries in SQL
  • SPARQL, The SPARQL query language
  • SQL, Normalization, Denormalization, and Joins
• query optimizers, Query languages
• query plans, Query Execution: Compilation and Vectorization
• queueing delays, Network congestion and queueing
  • head-of-line blocking, Latency and Response Time
  • latency and response time, Latency and Response Time
• queues (messaging), Message brokers
• QUIC (protocol), The Limitations of TCP
• quorums, Quorums for reading and writing-Multi-region operation, Glossary
  • for leaderless replication, Quorums for reading and writing
  • in consensus algorithms, From single-leader replication to consensus
  • limitations of consistency, Limitations of Quorum Consistency-Monitoring staleness, Linearizability and quorums
  • making decisions in distributed systems, The Majority Rules
  • monitoring staleness, Monitoring staleness
  • multi-region replication, Multi-region operation
  • relying on durability, Mapping system models to the real world
• quotas, Operations in the Cloud Era

R

• R (language), From data warehouse to data lake, DataFrames, Matrices, and Arrays, DataFrames
• R-trees (indexes), Multidimensional and Full-Text Indexes
• R2 (object storage), Layering of cloud services, Distributed Filesystems
• RabbitMQ (messaging), Message brokers, Message brokers compared to databases
  • quorum queues (replication), Single-Leader Replication
• race conditions, Isolation
  • (see also concurrency)
  • avoiding with linearizability, Cross-channel timing dependencies
  • caused by dual writes, Keeping Systems in Sync
  • causing loss of money, Weak Isolation Levels
  • dirty writes, No dirty writes
  • in counter increments, No dirty writes
  • lost updates, Preventing Lost Updates-Conflict resolution and replication
  • preventing with event logs, Concurrency control, Dataflow: Interplay between state changes and application code
  • preventing with serializable isolation, Serializability
  • weak transaction isolation, Weak Isolation Levels
  • write skew, Write Skew and Phantoms-Materializing conflicts
• Raft (consensus algorithm), Consensus, Consensus in Practice
  • leader-based replication, Single-Leader Replication
  • sensitivity to network problems, Pros and cons of consensus
  • term number, From single-leader replication to consensus
  • use in etcd, Implementing Linearizable Systems
• RAID (Redundant Array of Independent Disks), Separation of storage and compute, Tolerating hardware faults through redundancy, Distributed Filesystems
• railways, schema migration on, Reprocessing data for application evolution
• RAM (see memory)
• RAMCloud (in-memory storage), Keeping everything in memory
• random writes (access pattern), Sequential versus random writes
• range queries
  • in B-trees, B-Trees, Read performance
  • in LSM-trees, Read performance
  • not efficient in hash maps, Log-Structured Storage
  • with hash sharding, Sharding by hash range
• ranking algorithms, Machine Learning
• Ray (workflow scheduler), Machine Learning
• RDF (Resource Description Framework), The RDF data model
  • querying with SPARQL, The SPARQL query language
• RDMA (Remote Direct Memory Access), Layering of cloud services, Cloud Computing Versus Supercomputing
• React (user interface library), End-to-end event streams
• reactive programming, Pros and cons of sync engines
• read committed isolation level, Read Committed-Implementing read committed
  • implementing, Implementing read committed
  • multi-version concurrency control (MVCC), Multi-version concurrency control (MVCC)
  • no dirty reads, No dirty reads
  • no dirty writes, No dirty writes
• read models (event sourcing), Event Sourcing and CQRS
• read path (derived data), Observing Derived State
• read repair (leaderless replication), Catching up on missed writes
  • for linearizability, Linearizability and quorums
• read replicas (see leader-based replication)
• read skew (transaction isolation), Snapshot Isolation and Repeatable Read, Summary
• read uncommitted isolation level, Implementing read committed
• read-after-write consistency, Reading Your Own Writes, Timeliness and Integrity
  • cross-device, Reading Your Own Writes
  • in derived data systems, Derived data versus distributed transactions
• read-modify-write cycle, Preventing Lost Updates
• read-scaling architecture, Problems with Replication Lag, Single-Leader Versus Leaderless Replication Performance
  • versus sharding, Pros and Cons of Sharding
• reads as events, Reads are events too
• real-time
  • analytics (see product analytics)
  • collaborative editing, Real-time collaboration, offline-first, and local-first apps
  • publish/subscribe dataflow, End-to-end event streams
  • response time guarantees, Response time guarantees
  • time-of-day clocks, Time-of-day clocks
• Realm (database), Pros and cons of sync engines
• rebalancing shards, Rebalancing key-range sharded data-Operations: Automatic or Manual Rebalancing, Glossary
  • (see also sharding)
  • automatic or manual rebalancing, Operations: Automatic or Manual Rebalancing
  • fixed number of shards, Fixed number of shards
  • fixed number of shards per node, Sharding by hash range
  • problems with hash mod N, Hash modulo number of nodes
• recency guarantee, Linearizability
• recommendation engines, Operational Versus Analytical Systems
  • building using DataFrames, DataFrames, Matrices, and Arrays
  • iterative processing, Machine Learning
• reconfiguration (consensus), Subtleties of consensus
• records, MapReduce
  • events in stream processing, Transmitting Event Streams
• recursive queries
  • in Cypher, The Cypher Query Language
  • in Datalog, Datalog: Recursive Relational Queries
  • in SPARQL, The SPARQL query language
  • lack of, in GraphQL, GraphQL
  • SQL common table expressions, Graph Queries in SQL
• Red Hat
  • Apicurio Registry, JSON Schema
• red-black tree, Constructing and merging SSTables
• redelivery (messaging), Acknowledgments and redelivery
• Redis (database)
  • atomic operations, Atomic write operations
  • CRDT support, CRDTs and Operational Transformation
  • durability, Keeping everything in memory
  • Lua scripting, Pros and cons of stored procedures
  • multi-leader replication, Geographically Distributed Operation
  • process-per-core model, Pros and Cons of Sharding
  • single-threaded execution, Actual Serial Execution
• redo log (see write-ahead log)
• Redpanda (messaging), Message brokers, Setting Up New Followers
  • tiered storage, Disk space usage
• Redshift (database), Cloud Data Warehouses
• redundancy
  • hardware components, Tolerating hardware faults through redundancy
  • of derived data, Systems of Record and Derived Data
    • (see also derived data)
• Reed--Solomon codes (error correction), Distributed Filesystems
• refactoring, Evolvability: Making Change Easy
  • (see also evolvability)
• regions (geographic distribution), Reading Your Own Writes
  • (see also datacenters)
  • consensus across, Pros and cons of consensus
  • definition, Reading Your Own Writes
  • latency, Distributed Versus Single-Node Systems
  • linearizable ID generation, Implementing a linearizable ID generator
  • replication across, Geographically Distributed Operation-Problems with different topologies, The Cost of Linearizability, The limits of total ordering
    • leaderless, Multi-region operation
    • multi-leader, Geographically Distributed Operation
• regions (sharding), Sharding
• register (data structure), What Makes a System Linearizable?
• regulation (see legal matters)
• relational data model, From data warehouse to data lake, Relational Model versus Document Model-Convergence of document and relational databases
  • comparison to document model, When to Use Which Model-Convergence of document and relational databases
  • graph queries in SQL, Graph Queries in SQL
  • in-memory databases with, Keeping everything in memory
  • many-to-one and many-to-many relationships, Many-to-One and Many-to-Many Relationships
  • multi-object transactions, need for, The need for multi-object transactions
  • object-relational mismatch, The Object-Relational Mismatch
  • representing a reorderable list, When to Use Which Model
  • versus document model
    • convergence of models, Convergence of document and relational databases
    • data locality, Data locality for reads and writes
• relational databases
  • eventual consistency, Problems with Replication Lag
  • history, Relational Model versus Document Model
  • leader-based replication, Single-Leader Replication
  • logical logs, Logical (row-based) log replication
  • philosophy compared to Unix, Unbundling Databases, The meta-database of everything
  • schema changes, Schema flexibility in the document model, Encoding and Evolution, Different values written at different times
  • sharded secondary indexes, Sharding and Secondary Indexes
  • statement-based replication, Statement-based replication
  • use of B-tree indexes, B-Trees
• relationships (see edges)
• reliability, Reliability and Fault Tolerance-Humans and Reliability, A Philosophy of Streaming Systems
  • building a reliable system from unreliable components, Faults and Partial Failures
  • hardware faults, Hardware and Software Faults
  • human errors, Humans and Reliability
  • importance of, Humans and Reliability
  • of messaging systems, Messaging Systems
  • software faults, Software faults
• Remote Method Invocation (Java RMI), The problems with remote procedure calls (RPCs)
• remote procedure calls (RPCs), The problems with remote procedure calls (RPCs)-Data encoding and evolution for RPC
  • (see also services)
  • data encoding and evolution, Data encoding and evolution for RPC
  • issues with, The problems with remote procedure calls (RPCs)
  • using Avro, But what is the writer's schema?
  • versus message brokers, Event-Driven Architectures
• renewable energy, Distributed Versus Single-Node Systems
• repeatable reads (transaction isolation), Snapshot isolation, repeatable read, and naming confusion
• replicas, Single-Leader Replication
• replication, Replication-Summary, Glossary
  • and durability, Durability
  • conflict resolution and, Conflict resolution and replication
  • consistency properties, Problems with Replication Lag-Solutions for Replication Lag
    • consistent prefix reads, Consistent Prefix Reads
    • monotonic reads, Monotonic Reads
    • reading your own writes, Reading Your Own Writes
  • in distributed filesystems, Distributed Filesystems
  • leaderless, Leaderless Replication-Version vectors
    • detecting concurrent writes, Detecting Concurrent Writes-Version vectors
    • limitations of quorum consistency, Limitations of Quorum Consistency-Monitoring staleness, Linearizability and quorums
  • monitoring staleness, Monitoring staleness
  • multi-leader, Multi-Leader Replication-Types of conflict
    • across multiple regions, Geographically Distributed Operation, The Cost of Linearizability
    • conflict resolution, Dealing with Conflicting Writes-Types of conflict
    • replication topologies, Multi-leader replication topologies-Problems with different topologies
  • reasons for using, Distributed Versus Single-Node Systems, Replication
  • sharding and, Sharding
  • single-leader, Single-Leader Replication-Logical (row-based) log replication
    • failover, Leader failure: Failover
    • implementation of replication logs, Implementation of Replication Logs-Logical (row-based) log replication
    • relation to consensus, From single-leader replication to consensus, Pros and cons of consensus
    • setting up new followers, Setting Up New Followers
    • synchronous versus asynchronous, Synchronous Versus Asynchronous Replication-Synchronous Versus Asynchronous Replication
  • state machine replication, Statement-based replication, Pros and cons of stored procedures, Using shared logs, Databases and Streams
    • event sourcing, Event Sourcing and CQRS
    • reliance on determinism, Deterministic simulation testing
  • using consensus, Pros and cons of consensus
  • using erasure coding, Distributed Filesystems
  • using object storage, Setting Up New Followers
  • versus backups, Replication
  • with heterogeneous data systems, Keeping Systems in Sync
• replication logs (see logs)
• representations of data (see data models)
• reprocessing data, Reprocessing data for application evolution, Unifying batch and stream processing
  • (see also evolvability)
  • from log-based messaging, Replaying old messages
• request hedging, Single-Leader Versus Leaderless Replication Performance
• request identifiers, Uniquely identifying requests, Multi-shard request processing
• request routing, Request Routing-Request Routing
  • approaches to, Request Routing
• residence laws for data, Distributed Versus Single-Node Systems, Sharding for Multitenancy
• resilient systems, Reliability and Fault Tolerance
  • (see also fault tolerance)
• resource isolation, Cloud Computing Versus Supercomputing, Sharding for Multitenancy
• resource limits, Operations in the Cloud Era
• response time
  • as performance metric, Describing Performance, Batch Processing
  • guarantees on, Response time guarantees
  • impact on users, Average, Median, and Percentiles
  • in replicated systems, Single-Leader Versus Leaderless Replication Performance
  • latency versus, Latency and Response Time
  • mean and percentiles, Average, Median, and Percentiles
  • user experience, Average, Median, and Percentiles
• responsibility and accountability, Responsibility and Accountability
• REST (Representational State Transfer), Web services
  • (see also services)
• Restate (workflow engine), Durable Execution and Workflows
• RethinkDB (database)
  • join support, Convergence of document and relational databases
  • key-range sharding, Sharding by Key Range
• retry storm, Describing Performance, Software faults
• reverse ETL, Beyond the data lake
• Riak (database)
  • CRDT support, CRDTs and Operational Transformation, Detecting Concurrent Writes
  • dotted version vectors, Version vectors
  • gossip protocol, Request Routing
  • hash sharding, Fixed number of shards
  • leaderless replication, Leaderless Replication
  • linearizability, lack of, Linearizability and quorums
  • multi-region support, Multi-region operation
  • rebalancing, Operations: Automatic or Manual Rebalancing
  • secondary indexes, Local Secondary Indexes
  • sloppy quorums, Single-Leader Versus Leaderless Replication Performance
  • vnodes (sharding), Sharding
• ring buffers, Disk space usage
• RisingWave (database)
  • incremental view maintenance, Maintaining materialized views
• rockets, Byzantine Faults
• RocksDB (storage engine), Constructing and merging SSTables
  • as embedded storage engine, Compaction strategies
  • leveled compaction, Compaction strategies
  • serving derived data, Serving Derived Data
• rollbacks (transactions), Transactions
• rolling upgrades, Tolerating hardware faults through redundancy, Encoding and Evolution, Faults and Partial Failures
  • in a multitenant system, Sharding for Multitenancy
• routing (see request routing)
• row-based replication, Logical (row-based) log replication
• row-oriented storage, Column-Oriented Storage
• rowhammer (memory corruption), Hardware and Software Faults
• RPCs (see remote procedure calls)
• rules (Datalog), Datalog: Recursive Relational Queries
• Rust (programming language)
  • memory management, Limiting the impact of garbage collection

S

• S3 (object storage), Layering of cloud services, Setting Up New Followers, Batch Processing, Distributed Filesystems, Object Stores
  • checking data integrity, Don't just blindly trust what they promise
  • conditional writes, Fencing off zombies and delayed requests
  • object size, Separation of storage and compute
  • S3 Express One Zone, Object Stores, Object Stores
  • use in MapReduce, MapReduce
  • workflow example, Scheduling Workflows
• SaaS (see software as a service (SaaS))
• safety and liveness properties, Safety and liveness
  • in consensus algorithms, Single-value consensus
  • in transactions, Transactions
• sagas (see compensating transactions)
• Samza (stream processor), Stream analytics
• SAP HANA (database), Data Storage for Analytics
• scalability, Scalability-Principles for Scalability, A Philosophy of Streaming Systems
  • auto-scaling, Operations: Automatic or Manual Rebalancing
  • by sharding, Pros and Cons of Sharding
  • describing load, Describing Load
  • describing performance, Describing Performance
  • linear, Describing Load
  • principles for, Principles for Scalability
  • replication and, Problems with Replication Lag
  • scaling up versus scaling out, Shared-Memory, Shared-Disk, and Shared-Nothing Architecture
• scaling out, Shared-Memory, Shared-Disk, and Shared-Nothing Architecture
  • (see also shared-nothing architecture)
  • by sharding, Pros and Cons of Sharding
• scaling up, Shared-Memory, Shared-Disk, and Shared-Nothing Architecture
• SCD (slowly changing dimension), Time-dependence of joins
• scheduling
  • algorithms, Resource Allocation
  • batch jobs, Distributed Job Orchestration-Scheduling Workflows
  • gang scheduling, Resource Allocation
• schema-on-read, Schema flexibility in the document model
  • comparison to evolvable schema, The Merits of Schemas
• schema-on-write, Schema flexibility in the document model
• schemaless databases (see schema-on-read)
• schemas, Glossary
  • Avro, Avro-Dynamically generated schemas
    • reader determining writer's schema, But what is the writer's schema?
    • schema evolution, The writer's schema and the reader's schema
  • dynamically generated, Dynamically generated schemas
  • evolution of, Reprocessing data for application evolution
    • affecting application code, Encoding and Evolution
    • compatibility checking, But what is the writer's schema?
    • in databases, Dataflow Through Databases-Archival storage
    • in service calls, Data encoding and evolution for RPC
  • flexibility in document model, Schema flexibility in the document model
  • for analytics, Stars and Snowflakes: Schemas for Analytics-Stars and Snowflakes: Schemas for Analytics
  • for JSON and XML, JSON, XML, and Binary Variants, JSON Schema
  • generation and migration using ORMs, Object-relational mapping (ORM)
  • merits of, The Merits of Schemas
  • migration, Schema flexibility in the document model
  • Protocol Buffers, Protocol Buffers-Field tags and schema evolution
    • schema evolution, Field tags and schema evolution
  • schema migration on railways, Reprocessing data for application evolution
  • traditional approach to design, fallacy in, Deriving several views from the same event log
• scientific computing, Cloud Computing Versus Supercomputing
• scikit-learn (Python library), From data warehouse to data lake
• ScyllaDB (database)
  • cluster metadata, Request Routing
  • consistency level ANY, Single-Leader Versus Leaderless Replication Performance
  • hash-range sharding, Sharding by Hash of Key, Sharding by hash range
  • last-write-wins conflict resolution, Detecting Concurrent Writes
  • leaderless replication, Leaderless Replication
  • lightweight transactions, Single-object writes
  • linearizability, lack of, Implementing Linearizable Systems
  • log-structured storage, Constructing and merging SSTables
  • multi-region support, Multi-region operation
  • use of clocks, Limitations of Quorum Consistency, Timestamps for ordering events
  • vnodes (sharding), Sharding
• search engines (see full-text search)
• searching on streams, Search on streams
• secondaries (see leader-based replication)
• secondary indexes, Multi-Column and Secondary Indexes, Glossary
  • for many-to-many relationships, Many-to-One and Many-to-Many Relationships
  • problems with dual writes, Keeping Systems in Sync, Reasoning about dataflows
  • sharding, Sharding and Secondary Indexes-Global Secondary Indexes, Summary
    • global, Global Secondary Indexes
    • index maintenance, Maintaining derived state
    • local, Local Secondary Indexes
  • updating, transaction isolation and, The need for multi-object transactions
• secondary sort (MapReduce), JOIN and GROUP BY
• sed (Unix tool), Simple Log Analysis
• self-hosting, Cloud Versus Self-Hosting
  • data warehouses, Cloud Data Warehouses
• self-joins, Summary
• self-validating systems, Don't just blindly trust what they promise
• semantic search, Vector Embeddings
• semantic similarity, Vector Embeddings
• semantic web, Triple-Stores and SPARQL
• semi-synchronous replication, Synchronous Versus Asynchronous Replication
• sequential writes (access pattern), Sequential versus random writes
• serializability, Isolation, Weak Isolation Levels, Serializability-Performance of serializable snapshot isolation, Glossary
  • linearizability versus, What Makes a System Linearizable?
  • pessimistic versus optimistic concurrency control, Pessimistic versus optimistic concurrency control
  • serial execution, Actual Serial Execution-Summary of serial execution
    • sharding, Sharding
    • using stored procedures, Encapsulating transactions in stored procedures, Using shared logs
  • serializable snapshot isolation (SSI), Serializable Snapshot Isolation (SSI)-Performance of serializable snapshot isolation
    • detecting stale MVCC reads, Detecting stale MVCC reads
    • detecting writes that affect prior reads, Detecting writes that affect prior reads
    • distributed execution, Performance of serializable snapshot isolation, Database-internal Distributed Transactions
    • performance of SSI, Performance of serializable snapshot isolation
    • preventing write skew, Decisions based on an outdated premise-Detecting writes that affect prior reads
  • strict serializability, What Makes a System Linearizable?
    • timeliness vs. integrity, Timeliness and Integrity
  • two-phase locking (2PL), Two-Phase Locking (2PL)-Index-range locks
    • index-range locks, Index-range locks
    • performance, Performance of two-phase locking
• Serializable (Java), Language-Specific Formats
• serialization, Formats for Encoding Data
  • (see also encoding)
• serverless, Microservices and Serverless
• service discovery, Load balancers, service discovery, and service meshes, Request Routing, Service discovery
  • registration, Load balancers, service discovery, and service meshes
  • using DNS, Load balancers, service discovery, and service meshes, Request Routing, Service discovery
• service level agreements (SLAs), Use of Response Time Metrics, Describing Load
• service mesh, Load balancers, service discovery, and service meshes
• Service Organization Control (SOC), Data Systems, Law, and Society
• service time, Latency and Response Time
• service-oriented architecture (SOA), Microservices and Serverless
  • (see also services)
• services, Dataflow Through Services: REST and RPC-Data encoding and evolution for RPC
  • microservices, Microservices and Serverless
    • causal dependencies across services, The limits of total ordering
    • loose coupling, Making unbundling work
  • relation to batch/stream processors, Batch Processing, Stream processors and services
  • remote procedure calls (RPCs), The problems with remote procedure calls (RPCs)-Data encoding and evolution for RPC
    • issues with, The problems with remote procedure calls (RPCs)
  • similarity to databases, Dataflow Through Services: REST and RPC
  • web services, Web services
• session windows (stream processing), Types of windows
  • (see also windows)
• sharding, Sharding-Summary, Glossary
  • and consensus, Using shared logs
  • and replication, Sharding
  • distributed transactions across shards, Distributed Transactions
  • hot shards, Sharding of Key-Value Data
  • in batch processing, Batch Processing
  • key-range splitting, Rebalancing key-range sharded data
  • multi-shard operations, Multi-shard data processing
    • enforcing constraints, Multi-shard request processing
    • secondary index maintenance, Maintaining derived state
  • of key-value data, Sharding of Key-Value Data-Skewed Workloads and Relieving Hot Spots
    • by key range, Sharding by Key Range
    • skew and hot spots, Skewed Workloads and Relieving Hot Spots
  • origin of the term, Sharding
  • partition key, Pros and Cons of Sharding, Sharding of Key-Value Data
  • rebalancing
    • of key-range sharded data, Rebalancing key-range sharded data
  • rebalancing shards, Rebalancing key-range sharded data-Operations: Automatic or Manual Rebalancing
    • automatic or manual rebalancing, Operations: Automatic or Manual Rebalancing
    • problems with hash mod N, Hash modulo number of nodes
    • using fixed number of shards, Fixed number of shards
    • using N shards per node, Sharding by hash range
  • request routing, Request Routing-Request Routing
  • secondary indexes, Sharding and Secondary Indexes-Global Secondary Indexes
    • global, Global Secondary Indexes
    • local, Local Secondary Indexes
  • serial execution of transactions and, Sharding
  • sorting sharded data, Shuffling Data
• shared logs, Consensus in Practice-Pros and cons of consensus, The limits of total ordering, Uniqueness in log-based messaging
  • algorithms, Consensus in Practice
  • for event sourcing, Event Sourcing and CQRS
  • for messaging, Log-based Message Brokers-Replaying old messages
  • relation to consensus, Shared logs as consensus
  • using, Using shared logs
• shared mode (locks), Implementation of two-phase locking
• shared-disk architecture, Shared-Memory, Shared-Disk, and Shared-Nothing Architecture, Distributed Filesystems
• shared-memory architecture, Shared-Memory, Shared-Disk, and Shared-Nothing Architecture
• shared-nothing architecture, Shared-Memory, Shared-Disk, and Shared-Nothing Architecture, Glossary
  • distributed filesystems, Distributed Filesystems
    • (see also distributed filesystems)
  • use of network, Unreliable Networks
• sharks
  • biting undersea cables, Network Faults in Practice
  • counting (example), Query languages for documents
• shredding (deletion) (see crypto-shredding)
• shredding (in columnar encoding), Column-Oriented Storage
• shredding (in relational model), When to Use Which Model
• shuffle (batch processing), Shuffling Data-Shuffling Data
• siblings (concurrent values), Manual conflict resolution, Capturing the happens-before relationship, Conflict resolution and replication
  • (see also conflicts)
• silo, Data Warehousing
• similarity search
  • edit distance, Full-Text Search
  • genome data, Summary
• simplicity, Simplicity: Managing Complexity
• Singer, Data Warehousing
• single-instruction-multi-data (SIMD) instructions, Query Execution: Compilation and Vectorization
• single-leader replication (see leader-based replication)
• single-threaded execution, Atomic write operations, Actual Serial Execution
  • in stream processing, Logs compared to traditional messaging, Concurrency control, Uniqueness in log-based messaging
• SingleStore (database)
  • in-memory storage, Keeping everything in memory
• site reliability engineer, Operations in the Cloud Era
• size-tiered compaction, Compaction strategies, Disk space usage
• skew, Glossary
  • clock skew, Relying on Synchronized Clocks-Clock readings with a confidence interval, Implementing Linearizable Systems
  • in transaction isolation
    • read skew, Snapshot Isolation and Repeatable Read, Summary
    • write skew, Write Skew and Phantoms-Materializing conflicts, Decisions based on an outdated premise-Detecting writes that affect prior reads
      • (see also write skew)
  • meanings of, Snapshot Isolation and Repeatable Read
  • unbalanced workload, Sharding of Key-Value Data
    • compensating for, Skewed Workloads and Relieving Hot Spots
    • due to celebrities, Skewed Workloads and Relieving Hot Spots
    • for time-series data, Sharding by Key Range
• skip list, Constructing and merging SSTables
• SLA (see service level agreements)
• Slack (group chat)
  • GraphQL example, GraphQL
• SlateDB (database), Constructing and merging SSTables, Setting Up New Followers
• sliding windows (stream processing), Types of windows
  • (see also windows)
• sloppy quorums, Single-Leader Versus Leaderless Replication Performance
• slowly changing dimension (data warehouses), Time-dependence of joins
• smearing (leap seconds adjustments), Clock Synchronization and Accuracy
• snapshots (databases)
  • as backups, Replication
  • computing derived data, Creating an index
  • in change data capture, Initial snapshot
  • serializable snapshot isolation (SSI), Serializable Snapshot Isolation (SSI)-Performance of serializable snapshot isolation
  • setting up a new replica, Setting Up New Followers
  • snapshot isolation and repeatable read, Snapshot Isolation and Repeatable Read-Snapshot isolation, repeatable read, and naming confusion
    • implementing with MVCC, Multi-version concurrency control (MVCC)
    • indexes and MVCC, Indexes and snapshot isolation
    • visibility rules, Visibility rules for observing a consistent snapshot
  • synchronized clocks for global snapshots, Synchronized clocks for global snapshots
• Snowflake (database), Cloud-Native System Architecture, Layering of cloud services, Cloud Data Warehouses, Batch Processing
  • column-oriented storage, Column-Oriented Storage
  • handling writes, Writing to Column-Oriented Storage
  • sharding and clustering, Sharding by hash range
  • Snowpark, Query languages
• Snowflake (ID generator), ID Generators and Logical Clocks
• snowflake schemas, Stars and Snowflakes: Schemas for Analytics
• SOAP (web services), The problems with remote procedure calls (RPCs)
• SOC2 (see Service Organization Control (SOC))
• social graph, Graph-Like Data Models
• society
  • responsibility towards, Data Systems, Law, and Society, Legislation and Self-Regulation
• sociotechnical systems, Humans and Reliability
• software as a service (SaaS), Trade-offs in Data Systems Architecture, Cloud Versus Self-Hosting
  • ETL from, Data Warehousing
  • multitenancy, Sharding for Multitenancy
• software bugs, Software faults
  • maintaining integrity, Maintaining integrity in the face of software bugs
• solar storm, Hardware and Software Faults
• solid state drives (SSDs)
  • access patterns, Sequential versus random writes
  • compared to object storage, Setting Up New Followers
  • detecting corruption, The end-to-end argument, Don't just blindly trust what they promise
  • failure rate, Hardware and Software Faults
  • faults in, Durability
  • firmware bugs, Software faults
  • read throughput, Read performance
  • sequential vs. random writes, Sequential versus random writes
• Solr (search server)
  • local secondary indexes, Local Secondary Indexes
  • request routing, Request Routing
  • use of Lucene, Full-Text Search
• sort (Unix tool), Simple Log Analysis, Simple Log Analysis, Sorting Versus In-memory Aggregation, Distributed Job Orchestration
• sort-merge joins (MapReduce), JOIN and GROUP BY
• Sorted String Tables (see SSTables)
• sorting
  • sort order in column storage, Sort Order in Column Storage
• source of truth (see systems of record)
• Spanner (database)
  • consistency model, What Makes a System Linearizable?
  • data locality, Data locality for reads and writes
  • in the cloud, Cloud-Native System Architecture
  • snapshot isolation using clocks, Synchronized clocks for global snapshots
  • transactions, What Exactly Is a Transaction?, Database-internal Distributed Transactions
  • TrueTime API, Clock readings with a confidence interval
• Spark (processing framework), From data warehouse to data lake, Cloud-Native System Architecture, Batch Processing, Dataflow Engines
  • cost efficiency, Query languages
  • DataFrames, DataFrames, Matrices, and Arrays, DataFrames
  • fault tolerance, Handling Faults
  • for data warehouses, Cloud Data Warehouses
  • high availability using ZooKeeper, Coordination Services
  • MLlib, Machine Learning
  • query optimizer, Query languages
  • shuffling data, Shuffling Data
  • Spark Streaming, Stream analytics
    • microbatching, Microbatching and checkpointing
  • streaming SQL support, Complex event processing
  • use for ETL, Extract--Transform--Load (ETL)
• SPARQL (query language), The SPARQL query language
• sparse index, The SSTable file format
• sparse matrices, DataFrames, Matrices, and Arrays
• split brain, Leader failure: Failover, Request Routing, Glossary
  • enforcing constraints, Uniqueness constraints require consensus
  • in consensus algorithms, Consensus, From single-leader replication to consensus
  • preventing, Implementing Linearizable Systems
  • using fencing tokens to avoid, Fencing off zombies and delayed requests-Fencing with multiple replicas
• spot instances, Handling Faults
• spreadsheets, Trade-offs in Data Systems Architecture, DataFrames, Matrices, and Arrays
  • dataflow programming, Designing Applications Around Dataflow
  • pivot table, DataFrames, Matrices, and Arrays
• SQL (Structured Query Language), Simplicity: Managing Complexity, Relational Model versus Document Model, Cloud Data Warehouses
  • for analytics, Data Warehousing, Column-Oriented Storage
  • graph queries in, Graph Queries in SQL
  • isolation levels standard, issues with, Snapshot isolation, repeatable read, and naming confusion
  • joins, Normalization, Denormalization, and Joins
  • résumé (example), The document data model for one-to-many relationships
  • social network home timelines (example), Representing Users, Posts, and Follows
  • SQL injection vulnerability, Byzantine Faults
  • statement-based replication, Statement-based replication
  • stored procedures, Pros and cons of stored procedures
  • support in batch processing frameworks, Batch Processing
  • views, Datalog: Recursive Relational Queries
• SQL Server (database)
  • archiving WAL to object stores, Setting Up New Followers
  • change data capture, Implementing change data capture
  • data warehousing support, Data Storage for Analytics
  • distributed transaction support, XA transactions
  • leader-based replication, Single-Leader Replication
  • multi-leader replication, Geographically Distributed Operation
  • preventing lost updates, Automatically detecting lost updates
  • preventing write skew, Characterizing write skew, Implementation of two-phase locking
  • read committed isolation, Implementing read committed
  • serializable isolation, Implementation of two-phase locking
  • snapshot isolation support, Snapshot Isolation and Repeatable Read
  • T-SQL language, Pros and cons of stored procedures
• SQLite (database), Problems with Distributed Systems, Compaction strategies
  • archiving WAL to object stores, Setting Up New Followers
• SRE (site reliability engineer), Operations in the Cloud Era
• SSDs (see solid state drives)
• SSTables (storage format), The SSTable file format-Compaction strategies
  • constructing and maintaining, Constructing and merging SSTables
  • making LSM-Tree from, Constructing and merging SSTables
• staged rollout (see rolling upgrades)
• staleness (old data), Reading Your Own Writes
  • cross-channel timing dependencies, Cross-channel timing dependencies
  • in leaderless databases, Writing to the Database When a Node Is Down
  • in multi-version concurrency control, Detecting stale MVCC reads
  • monitoring for, Monitoring staleness
  • of client state, Pushing state changes to clients
  • versus linearizability, Linearizability
  • versus timeliness, Timeliness and Integrity
• standbys (see leader-based replication)
• star replication topologies, Multi-leader replication topologies
• star schemas, Stars and Snowflakes: Schemas for Analytics-Stars and Snowflakes: Schemas for Analytics
• Star Wars analogy (event time versus processing time), Event time versus processing time
• starvation (scheduling), Resource Allocation
• state
  • derived from log of immutable events, State, Streams, and Immutability
  • interplay between state changes and application code, Dataflow: Interplay between state changes and application code
  • maintaining derived state, Maintaining derived state
  • maintenance by stream processor in stream-stream joins, Stream-stream join (window join)
  • observing derived state, Observing Derived State-Multi-shard data processing
  • rebuilding after stream processor failure, Rebuilding state after a failure
  • separation of application code and, Separation of application code and state
• state machine replication, Statement-based replication, Pros and cons of stored procedures, Using shared logs, Databases and Streams
  • event sourcing, Event Sourcing and CQRS
  • reliance on determinism, Deterministic simulation testing
• stateless systems, Trade-offs in Data Systems Architecture
• statement-based replication, Statement-based replication
  • reliance on determinism, Deterministic simulation testing
• statically typed languages
  • analogy to schema-on-write, Schema flexibility in the document model
• statistical and numerical algorithms, DataFrames, Matrices, and Arrays
• StatsD (metrics aggregator), Direct messaging from producers to consumers
• stock market feeds, Direct messaging from producers to consumers
• STONITH (Shoot The Other Node In The Head), Leader failure: Failover
  • problems with, Fencing off zombies and delayed requests
• stop-the-world (see garbage collection)
• storage
  • composing data storage technologies, Composing Data Storage Technologies-Unbundled versus integrated systems
• Storage Area Network (SAN), Shared-Memory, Shared-Disk, and Shared-Nothing Architecture, Distributed Filesystems
• storage engines, Storage and Retrieval-Summary
  • column-oriented, Column-Oriented Storage-Query Execution: Compilation and Vectorization
    • column compression, Column Compression-Column Compression
    • defined, Column-Oriented Storage
    • Parquet, Cloud Data Warehouses, Column-Oriented Storage, Archival storage
    • sort order in, Sort Order in Column Storage-Sort Order in Column Storage
    • versus wide-column model, Column Compression
    • writing to, Writing to Column-Oriented Storage
  • in-memory storage, Keeping everything in memory
    • durability, Durability
  • row-oriented, Storage and Indexing for OLTP-Keeping everything in memory
    • B-trees, B-Trees-B-tree variants
    • comparing B-trees and LSM-trees, Comparing B-Trees and LSM-Trees-Disk space usage
    • defined, Column-Oriented Storage
    • log-structured, Log-Structured Storage-Compaction strategies
• stored procedures, Encapsulating transactions in stored procedures-Pros and cons of stored procedures, Glossary
  • and shared logs, Using shared logs
  • pros and cons of, Pros and cons of stored procedures
  • similarity to stream processors, Application code as a derivation function
• Storm (stream processor), Stream analytics
  • distributed RPC, Event-Driven Architectures and RPC, Multi-shard data processing
  • Trident state handling, Idempotence
• straggler events, Handling straggler events
• Stream Control Transmission Protocol (SCTP), The Limitations of TCP
• stream processing, Processing Streams-Summary, Glossary
  • accessing external services within job, Stream-table join (stream enrichment), Microbatching and checkpointing, Idempotence, Exactly-once execution of an operation
  • combining with batch processing, Unifying batch and stream processing
  • comparison to batch processing, Processing Streams
  • complex event processing (CEP), Complex event processing
  • fault tolerance, Fault Tolerance-Rebuilding state after a failure
    • atomic commit, Atomic commit revisited
    • idempotence, Idempotence
    • microbatching and checkpointing, Microbatching and checkpointing
    • rebuilding state after a failure, Rebuilding state after a failure
  • for data integration, Batch and Stream Processing-Unifying batch and stream processing
  • for event sourcing, Event Sourcing and CQRS
  • maintaining derived state, Maintaining derived state
  • maintenance of materialized views, Maintaining materialized views
  • messaging systems (see messaging systems)
  • reasoning about time, Reasoning About Time-Types of windows
    • event time versus processing time, Event time versus processing time, Microbatching and checkpointing, Unifying batch and stream processing
    • knowing when window is ready, Handling straggler events
    • types of windows, Types of windows
  • relation to databases (see streams)
  • relation to services, Stream processors and services
  • relationship to batch processing, Batch Processing
  • search on streams, Search on streams
  • single-threaded execution, Logs compared to traditional messaging, Concurrency control
  • stream analytics, Stream analytics
  • stream joins, Stream Joins-Time-dependence of joins
    • stream-stream join, Stream-stream join (window join)
    • stream-table join, Stream-table join (stream enrichment)
    • table-table join, Table-table join (materialized view maintenance)
    • time-dependence of, Time-dependence of joins
• streams, Stream Processing-Replaying old messages
  • end-to-end, pushing events to clients, End-to-end event streams
  • messaging systems (see messaging systems)
  • processing (see stream processing)
  • relation to databases, Databases and Streams-Limitations of immutability
    • (see also changelogs)
    • API support for change streams, API support for change streams
    • change data capture, Change Data Capture-API support for change streams
    • derivative of state by time, State, Streams, and Immutability
    • event sourcing, Change data capture versus event sourcing
    • keeping systems in sync, Keeping Systems in Sync-Keeping Systems in Sync
    • philosophy of immutable events, State, Streams, and Immutability-Limitations of immutability
  • topics, Transmitting Event Streams
• strict serializability, What Makes a System Linearizable?
  • timeliness vs. integrity, Timeliness and Integrity
• striping (in columnar encoding), Column-Oriented Storage
• strong consistency (see linearizability)
• strong eventual consistency, Automatic conflict resolution
• strong one-copy serializability, What Makes a System Linearizable?
• subjects, predicates, and objects (in triple-stores), Triple-Stores and SPARQL
• subscribers (message streams), Transmitting Event Streams
  • (see also consumers)
• supercomputers, Cloud Computing Versus Supercomputing
• Superset (data visualization software), Analytics
• surveillance, Surveillance
  • (see also privacy)
• sushi principle, From data warehouse to data lake
• sustainability, Distributed Versus Single-Node Systems
• Swagger (service definition format), Web services
• swapping to disk (see virtual memory)
• Swift (programming language)
  • memory management, Limiting the impact of garbage collection
• sync engines, Sync Engines and Local-First Software-Pros and cons of sync engines
  • examples of, Pros and cons of sync engines
  • for local-first software, Real-time collaboration, offline-first, and local-first apps
• synchronous networks, Synchronous Versus Asynchronous Networks, Glossary
  • comparison to asynchronous networks, Synchronous Versus Asynchronous Networks
  • system model, System Model and Reality
• synchronous replication, Synchronous Versus Asynchronous Replication, Glossary
  • with multiple leaders, Multi-Leader Replication
• system administrator, Operations in the Cloud Era
• system models, Knowledge, Truth, and Lies, System Model and Reality-Deterministic simulation testing
  • assumptions in, Trust, but Verify
  • correctness of algorithms, Defining the correctness of an algorithm
  • mapping to the real world, Mapping system models to the real world
  • safety and liveness, Safety and liveness
• systems of record, Systems of Record and Derived Data, Glossary
  • change data capture, Implementing change data capture, Reasoning about dataflows
  • event logs, Event Sourcing and CQRS
  • treating event log as, State, Streams, and Immutability
• systems thinking, Feedback Loops

T

• t-digest (algorithm), Use of Response Time Metrics
• table-table joins, Table-table join (materialized view maintenance)
• Tableau (data visualization software), Characterizing Transaction Processing and Analytics, Analytics
• tail (Unix tool), Using logs for message storage
• tail latency (see latency)
• tail vertex (property graphs), Property Graphs
• task (workflows) (see workflow engines)
• TCP (Transmission Control Protocol), The Limitations of TCP
  • comparison to circuit switching, Can we not simply make network delays predictable?
  • comparison to UDP, Network congestion and queueing
  • connection failures, Detecting Faults
  • flow control, Network congestion and queueing, Messaging Systems
  • packet checksums, Weak forms of lying, The end-to-end argument, Trust, but Verify
  • reliability and duplicate suppression, Duplicate suppression
  • retransmission timeouts, Network congestion and queueing
  • use for transaction sessions, Single-Object and Multi-Object Operations
• Temporal (workflow engine), Durable Execution and Workflows
• Tensorflow (machine learning library), Machine Learning
• Teradata (database), Cloud-Native System Architecture, Cloud Data Warehouses
• term-partitioned indexes (see global secondary indexes)
• termination (consensus), Single-value consensus, Atomic commitment as consensus
• testing, Humans and Reliability
• thrashing (out of memory), Process Pauses
• threads (concurrency)
  • actor model, Distributed actor frameworks, Event-Driven Architectures and RPC
    • (see also event-driven architecture)
  • atomic operations, Atomicity
  • background threads, Constructing and merging SSTables
  • execution pauses, Can we not simply make network delays predictable?, Process Pauses-Process Pauses
  • memory barriers, Linearizability and network delays
  • preemption, Process Pauses
  • single (see single-threaded execution)
• three-phase commit, Three-phase commit
• three-way relationships, Property Graphs
• Thrift (data format), Protocol Buffers
• throughput, Describing Performance, Describing Load, Batch Processing
• TIBCO, Message brokers
  • Enterprise Message Service, Message brokers compared to databases
  • StreamBase (stream analytics), Complex event processing
• TiDB (database)
  • consensus-based replication, Single-Leader Replication
  • regions (sharding), Sharding
  • request routing, Request Routing
  • serving derived data, Serving Derived Data
  • sharded secondary indexes, Global Secondary Indexes
  • snapshot isolation support, Snapshot Isolation and Repeatable Read
  • timestamp oracle, Implementing a linearizable ID generator
  • transactions, What Exactly Is a Transaction?, Database-internal Distributed Transactions
  • use of model-checking, Model checking and specification languages
• tiered storage, Setting Up New Followers, Disk space usage
• TigerBeetle (database), Summary
  • deterministic simulation testing, Deterministic simulation testing
• TigerGraph (database)
  • GSQL language, Graph Queries in SQL
• Tigris (object storage), Distributed Filesystems
• TileDB (database), DataFrames, Matrices, and Arrays
• time
  • concurrency and, The "happens-before" relation and concurrency
  • cross-channel timing dependencies, Cross-channel timing dependencies
  • in distributed systems, Unreliable Clocks-Limiting the impact of garbage collection
    • (see also clocks)
    • clock synchronization and accuracy, Clock Synchronization and Accuracy
    • relying on synchronized clocks, Relying on Synchronized Clocks-Synchronized clocks for global snapshots
  • process pauses, Process Pauses-Limiting the impact of garbage collection
  • reasoning about, in stream processors, Reasoning About Time-Types of windows
    • event time versus processing time, Event time versus processing time, Microbatching and checkpointing, Unifying batch and stream processing
    • knowing when window is ready, Handling straggler events
    • timestamp of events, Whose clock are you using, anyway?
    • types of windows, Types of windows
  • system models for distributed systems, System Model and Reality
  • time-dependence in stream joins, Time-dependence of joins
• time series data
  • as DataFrames, DataFrames, Matrices, and Arrays
  • column-oriented storage, Column-Oriented Storage
• time-of-day clocks, Time-of-day clocks
  • hybrid logical clocks, Hybrid logical clocks
• timeliness, Timeliness and Integrity
  • coordination-avoiding data systems, Coordination-avoiding data systems
  • correctness of dataflow systems, Correctness of dataflow systems
• timeouts, Unreliable Networks, Glossary
  • dynamic configuration of, Network congestion and queueing
  • for failover, Leader failure: Failover
  • length of, Timeouts and Unbounded Delays
• TimescaleDB (database), Column-Oriented Storage
• timestamps, Logical Clocks
  • assigning to events in stream processing, Whose clock are you using, anyway?
  • for read-after-write consistency, Reading Your Own Writes
  • for transaction ordering, Synchronized clocks for global snapshots
  • insufficiency for enforcing constraints, Enforcing constraints using logical clocks
  • key range sharding by, Sharding by Key Range
  • Lamport, Lamport timestamps
  • logical, Ordering events to capture causality
  • ordering events, Timestamps for ordering events
  • timestamp oracle, Implementing a linearizable ID generator
• TLA+ (specification language), Model checking and specification languages
• token bucket (limiting retries), Describing Performance
• tombstones, Constructing and merging SSTables, Disk space usage, Log compaction
• topics (messaging), Message brokers, Transmitting Event Streams
• torn pages (B-trees), Making B-trees reliable
• total order, Glossary
  • broadcast (see shared logs)
  • limits of, The limits of total ordering
  • on logical timestamps, Logical Clocks
• tracing, Problems with Distributed Systems
• tracking behavioral data, Privacy and Tracking
  • (see also privacy)
• trade-offs, Trade-offs in Data Systems Architecture-Data Systems, Law, and Society
• transaction coordinator (see coordinator)
• transaction manager (see coordinator)
• transaction processing, Characterizing Transaction Processing and Analytics-Characterizing Transaction Processing and Analytics
  • comparison to analytics, Characterizing Transaction Processing and Analytics
  • comparison to data warehousing, Data Storage for Analytics
• transactions, Transactions-Summary, Glossary
  • ACID properties of, The Meaning of ACID
    • atomicity, Atomicity
    • consistency, Consistency
    • durability, Making B-trees reliable, Durability
    • isolation, Isolation
  • and derived data integrity, Timeliness and Integrity
  • and replication, Solutions for Replication Lag
  • compensating (see compensating transactions)
  • concept of, What Exactly Is a Transaction?
  • distributed transactions, Distributed Transactions-Exactly-once message processing revisited
    • avoiding, Derived data versus distributed transactions, Making unbundling work, Enforcing Constraints-Coordination-avoiding data systems
    • failure amplification, Maintaining derived state
    • for sharded systems, Pros and Cons of Sharding
    • in doubt/uncertain status, Coordinator failure, Holding locks while in doubt
    • two-phase commit, Two-Phase Commit (2PC)-Three-phase commit
    • use of, Distributed Transactions Across Different Systems-Exactly-once message processing
    • XA transactions, XA transactions-Problems with XA transactions
  • OLTP versus analytics queries, Analytics
  • purpose of, Transactions
  • serializability, Serializability-Performance of serializable snapshot isolation
    • actual serial execution, Actual Serial Execution-Summary of serial execution
    • pessimistic versus optimistic concurrency control, Pessimistic versus optimistic concurrency control
    • serializable snapshot isolation (SSI), Serializable Snapshot Isolation (SSI)-Performance of serializable snapshot isolation
    • two-phase locking (2PL), Two-Phase Locking (2PL)-Index-range locks
  • single-object and multi-object, Single-Object and Multi-Object Operations-Handling errors and aborts
    • handling errors and aborts, Handling errors and aborts
    • need for multi-object transactions, The need for multi-object transactions
    • single-object writes, Single-object writes
  • snapshot isolation (see snapshots)
  • strict serializability, What Makes a System Linearizable?
  • weak isolation levels, Weak Isolation Levels-Materializing conflicts
    • preventing lost updates, Preventing Lost Updates-Conflict resolution and replication
    • read committed, Read Committed-Snapshot Isolation and Repeatable Read
• traversal (graphs), Property Graphs
• trie (data structure), Constructing and merging SSTables, Full-Text Search
  • as SSTable index, The SSTable file format
• triggers (databases), Transmitting Event Streams
• Trino (data warehouse), Cloud Data Warehouses
  • federated databases, The meta-database of everything
  • query optimizer, Query languages
  • use for ETL, Extract--Transform--Load (ETL)
  • workflow example, Scheduling Workflows
• triple-stores, Triple-Stores and SPARQL-The SPARQL query language
  • SPARQL query language, The SPARQL query language
• tumbling windows (stream processing), Types of windows
  • (see also windows)
  • in microbatching, Microbatching and checkpointing
• Turbopuffer (vector search), Setting Up New Followers
• Turtle (RDF data format), Triple-Stores and SPARQL
• Twitter (see X (social network))
• two-phase commit (2PC), Two-Phase Commit (2PC)-Coordinator failure, Glossary
  • confusion with two-phase locking, Two-Phase Locking (2PL)
  • coordinator failure, Coordinator failure
  • coordinator recovery, Recovering from coordinator failure
  • how it works, A system of promises
  • performance cost, Distributed Transactions Across Different Systems
  • problems with XA transactions, Problems with XA transactions
  • transactions holding locks, Holding locks while in doubt
• two-phase locking (2PL), Two-Phase Locking (2PL)-Index-range locks, What Makes a System Linearizable?, Glossary
  • confusion with two-phase commit, Two-Phase Locking (2PL)
  • growing and shrinking phases, Implementation of two-phase locking
  • index-range locks, Index-range locks
  • performance of, Performance of two-phase locking
• type checking, dynamic versus static, Schema flexibility in the document model

U

• UDP (User Datagram Protocol)
  • comparison to TCP, Network congestion and queueing
  • multicast, Direct messaging from producers to consumers
• Ultima Online (game), Sharding
• unbounded datasets, Stream Processing, Glossary
  • (see also streams)
• unbounded delays, Glossary
  • in networks, Timeouts and Unbounded Delays
  • process pauses, Process Pauses
• unbundling databases, Unbundling Databases-Multi-shard data processing
  • composing data storage technologies, Composing Data Storage Technologies-Unbundled versus integrated systems
    • federation versus unbundling, The meta-database of everything
  • designing applications around dataflow, Designing Applications Around Dataflow-Stream processors and services
  • observing derived state, Observing Derived State-Multi-shard data processing
    • materialized views and caching, Materialized views and caching
    • multi-shard data processing, Multi-shard data processing
    • pushing state changes to clients, Pushing state changes to clients
• uncertain (transaction status) (see in doubt)
• union type (in Avro), Schema evolution rules
• uniq (Unix tool), Simple Log Analysis, Simple Log Analysis, Distributed Job Orchestration
• uniqueness constraints
  • asynchronously checked, Loosely interpreted constraints
  • requiring consensus, Uniqueness constraints require consensus
  • requiring linearizability, Constraints and uniqueness guarantees
  • uniqueness in log-based messaging, Uniqueness in log-based messaging
• Unity (data catalog), Cloud Data Warehouses
• universally unique identifiers (see UUIDs)
• Unix philosophy
  • comparison to relational databases, Unbundling Databases, The meta-database of everything
  • comparison to stream processing, Processing Streams
• Unix pipes, Simple Log Analysis
  • compared to distributed batch processing, Scheduling Workflows
• UPDATE statement (SQL), Schema flexibility in the document model
• updates
  • preventing lost updates, Preventing Lost Updates-Conflict resolution and replication
    • atomic write operations, Atomic write operations
    • automatically detecting lost updates, Automatically detecting lost updates
    • compare-and-set (CAS), Conditional writes (compare-and-set)
    • conflict resolution and replication, Conflict resolution and replication
    • using explicit locking, Explicit locking
  • preventing write skew, Write Skew and Phantoms-Materializing conflicts
• utilization
  • batch process scheduling, Resource Allocation
  • increasing through preemption, Handling Faults
  • trade-off with latency, Can we not simply make network delays predictable?
• uTP protocol (BitTorrent), The Limitations of TCP
• UUIDs, ID Generators and Logical Clocks

V

• validity (consensus), Single-value consensus, Atomic commitment as consensus
• vBuckets (sharding), Sharding
• vector clocks, Version vectors
  • (see also version vectors)
  • and Lamport/hybrid logical clocks, Lamport/hybrid logical clocks versus vector clocks
  • and version vectors, Version vectors
• vector embedding, Vector Embeddings
• vectorized processing, Query Execution: Compilation and Vectorization
• vendor lock-in, Pros and Cons of Cloud Services
• Venice (database), Serving Derived Data
• verification, Trust, but Verify-Tools for auditable data systems
  • avoiding blind trust, Don't just blindly trust what they promise
  • designing for auditability, Designing for auditability
  • end-to-end integrity checks, The end-to-end argument again
  • tools for auditable data systems, Tools for auditable data systems
• version control systems
  • merge conflicts, Manual conflict resolution
  • reliance on immutable data, Concurrency control
• version vectors, Problems with different topologies, Version vectors
  • dotted, Version vectors
  • versus vector clocks, Version vectors
• Vertica (database), Cloud Data Warehouses
  • handling writes, Writing to Column-Oriented Storage
• vertical scaling (see scaling up)
• vertices (in graphs), Graph-Like Data Models
  • property graph model, Property Graphs
• video games, Pros and cons of sync engines
• video transcoding (example), Cross-channel timing dependencies
• views (SQL queries), Datalog: Recursive Relational Queries
  • materialized views (see materialization)
• Viewstamped Replication (consensus algorithm), Consensus, Consensus in Practice
  • use of model-checking, Model checking and specification languages
  • view number, From single-leader replication to consensus
• virtual block device, Separation of storage and compute
• virtual file system, Distributed Filesystems
  • comparison to distributed filesystems, Distributed Filesystems
• virtual machines, Layering of cloud services
  • context switches, Process Pauses
  • network performance, Network congestion and queueing
  • noisy neighbors, Network congestion and queueing
  • virtualized clocks in, Clock Synchronization and Accuracy
• virtual memory
  • process pauses due to page faults, Latency and Response Time, Process Pauses
• Virtuoso (database), The SPARQL query language
• VisiCalc (spreadsheets), Designing Applications Around Dataflow
• Vitess (database)
  • key-range sharding, Sharding by Key Range
• vnodes (sharding), Sharding
• vocabularies, Triple-Stores and SPARQL
• Voice over IP (VoIP), Network congestion and queueing
• VoltDB (database)
  • cross-shard serializability, Sharding
  • deterministic stored procedures, Pros and cons of stored procedures
  • in-memory storage, Keeping everything in memory
  • process-per-core model, Pros and Cons of Sharding
  • secondary indexes, Local Secondary Indexes
  • serial execution of transactions, Actual Serial Execution
  • statement-based replication, Statement-based replication, Rebuilding state after a failure
  • transactions in stream processing, Atomic commit revisited

W

• WAL (write-ahead log), Making B-trees reliable
• WAL-G (backup tool), Setting Up New Followers
• WarpStream (messaging), Disk space usage
• web services (see services)
• webhooks, Direct messaging from producers to consumers
• webMethods (messaging), Message brokers
• WebSocket (protocol), Pushing state changes to clients
• wide-column data model, Data locality for reads and writes
  • versus column-oriented storage, Column Compression
• windows (stream processing), Stream analytics, Reasoning About Time-Types of windows
  • infinite windows for changelogs, Maintaining materialized views, Stream-table join (stream enrichment)
  • knowing when all events have arrived, Handling straggler events
  • stream joins within a window, Stream-stream join (window join)
  • types of windows, Types of windows
• WITH RECURSIVE syntax (SQL), Graph Queries in SQL
• Word2Vec (language model), Vector Embeddings
• workflow engines, Durable Execution and Workflows
  • Airflow (see Airflow (workflow scheduler))
  • batch processing, Scheduling Workflows
  • Camunda (see Camunda (workflow engine))
  • Dagster (see Dagster (workflow scheduler))
  • durable execution, Durable Execution and Workflows
  • ETL (see ETL (extract-transform-load))
  • executor, Durable Execution and Workflows
  • orchestrators, Durable Execution and Workflows, Batch Processing
  • Orkes (see Orkes (workflow engine))
  • Prefect (see Prefect (workflow scheduler))
  • reliance on determinism, Deterministic simulation testing
  • Restate (see Restate (workflow engine))
  • Temporal (see Temporal (workflow engine))
• working set, Sorting Versus In-memory Aggregation
• write amplification, Write amplification
• write path (derived data), Observing Derived State
• write skew (transaction isolation), Write Skew and Phantoms-Materializing conflicts
  • characterizing, Write Skew and Phantoms-Phantoms causing write skew, Decisions based on an outdated premise
  • examples of, Write Skew and Phantoms, More examples of write skew
  • materializing conflicts, Materializing conflicts
  • occurrence in practice, Maintaining integrity in the face of software bugs
  • phantoms, Phantoms causing write skew
  • preventing
    • in snapshot isolation, Decisions based on an outdated premise-Detecting writes that affect prior reads
    • in two-phase locking, Predicate locks-Index-range locks
    • options for, Characterizing write skew
• write-ahead log (WAL), Making B-trees reliable, Write-ahead log (WAL) shipping
  • in durable execution, Durable execution
• writes (database)
  • atomic write operations, Atomic write operations
  • detecting writes affecting prior reads, Detecting writes that affect prior reads
  • preventing dirty writes with read committed, No dirty writes
• WS-* framework, The problems with remote procedure calls (RPCs)
• WS-AtomicTransaction (2PC), Two-Phase Commit (2PC)

X

• X (social network)
  • constructing home timelines (example), Case Study: Social Network Home Timelines, Deriving several views from the same event log, Table-table join (materialized view maintenance), Materialized views and caching
    • cost of joins, Denormalization in the social networking case study
    • describing load, Describing Load
    • fault tolerance, Fault Tolerance
    • performance metrics, Describing Performance
  • DistributedLog (event log), Using logs for message storage
  • Snowflake (ID generator), ID Generators and Logical Clocks
• XA transactions, Two-Phase Commit (2PC), XA transactions-Problems with XA transactions
  • heuristic decisions, Recovering from coordinator failure
  • problems with, Problems with XA transactions
• xargs (Unix tool), Simple Log Analysis
• XFS (file system), Distributed Filesystems
• XGBoost (machine learning library), Machine Learning
• XML
  • binary variants, Binary encoding
  • data locality, Data locality for reads and writes
  • encoding RDF data, The RDF data model
  • for application data, issues with, JSON, XML, and Binary Variants
  • in relational databases, Schema flexibility in the document model
  • XML databases, Relational Model versus Document Model, Query languages for documents
• Xorq (query engine), The meta-database of everything
• XPath, Query languages for documents
• XQuery, Query languages for documents

Y

• Yahoo
  • response time study, Average, Median, and Percentiles
• YARN (job scheduler), Distributed Job Orchestration, Separation of application code and state
  • ApplicationMaster, Distributed Job Orchestration
• Yjs (CRDT library), Pros and cons of sync engines
• YugabyteDB (database)
  • hash-range sharding, Sharding by hash range
  • key-range sharding, Sharding by Key Range
  • multi-leader replication, Geographically Distributed Operation
  • request routing, Request Routing
  • sharded secondary indexes, Global Secondary Indexes
  • tablets (sharding), Sharding
  • transactions, What Exactly Is a Transaction?, Database-internal Distributed Transactions
  • use of clock synchronization, Synchronized clocks for global snapshots

Z

• Zab (consensus algorithm), Consensus, Consensus in Practice
  • use in ZooKeeper, Implementing Linearizable Systems
• zero-copy, Formats for Encoding Data
• zero-disk architecture (ZDA), Setting Up New Followers
• ZeroMQ (messaging library), Direct messaging from producers to consumers
• zombies (split brain), Fencing off zombies and delayed requests
• zones (cloud computing) (see availability zones)
• ZooKeeper (coordination service), Coordination Services-Service discovery
  • generating fencing tokens, Fencing off zombies and delayed requests, Using shared logs, Coordination Services
  • linearizable operations, Implementing Linearizable Systems
  • locks and leader election, Locking and leader election
  • observers, Service discovery
  • use for service discovery, Load balancers, service discovery, and service meshes, Service discovery
  • use for shard assignment, Request Routing
  • use of Zab algorithm, Consensus