Read Model

Connector can scan offline, hybrid and realtime tables. Base two options <table and tableType> parameters have to given like below;

• For offline table: table: tbl, tableType: OFFLINE or offline
• For realtime table table: tbl, tableType: REALTIME or realtime
• For hybrid table table: tbl, tableType: HYBRID or hybrid

An example scan;

val df = spark.read
      .format("pinot")
      .option("table", "airlineStats")
      .option("tableType", "offline")
      .load()

Custom schema can be specified directly. If schema is not specified, connector read table schema from Pinot controller, and then convert to the Spark schema.

Architecture

Connector reads data from Pinot Servers directly. For this operation, firstly, connector creates query with given filters(if filter push down is enabled) and columns, then finds routing table for created query. It creates pinot splits that contains ONE PINOT SERVER and ONE OR MORE SEGMENT per spark partition, based on the routing table and segmentsPerSplit(detailed explain is defined below). Lastly, each partition read data from specified pinot server in parallel.

Each Spark partition open connection with Pinot server, and read data. For example, assume that routing table informations for specified query is like that:

- realtime ->
   - realtimeServer1 -> (segment1, segment2, segment3)
   - realtimeServer2 -> (segment4)
- offline ->
   - offlineServer10 -> (segment10, segment20)

If segmentsPerSplit is equal to 3, there will be created 3 Spark partition like below;

If segmentsPerSplit is equal to 1, there will be created 6 Spark partition;

If segmentsPerSplit value is too low, that means more parallelism. But this also mean that a lot of connection will be opened with Pinot servers, and will increase QPS on the Pinot servers.

If segmentsPerSplit value is too high, that means less parallelism. Each Pinot server will scan more segments per request.

Note: Pinot servers prunes segments based on the segment metadata when query comes. In some cases(for example filtering based on the some columns), some servers may not return data. Therefore, some Spark partitions will be empty. In this cases, repartition() may be applied for efficient data analysis after loading data to Spark.

Filter And Column Push Down

Connector supports filter and column push down. Filters and columns are pushed to the pinot servers. Filter and column push down improves the performance while reading data because of its minimizing data transfer between Pinot and Spark. In default, filter push down enabled. If filters are desired to be applied in Spark, usePushDownFilters should be set as false.

Connector uses SQL, as a result all sql filters are supported.

Segment Pruning

Connector receives routing table of given query to get information on which Pinot servers to will be queried and which segments will be scan. If partitioning is enabled for given Pinot table, and created query in Spark will be scan the specific partitions, only required Pinot server and segment informations will be got(that means segment pruning operation will be applied before data reading like Pinot brokers). For more information; Optimizing Scatter and Gather in Pinot

Table Querying

Connector uses SQL to query Pinot tables.

Connector creates realtime and offline queries based on the filters and required columns.

• If queried table type is OFFLINE or REALTIME, routing table information will be got for specific table type.
• If queried table type is HYBRID, realtime and offline routing table information will be got. Also, connector receives TimeBoundary information for given table, and use it in query to ensure that the overlap between realtime and offline segment data is queried exactly once. For more information; Pinot Broker

Query Generation

Example generated queries for given usages(assume that airlineStats table is hybrid and TimeBoundary information is DaysSinceEpoch, 16084);

val df = spark.read
      .format("pinot")
      .option("table", "airlineStats")
      .option("tableType", "hybrid")
      .load()

For above usage, realtime and offline SQL queries will be created;

• Offline query: select * from airlineStats_OFFLINE where DaysSinceEpoch < 16084 LIMIT {Int.MaxValue}

• Realtime query: select * from airlineStats_REALTIME where DaysSinceEpoch >= 16084 LIMIT {Int.MaxValue}

val df = spark.read
      .format("pinot")
      .option("table", "airlineStats")
      .option("tableType", "offline")
      .load()
      .filter($"DestStateName" === "Florida")
      .filter($"Origin" === "ORD")
      .select($"DestStateName", $"Origin", $"Carrier")

• Offline query: select DestStateName, Origin, Carrier from airlineStats_OFFLINE where DestStateName = 'Florida and Origin = 'ORD' LIMIT {Int.MaxValue}

Note: Limit is added to every query. Because, generated queries will be converted to the Pinot BrokerRequest class. In this operation, pinot sets limit to 10 automatically. Therefore, LIMIT was set to Int.MaxValue to prevent this issue.

Connector Read Parameters