ContextQMD
Back to Qdrant

Developer's guide to Qdrant

docs/DEVELOPMENT.md

1.17.114.0 KB
Original Source

Developer's guide to Qdrant

Build Qdrant

Docker 🐳

Build your own from source

bash
docker build . --tag=qdrant/qdrant

Or use latest pre-built image from DockerHub

bash
docker pull qdrant/qdrant

To run the container, use the command:

bash
docker run -p 6333:6333 qdrant/qdrant

And once you need a fine-grained setup, you can also define a storage path and custom configuration:

bash
docker run -p 6333:6333 \
    -v $(pwd)/path/to/data:/qdrant/storage \
    -v $(pwd)/path/to/snapshots:/qdrant/snapshots \
    -v $(pwd)/path/to/custom_config.yaml:/qdrant/config/production.yaml \
    qdrant/qdrant
  • /qdrant/storage - is the place where Qdrant persists all your data. Make sure to mount it as a volume, otherwise docker will drop it with the container.
  • /qdrant/snapshots - is the place where Qdrant stores snapshots
  • /qdrant/config/production.yaml - is the file with engine configuration. You can override any value from the reference config

Now Qdrant should be accessible at localhost:6333.

Docker image build parameters

As it was mentioned before, you can build your own Qdrant docker image with

bash
docker build . --tag=qdrant/qdrant

You may also configure certain Docker arguments to fine tune your image:

  • FEATURES -- define cargo features;
  • RUSTFLAGS -- define rustc compilation flags;
  • LINKER -- define linker in RUSTFLAGS;
  • TARGET_CPU -- define target-cpu in RUSTFLAGS;
  • JEMALLOC_SYS_WITH_LG_PAGE -- define jemalloc's page size in base 2 log; may be useful when compiling for some ARM machines; it corresponds to jemalloc's --with-lg-page configure argument.
  • GPU -- add GPU support, either nvidia or amd.

For example:

bash
docker build . --tag=qdrant/qdrant \
    --build-arg TARGET_CPU=native --build-arg GPU=amd

Local development

Linux/Debian/MacOS

To run Qdrant on local development environment you need to install below:

  • Install Rust, follow: install rust

  • Install rustfmt toolchain for Rust

    shell
    rustup component add rustfmt

  • Install dependencies:

    shell
    sudo apt-get update -y
sudo apt-get upgrade -y
sudo apt-get install -y curl unzip gcc-multilib \
    clang cmake jq \
    g++-9-aarch64-linux-gnu \
    gcc-9-aarch64-linux-gnu

  • Install protoc from source

    shell
    PROTOC_VERSION=22.2
PKG_NAME=$(uname -s | awk '{print ($1 == "Darwin") ? "osx-universal_binary" : (($1 == "Linux") ? "linux-x86_64" : "")}')

# curl `proto` source file
curl -LO https://github.com/protocolbuffers/protobuf/releases//download/v$PROTOC_VERSION/protoc-$PROTOC_VERSION-$PKG_NAME.zip

unzip protoc-$PROTOC_VERSION-$PKG_NAME.zip -d $HOME/.local

export PATH="$PATH:$HOME/.local/bin"

# remove source file if not needed
rm protoc-$PROTOC_VERSION-$PKG_NAME.zip

# check installed `protoc` version
protoc --version

  • Build and run the app

    shell
    cargo build --release --bin qdrant

./target/release/qdrant

  • Install uv for testing Then you can use uv --project tests run pytest tests/openapi and uv --project tests run pytest tests/consensus_tests to run the tests.

  • Use the web UI

    Web UI repo is in a separate repo, but there's a utility script to sync it to the static folder:

    shell
    ./tools/sync-web-ui.sh

Nix/NixOS

If you are using Nix package manager (available for Linux and MacOS), you can run nix-shell in the project root to get a shell with all dependencies installed. It includes dependencies to build Rust code as well as to run Python tests and various tools in the ./tools directory.

Profiling

There are several benchmarks implemented in Qdrant. Benchmarks are not included in CI/CD and might take some time to execute. So the expected approach to benchmarking is to run only ones which might be affected by your changes.

To run benchmark, use the following command inside a related sub-crate:

bash
cargo bench --bench name_of_benchmark

In this case you will see the execution timings and, if you launched this bench earlier, the difference in execution time.

Example output:

scoring-vector/basic-score-point
                        time:   [111.81 us 112.07 us 112.31 us]
                        change: [+19.567% +20.454% +21.404%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 9 outliers among 100 measurements (9.00%)
  3 (3.00%) low severe
  3 (3.00%) low mild
  2 (2.00%) high mild
  1 (1.00%) high severe
scoring-vector/basic-score-point-10x
                        time:   [111.86 us 112.44 us 113.04 us]
                        change: [-1.6120% -0.5554% +0.5103%] (p = 0.32 > 0.05)
                        No change in performance detected.
Found 1 outliers among 100 measurements (1.00%)
  1 (1.00%) high mild

FlameGraph and call-graph visualisation

To run benchmarks with profiler to generate FlameGraph - use the following command:

bash
cargo bench --bench name_of_benchmark -- --profile-time=60

This command will run each benchmark iterator for 60 seconds and generate FlameGraph svg along with profiling records files. These records could later be used to generate visualisation of the call-graph.

Use pprof and the following command to generate svg with a call graph:

bash
~/go/bin/pprof -output=profile.svg -svg ${qdrant_root}/target/criterion/${benchmark_name}/${function_name}/profile/profile.pb

Coverage reports

We generate coverage reports every day that can be accessed here

Note: These reports only cover the Rust unit tests (for now)

You can also generate coverage reports locally with the following commands

bash
cd qdrant
cargo install cargo-llvm-cov
./tools/coverage.sh

cd target/llvm-cov/html
python -m http.server
open http://localhost:8000

Tango.rs-based benchmarks

Some benchmarks are implemented using the Tango.rs framework. It enables more precise comparisons between two revisions of the code by running them simultaneously. Basic usage:

  1. Compile and run the baseline version in the solo mode:

    console
    $ cargo bench -p common --bench bitpacking_tango -- solo
    Finished `bench` profile [optimized + debuginfo] target(s) in 0.22s
     Running benches/bitpacking_tango.rs (target/release/deps/bitpacking_tango-9713980dd08cde85)
bitpacking/read                                     [  30.8 ns ...  43.9 ns ... 125.3 ns ]  stddev:   6.7 ns
bitpacking/write                                    [  32.4 ns ...  50.1 ns ...  91.1 ns ]  stddev:   7.3 ns
bitpacking_links/read                               [ 343.3 ns ... 378.3 ns ... 419.4 ns ]  stddev:  16.5 ns

  2. Note the binary name in the output above. Copy it to compare against, or use the cargo-export tool to automate this step.

    console
    $ cp target/release/deps/bitpacking_tango-9713980dd08cde85 ./baseline

  3. Change the code and run the benchmark in the compare mode to compare the performance against the baseline.

    console
    $ cargo bench -p common --bench bitpacking_tango -- compare ./baseline
    Finished `bench` profile [optimized + debuginfo] target(s) in 0.14s
     Running benches/bitpacking_tango.rs (target/release/deps/bitpacking_tango-9713980dd08cde85)
bitpacking/read                                    [  41.8 ns ...  41.9 ns ]      +0.08%
bitpacking/write                                   [  45.7 ns ...  45.5 ns ]      -0.46%
bitpacking_links/read                              [ 369.4 ns ... 368.4 ns ]      -0.27%

Real-time profiling

Qdrant have basic tracing support with Tracy profiler and tokio-console integrations that can be enabled with optional features.

  • tracing is an optional dependency that can be enabled with tracing feature
  • tracy feature enables Tracy profiler integration
  • console feature enables tokio-console integration
    • note, that you'll also have to pass --cfg tokio_unstable arguments to rustc to enable this feature
    • by default tokio-console binds to 127.0.0.1:6669
    • if you want to connect tokio-console to Qdrant instance running inside a Docker container or on remote server, you can define TOKIO_CONSOLE_BIND when running Qdrant to override it (e.g., TOKIO_CONSOLE_BIND=0.0.0.0:6669 to listen on all interfaces)
  • tokio-tracing feature explicitly enables Tokio crate tracing
    • note, that you'll also have to pass --cfg tokio_unstable arguments to rustc to enable this feature
    • this is required (and enabled automatically) by the console feature
    • but you can enable it explicitly with the tracy feature, to see Tokio traces in Tracy profiler

Qdrant code is not instrumented by default, so you'll have to manually add #[tracing::instrument] attributes on functions and methods that you want to profile.

Qdrant uses tracing-log as the log backend, so log and log-always features of the tracing crate should not be enabled!

rust
// `tracing` crate is an *optional* dependency in `lib/*` crates, so if you want the code to compile
// when `tracing` feature is disabled, you have to use `#[cfg_attr(...)]`...
//
// See https://doc.rust-lang.org/reference/conditional-compilation.html#the-cfg_attr-attribute
#[cfg_attr(feature = "tracing", tracing::instrument)]
fn my_function(some_parameter: String) {
    // ...
}

// ...or if you just want to do some quick-and-dirty profiling, you can use `#[tracing::instrument]`
// directly, just don't forget to add `--features tracing` when running `cargo` (or add `tracing`
// to default features in `Cargo.toml`)
#[tracing::instrument]
fn some_other_function() {
    // ...
}

Pyroscope (continuous CPU profiling)

Qdrant has built-in Pyroscope integration for continuous CPU profiling. Linux only.

Local setup — start Pyroscope:

bash
docker run -d --name pyroscope -p 4040:4040 grafana/pyroscope:latest

Pyroscope UI is available at http://localhost:4040.

Enable on a running Qdrant instance:

bash
curl -X PATCH http://localhost:6333/debugger \
  -H 'Content-Type: application/json' \
  -d '{"pyroscope": {"url": "http://localhost:4040", "identifier": "qdrant-local"}}'

Disable:

bash
curl -X PATCH http://localhost:6333/debugger \
  -H 'Content-Type: application/json' \
  -d '{"pyroscope": null}'

View flame graphs at http://localhost:4040 — select application qdrant, filter by identifier = qdrant-local (process_cpu:cpu:nanoseconds:cpu:nanoseconds{service_name="qdrant", identifier="qdrant-local"}).

Pyroscope (continuous Heap profiling)

If Jemalloc profiler is enabled, you can also use Pyroscope for continuous heap profiling. To enable the profiler start Qdrant with MALLOC_CONF="prof:true,prof_active:true" environment variable. Then when Pyroscope is activated via POST /debugger it will also start collecting heap stats and send them to the server. You can view them in the Pyroscope UI by selecting memory:inuse_space:bytes:space:bytes{service_name="qdrant", identifier="your-identifier"}.

API changes

REST

Qdrant uses the openapi specification to document its API.

This means changes to the API must be followed by changes to the specification. This is enforced by CI.

Here is a quick step-by-step guide:

  1. code endpoints and model in Rust
  2. change specs in /openapi/*ytt.yaml
  3. add new schema definitions to src/schema_generator.rs
  4. run ./tools/generate_openapi_models.sh to generate specs
  5. update integration tests tests/openapi and run them with uv --project tests run pytest tests/openapi
  6. expose file by starting an HTTP server, for instance python -m http.server, in /docs/redoc
  7. validate specs by browsing redoc on http://localhost:8000/?v=master
  8. validate openapi-merged.yaml using swagger editor

gRPC

Qdrant uses tonic to serve gRPC traffic.

Our protocol buffers are defined in lib/api/src/grpc/proto/*.proto

  1. define request and response types using protocol buffers (use oneof for enums payloads)
  2. specify RPC methods inside the service definition using protocol buffers
  3. cargo build or cargo build -p api will generate the struct definitions and a service trait
  4. implement the service trait in Rust
  5. start server cargo run --bin qdrant
  6. run integration test ./tests/basic_grpc_test.sh

Here is a good tonic tutorial for reference.

System integration

On top of the API definitions, Qdrant has a few system integrations that need to be considered when making changes:

  1. add new endpoints to the metrics allow lists in src/common/metrics.rs
  2. test the JWT integration in tests/auth_tests