qdrant/docs/DEVELOPMENT.md

Developer's guide to Qdrant

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:

cargo bench --bench name_of_banchmark

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:

cargo bench --bench name_of_banchmark -- --profile-time=60

This command will run each benchmark iterator for 60 seconds and generate FlameGraph svg along with profiling records file. 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:

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

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.

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. move newly created openapi.json cp docs/redoc/openapi.json docs/redoc/master/openapi.json
6. update integration tests openapi/tests/openapi_integration
7. expose file by starting an HTTP server, for instance python -m http.server, in /docs/redoc
8. validate specs by browsing redoc on http://localhost:8000/?v=master
9. 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 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
7. generate docs ./tools/generate_grpc_docs.sh

Here is a good tonic tutorial for reference.