diff --git a/.github/workflows/.static-type-check.yml b/.github/workflows/.static-type-check.yml index 365dcbb4a5..8c2ae30b87 100644 --- a/.github/workflows/.static-type-check.yml +++ b/.github/workflows/.static-type-check.yml @@ -143,6 +143,7 @@ jobs: uv run poe static-type-check-python --package=data-storage.hm-duckdb.query-protobuf uv run poe static-type-check-python --package=data-storage.hm-lancedb uv run poe static-type-check-python --package=data-storage.hm-protobuf + uv run poe static-type-check-python --package=data-storage.lance uv run poe static-type-check-python --package=data-visualization.grafana.hm-dashboard uv run poe static-type-check-python --package=data-visualization.iads.iads-data-manager.iads-config-reader uv run poe static-type-check-python --package=data-visualization.iads.iads-data-manager.iads-data-reader diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 98d4aa537f..6d4a3f6307 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -102,6 +102,7 @@ jobs: iads-iads-data-producer: ${{ steps.filter.outputs.iads-iads-data-producer }} iads-zeromq-iads-bridge: ${{ steps.filter.outputs.iads-zeromq-iads-bridge }} hugging-face-analyze-sentiment: ${{ steps.filter.outputs.hugging-face-analyze-sentiment }} + lance: ${{ steps.filter.outputs.lance }} kafka-rust-proto-consumer: ${{ steps.filter.outputs.kafka-rust-proto-consumer }} kafka-rust-proto-producer: ${{ steps.filter.outputs.kafka-rust-proto-producer }} kafka-rust-udp-kafka-bridge: ${{ steps.filter.outputs.kafka-rust-udp-kafka-bridge }} @@ -383,6 +384,9 @@ jobs: iads-zeromq-iads-bridge: - '.github/workflows/test.yml' - 'data-visualization/iads/iads-rtstation/zeromq-iads-bridge/**' + lance: + - '.github/workflows/test.yml' + - 'data-storage/lance/**' mineru: - '.github/workflows/test.yml' - 'machine-learning/mineru/**' @@ -1406,6 +1410,39 @@ jobs: with: directory: data-storage/hm-duckdb/query-protobuf + lance-test: + name: Lance | Test + needs: detect-changes + if: ${{ needs.detect-changes.outputs.lance == 'true' }} + runs-on: ubuntu-24.04 + environment: test + timeout-minutes: 10 + steps: + - name: Checkout + uses: actions/checkout@v4.2.2 + - name: Install uv + uses: astral-sh/setup-uv@v5.1.0 + with: + version: 0.5.11 + enable-cache: true + cache-dependency-glob: data-storage/lance/uv.lock + - name: Set up Python + uses: actions/setup-python@v5.3.0 + with: + python-version-file: data-storage/lance/pyproject.toml + - name: Install dependencies + working-directory: data-storage/lance + run: | + uv sync --dev + - name: Test + working-directory: data-storage/lance + run: | + uv run poe test-coverage + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v5.1.2 + with: + directory: data-storage/lance + lancedb-test: name: LanceDB | Test needs: detect-changes diff --git a/.mergify.yml b/.mergify.yml index 7beb04fd05..5cc38779be 100644 --- a/.mergify.yml +++ b/.mergify.yml @@ -263,6 +263,9 @@ pull_request_rules: - or: - check-success=DuckDB (query-protobuf) | Test - check-skipped=DuckDB (query-protobuf) | Test + - or: + - check-success=Lance | Test + - check-skipped=Lance | Test - or: - check-success=LanceDB | Test - check-skipped=LanceDB | Test diff --git a/Makefile b/Makefile index 5bfbc67651..8e21027eab 100644 --- a/Makefile +++ b/Makefile @@ -360,6 +360,7 @@ static-type-check-python: uv run poe static-type-check-python --package=data-storage.hm-duckdb.query-protobuf uv run poe static-type-check-python --package=data-storage.hm-lancedb uv run poe static-type-check-python --package=data-storage.hm-protobuf + uv run poe static-type-check-python --package=data-storage.lance uv run poe static-type-check-python --package=data-visualization.grafana.hm-dashboard uv run poe static-type-check-python --package=data-visualization.iads.iads-data-manager.iads-config-reader uv run poe static-type-check-python --package=data-visualization.iads.iads-data-manager.iads-data-reader diff --git a/README.md b/README.md index 1b142c5c13..4bf1870163 100644 --- a/README.md +++ b/README.md @@ -231,6 +231,12 @@ The diagram illustrates the repository's architecture, which is considered overl ## Data +## Data Format + +- **Protocol Buffers (Protobuf)** - Data serialization format +- **Apache Parquet** - Columnar file format for big data +- **Lance** - Columnar file format for machine learning + ### Database, Data Warehouse, Data Lakehouse - **Trino** - Distributed SQL query engine diff --git a/data-storage/lance/Makefile b/data-storage/lance/Makefile new file mode 100644 index 0000000000..452d774fb9 --- /dev/null +++ b/data-storage/lance/Makefile @@ -0,0 +1,13 @@ +uv-install-python:: + uv python install +uv-update-lock-file: + uv lock +uv-install-dependencies: + uv sync --dev + +uv-run-dev: + uv run poe dev +uv-run-test: + uv run poe test +uv-run-test-coverage: + uv run poe test-coverage diff --git a/data-storage/lance/pyproject.toml b/data-storage/lance/pyproject.toml new file mode 100644 index 0000000000..92093315f2 --- /dev/null +++ b/data-storage/lance/pyproject.toml @@ -0,0 +1,25 @@ +[project] +name = "lance" +version = "1.0.0" +requires-python = "~=3.13.0" +dependencies = [ + "pandas==2.2.3", + "polars==1.18.0", + "pylance==0.21.0", + "tqdm==4.67.1", +] + +[dependency-groups] +dev = [ + "poethepoet==0.31.1", + "pytest==8.3.4", + "pytest-cov==6.0.0", +] + +[tool.uv] +package = false + +[tool.poe.tasks] +dev = "python src/main.py" +test = "pytest --verbose --verbose" +test-coverage = "pytest --cov=. --cov-report=xml" diff --git a/data-storage/lance/src/dummy_test.py b/data-storage/lance/src/dummy_test.py new file mode 100644 index 0000000000..82960647c8 --- /dev/null +++ b/data-storage/lance/src/dummy_test.py @@ -0,0 +1,3 @@ +class TestDummy: + def test_dummy(self): + assert 1 + 1 == 2 diff --git a/data-storage/lance/src/main.py b/data-storage/lance/src/main.py new file mode 100644 index 0000000000..28d1c4fc9b --- /dev/null +++ b/data-storage/lance/src/main.py @@ -0,0 +1,85 @@ +import logging + +import lance +import numpy as np +from lance.vector import vec_to_table + + +def main() -> None: + # Create sample vectors (minimum 5000 recommended for meaningful indexing) + num_vectors = 5000 # Increased from 1000 to meet minimum recommendation + vector_dim = 128 # Dimension of each vector (common for embeddings) + vectors = np.random.randn(num_vectors, vector_dim) + + # Create some distinct vectors at the beginning for demonstration + # Make the first vector have a clear pattern + vectors[0] = np.array([1.0] * 32 + [2.0] * 32 + [3.0] * 32 + [4.0] * 32) + # Make the second vector similar to the first but with some variation + vectors[1] = vectors[0] + np.random.randn(vector_dim) * 0.1 + + # Convert to Lance table + vector_table = vec_to_table(vectors) + + # Save to Lance dataset + uri = "/tmp/lancedb/vectors.lance" + dataset = lance.write_dataset(vector_table, uri, mode="overwrite") + logging.info( + "Dataset saved to %s with %d vectors of dimension %d", + uri, + num_vectors, + vector_dim, + ) + + # https://lancedb.github.io/lancedb/concepts/index_ivfpq/ + # Create an index for vector similarity search + # IVF-PQ is a composite index that combines inverted file index (IVF) and product quantization (PQ) + # - IVF divides the vector space into Voronoi cells using K-means clustering + # - PQ reduces dimensionality by dividing vectors into sub-vectors and quantizing them + dataset.create_index( + "vector", + index_type="IVF_PQ", + # num_partitions: The number of partitions (Voronoi cells) in the IVF portion + # - Controls how the vector space is divided + # - Higher values increase query throughput but may reduce recall + # - Should be chosen to target a particular number of vectors per partition + # - For 5000 vectors, we use 64 partitions (~78 vectors per partition) + num_partitions=64, + # num_sub_vectors: The number of sub-vectors created during Product Quantization (PQ) + # - Controls the compression level and search accuracy + # - Chosen based on desired recall and vector dimensionality + # - Trade-off: more sub-vectors = better compression but potentially lower accuracy + num_sub_vectors=16, + ) + logging.info("Created vector similarity index") + + # Read back the dataset + dataset = lance.dataset(uri) + + # Perform vector similarity search + query_vector = vectors[1] + logging.info( + "Performing similarity search for vector with pattern [1.0]*32 + [2.0]*32 + [3.0]*32 + [4.0]*32" + ) + + # Find 5 nearest neighbors + # Note: For better accuracy, you can use nprobes (5-10% of dataset) and refine_factor + k = 5 + results = dataset.to_table( + nearest={ + "column": "vector", + "k": k, + "q": query_vector, + } + ).to_pandas() + + logging.info("Nearest neighbors (distances show similarity, lower = more similar):") + for idx, row in results.iterrows(): + vector_preview = np.array(row["vector"]) + logging.info( + f"Result {idx + 1}/{k}: Distance: {row['_distance']:.4f}, Vector preview: {vector_preview[:8]}..." + ) + + 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