HyperStreamDB

Serverless Index-Streaming Database with Overlay Indexing

An indexed lakehouse storage and search engine designed for production workloads, combining the transactional guarantees of Apache Iceberg with reconstructible persistent index overlays (scalar bitmaps, BM25 Okapi, and HNSW vector search) for blazing-fast queries directly on object storage.

🎯 Architecture: The Indexed Lakehouse

HyperStreamDB implements an indexed, compute-disaggregated lakehouse storage architecture that pairs authoritative open table storage with advisory, persistent secondary indexes and a unified retrieval layer:

               Iceberg Table
                     │
       ┌─────────────┴──────────────┐
       │                            │
Authoritative Storage        Advisory Index Overlay
       │                            │
  Parquet Files              Bitmap / Bloom / BM25 / HNSW / TQ

Core Architecture Invariants

1. The Overlay Invariant: Index files are derived, reconstructible state. They may be absent, stale, or deleted without compromising snapshot correctness. Queries may degrade to Parquet scanning or background recovery, but never return incorrect results.
2. Publication Invariant: A published manifest may reference only immutable artifacts that have already been successfully uploaded and verified to storage.
3. Durability Invariant: WAL truncation is permitted only after the corresponding data is durably represented by a committed manifest snapshot.
4. Maintenance Invariant: Maintenance operations may delete an artifact only if it is neither referenced by any active snapshot nor currently in-flight.

⚡ Iceberg V2/V3 Compatibility

HyperStreamDB implements 100% of the core required Apache Iceberg table format V2 and V3 specifications:

New APIs

import hyperstreamdb as hdb

# Create table with sort order (V2)
table = hdb.Table("s3://bucket/table")
table.replace_sort_order(["timestamp", "user_id"], ascending=[False, True])

# V3 tables automatically include row lineage
# _row_id (UUID) and _last_updated_sequence_number are added when format_version >= 3

Migration Guide: V2 → V3

Upgrading to V3 enables row-level operations and enhanced tracking:

1. Automatic: V3 metadata columns added transparently when format_version >= 3
2. No Data Rewrite: Existing data remains compatible
3. New Columns: _row_id (UUID v4), _last_updated_sequence_number (i64)

🌐 REST APIs (OpenSearch & Qdrant)

HyperStreamDB includes a highly optimized HTTP frontend (hyperstreamdb-search) that exposes the core engine over standard REST protocols. By translating incoming requests into native HyperStreamDB columnar operations, it allows you to use existing tools without running traditional clustered databases.

• OpenSearch / Elasticsearch 7.10 API (Port 9200): Drop-in compatibility for standard text indexing, bulk writes, and keyword search. (e.g., connect Kibana or Grafana directly).
• Qdrant Vector API (Port 6333): Native vector database emulation. Fully compatible with Qdrant's unstructured JSON payloads, which are dynamically inferred and converted into highly compressed Arrow columns on write.

Both APIs are hosted concurrently from a single binary, completely share the exact same underlying AppState and data files, and require zero data duplication. You can write a collection of embeddings via the Qdrant API and instantly query it via the OpenSearch API!

To start the dual-API server:

# Uses HYPERSEARCH_PORT=9200 and HYPERSEARCH_QDRANT_PORT=6333 by default
cargo run -p hyperstreamdb-search

🚀 Quick Start

🐳 Docker Quickstart (3 Minutes to First Query)

Run the full HyperStreamDB gateway stack with one command:

# Standalone All-in-One Container (Local storage)
docker run -d --name hyperstreamdb \
  -p 9200:9200 \
  -p 6333:6333 \
  -p 50051:50051 \
  hyperstreamdb/quickstart:latest

# Or Full-Stack Compose (MinIO S3 + Nessie Catalog + HyperStreamDB)
docker compose -f docker/docker-compose.quickstart.yml up -d

Verify cluster health:

curl http://localhost:9200/_cluster/health

GPU-Accelerated Docker (NVIDIA CUDA, AMD ROCm, Intel XPU)

Run with hardware acceleration across NVIDIA, AMD, or Intel GPUs:

# Launch with GPU override:
docker compose -f docker/docker-compose.quickstart.yml -f docker/docker-compose.gpu.yml up -d

# Verify compute engine reported by the Search API:
curl -s http://localhost:9200/

Output:

{
  "name": "hypersearch-1",
  "cluster_name": "hypersearch",
  "version": { "number": "7.10.2", ... },
  "compute": {
    "backend": "cuda",
    "device_id": 0,
    "gpu_accelerated": true,
    "available": true
  },
  "tagline": "You know, you search"
}

Python Installation

Standard Install (CPU + WGPU/Vulkan): The default package includes automatic high-performance hardware detection for NVIDIA CUDA, Apple Metal, Intel Graphics/XPU, and AMD ROCm.

pip install hyperstreamdb

Windows Users: HyperStreamDB is optimized for Linux/POSIX. Windows users should use WSL2.

GPU Acceleration (Optional)

For GPU-accelerated vector operations, install the appropriate backend:

NVIDIA CUDA:

# Ubuntu/Debian
sudo apt-get install cuda-toolkit-12-3
# Verify: nvidia-smi

AMD ROCm: ROCm support is now native on Linux via WGPU/Vulkan.

# Verify Vulkan support (standard in modern ROCm drivers)
vulkaninfo | grep vendor
# Verify: rocm-smi

Apple Metal:

• Included with macOS 12.3+ on Apple Silicon (M1, M2, M3, M4, M5)
• No additional installation required

Intel XPU / Graphics: Intel Arc and Data Center GPUs are supported natively on Linux.

# Verify intel-media-va-driver or similar is present
clinfo | grep Intel

See Python Vector API Documentation for detailed GPU setup instructions.

pgvector SQL Compatibility

HyperStreamDB provides full pgvector-compatible SQL syntax for vector operations:

-- Use familiar pgvector operators
SELECT id, content, 
       embedding <-> '[0.1, 0.2, 0.3]'::vector AS l2_distance,
       embedding <=> '[0.1, 0.2, 0.3]'::vector AS cosine_distance
FROM documents
WHERE category = 'science'
ORDER BY l2_distance
LIMIT 10;

-- All six distance operators supported
-- <->  L2 (Euclidean)
-- <=>  Cosine  
-- <#>  Inner Product
-- <+>  L1 (Manhattan)
-- <~>  Hamming (direct on ::vector)
-- <%>  Jaccard (direct on ::vector)

💡 pgvector Compatibility Note on <~> (Hamming) & <%> (Jaccard):
In HyperStreamDB, <~> and <%> operate directly on standard float ::vector embeddings (evaluating binary indicator sets and quantized vectors) for developer convenience. In upstream PostgreSQL pgvector, these two operators are restricted exclusively to the bit data type.

PostgreSQL Conversion Equivalent:

-- HyperStreamDB:
SELECT * FROM documents ORDER BY embedding <~> '[1, 0, 1]'::vector LIMIT 10;

-- PostgreSQL (pgvector 0.7.0+): requires binary_quantize() to produce bit types
SELECT * FROM documents ORDER BY binary_quantize(embedding) <~> binary_quantize('[1, 0, 1]'::vector) LIMIT 10;

See pgvector SQL Guide for complete documentation and conversion guide.

Basic Usage

import hyperstreamdb as hdb

# Create table
table = hdb.Table("s3://bucket/my-table")

# Write data (Pandas/PyArrow)
import pandas as pd
df = pd.DataFrame({
    "id": [1, 2, 3],
    "embedding": [[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]]
})
table.write_pandas(df)
table.commit()

# Create high-performance vector index (TQ8 - 4x compression)
table.add_index("embedding", "hnsw_tq8")

# Query with filters (uses indexes!)
results = table.to_pandas(filter="id > 1")

# Vector search
query_vec = [0.15, 0.25]
results = table.to_pandas(
    vector_filter={"column": "embedding", "query": query_vec, "k": 10}
)

# Hybrid query (scalar + vector)
results = table.to_pandas(
    filter="category = 'science'",
    vector_filter={"column": "embedding", "query": query_vec, "k": 10}
)

🔄 Fluent Query API

HyperStreamDB features a fluent query API in Rust with method chaining. Python uses the to_pandas() API with filter and vector_filter arguments.

Rust Fluent API

The same fluent interface is available in native Rust:

use hyperstreamdb::{Table, VectorValue};

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    let table = Table::new("s3://bucket/my-table")?;
    
    // Method chaining
    let results = table
        .query()
        .filter("age > 25")
        .vector_search("embedding", VectorValue::Float32(query_vec), 10)
        .select(vec!["name".to_string(), "score".to_string()])
        .to_batches()
        .await?;
    
    println!("Found {} result batches", results.len());
    Ok(())
}

Benefits

• Method Chaining: Intuitive, readable query construction
• Type Safe: Compile-time validation in Rust, runtime validation in Python
• Performance: Same underlying optimized execution as traditional APIs
• Interoperable: Mix with SQL queries and traditional to_pandas() calls
• GPU Acceleration: Automatic GPU context propagation for vector operations
• TurboQuant Optimized: Seamless integration with 8-bit/4-bit quantization

TurboQuant Quantization (TQ8 / TQ4)

HyperStreamDB features TurboQuant, an optimized quantization engine that reduces vector storage costs while maintaining high search accuracy:

• TQ8 (8-bit): 4x compression vs. float32. Near-lossless accuracy (typically >99% recall retention). Ideal for general-purpose RAG.
• TQ4 (4-bit): 8x compression vs. float32. Maximum efficiency for massive datasets where storage cost is the primary bottleneck.

# High-performance community default (HNSW-TQ8)
table.add_index("embedding", "hnsw_tq8")

# High-compression mode
table.add_index("embedding", "hnsw_tq4")

# Custom HNSW-PQ configuration
table.add_index("embedding", {
    "type": "hnsw_pq",
    "complexity": 32,
    "quality": 300,
    "compression": 32 # PQ subspaces
})

Python Vector Distance API with GPU Acceleration

HyperStreamDB provides a comprehensive Python API for vector distance computations with GPU acceleration:

import hyperstreamdb as hdb
import numpy as np

# GPU-accelerated batch distance computation
ctx = hdb.GPUContext.auto_detect()  # Auto-detect CUDA/ROCm/Metal/XPU
print(f"Using GPU backend: {ctx.backend}")

# Create query and database vectors
query = np.random.randn(768).astype(np.float32)
database = np.random.randn(100000, 768).astype(np.float32)

# Compute distances on GPU (10x+ faster for large databases)
distances = hdb.l2_distance_batch(query, database, context=ctx)

# Find top-k nearest neighbors
k = 10
top_k_indices = np.argsort(distances)[:k]

# Single-pair distance computation
vec1 = np.array([1.0, 2.0, 3.0])
vec2 = np.array([4.0, 5.0, 6.0])
distance = hdb.cosine_distance(vec1, vec2)

# Sparse vector support for high-dimensional sparse data
sparse1 = hdb.SparseVector(
    indices=np.array([0, 5, 100], dtype=np.int32),
    values=np.array([1.0, 2.5, 0.8], dtype=np.float32),
    dim=1000
)
sparse2 = hdb.SparseVector(
    indices=np.array([5, 50, 100], dtype=np.int32),
    values=np.array([2.0, 1.5, 0.9], dtype=np.float32),
    dim=1000
)
distance = hdb.l2_distance_sparse(sparse1, sparse2)

# Binary vector operations (bit-packed for efficiency)
binary1 = np.packbits(np.random.randint(0, 2, 128))
binary2 = np.packbits(np.random.randint(0, 2, 128))
distance = hdb.hamming_distance_packed(binary1, binary2)

Supported GPU Backends:

• CUDA - NVIDIA GPUs (Linux, Windows via WSL2)
• ROCm - AMD GPUs (Native Linux via WGPU)
• Intel XPU - Intel Graphics (Native Linux via WGPU)
• Metal (MPS) - Apple Silicon (macOS)
• Torch Alignment - Automatically aliases cuda to rocm on AMD hardware if torch.version.hip is detected.
• CPU - Fallback for all platforms

Supported Distance Metrics:

• L2 (Euclidean), Cosine, Inner Product, L1 (Manhattan), Hamming, Jaccard

See Python Vector API Documentation for complete API reference and GPU installation instructions

SQL queries (full DataFusion support with pgvector syntax)

import hyperstreamdb as hdb
session = hdb.Session()
session.register("users", table)

# Optional: Enable GPU acceleration for SQL queries
device = hdb.Device.auto_detect()
device.activate()

# Simple SQL (via table — registers as table 't')
results = table.execute_sql("SELECT * FROM t WHERE id > 100")

# Vector similarity search with pgvector operators (GPU-accelerated)
results = session.sql("""
    SELECT id, content,
           embedding <-> '[0.1, 0.2, 0.3]'::vector AS distance
    FROM documents
    WHERE category = 'science'
    ORDER BY distance
    LIMIT 10
""")

# Joins (uses Index Nested Loop Join optimization)
results = session.sql("""
    SELECT u.name, o.amount
    FROM users u
    JOIN orders o ON u.id = o.user_id
    WHERE u.category = 'premium'
""")

# Maintenance
table.compact()

📊 Production Benchmarks & Verification

HyperStreamDB performance has been validated across large-scale synthetic, real-world datasets, and head-to-head competitive benchmarks against industry-standard engines like OpenSearch 2.11:

🚀 Competitive Benchmarks: HyperStreamDB vs. OpenSearch 2.11 (4 CPUs, 4GB RAM)

Conducted under identical, strictly constrained container environments (4 CPU cores, 4GB RAM, 64-dimensional float32 vectors, Wikipedia text payloads):

Key Architectural Takeaways:

• Ironclad Vector Latency Stability: At 1M vectors, OpenSearch tail latency collapses to 478.77 ms due to JVM garbage collection pauses and Lucene segment merging. HyperStreamDB query latency stays completely flat (1.91 ms p50 / 3.74 ms p99) thanks to its Hot Row Cache bypassing disk I/O on scattered row lookups.
• Zero Data Duplication (6.6x–7x Storage Savings): OpenSearch requires maintaining a separate primary data lake plus duplicating all vectors into amplified Lucene index files (~1.85 GB total). HyperStreamDB is the data lake, writing compressed Parquet files with compact .hnsw sidecar files (~280 MB total).
• Instant Stateless Cold Starts: HyperStreamDB eliminates the JVM boot, translog replay, and Lucene warmup delays of clustered search engines—memory mapping Parquet and .hnsw sidecars directly from the OS page cache for immediate query readiness.

📖 For complete test methodology, memory safety metrics, and replication scripts, see the Benchmarking Guide.

Workload & Engine Baselines

To run the integration and benchmark suite:

# Criterion micro-benchmarks
cargo bench

# Competitive 100k/1M OpenSearch benchmarks
./run_comparison.sh
./run_1m_comparison.sh

# Integration benchmarks
python tests/integration/test_nyc_taxi.py
python tests/benchmarks/benchmark_vs_iceberg.py

🏗️ Architecture

Overlay Indexing

HyperStreamDB stores indexes as sidecar files alongside Parquet data:

s3://bucket/table/
├── data/
│   ├── segment_001.parquet                   # Main Data (Parquet)
│   ├── segment_001.id.inv.parquet           # Scalar index (Inverted Parquet)
│   ├── segment_001.emb.centroids.parquet    # Vector index centroids
│   └── segment_001.emb.cluster_0.hnsw.graph # Vector index graph (HNSW)
├── _manifest/
│   ├── v1.avro                              # Manifest (Iceberg/Avro)
│   └── v2.avro
└── _metadata/
    └── v1.metadata.json

Manifest Format

Apache Iceberg V2/V3 compliant (Avro encoding):

{
  "version": 2,
  "timestamp_ms": 1705512000000,
  "entries": [
    {
      "file_path": "segment_001.parquet",
      "file_size_bytes": 104857600,
      "record_count": 1000000,
      "index_files": [
        {
          "file_path": "segment_001.id.inv.parquet",
          "index_type": "scalar",
          "column_name": "id"
        },
        {
          "file_path": "segment_001.embedding.cluster_0.hnsw.graph",
          "index_type": "vector",
          "column_name": "embedding"
        }
      ]
    }
  ],
  "prev_version": 1
}

🔌 Connectors

[!NOTE] MERGE INTO Support While Apache DataFusion's native SQL engine does not currently support MERGE INTO syntax out-of-the-box, you can seamlessly use MERGE INTO with HyperStreamDB via the Spark and Trino connectors. Spark and Trino parse the SQL statements using their respective query engines, determine the row-level changes, and send standard Iceberg Position Deletes and Data Appends to the HyperStreamDB core via our optimized JNI bridges.

Spark

The Spark connector supports Spark 3.5, 4.0, and 4.1 via a shared JNI FFI bridge. It intercepts row-level operations (like MERGE INTO) to take advantage of HyperStreamDB's fast indexing and supports configuring GPU backends.

// Read
val df = spark.read
  .format("hyperstream")
  .option("path", "s3://bucket/table")
  // Optionally configure the GPU device (cuda, mps, intel, rocm, auto, or cpu)
  .option("hyperstream.gpu_device", "cuda")
  .load()

// Write
df.write
  .format("hyperstream")
  .option("path", "s3://bucket/table")
  .save()

You can also globally configure the GPU for Spark stored procedures (e.g. index building):

spark.conf.set("spark.hyperstream.gpu.device", "cuda")

Trino

The Trino connector intercepts reads to natively push down scalar and vector filtering to the HyperStreamDB core, drastically reducing IO.

SELECT * FROM hyperstream.default.my_table
WHERE id > 100;  -- Uses scalar index natively via JNI pushdown

You can configure the GPU backend for Trino globally or per-catalog using the properties file (e.g. etc/catalog/hyperstream.properties):

connector.name=hyperstreamdb
hyperstream.gpu-device=cuda

Arrow Flight SQL Gateway

HyperStreamDB provides a high-performance Arrow Flight SQL server (hyperstreamdb-flight) running over gRPC (port 50051). This enables any JDBC, ODBC, ADBC, or Arrow-native client (including BI tools and distributed query engines) to query HyperStreamDB with zero-copy Arrow serialization and native index pushdown.

cargo run -p hyperstreamdb-flight

dbt (dbt-hyperstreamdb)

Official dbt adapter for HyperStreamDB over Arrow Flight SQL. Provides native vector search macros and custom materializations:

• Vector Macros: vector_distance(...), knn_search(...), vector_avg(...), type_vector(...), type_sparsevec(...) with pgvector-compatible operators.
• Custom Materializations: Table and incremental materialization with support for append, delete+insert, and partition-looping insert_overwrite.
• DDL Support: Iceberg-compatible PARTITIONED BY syntax.

cd dbt-hyperstreamdb
pip install -e .

Python (Direct)

# No Spark needed for local/notebook work
import hyperstreamdb as hdb
df = hdb.Table("s3://bucket/table").query().execute()
# Or using traditional API: df = hdb.Table("s3://bucket/table").to_pandas()

🔨 Building Connectors

The Spark and Trino connectors require building shaded "fat" JARs that bundle the native Rust core.

Matrix Build

We provide a script to build a full matrix of connectors (Java 17/21, Spark 3.5/4.0):

./build-connectors.sh

Hardware Acceleration

• Standard: Build with CPU + Intel Graphics/XPU support (default).
• CUDA: Build for NVIDIA GPUs:

  ./build-connectors.sh --cuda
  

Portable Toolchain

The build script automatically downloads a project-local Maven and JDK 21 if they are missing from your system, ensuring a consistent build environment.

Artifacts

Final JARs and ZIPs are collected in the connector-artifacts/ directory.

🧪 Development

Build & Test

# Build Rust library
cargo build --release

# Run tests
cargo test

# Run benchmarks
cargo bench

# Build Python bindings
maturin develop

# Python tests
pytest tests/

Project Structure

hyperstreamdb/
├── src/
│   ├── lib.rs                  # Main library & PyO3 module registration
│   ├── core/
│   │   ├── table/              # Table API (read, write, schema, fluent query)
│   │   ├── reader/             # Index-aware Parquet reader
│   │   ├── manifest/           # Manifest management (Iceberg/Avro)
│   │   ├── index/              # HNSW, inverted, bitmap indexes
│   │   ├── catalog/            # REST, Nessie, Glue, Hive, Unity catalogs
│   │   ├── sql/                # DataFusion integration & pgvector operators
│   │   ├── planner/            # Query planner & optimizer
│   │   ├── iceberg/            # Iceberg V2/V3 metadata & schema
│   │   ├── lock.rs             # Vendor-neutral distributed locking (FileBasedLock via object store CAS)
│   │   ├── compaction.rs       # Compaction engine
│   │   ├── maintenance.rs      # Vacuum/GC
│   │   ├── storage.rs          # Multi-cloud storage (S3, GCS, Azure, local)
│   │   ├── wal.rs              # Write-Ahead Log
│   │   ├── ffi.rs              # JNI bindings (Spark/Trino)
│   │   └── error.rs            # Structured error types
│   ├── telemetry/              # Structured tracing (OpenTelemetry) & Prometheus metrics
│   ├── python_binding.rs       # PyO3 bindings
│   ├── python_distance.rs      # Vector distance API
│   └── python_gpu_context.rs   # GPU device management
├── hyperstreamdb-flight/        # Arrow Flight SQL gRPC server
├── hyperstreamdb-search/        # OpenSearch 7.10 & Qdrant REST search gateway
├── dbt-hyperstreamdb/           # Official dbt adapter (Arrow Flight SQL)
├── hyperstreamdb-enterprise/    # Enterprise extensions (Continuous Indexing, Enterprise Security)
├── spark-hyperstream/          # Spark connector (Java)
├── trino-hyperstream/          # Trino connector (Java)
├── tests/
│   ├── integration/            # Infrastructure integration tests
│   ├── benchmarks/             # Performance benchmarks
│   └── python/                 # Python binding tests
└── benches/                    # Criterion benchmarks

🔎 Search API (OpenSearch / Elasticsearch 7.10-compatible)

HyperStreamDB ships an optional add-on, hypersearch (hyperstreamdb-search), that serves an OpenSearch 1.x / Elasticsearch 7.10-compatible REST API on top of the engine — plus a Qdrant-compatible API for vector workloads. It is built for website search, document catalogs, and knowledge bases where a 50–200 ms query latency envelope is acceptable and object-storage-native, scale-to-zero hosting is desired.

cargo build --release -p hyperstreamdb-search --bin hypersearch
HYPERSEARCH_BIND=127.0.0.1 HYPERSEARCH_PORT=9200 ./target/release/hypersearch

# Index + search (ES 7.10 wire format)
curl -X POST localhost:9200/articles/_doc -H 'content-type: application/json' \
     -d '{"title":"Hello","body":"Welcome to HyperStreamDB"}'
curl -X POST localhost:9200/articles/_refresh
curl -X POST localhost:9200/articles/_search -H 'content-type: application/json' \
     -d '{"query":{"match":{"body":"HyperStreamDB"}}}'

Supported: cluster/health/cat/stats, index CRUD, mapping GET/PUT, _doc, _bulk, _refresh, _search (match BM25, knn HNSW, hybrid RRF, filter/bool with term/terms/range/exists, match_all), _count, _source filtering, from/size, and Prometheus /metrics.

Not supported (v1): per-document delete (501, append-only), aggregations, aliases, reindex, ILM, snapshots, auth, multi-node. See OPENSEARCH_COMPATIBILITY.md for the full matrix and GETTING_STARTED.md for a complete quickstart.

📈 Roadmap

✅ Completed

• Hybrid segment format (Parquet + indexes)
• Manifest management (Iceberg-like)
• Compaction engine
• Maintenance (expire_snapshots, remove_orphan_files)
• Python bindings (Pandas-compatible)
• Native SQL support (DataFusion integration)
• pgvector-compatible SQL operators and syntax
• Index Nested Loop Join optimization
• Boolean column indexing
• Multi-table JOIN support
• Real-world testing (NYC Taxi, Wikipedia, embeddings)
• Multi-catalog support (Nessie, REST, AWS Glue, Hive Metastore, Unity)
• Iceberg V2 compliance (Sort Orders, Partition Evolution, Statistics)
• Iceberg V3 features (Row Lineage, Default Values, HyperLogLog NDV)
• Standard Iceberg API (update_spec, replace_sort_order, rewrite_data_files, rollback_to_snapshot)
• Python Vector Distance API with GPU acceleration
• Multi-backend GPU support (CUDA, ROCm, Metal, XPU)
• Sparse and binary vector operations
• Spark and Trino connectors (JNI Bridge & Native GPU support)
• Schema evolution & Partition evolution
• CLI tools (hyperstream admin and REPL SQL)
• Prometheus metrics & Grafana dashboards
• OpenSearch / Elasticsearch 7.10 & Qdrant REST Search API (hyperstreamdb-search)
• Arrow Flight SQL Gateway (hyperstreamdb-flight gRPC server on port 50051)
• Official dbt adapter (dbt-hyperstreamdb with vector macros & partition-looping incremental materialization)
• Cloud-agnostic distributed locking (FileBasedLock using object storage CAS / PutMode::Create)
• Optimistic Concurrency Control (OCC) with atomic snapshot swaps and retries
• Resilient chaos recovery (transparent fallback to Parquet scans on index corruption)
• Comprehensive documentation suite in docs/ (Sphinx/ReadTheDocs, pgvector SQL, Python API, GPU guides)
• 100k / 1M doc competitive benchmarks vs OpenSearch 2.11 / Elasticsearch 7.10
• Apache Polaris & Lakekeeper REST catalog integration with Snowflake zero-copy querying (Guide)
• Multi-vector search (simultaneous multi-embedding column search with Reciprocal Rank Fusion)
• Composite scalar indexes (multi-column composite roaring bitmaps)
• Community TurboQuant™ (TQ4 / TQ8) scalar quantization

🔄 In Progress

• Trino connector sidecar index predicate pushdown (direct .hnsw and .idx pre-filtering)
• Micro-batch streaming ingest buffer (5–30s Iceberg snapshot buffer for Kafka/Kinesis)

📋 Planned

• Zero-copy Arrow IPC vector index traversal for Spark/Trino
• Universal GPU PyPI wheel with cudarc runtime dynamic loading and automated CUDA CI
• Native Lakehouse Graph Analytics & Graph RAG SQL functions

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

📄 License

The Python wrapper is licensed under the MIT License. The underlying Rust engine and core database logic is licensed under the Apache License 2.0.

This project contains modified source code from various upstream open-source projects (including hnsw_rs for pre-filtering support), which were originally licensed under Apache 2.0. HyperStreamDB maintains compliance by retaining all original copyright notices and providing prominent notice of modifications in the relevant source files.

🙏 Acknowledgments

• Apache Iceberg - Inspiration for manifest design
• Apache Arrow - Columnar format
• hnsw_rs - Vector indexing
• RoaringBitmap - Scalar indexing

Built with ❤️ in Rust