Show HN: Glider: A single-node vector database with S3 as its only durable state

Developer Omer Feyzioglu released Glider, a single-node vector database that treats S3-compatible object storage as its only durable state, acknowledging each write only after it is persisted to the bucket. Glider serves nearest-neighbor search with metadata filters over an HTTP/JSON API and ships a dependency-free Python client plus an MCP memory server for AI agents, with optional server-side embedding via a local 384-dimension ONNX model, Ollama, or OpenAI's text-embedding-3-small. Because the bucket is the database, a container can be stopped and restarted anywhere with the same S3 settings and credentials and retain its data, with local RAM and SSD used only to speed reads.

Vector search with S3 as the source of truth. Website and interactive storage simulation https://gliderdb.com/ Glider is a single-node vector database. Every acknowledged write is durable in S3-compatible object storage before the server answers; local RAM and SSD only speed up reads and never hold the only copy. It serves nearest-neighbor search with metadata filters over an HTTP/JSON API, with a Python client and an MCP memory server for AI agents. Start a server with a local directory as storage data is lost when the container stops : docker run --rm -p 8080:8080 \ -e GLIDER DATA DIR=/var/lib/glider/data ghcr.io/omerfeyzioglu/glider:latest Create a collection, write two points and query them: curl -sS localhost:8080/v1/collections -H 'content-type: application/json' \ -d '{"name":"demo","dimensions":3}' curl -sS localhost:8080/v1/collections/demo/write -H 'content-type: application/json' \ -d '{"upsert": {"id":1,"vector": 0,0,0 ,"metadata":{"color":"red"}},{"id":2,"vector": 1,1,1 } }' curl -sS localhost:8080/v1/collections/demo/query -H 'content-type: application/json' \ -d '{"vector": 1,1,0.9 ,"k":2,"include metadata":true}' The same with the dependency-free Python client: pip install "git+https://github.com/omerfeyzioglu/glider subdirectory=clients/python" from glider client import Client client = Client "http://localhost:8080" client.create collection "docs", dimensions=3, metric="cosine" docs = client.collection "docs" docs.upsert {"id": 1, "vector": 0, 0, 1 , "metadata": {"lang": "en"}} print docs.query 0, 0, 1 , k=1, include metadata=True Open http://localhost:8080/console http://localhost:8080/console to browse collections and run queries in the browser. For S3 or MinIO storage, the Docker Compose demo and building from source, see installation https://github.com/omerfeyzioglu/glider/blob/main/docs/INSTALL.md ; every setting is listed in configuration https://github.com/omerfeyzioglu/glider/blob/main/docs/CONFIGURATION.md . With a bucket and AWS credentials exported in your shell: docker run --rm -p 8080:8080 -v glider-cache:/var/lib/glider/cache \ -e GLIDER S3 BUCKET=my-bucket -e GLIDER S3 NAMESPACE=glider \ -e GLIDER S3 REGION=eu-central-1 \ -e AWS ACCESS KEY ID -e AWS SECRET ACCESS KEY -e AWS SESSION TOKEN \ ghcr.io/omerfeyzioglu/glider:latest Nothing else is needed: the bucket is the database; stop the container and start a new one anywhere with the same settings and access, and the data is there. See Run on AWS https://github.com/omerfeyzioglu/glider/blob/main/docs/INSTALL.md run-on-aws for setup and IAM, or S3-compatible storage https://github.com/omerfeyzioglu/glider/blob/main/docs/INSTALL.md s3-compatible-storage for MinIO. Enable server-side embedding to query with {"text":"a sleepy cat"} or use Text mode in the console. Embed documents with POST /v1/embed , then write its vectors with optional metadata.text . Stored and acknowledged data stays vectors. The default local model needs a 384-dimension cosine collection. Local ONNX model; downloaded on first use into the persistent volume. docker run --rm -p 8080:8080 -v glider-text:/var/lib/glider \ -e GLIDER DATA DIR=/var/lib/glider/data -e GLIDER EMBED PROVIDER=local \ ghcr.io/omerfeyzioglu/glider:latest Ollama on the host pull nomic-embed-text in Ollama first . docker run --rm -p 8080:8080 -e GLIDER DATA DIR=/var/lib/glider/data \ -e GLIDER EMBED PROVIDER=openai -e GLIDER EMBED MODEL=nomic-embed-text \ -e GLIDER EMBED URL=http://host.docker.internal:11434/v1 \ ghcr.io/omerfeyzioglu/glider:latest For OpenAI, set GLIDER EMBED URL=https://api.openai.com/v1 , a model such as text-embedding-3-small , and GLIDER EMBED API KEY . Use the response's dimensions when creating a collection. Embedding is off by default; source builds opt into embed-local and/or embed-openai . See configuration https://github.com/omerfeyzioglu/glider/blob/main/docs/CONFIGURATION.md text-embedding-optional . - Durable on S3. Each write batch becomes one immutable log object, created conditionally before it is acknowledged; nothing is overwritten in place. - Crash-safe takeover. A restarted server waits out the old writer's lease, fences it at the object store and replays the log; no operator step is needed after a crash. - Retry-safe writes. Every write carries a request ID; resending it within the retained 128-commit window returns the original outcome instead of applying the write twice. Supply and keep the ID before sending so a lost response can be retried retry contract https://github.com/omerfeyzioglu/glider/blob/main/docs/API.md request-ids-and-retries . - Collections. One server creates and serves many collections, each with its own dimension and metric squared Euclidean, Manhattan or cosine . - Metadata filters. Equality, set, existence, numeric and logical filters; exact: true answers any filter exhaustively. - Clustered ANN that builds itself. The server clusters a collection in the background at 250,000 rows and rebuilds the clusters as it grows. - SSD cache. A local block cache warms in the background so warm queries need no remote reads; losing it loses no data. - Operations. Prometheus metrics, a status endpoint, bearer-token authentication, and glider-admin for backup and restore. The Python client includes glider-mcp , an MCP server that gives agents Claude Code, Claude Desktop, Cursor and other MCP clients durable remember , recall and forget tools backed by Glider: docker run -d --name glider -p 8080:8080 -v glider-data:/var/lib/glider \ -e GLIDER DATA DIR=/var/lib/glider/data ghcr.io/omerfeyzioglu/glider:latest pip install "glider-client mcp @ git+https://github.com/omerfeyzioglu/glider subdirectory=clients/python" claude mcp add glider -e GLIDER COLLECTION=memory -- glider-mcp The client guide https://github.com/omerfeyzioglu/glider/blob/main/clients/python/README.md mcp-memory-for-agents covers other MCP clients and settings. 1,000,000 SIFT vectors 128 dimensions, k=10 in AWS S3 Standard, served from a c7g.2xlarge 8 vCPU, 16 GiB in eu-central-1 while four writers and four readers run concurrently: | Measure | Result | |---|---| | Recall@10, static mean / p5 | 0.998 / 1.0 | | Recall@10 after updates, cold cache mean / p5 | 0.964 / 0.8 | | Query p95, warm cache | 29.8 ms | | Query p95, cold cache | 57.0 ms | | Write p95 | 86.9 ms | | Open / reopen | 2.93 / 3.84 s | The run at revision 35f9b46 lost no acknowledged writes and returned equal results after cache loss and backup restore. Methodology and raw results are in the benchmark report https://github.com/omerfeyzioglu/glider/blob/main/benchmarks/M39.md current-main-on-s3 and BENCHMARKS.md https://github.com/omerfeyzioglu/glider/blob/main/BENCHMARKS.md . Many small collections on one server, same instance and S3 Standard: 10,000 collections of 1,000 vectors 10,000,000 vectors were created and loaded in 22 minutes with the server killed SIGKILL halfway; every tenant was verified with no lost or duplicated write. Warm queries over 64 active collections ran at 2,457 per second with a p95 of 18.5 ms and recall@10 of 1.0; a cold tenant opened from S3 and answered its first query in 716 ms p95 . Idle collections close, so they cost no S3 requests details https://github.com/omerfeyzioglu/glider/blob/main/BENCHMARKS.md multi-tenant-server-scenario . - Write: a single committer publishes each batch as one conditional log object, then acknowledges it. - Maintenance: idle time seals the log into immutable packs and indexes, then publishes a new root generation that names them. - Read: queries route to candidate blocks, read bounded byte ranges from S3 or the SSD cache, and rerank full vectors. The architecture guide https://github.com/omerfeyzioglu/glider/blob/main/docs/ARCHITECTURE.md adds an AWS deployment pattern; DESIGN.md https://github.com/omerfeyzioglu/glider/blob/main/DESIGN.md specifies formats, invariants and recovery. | Document | Contents | |---|---| | Installation https://github.com/omerfeyzioglu/glider/blob/main/docs/INSTALL.md | Docker, Docker Compose with MinIO, building from source | | Configuration https://github.com/omerfeyzioglu/glider/blob/main/docs/CONFIGURATION.md | Environment variables and serving profile | | HTTP API https://github.com/omerfeyzioglu/glider/blob/main/docs/API.md | Endpoints, filters, errors, retries and consistency | | Python client https://github.com/omerfeyzioglu/glider/blob/main/clients/python/README.md | Client API and MCP memory server | | Operations https://github.com/omerfeyzioglu/glider/blob/main/docs/SERVING.md | Running, backup, restore, cache and clustering | | Recovery https://github.com/omerfeyzioglu/glider/blob/main/docs/RECOVERY.md | Crash, takeover and restore procedures | | Architecture https://github.com/omerfeyzioglu/glider/blob/main/docs/ARCHITECTURE.md | Runtime and AWS deployment diagrams | | Design https://github.com/omerfeyzioglu/glider/blob/main/DESIGN.md | Formats, invariants and guarantees | | Rust library https://github.com/omerfeyzioglu/glider/blob/main/docs/LIBRARY.md | Embedding the engine in Rust | | Benchmarks https://github.com/omerfeyzioglu/glider/blob/main/BENCHMARKS.md | Measurements and how to reproduce them | | Changelog https://github.com/omerfeyzioglu/glider/blob/main/CHANGELOG.md | Release notes | | Contributing https://github.com/omerfeyzioglu/glider/blob/main/CONTRIBUTING.md | Development workflow and checks | | Website https://github.com/omerfeyzioglu/glider/blob/main/docs/WEBSITE.md | Previewing the site and regenerating its playground results | | Security https://github.com/omerfeyzioglu/glider/blob/main/SECURITY.md | Reporting vulnerabilities and deployment security | - Single node: no replication, sharding or standby; after a crash, the next server waits for the writer lease 10 s by default before taking over. - Filtered queries are approximate and may return fewer than k results unless they use exact: true or the collection's resident filter. - Cache loss can lower approximate recall as well as increase latency; acknowledged data remains durable. Use exact: true for exhaustive results. - A collection's dimension and metric are fixed when it is created. - The server speaks plain HTTP; terminate TLS in a reverse proxy. - The measured 1M-vector collection opens from S3 in a few seconds; larger collections have no fixed open-time guarantee. Contributions are welcome. See CONTRIBUTING.md https://github.com/omerfeyzioglu/glider/blob/main/CONTRIBUTING.md for the workflow and the checks CI runs. Licensed under either of Apache License, Version 2.0 https://github.com/omerfeyzioglu/glider/blob/main/LICENSE-APACHE or MIT license https://github.com/omerfeyzioglu/glider/blob/main/LICENSE-MIT at your option. Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in this project, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.