Comments (4)
The main difference between Gunicorn and Uvicorn (as I understand it) is that Uvicorn is best as a single-worker model whereas Gunicorn has better performance and support for multiple workers, where each worker is its own python process.
So on a "traditional" server where you want parallel request processing by multiple isolated workers, Gunicorn or a Gunicorn+Uvicorn combo is going to perform better.
But if we're contemplating an MVP where the primary "hot" representation of the data is in-memory, then we can't have multiple workers, because multiple processes don't share memory. We'd end up with N diverging in-memory representations if we did that.
So Gunicorn only becomes relevant in a world where we have a "stateless" FastAPI server that's writing through each request to some shared-state persisted database incrementally. Which relates to the question you asked in #38 and does somewhat depend on the capabilities of DuckDB to support overlapping updates from multiple processes.
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Close relation to #38 - definitely agree.
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We could move forward with this now I believe. @levand
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Moving to backlog
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Related Issues (20)
- [Bug]: Client & Persistent Client are retrieving different documents HOT 4
- [Install issue]: type object 'CreateCollection' has no attribute 'model_validate' during Create Index HOT 3
- [Feature Request]: TTL for entries HOT 1
- [Bug]: Upserting the same data causes the SQLite db to grow by 50-100% HOT 3
- [Bug]: sqlite3.OperationalError: no such column: collections.topic HOT 7
- [Install issue]: Use my own embedding function add data error HOT 3
- No OllamaEmbeddingFunction in latest release of chromadb/npm module HOT 5
- [BLD]: Matrix test for Node 16+
- [Feature Request]: Langchain plugin for Chroma always tries to create the collection even if the collection already exists. HOT 13
- [Feature Request]: Issue with Data Structure in Chroma DB Collections HOT 2
- [Bug]: Missing OllamaEmbeddingFunction when using chromadb==0.5.0 HOT 2
- [Feature Request]: Set ef_search per query HOT 3
- [Feature Request]: Bump black to 24.4.2
- [Bug]: "Cannot submit more than 5,461 embeddings at once. Please submit your embeddings in batches of size 5,461 or less." but on running *.delete* HOT 5
- [Feature Request]: Async.io suport for python HOT 1
- [Feature Request]: Docs to be crawler friendly, and LLM discoverable HOT 2
- [Bug]: "Collection is not created" message despite being returned by list_collections method HOT 6
- [Bug]: client/server mode RuntimeError: coroutine raised StopIteration HOT 6
- [Bug]: Type mismatch for embedding function HOT 6
- [Bug]: where not equals clause not working HOT 2
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