Database / ChromaDB Interview Questions
What is a production readiness checklist for a ChromaDB-based application?
Moving a ChromaDB application from prototype to production involves several architectural decisions around storage, concurrency, reliability, and observability. This checklist covers the key concerns.
| Area | Recommendation |
|---|---|
| Storage mode | Use HttpClient connecting to a ChromaDB server — not PersistentClient in multi-process apps |
| Embedding consistency | Store embedding model name in collection metadata; always re-supply EF on get_collection() |
| Distance metric | Set hnsw:space='cosine' at collection creation for text; cannot change later |
| Backups | Schedule regular directory snapshots or SQLite online backups; test restore procedure |
| Telemetry | Set ANONYMIZED_TELEMETRY=False for privacy |
| Batching | Insert in batches of 100–500; use upsert() for idempotent pipelines |
| Error handling | Catch IDAlreadyExistsError, InvalidCollectionException; implement retry logic for HttpClient |
| HNSW tuning | Increase hnsw:construction_ef to 200 and hnsw:search_ef to 50–100 for large collections |
| Metadata schema | Use ints for dates/booleans; document schema in collection metadata |
| Security | Run server behind a reverse proxy with TLS; add auth headers for HttpClient |
| Monitoring | Log query latency, collection size, and embedding function errors |
| Scale planning | Plan ~1.5 KB/doc for 384-dim vectors + 25% HNSW overhead; consider alternatives above 10M docs |
# Minimal production-ready ChromaDB setup import chromadb from chromadb.utils import embedding_functions from chromadb.config import Settings import os import logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) EMBEDDING_MODEL = "text-embedding-3-small" COLLECTION_NAME = "prod_knowledge_base" def create_client(): return chromadb.HttpClient( host=os.environ["CHROMA_HOST"], port=int(os.environ.get("CHROMA_PORT", 8000)), settings=Settings(anonymized_telemetry=False), ) def get_collection(client): ef = embedding_functions.OpenAIEmbeddingFunction( api_key=os.environ["OPENAI_API_KEY"], model_name=EMBEDDING_MODEL, ) return client.get_or_create_collection( name=COLLECTION_NAME, embedding_function=ef, metadata={ "hnsw:space": "cosine", "hnsw:construction_ef": 200, "hnsw:search_ef": 100, "embedding_model": EMBEDDING_MODEL, }, ) client = create_client() client.heartbeat() # fail fast if server is unreachable collection = get_collection(client) logger.info(f"Connected to collection with {collection.count()} documents")
Which ChromaDB client should a production multi-service application use?
What is the recommended first check after connecting to a production ChromaDB HttpClient?
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