# High Throughput

> Browse 5 items tagged with High Throughput.

## Items

- [Redis Vector Search](https://vectordb.ever.works/items/redis-vector-search) — Redis Vector Search (part of Redis Stack) enables vector similarity search on Redis with HNSW indexing, hybrid BM25+vector, and metadata filtering. It leverages Redis caching for low-latency real-time apps like semantic search. Vs dedicated DBs like Pinecone, Redis offers multi-model (JSON/KV + vectors) but requires more config for scale.
- [ClickHouse](https://vectordb.ever.works/items/clickhouse) — ClickHouse is a columnar OLAP database with vector indexes (ANN via AMM, brute-force), supporting SQL queries over vectors + structured data at petabyte scale. Excels in aggregations with vectors. For analytics workloads with embeddings; faster ingestion than Postgres pgvector for big data.
- [Redis](https://vectordb.ever.works/items/redis) — Redis Stack with vector search via RediSearch module, HNSW/Flat indexes on in-memory store. Features: Hybrid BM25+vector, real-time cache, multi-tenancy. Use cases: Caching LLM responses, high-throughput RAG. Comparisons: Faster than disk DBs for hot data; vs Memgraph: simpler key-value.
- [RediSearch](https://vectordb.ever.works/items/redisearch) — Redis Stack with vector search via RediSearch module, HNSW/Flat indexes on in-memory store. Features: Hybrid BM25+vector, real-time cache, multi-tenancy. Use cases: Caching LLM responses, high-throughput RAG. Comparisons: Faster than disk DBs for hot data; vs Memgraph: simpler key-value.
- [BatANN: Passing the Baton: High Throughput Distributed Disk-Based Vector Search](https://vectordb.ever.works/items/batann-passing-the-baton-high-throughput-distributed-disk-based-vector-search) — BatANN system by Dang et al. for high-throughput distributed disk-based vector search. Supports scalable ANN in distributed environments.

---

_Canonical page: https://vectordb.ever.works/tags/high-throughput_