---
title: "Indexing"
description: "Overview of Weaviate's indexing systems for optimized search performance and data retrieval efficiency."
url: https://docs.weaviate.io/weaviate/concepts/indexing
---

# Indexing

Weaviate supports several types of indexes.

1. **[Vector indexes](https://docs.weaviate.io/weaviate/concepts/vector-index)** - a vector index (e.g. HNSW or flat) is used to serve all vector-search queries.

   - **HNSW** - an approximate nearest neighbor (ANN) search-based vector index. HNSW indexes scale well with large datasets.
   - **Flat** - a vector index that is used for brute-force searches. This is useful for small datasets.
   - **Dynamic** - a vector index that is flat when the dataset is small and switches to HNSW when the dataset is large.

2. **[Inverted indexes](https://docs.weaviate.io/weaviate/concepts/indexing/inverted-index)** - inverted indexes enable BM25 queries or speed up filtering.

You can configure indexes in Weaviate per collection.

> [!TIP]
>
> **Tips for indexing**
>
> Especially for large datasets, configuring the indexes is important because the more you index, the more storage is needed. A rule of thumb - if you don't query over a specific field or vector space, don't index it.