---
title: "Query acceleration with bloom filters | Grafana Cloud documentation"
description: "Briefly describes query acceleration with bloom filters."
---

> For a curated documentation index, see [llms.txt](/llms.txt). For the complete documentation index, see [llms-full.txt](/llms-full.txt).

# Query acceleration with bloom filters

> Note
> 
> Query acceleration using Bloom filters is enabled as a [public preview](/docs/release-life-cycle/) for select large-scale Grafana Cloud customers that are ingesting more that 75TB of logs a month. Limited support and no SLA are provided.

Loki leverages [bloom filters](https://en.wikipedia.org/wiki/Bloom_filter) to speed up queries by reducing the amount of data Loki needs to load from the store and iterate through. Loki is often used to run “needle in a haystack” queries; these are queries where a large number of log lines are searched, but only a few log lines match the query. Some common use cases are searching all logs tied to a specific trace ID or customer ID.

An example of such queries would be looking for a trace ID on a whole cluster for the past 24 hours:

logql ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy

```logql
{cluster="prod"} | traceID="3c0e3dcd33e7"
```

Without accelerated filtering, Loki downloads all the chunks for all the streams matching `{cluster="prod"}` for the last 24 hours and iterates through each log line in the chunks, checking if the \[structured metadata]\[] key `traceID` with value `3c0e3dcd33e7` is present.

With accelerated filtering, Loki is able to skip most of the chunks and only process the ones where we have a statistical confidence that the structured metadata pair might be present.

To learn how to write queries to use bloom filters, refer to [Query acceleration](/docs/loki/next/query/query_acceleration/).

For more information about the underlying components, refer to the [Bloom filters](/docs/loki/next/operations/bloom-filters/) topic in the Loki documentation.