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docs-v2/content/influxdb/clustered/query-data/troubleshoot-and-optimize/optimize-queries.md

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title description weight menu influxdb/clustered/tags related aliases
Optimize queries Optimize queries to improve performance and reduce their memory and compute (CPU) requirements in InfluxDB. Learn how to use observability tools to analyze query execution and view metrics. 201
influxdb_clustered
name parent
Optimize queries Troubleshoot and optimize queries
query
performance
observability
errors
sql
influxql
/influxdb/clustered/query-data/sql/
/influxdb/clustered/query-data/influxql/
/influxdb/clustered/query-data/execute-queries/optimize-queries/
/influxdb/clustered/query-data/execute-queries/analyze-query-plan/

Optimize SQL and InfluxQL queries to improve performance and reduce their memory and compute (CPU) requirements. Learn how to use observability tools to analyze query execution and view metrics.

Why is my query slow?

Query performance depends on time range and complexity. If a query is slower than you expect, it might be due to the following reasons:

  • It queries data from a large time range.
  • It includes intensive operations, such as querying many string values or ORDER BY sorting or re-sorting large amounts of data.

Strategies for improving query performance

The following design strategies generally improve query performance and resource use:

  • Follow schema design best practices to make querying easier and more performant.

  • Query only the data you need--for example, include a WHERE clause that filters data by a time range. InfluxDB v3 stores data in a Parquet file for each measurement and day, and retrieves files from the Object store to answer a query. The smaller the time range in your query, the fewer files InfluxDB needs to retrieve from the Object store.

  • Downsample data to reduce the amount of data you need to query.

Some bottlenecks may be out of your control and are the result of a suboptimal execution plan, such as:

  • Applying the same sort (ORDER BY) to already sorted data.
  • Retrieving many Parquet files from the Object store--the same query performs better if it retrieves fewer - though, larger - files.
  • Querying many overlapped Parquet files.
  • Performing a large number of table scans.

{{% note %}}

Analyze query plans to view metrics and recognize bottlenecks

To view runtime metrics for a query, such as the number of files scanned, use the EXPLAIN ANALYZE keywords and learn how to analyze a query plan. {{% /note %}}

Analyze and troubleshoot queries

Learn how to analyze a query plan to troubleshoot queries and find performance bottlenecks.