Optimize queries

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?
• Strategies for improving query performance
  • Query only the data you need
• Analyze and troubleshoot queries

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.
• 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.

Query only the data you need

Include a WHERE clause

InfluxDB v3 stores data in a Parquet file for each partition. By default, InfluxDB Clustered partitions tables by day, but you can also custom-partition your data. At query time, InfluxDB retrieves files from the Object store to answer a query. To reduce the number of files that a query needs to retrieve from the Object store, include a WHERE clause that filters data by a time range or by specific tag values.

SELECT only columns you need

Because InfluxDB v3 is a columnar database, it only processes the columns selected in a query, which can mitigate the query performance impact of wide schemas.

However, a non-specific query that retrieves a large number of columns from a wide schema can be slower and less efficient than a more targeted query–for example, consider the following queries:

• SELECT time,a,b,c
• SELECT *

If the table contains 10 columns, the difference in performance between the two queries is minimal. In a table with over 1000 columns, the SELECT * query is slower and less efficient.

Analyze and troubleshoot queries

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

If you need help troubleshooting, follow the guidelines to report query performance issues.