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
• Recognize and address bottlenecks

Why is my query slow?

Query performance depends on factors like the time range and query complexity. If a query is slower than expected, consider the following potential causes:

• The query spans a large time range, which increases the amount of data being processed.
• The query performs intensive operations, such as:
  • Sorting or re-sorting large datasets with ORDER BY.
  • Querying many string values, which can be computationally expensive.

Strategies for improving query performance

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

• Follow schema design best practices to simplify and improve queries.
• Query only the data you need to reduce unnecessary processing.
• Downsample data to decrease the volume of data queried.

Query only the data you need

Include a WHERE clause

InfluxDB v3 stores data in a Parquet file for each partition. By default, InfluxDB Cloud Dedicated 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.

Recognize and address bottlenecks

To identify performance bottlenecks, learn how to analyze a query plan. Query plans provide runtime metrics, such as the number of files scanned, that may reveal inefficiencies in query execution.