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?
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.
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.
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.
Analyze and troubleshoot queries
Use the following tools to analyze and troubleshoot queries and find performance bottlenecks:
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