Optimize queries
Troubleshoot query performance
Use the following tools to help you identify performance bottlenecks and troubleshoot problems in queries:
EXPLAIN and ANALYZE
To view the query engine’s execution plan and metrics for an SQL query, prepend EXPLAIN or EXPLAIN ANALYZE to the query.
The report can reveal query bottlenecks such as a large number of table scans or parquet files, and can help triage the question, “Is the query slow due to the amount of work required or due to a problem with the schema, compactor, etc.?”
The following example shows how to use the InfluxDB v3 Python client library and pandas to view EXPLAIN and EXPLAIN ANALYZE results for a query:
from influxdb_client_3 import InfluxDBClient3
import pandas as pd
import tabulate # Required for pandas.to_markdown()
def explain_and_analyze():
print('Use SQL EXPLAIN and ANALYZE to view query plan information.')
# Instantiate an InfluxDB client.
client = InfluxDBClient3(token = f"API_TOKEN",
host = f"cloud2.influxdata.com",
database = f"BUCKET_NAME")
sql_explain = '''EXPLAIN SELECT *
FROM home
WHERE time >= now() - INTERVAL '90 days'
ORDER BY time'''
table = client.query(sql_explain)
df = table.to_pandas()
sql_explain_analyze = '''EXPLAIN ANALYZE SELECT *
FROM home
WHERE time >= now() - INTERVAL '90 days'
ORDER BY time'''
table = client.query(sql_explain_analyze)
# Combine the Dataframes and output the plan information.
df = pd.concat([df, table.to_pandas()])
assert df.shape == (3, 2) and df.columns.to_list() == ['plan_type', 'plan']
print(df[['plan_type', 'plan']].to_markdown(index=False))
client.close()
explain_and_analyze()
Replace the following:
BUCKET_NAME: the bucket to queryAPI_TOKEN: a token with sufficient permissions to the specified database
The output is similar to the following:
| plan_type | plan |
|:------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|
| logical_plan | Sort: home.time ASC NULLS LAST |
| | TableScan: home projection=[co, hum, room, sensor, temp, time], full_filters=[home.time >= TimestampNanosecond(1688491380936276013, None)] |
| physical_plan | SortExec: expr=[time@5 ASC NULLS LAST] |
| | EmptyExec: produce_one_row=false |
| Plan with Metrics | SortExec: expr=[time@5 ASC NULLS LAST], metrics=[output_rows=0, elapsed_compute=1ns, spill_count=0, spilled_bytes=0] |
| | EmptyExec: produce_one_row=false, metrics=[]
Enable trace logging
Customers with an InfluxDB Cloud Serverless annual or support contract can contact InfluxData Support to enable tracing and request help troubleshooting your query. With tracing enabled, InfluxDB Support can trace system processes and analyze log information for a query instance. The tracing system follows the OpenTelemetry traces model for providing observability into a request.
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Support and feedback
Thank you for being part of our community! We welcome and encourage your feedback and bug reports for InfluxDB and this documentation. To find support, use the following resources:
Customers with an annual or support contract can contact InfluxData Support.