Report query performance issues

These guidelines are intended to faciliate collaboration between InfluxData engineers and you. They allow engineers to conduct timely analyses of any performance issues that you have not been able to resolve following our guide on troubleshooting and optimizing queries.

1. Send InfluxData output artifacts
2. Document your test process
3. Document your environment
4. Document your data schema
5. Establish query performance degradation conditions
6. Reduce query noise
7. Establish baseline single-query performance
8. Run queries at multiple load scales
9. Gather debug information
   1. Kubernetes-specific information
   2. Clustered-specific information
   3. Query analysis
      1. EXPLAIN
      2. EXPLAIN VERBOSE
      3. EXPLAIN ANALYZE
10. Gather system information
    • Collect table information
    • Collect compaction information for the table
    • Collect partition information for multiple tables

Send InfluxData output artifacts

As you follow these guidelines, package all produced output artifacts in the following form:

Outputs:

• test-artifact-name.tar.gz

Send InfluxData engineers all produced artifacts for analysis.

Document your test process

There currently is no standardized performance test suite that you can run in your environment, so please document your process so it can be replicated. Include the following:

• The steps you take when performance testing.
• Timestamps of the tests you perform so they can be correlated with associated logs.

Document your environment

Provide as much detail about your environment as your organization allows, including the following:

• Your kubernetes cluster
• The cloud provider where it runs or indicate that it’s “on-prem”
• The hardware it runs on
• The type and size of disk in use–for example: hard disk, SSD, NVMe, etc.
• CPU and memory resources set on each type of InfluxDB pod
• The number of pods in each InfluxDB StatefulSet and Deployment
• The type of object store used and how it is hosted
• How the Catalog (PostgreSQL-compatible database) is hosted
• Indicate if either the Object store or the Catalog is shared by more than one InfluxDB Clustered product
  • If so, describe the network-level topology of your setup

Document your data schema

Document your the data schema to help InfluxData engineers better understand the conditions that reproduce your issue.

Establish query performance degradation conditions

The most effective way to investigate query performance is to have a good understanding of the conditions in which you don’t see the expected performance. Things to think about and provide:

• Does this always happen, or only sometimes?
  • If only sometimes, is it at a consistent time of day or over a consistent period?
• Will a single query execution reproduce the issue, or does it only appear with multiple queries are running at the same time?
• How are you executing the queries? For example:
  • influxctl
  • Client libraries
  • Other environments or tools

Reduce query noise

To get a sense of the baseline performance of your system without the noise of additional queries, test in an environment that doesn’t have periodic or intermittent queries running concurrently.

Additionally, when running multiple tests with different queries, let the system recover between tests by waiting at least a minute after receiving a query result before executing the next query.

Establish baseline single-query performance

To get a sense of the baseline performance of your system without the noise of additional queries, perform at least some of your testing with single queries in isolation from one another.

This is may be useful for the purposes of analysis by InfluxData engineers even if a single query in isolation isn’t enough to reproduce the issue you are having.

Run queries at multiple load scales

Once you’ve established baseline performance with a single query and your performance issue can’t be replicated with a single query, use a systematic approach to identify the scale at which it does become a problem. This involves systematic incremental increases to your query concurrency until you identify a threshold at which the issue can be reproduced.

This, along with information about your Kubernetes environment, can provide insight necessary to recommend changes to your configuration to improve query performance characteristis as your usage scales.

As an example, consider the following test plan outline:

1. Turn off intermittent or periodic InfluxDB queries and allow the cluster to recover.
2. Run Query A and allow the cluster to recover for 1 minute.
3. Run 5 concurrent instances of Query A and allow the cluster to recover for 1 minute.
4. Run 10 concurrent instances of Query A and allow the cluster to recover for 1 minute.
5. Run 20 concurrent instances of Query A and allow the cluster to recover for 1 minute.
6. Run 40 concurrent instances of Query A and allow the cluster to recover for 1 minute.
7. Provide InfluxData the debug information described below.

Gather debug information

The following debug information should be collected shortly after a problematic query has been tried against your InfluxDB cluster.

Kubernetes-specific information

Outputs:

• ${DATETIME}-cluster-info.tar.gz

DATETIME="$(date -Iminutes)"
kubectl cluster-info dump --namespace influxdb --output-directory "${DATETIME}-cluster-info/"
tar -czf "${DATETIME}-cluster-info.tar.gz" "${DATETIME}-cluster-info/"

Clustered-specific information

Outputs:

• app-instance.yml: Provide a copy of your AppInstance manifest.

Query analysis

Outputs (InfluxQL):

• explain.csv
• explain-verbose.csv
• explain-analyze.csv

Outputs (SQL):

• explain.txt
• explain-verbose.txt
• explain-analyze.txt

For any known long-running queries, it may be helpful to execute variations of the EXPLAIN command on them.

In the examples below, replace the following:

• DATABASE_NAME: The name of the database to query
• DATABASE_TOKEN: A database token with read permissions on the queried database
• YOUR_QUERY: Your long-running query (formatted as a single line with escaped double quotes (\"))

EXPLAIN

influxctl \
  --config config.toml \
    query \
  --database 
DATABASE_NAME
 \
  --format table \
  --token 
DATABASE_TOKEN
 \
  "EXPLAIN 
YOUR_QUERY
;" > explain.txt

curl --get "https://cluster-host.com/query" \
  --output "./explain.csv" \
  --header "Authorization: Bearer 
DATABASE_TOKEN
" \
  --header "Accept: application/csv" \
  --data-urlencode "db=
DATABASE_NAME
" \
  --data-urlencode "q=EXPLAIN 
YOUR_QUERY
"

EXPLAIN VERBOSE

influxctl \
  --config config.toml \
    query \
  --database 
DATABASE_NAME
 \
  --format table \
  --token 
DATABASE_TOKEN
 \
  "EXPLAIN VERBOSE 
YOUR_QUERY
;" > explain-verbose.txt

curl --get "https://cluster-host.com/query" \
  --output "./explain-verbose.csv" \
  --header "Authorization: Bearer 
DATABASE_TOKEN
" \
  --header "Accept: application/csv" \
  --data-urlencode "db=
DATABASE_NAME
" \
  --data-urlencode "q=EXPLAIN VERBOSE 
YOUR_QUERY
"

EXPLAIN ANALYZE

influxctl \
  --config config.toml \
    query \
  --database 
DATABASE_NAME
 \
  --format table \
  --token 
DATABASE_TOKEN
 \
  "EXPLAIN ANALYZE 
YOUR_QUERY
;" > explain-analyze.txt

curl --get "https://cluster-host.com/query" \
  --output "./explain-analyze.csv" \
  --header "Authorization: Bearer 
DATABASE_TOKEN
" \
  --header "Accept: application/csv" \
  --data-urlencode "db=
DATABASE_NAME
" \
  --data-urlencode "q=EXPLAIN ANALYZE 
YOUR_QUERY
"

Gather system information

If queries are slow for a specific table, run the following system queries to collect information for troubleshooting.

• Collect table information
• Collect compaction information for the table
• Collect partition information for multiple tables

To optimize system queries, use table_name, partition_key, and partition_id filters. In your queries, replace the following:

• TABLE_NAME: the table to retrieve partitions for
• PARTITION_ID: a partition ID (int64)
• PARTITION_KEY: a partition key derived from the table’s partition template. The default format is %Y-%m-%d (for example, 2024-01-01).

Collect table information

SELECT *
FROM system.tables
WHERE table_name = '
TABLE_NAME
';

Collect compaction information for the table

Query the system.compactor table to collect compaction information–for example, run one of the following queries:

SELECT * 
FROM system.compactor 
WHERE
  table_name = '
TABLE_NAME
' 
    AND partition_key = '
PARTITION_KEY
';

SELECT * 
FROM system.compactor 
WHERE
  table_name = '
TABLE_NAME
' 
    AND partition_id = '
PARTITION_ID
';

Collect partition information for multiple tables

If the same queries are slow on more than 1 table, also run the following query to collect the size and number of partitions for all tables:

SELECT table_name,
  COUNT(*) as partition_count,
  MAX(last_new_file_created_at) as last_new_file_created_at,
  SUM(total_size_mb) as total_size_mb
FROM system.partitions
WHERE table_name IN ('foo', 'bar', 'baz')
GROUP BY table_name;