ClickHouse¶

Connect HolmesGPT to ClickHouse databases to analyze OLAP query performance, investigate slow aggregations, check table compression, examine cluster health, and read data for troubleshooting.

You can configure multiple ClickHouse instances with different names (e.g., clickhouse-analytics, clickhouse-metrics, clickhouse-staging).

Creating a Read-Only User¶

-- Create user
CREATE USER holmes_readonly IDENTIFIED BY 'your_secure_password';

-- Grant read-only access to specific database
GRANT SELECT ON your_database.* TO holmes_readonly;

-- Grant access to system tables for performance analysis
GRANT SELECT ON system.* TO holmes_readonly;
GRANT SELECT ON information_schema.* TO holmes_readonly;

For all databases:

CREATE USER holmes_readonly IDENTIFIED BY 'your_secure_password';
GRANT SELECT ON *.* TO holmes_readonly;
GRANT SELECT ON system.* TO holmes_readonly;

Configuration¶

Connection URL format:

clickhouse://[username]:[password]@[host]:[port]/[database]
clickhouse+http://[username]:[password]@[host]:[port]/[database]

Note: Use native protocol (port 9000) or HTTP interface (port 8123).

Holmes CLIHolmes Helm ChartRobusta Helm Chart

~/.holmes/config.yaml:

toolsets:
  clickhouse-analytics:
    type: database
    config:
      connection_url: "clickhouse://holmes_readonly:your_secure_password@clickhouse.example.com:9000/metrics"
    llm_instructions: "ClickHouse analytics warehouse with event streams and metrics"

  clickhouse-logs:
    type: database
    config:
      connection_url: "clickhouse+http://log_reader:pass@clickhouse-logs.internal:8123/logs"
      clickhouse_use_http_json: true
    llm_instructions: "Log analytics database with application and system logs"

Using environment variables:

toolsets:
  clickhouse-analytics:
    type: database
    config:
      connection_url: "{{ env.CLICKHOUSE_URL }}"

Create a Kubernetes secret in the namespace Holmes runs in:

kubectl create secret generic holmes-database-clickhouse \
  --from-literal=CLICKHOUSE_URL='clickhouse://holmes_readonly:your_secure_password@clickhouse.example.com:9000/metrics' \
  -n <namespace>

When using the standalone Holmes Helm Chart, update your values.yaml:

extraEnvVarsSecrets:
  - holmes-database-clickhouse

toolsets:
  clickhouse-analytics:
    type: database
    config:
      connection_url: "{{ env.CLICKHOUSE_URL }}"
    llm_instructions: "ClickHouse analytics warehouse with event streams and metrics"

Apply the configuration:

helm upgrade holmes robusta/holmes -f values.yaml

Create a Kubernetes secret in the namespace Holmes runs in:

kubectl create secret generic holmes-database-clickhouse \
  --from-literal=CLICKHOUSE_URL='clickhouse://holmes_readonly:your_secure_password@clickhouse.example.com:9000/metrics' \
  -n <namespace>

When using the Robusta Helm Chart (which includes HolmesGPT), update your generated_values.yaml:

holmes:
  extraEnvVarsSecrets:
    - holmes-database-clickhouse

  toolsets:
    clickhouse-analytics:
      type: database
      config:
        connection_url: "{{ env.CLICKHOUSE_URL }}"
      llm_instructions: "ClickHouse analytics warehouse with event streams and metrics"

Apply the configuration:

helm upgrade robusta robusta/robusta -f generated_values.yaml --set clusterName=<YOUR_CLUSTER_NAME>

Multiple instances¶

Holmes Helm ChartRobusta Helm Chart

Create a Kubernetes secret in the namespace Holmes runs in:

kubectl create secret generic holmes-database-clickhouse-instances \
  --from-literal=CLICKHOUSE_ANALYTICS_URL='clickhouse://holmes_readonly:your_secure_password@clickhouse.example.com:9000/metrics' \
  --from-literal=CLICKHOUSE_LOGS_URL='clickhouse+http://log_reader:pass@clickhouse-logs.internal:8123/logs' \
  -n <namespace>

When using the standalone Holmes Helm Chart, update your values.yaml:

extraEnvVarsSecrets:
  - holmes-database-clickhouse-instances

toolsets:
  clickhouse-analytics:
    type: database
    config:
      connection_url: "{{ env.CLICKHOUSE_ANALYTICS_URL }}"

  clickhouse-logs:
    type: database
    config:
      connection_url: "{{ env.CLICKHOUSE_LOGS_URL }}"

Apply the configuration:

helm upgrade holmes robusta/holmes -f values.yaml

Create a Kubernetes secret in the namespace Holmes runs in:

kubectl create secret generic holmes-database-clickhouse-instances \
  --from-literal=CLICKHOUSE_ANALYTICS_URL='clickhouse://holmes_readonly:your_secure_password@clickhouse.example.com:9000/metrics' \
  --from-literal=CLICKHOUSE_LOGS_URL='clickhouse+http://log_reader:pass@clickhouse-logs.internal:8123/logs' \
  -n <namespace>

When using the Robusta Helm Chart (which includes HolmesGPT), update your generated_values.yaml:

holmes:
  extraEnvVarsSecrets:
    - holmes-database-clickhouse-instances

  toolsets:
    clickhouse-analytics:
      type: database
      config:
        connection_url: "{{ env.CLICKHOUSE_ANALYTICS_URL }}"

    clickhouse-logs:
      type: database
      config:
        connection_url: "{{ env.CLICKHOUSE_LOGS_URL }}"

Apply the configuration:

helm upgrade robusta robusta/robusta -f generated_values.yaml --set clusterName=<YOUR_CLUSTER_NAME>

Configuration Options¶

connection_url (required): ClickHouse connection URL
read_only (default: true): Only allow SELECT/SHOW/DESCRIBE/EXPLAIN/WITH statements
verify_ssl (default: true): Verify SSL certificates
max_rows (default: 200): Maximum rows to return (1-10000)
timeout_seconds (default: 60): HTTP JSONEachRow query timeout in seconds (1-600); used when clickhouse_use_http_json is enabled
clickhouse_use_http_json (default: false): Use ClickHouse HTTP API with JSONEachRow instead of the SQLAlchemy driver's TSV format for query results
llm_instructions: Context about this database

HTTP JSONEachRow mode (`clickhouse_use_http_json`)¶

The default SQLAlchemy HTTP driver returns TabSeparatedWithNamesAndTypes and parses DateTime64 timestamps with Python strptime using microsecond precision (%f). Result sets with nanosecond timestamps (common in OpenTelemetry log tables, e.g. DateTime64(9)) can fail while reading rows with:

ValueError: unconverted data remains: 789

Enable JSONEachRow when you query tables that return high-precision DateTime64 columns:

toolsets:
  clickhouse-otel-logs:
    type: database
    config:
      connection_url: "clickhouse+http://user:pass@clickhouse:8123/otel"
      clickhouse_use_http_json: true
      read_only: true
      max_rows: 200

This path only affects query execution (execute_query); list/describe tools still use SQLAlchemy. Use clickhouse+http:// (port 8123), not the native protocol, with this option.

Common Use Cases¶

"Analyze query performance: SELECT count() FROM events WHERE date >= today() - 30"

"Show table sizes and compression ratios"

"Check for inefficient queries scanning too many rows"

ClickHouse¶

Creating a Read-Only User¶

Configuration¶

Multiple instances¶

Configuration Options¶

HTTP JSONEachRow mode (clickhouse_use_http_json)¶

Common Use Cases¶

HTTP JSONEachRow mode (`clickhouse_use_http_json`)¶