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Set Langfuse Sampling to 7% and Check ClickHouse Logs

A 0.07 Langfuse sampling rate targets 7% of traces, while ClickHouse system logs may grow independently of Langfuse trace retention. Learn what to check before changing either setting.
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To target 7% of Langfuse traces, set the client-side sampling rate to 0.07. That deliberately leaves most traces—and all observations and scores attached to unsampled traces—out of Langfuse. Separately, Langfuse’s trace-retention setting does not necessarily control ClickHouse’s own system logs: tables such as trace_log, text_log, and opentelemetry_span_log can grow without a TTL. Check those tables before concluding that retained Langfuse traces are responsible for disk growth.

How do I sample only 7% of Langfuse traces?

Sampling is configured on the client, where the Langfuse SDK decides whether to send a trace. The documented setting accepts a rate from 0 to 1, with a default of 1 (all traces). Set it to 0.07 for a 7% trace-sampling target. Depending on the SDK, the configuration is exposed as sample_rate, sampleRate, or the LANGFUSE_SAMPLE_RATE environment variable. See Langfuse’s sampling documentation for the applicable SDK configuration.

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The title’s 7% is the intended rate, not evidence that exactly 7% of a particular person’s agent executions were captured. Actual captured counts can vary, especially across a small number of traces.

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What sampling removes

Langfuse makes the decision at the trace level. If a trace is not sampled, its observations and associated scores are not sent either. Sampling therefore reduces trace volume, but it also creates intentional gaps: the resulting data is not a complete accounting of agent runs, tool calls, or outcomes. Lower the rate only if that loss of visibility is acceptable for your debugging, evaluation, and monitoring needs.

Why is ClickHouse logging itself?

ClickHouse maintains its own system logs to record server activity. Langfuse’s self-hosted deployment requires ClickHouse; its documentation says no alternative OLAP database is supported for this deployment. The Langfuse retention feature for tracing data and media is distinct from retention for ClickHouse system tables. Langfuse warns that tables including trace_log, text_log, and opentelemetry_span_log have no TTL by default and may contribute to disk growth. The details are in the ClickHouse self-hosting guide.

That makes system-log retention a reasonable place to investigate when disk use continues to rise, but it does not establish the cause on any particular installation. Growing disk use could have another explanation; identify the tables consuming space before changing retention.

Check the installation before changing retention

  • Compare table sizes over time and identify which ClickHouse tables are growing. In particular, check the system-log tables named in Langfuse’s guidance rather than assuming Langfuse trace data is responsible.
  • Record the Langfuse release, deployment setup, and trace-ingestion path. Ingestion behavior can vary by SDK and version.
  • Review the relevant ClickHouse configuration and determine how much diagnostic history your operators need before setting a TTL.

Langfuse documents a TTL example for system.trace_log in its ClickHouse guide. Treat it as an operational configuration choice, not a universal retention period: reducing stored logs trades disk use against the amount of historical diagnostic detail available.

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Could ingestion delays look like missing traces?

Yes. Langfuse documents OpenTelemetry as its supported trace-ingestion path, and its v4 compatibility guidance notes that older SDKs or exporters that omit the v4 ingestion header can experience delays. Before interpreting gaps as a sampling problem, check the SDK or exporter version and whether the ingestion path meets the v4 requirements. The OpenTelemetry v4 compatibility guide describes the compatibility behavior.

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In Langfuse v4 specifically, events_full is the immutable, full-fidelity event table, while events_core is a query-optimized projection. LLM calls, tool executions, and agent steps appear as rows with trace-level attributes. These table names describe the v4 model; do not assume they apply to older installations. See the v4 data-model documentation.

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How should sensitive trace data be handled?

Sampling is not a privacy control: a sampled trace can still contain sensitive content. Langfuse documents client-side masking. Centralized server-side ingestion masking for self-hosted deployments is an Enterprise feature, and Langfuse describes it as a safety net rather than a replacement for client-side masking when sensitive data must not leave the application boundary. Review the masking documentation and apply the controls appropriate to your data before traces are sent.

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What the available deployment guidance does—and does not—tell you

Langfuse’s Kubernetes guide allows bundled or external data stores and recommends managed blob storage for production rather than bundled SeaweedFS. That is deployment guidance, not evidence that a particular storage change will resolve a ClickHouse disk problem. Diagnose the growing tables and the actual deployment configuration first. See the Kubernetes deployment guide.

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