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ClickHouse can fill up for different reasons in self-hosted Langfuse and SigNoz: retained application telemetry, high-volume incoming data, or ClickHouse system logs. Find which tables are growing before changing retention or deleting data. Langfuse system logs can grow independently of Langfuse’s own data, while SigNoz’s production guidance focuses on identifying the signals and services driving ingestion.
Why is my ClickHouse disk filling up?
Start by separating three possible sources: application telemetry that remains stored, new telemetry arriving faster than it expires, and ClickHouse’s own system-log tables. A retention policy only affects the data it governs, so changing one setting may not address the largest consumer.
- Retained application data: traces, observations, scores, logs, metrics, or other product telemetry may remain within the configured retention window—or indefinitely if a policy is not configured.
- High ingestion volume: a busy service, signal, or high-cardinality metric labels can drive rapid growth even when retention is set.
- ClickHouse system logs: operational tables can accumulate separately from the product’s telemetry and may not be governed by the product’s retention setting.
Check free space and its rate of change, ClickHouse health, and the persistent volume or filesystem. If the disk is close to exhaustion, adding compatible capacity can provide headroom while you investigate, but it does not stop the underlying growth.
How do I find the biggest ClickHouse tables?
Langfuse: rank active table parts
Langfuse documents this query to sum bytes and rows for active ClickHouse parts by table and database, then sort by size. Run it against the ClickHouse instance used by Langfuse:
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SELECT table, formatReadableSize(size) AS size, rows FROM (
SELECT
table,
database,
sum(bytes) AS size,
sum(rows) AS rows
FROM system.parts
WHERE active
GROUP BY table, database
ORDER BY size DESC
)The result identifies the largest active-part consumers in that installation; do not assume the same tables will lead everywhere. Langfuse documents this diagnostic in its scaling guide and ClickHouse disk-size FAQ.
SigNoz: use diagnostics for the installed deployment
The reviewed SigNoz documentation describes retention and ingestion-volume controls but does not provide a current SigNoz-specific largest-table query. Inspect ClickHouse table and disk metrics using the ClickHouse version and deployment conventions you run. Validate table names and what they contain before changing or deleting anything; do not assume Langfuse’s table mix applies to SigNoz.
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What to do if Langfuse tables are growing
If Langfuse application data is the largest consumer
Langfuse uses ClickHouse as its primary analytical store for traces, observations, and scores. Its project-level Data Retention feature deletes old traces, observations, scores, and media assets nightly. On self-hosted Langfuse, this feature is available in Enterprise Edition; without a configured policy, event data is stored indefinitely by default. The minimum retention period is three days, and policies are configured per project. See the Langfuse data-retention guide.
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For self-hosted instances, the relevant IAM role needs s3:DeleteObject permission for buckets. If object storage is versioned, prior object versions or delete markers may also require separate cleanup. Do not substitute a broad bucket lifecycle deletion rule for product-aware media retention: Langfuse warns that deleting media objects this way can break references and uploads.
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Langfuse also documents ClickHouse TTLs for traces, observations, scores, and event_log when application retention is unavailable. Treat this as a ClickHouse-level option only after establishing the retention window your team needs and checking product and plan limitations. Langfuse’s ClickHouse guidance says v3 supports ClickHouse 24.3 and newer; v4 requires 25.12 or newer, with 26.4 recommended. Confirm your deployed versions before relying on version-sensitive behavior. Details are in the Langfuse ClickHouse guide and scaling guide.
If ClickHouse system logs are the largest consumer
Langfuse’s FAQ says ClickHouse’s built-in system-log tables can frequently dominate disk usage even after Langfuse retention is configured. It names trace_log, text_log, opentelemetry_span_log, asynchronous_metric_log, metric_log, and latency_log; these ship without TTLs by default, and the query profiler writes continuously to system.trace_log. Langfuse does not read these tables. See its FAQ.
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The Langfuse scaling guide documents two approaches: disable unused system-log tables in ClickHouse configuration, or retain them with aggressive TTLs and disable the query profiler. It recommends keeping query_log, part_log, and error_log enabled because they are useful for debugging and remain small. Check local operational and incident-response requirements before removing logs.
If retention deletion jobs are slow, the scaling guide lists LANGFUSE_CLICKHOUSE_DELETION_TIMEOUT_MS with a documented default of 600000 ms (10 minutes). It also describes lightweight deletion options on ClickHouse 25.7 and above. These are implementation details to consider only after identifying the issue and verifying the deployed ClickHouse version.
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How to reduce SigNoz storage growth
Check retention in the deployed workspace
SigNoz lets you set retention separately for logs, traces, and metrics. Its current Docker installation guide states defaults of 7 days for logs and traces and 30 days for metrics, while its separate retention guide states 15 days for logs and traces and 30 days for metrics. Because those first-party pages disagree, inspect the actual setting in your deployed version rather than treating either figure as universal. The UI location also varies in the documentation: installation pages refer to Settings under Workspace, while some pages refer to General. Consult the guide for your installation: Docker installation and retention period.
SigNoz’s retention guide says a changed setting applies only to newly ingested data; existing data keeps its earlier retention setting. TTL expiry permanently deletes data, and increasing retention later does not restore expired data. As a result, lowering retention may not immediately remove all data governed by an older TTL.
Find and control the high-volume signal or service
If SigNoz telemetry tables or ingestion trends point to fast incoming growth, identify the service and signal responsible before changing collection. The SigNoz production checklist recommends matching the intervention to the source:
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- For traces, apply sampling.
- For verbose logs, filter them at the Collector.
For long-term archival, SigNoz documents an S3 exporter path. The S3 copy is not queryable from the SigNoz UI, so if the aim is to reduce active storage, set online retention separately.
Which fix affects which data?
| Action | Data affected | Effect on existing data | Product availability or important qualification |
|---|---|---|---|
| Langfuse project Data Retention | Project traces, observations, scores, and media assets | Deletes data beyond the configured policy during nightly processing | Self-hosted availability is Enterprise Edition; bucket permissions and versioned-object cleanup may matter. |
| Langfuse ClickHouse system-log configuration or TTLs | ClickHouse system logs, not Langfuse-owned telemetry | Disabling or expiring logs limits their stored footprint; choose which logs to preserve for operations | Keep query_log, part_log, and error_log enabled per Langfuse’s scaling guidance. |
| SigNoz retention settings | Logs, traces, or metrics, according to the selected signal setting | Changed settings apply to newly ingested data; existing data retains its previous setting | Published defaults conflict for logs and traces; inspect the deployed setting. |
| SigNoz ingestion controls | Future incoming metrics, traces, or logs | Reduces future volume; does not itself remove stored data | Choose metric filtering or aggregation, trace sampling, or log filtering to match the source. |
| SigNoz S3 exporter | Archived copy of telemetry | Preserves a copy outside active ClickHouse storage; online retention is separate | The S3 copy is not queryable from the SigNoz UI. |
| Expand compatible storage capacity | Filesystem or persistent-volume headroom | Does not remove data or reduce growth | Compatibility depends on the host, storage interface, filesystem, and deployment topology. |
How to verify the fix and avoid a repeat incident
- Measure first: record free space, growth over time, ClickHouse health, and the largest tables or databases.
- Apply only the matching control: use product retention for product data, system-log controls for ClickHouse logs, and ingestion changes for future volume.
- Watch the outcome: confirm that the intended data is expiring or that incoming growth has changed, then observe disk usage over time. TTL processing, background merges, object storage, and filesystem or container-volume capacity are distinct measurements; confirm physical space is reclaimed rather than inferring it from a policy change.
If self-managed storage growth remains an operational bottleneck, managed services are an architecture and cost decision rather than a cleanup command. Langfuse names ClickHouse Cloud or BYOC in its scaling guidance; SigNoz documents Cloud-managed scaling and retention in its production checklist.
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