Why architecture matters here

TTL failures produce tombstone bloat that murders read performance. Architecture matters because TTL + compaction + strategy must work together.

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The architecture: every piece explained

The top strip is basics. TTL sources. Timestamp + TTL. Read filter. Compaction.

The middle row is nuance. Tombstones. gc_grace_seconds. TTL vs delete. TWCS.

The lower rows are ops. Tombstone bloat. Metrics. Ops — TTL design + strategy.

Cassandra TTL — default + per-column + tombstone bloat + compaction planningexpire data cleanly at scaleTTL sourcestable default / INSERT / UPDATETimestamp + TTLexpirationRead filterskip expiredCompactionphysical removalTombstonesfor deletesgc_grace_secondswhen tombstones removableTTL vs deletedifferent behaviorsTWCStime-window compactionTombstone bloatread amplificationMetricstombstones per readOps — TTL design + compaction strategy + monitoringdeletewaitcomparepreferdetectmeasuremeasureoperateoperate
Cassandra TTL + tombstone + compaction interaction.
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End-to-end flow

End-to-end: time-series data written with TTL=7d + TWCS. Data landed in daily windows. After 7d, expired; compaction reclaims full windows cleanly. Tombstone bloat avoided. Read metrics healthy.