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In Apache Cassandra, a delete is not an immediate physical erase. It writes a timestamped tombstone through the normal write path; reads may then have to reconcile that marker with older data in immutable SSTables. Compaction can eventually remove the tombstone and data it covers, but only when it is safe to do so.
What Cassandra writes when you delete data
Cassandra treats a deletion as an insertion: it records a timestamped deletion marker called a tombstone. The marker tells replicas to ignore values with timestamps older than the deletion. Because SSTables are immutable, Cassandra does not rewrite an existing SSTable in place to erase the value. The tombstone and older data can therefore coexist until compaction reconciles them. Apache Cassandra’s compaction overview describes this mechanism.
When tombstones affect read performance
A read may need to consult data spread across multiple SSTables and reconcile different versions. Duplicate data that must be read adds work; accumulated SSTables can add more. Compaction merges SSTables and removes eligible duplicates and tombstones, which can make reads faster. The cost is workload- and data-layout-dependent: a delete does not impose the same fixed latency penalty on every read, and the documentation establishes no universal slowdown factor. The compaction overview explains the read and cleanup effects.
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Why a tombstone is not purged as soon as it is written
A tombstone has to remain long enough for replicas that were temporarily unavailable to learn about the deletion. Cassandra’s stable documentation gives gc_grace_seconds a default of 864000 seconds, or ten days. This grace period is a safety window, not a timer that automatically frees disk space.
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After the grace period, a tombstone may become eligible for removal, but compaction still has to establish that purging it will not let an older value survive. Depending on the SSTables involved, the tombstone may need to be compacted together with SSTables containing the data it covers. Consequently, elapsed time alone does not reclaim the storage occupied by the marker and obsolete value. Apache Cassandra’s documentation details the purge conditions.
How cleanup can bring deleted data back
If a replica misses a deletion and remains unavailable beyond the tombstone’s retention window, compaction may purge the marker before that replica has processed it. When the replica returns, its old value may be treated as live and propagated: this is zombie data.
Repair and tombstone retention work together to reduce this risk. The safe grace-period value depends on how the cluster handles failures and repair; lowering gc_grace_seconds generically is not a safe cleanup shortcut. Cassandra also documents tombstone-purge settings and repaired-data safety behavior in its compaction subproperties reference.
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When a value’s TTL expires, Cassandra turns it into a tombstone rather than immediately erasing its bytes. Cleanup then depends on compaction and the same safety considerations that apply to explicit deletes. Mixing TTL and non-TTL data, or using different TTL durations for related data, can make tombstones harder to drop when partition data is spread across SSTables. The latest compaction overview describes TTL-related cleanup.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose compaction for the workload, not for deletes alone
Compaction strategy balances read amplification—the SSTables consulted for a read—against write amplification, or how much data is rewritten over its lifetime. Apache Cassandra’s Unified Compaction Strategy guidance describes the broad tradeoff this way:
| Strategy style | Documented workload fit | Tradeoff to consider |
|---|---|---|
| Leveled | Read-heavy workloads | Optimizes for lower read amplification; assess the accompanying rewrite work for the workload. |
| Tiered | Write-heavy workloads | Favors write efficiency; assess the resulting read amplification. |
These are workload guidelines, not a universal ranking. Review the actual read/write mix and data behavior before changing strategy. See Apache Cassandra’s UCS documentation for the strategy tradeoffs.
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