Cassandra Tombstone Accumulation

Tombstones are markers for deleted data in Cassandra. Excessive tombstones degrade read performance and can cause TombstoneOverwhelmingException.

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Symptoms

• TombstoneOverwhelmingException in logs or client errors
• Slow read performance on specific tables
• High "Tombstones scanned" in nodetool tablestats
• Read timeouts on range queries
• Increasing read latency over time

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Diagnosis

Step 1: Check Tombstone Counts

nodetool tablestats my_keyspace.my_table | grep -i tombstone

Key metrics:

• Average tombstones per slice (last five minutes): Should be < 100
• Maximum tombstones per slice (last five minutes): Alerts if > 1000

Step 2: Identify Problematic Tables

# Check all tables
for ks in $(nodetool tablestats 2>/dev/null | grep "Keyspace:" | awk '{print $2}'); do
    echo "=== $ks ==="
    nodetool tablestats $ks 2>/dev/null | grep -E "Table:|tombstones per slice" | head -20
done

Step 3: Check Tombstone Warning Threshold

grep tombstone_warn_threshold /etc/cassandra/cassandra.yaml
# Default: 1000

Step 4: Analyze Access Patterns

# Check recent warnings
grep -i "tombstone" /var/log/cassandra/system.log | tail -50

# Look for specific queries hitting tombstones
grep "Scanned over" /var/log/cassandra/system.log | tail -20

Step 5: Check gc_grace_seconds

cqlsh -e "SELECT table_name, gc_grace_seconds FROM system_schema.tables WHERE keyspace_name = 'my_keyspace';"

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Resolution

Immediate: Run Compaction

Force compaction to purge eligible tombstones:

# Compact specific table
nodetool compact my_keyspace my_table

# Or use garbagecollect for tombstone-only cleanup
nodetool garbagecollect my_keyspace my_table

Tombstone Eligibility

Tombstones are only purged after gc_grace_seconds (default 10 days) has passed AND the data has been repaired.

Short-term: Adjust Thresholds

Increase tombstone thresholds to prevent query failures (temporary fix):

# In cassandra.yaml
tombstone_warn_threshold: 10000
tombstone_failure_threshold: 100000

Per-Query Override

There is no per-query syntax to bypass tombstone thresholds. The only options are to adjust the thresholds in cassandra.yaml or fix the underlying data model.

Long-term: Fix Data Model

Problem Pattern 1: Deleting from wide partitions

-- Bad: Creates tombstone per deletion
DELETE FROM events WHERE user_id = ? AND event_time < ?;

Solution: Use TTL instead

-- Good: Data expires automatically, fewer tombstones
INSERT INTO events (...) VALUES (...) USING TTL 604800;

Problem Pattern 2: Null columns creating tombstones

-- Bad: Setting column to null creates tombstone
UPDATE users SET email = null WHERE id = ?;

Solution: Use separate table or don't update to null

Problem Pattern 3: Range deletes on time-series

Solution: Use Time-Window Compaction Strategy (TWCS)

ALTER TABLE events WITH compaction = {
    'class': 'TimeWindowCompactionStrategy',
    'compaction_window_unit': 'DAYS',
    'compaction_window_size': 1
};

Reduce gc_grace_seconds (With Caution)

If repairs run frequently, reduce gc_grace_seconds:

-- Only if repairs run more frequently than this value
ALTER TABLE my_table WITH gc_grace_seconds = 259200;  -- 3 days

Risk

Setting gc_grace_seconds too low risks resurrecting deleted data if repairs don't complete in time.

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Recovery

Verify Tombstone Reduction

# After compaction
nodetool tablestats my_keyspace.my_table | grep -i tombstone

# Should see reduced counts

Monitor Going Forward

Set up alerting on:

• Tombstones per read > 1000
• TombstoneOverwhelmingException count > 0

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Prevention

Strategy	Implementation
Use TTLs instead of deletes	INSERT ... USING TTL 86400
TWCS for time-series data	Change compaction strategy
Avoid null updates	Use default values or separate tables
Regular compaction	Monitor pending compactions
Proper data modeling	Avoid wide partitions with deletions
Run repairs	Enables tombstone purging

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Related Commands

Command	Purpose
nodetool tablestats	Check tombstone metrics
nodetool compact	Force compaction
nodetool garbagecollect	Targeted tombstone cleanup

Related Documentation

• Compaction Management - Compaction strategies
• Data Modeling - Best practices