Understanding the Problem

Latency Spikes Despite Low Resource Utilization

Senior architects often encounter unexpected latency spikes during stable operations. These are not always due to hardware saturation, but rather background processes such as compactions or tombstone garbage collection that are poorly timed or misconfigured.

Root Cause Scenarios

  • High tombstone count due to frequent deletes or TTL expirations.
  • Incorrect compaction strategy (e.g., SizeTiered vs LeveledCompactionStrategy).
  • Data model anti-patterns such as large partitions or unbounded clustering keys.
  • Over-sharding or skewed partition distribution across nodes.

Architectural Considerations

ScyllaDB Compaction: Friend or Foe?

ScyllaDB uses compaction to merge SSTables and reclaim disk space. However, improperly tuned compaction can compete with read/write I/O and degrade performance. SizeTiered compaction is efficient for append-heavy workloads, but LeveledCompactionStrategy is better suited for read-heavy OLTP applications.

compaction_strategy:
  class: LeveledCompactionStrategy
  sstable_size_in_mb: 160

Tombstone Pressure and Read Repair

Frequent deletions can produce massive tombstones, slowing down reads as the engine scans and filters them. Enabling tracing and examining read latency metrics often reveals tombstone-induced slowdowns.

Diagnostics and Monitoring

Key Metrics to Watch

  • scylla_storage_proxy_coordinator_read_latency
  • scylla_compaction_manager_pending_tasks
  • scylla_db_tombstone_filtered_cells
  • scylla_cache_hit_rate

Effective Use of Scylla Monitoring Stack

The Prometheus/Grafana-based monitoring stack provides detailed latency breakdowns. Correlate spikes in read latency with compaction activity or tombstone counts to pinpoint root causes.

Common Pitfalls

Data Model Anti-Patterns

Large partitions and wide rows are often culprits. Use Scylla's nodetool tablestats and nodetool cfstats to identify such patterns. Refactor tables with time-bucketing or hierarchical partition keys.

Improper CQL Usage

Queries that rely on ALLOW FILTERING or unbounded IN clauses can introduce serious latency bottlenecks. Use query tracing and audit logs to detect such patterns.

Step-by-Step Resolution Strategy

1. Analyze Compaction Activity

nodetool compactionstats
nodetool cfstats keyspace.table

Look for pending compactions and histograms showing I/O load. Adjust compaction throughput and strategy as needed.

2. Tombstone Scans

nodetool toppartitions --tombstone-read-count keyspace.table

Refactor tables or TTL settings if tombstones dominate read paths.

3. Adjust Table Schema

Introduce bucketing or change clustering keys to ensure even distribution and manageable partition sizes.

4. Monitor with Tracing

TRACING ON;
SELECT * FROM keyspace.table WHERE ...;

Trace logs reveal where time is being spent—particularly during filtering or tombstone resolution.

5. Tune Compaction and Cache

Increase row cache size or change to LCS compaction for frequently read datasets. Use the scylla.yaml to configure global thresholds.

Best Practices for Enterprise-Scale Stability

  • Run regular nodetool cleanup and repair cycles to maintain consistency.
  • Use leveled compaction for balanced I/O and predictable latencies.
  • Design schema with lifecycle in mind (e.g., TTL-aware data modeling).
  • Automate tombstone scans and partition distribution audits.
  • Utilize Scylla Manager for scheduling and anomaly detection.

Conclusion

ScyllaDB's performance hinges not just on hardware or capacity but on subtle interactions between schema design, compaction strategies, and workload patterns. The root causes of latency spikes are often architectural—tied to schema evolution, access patterns, and background tasks. Systematic diagnosis using tracing, metrics, and compaction insights allows senior engineers to detect and correct issues early. Implementing best practices around schema, compaction, and monitoring will future-proof your ScyllaDB deployment against performance regressions.

FAQs

1. Why does LeveledCompactionStrategy reduce read latencies in ScyllaDB?

Because it maintains fewer overlapping SSTables, leading to fewer disk seeks and more predictable I/O behavior for read-heavy workloads.

2. Can high tombstone counts crash a ScyllaDB node?

They typically won't crash a node, but they can severely degrade performance and cause timeouts, especially when tombstone scanning thresholds are breached.

3. How do I detect large partitions in production?

Use nodetool tablestats and enable Scylla's partition size warnings to alert on rows or partitions above recommended thresholds.

4. Should I use materialized views in high-throughput systems?

Materialized views can simplify read paths but may add write amplification and introduce eventual consistency issues. Use carefully and monitor their performance impact.

5. How does Scylla Manager help in troubleshooting?

Scylla Manager centralizes tasks like repairs, health checks, and alerts, helping teams proactively identify and resolve issues before they affect performance.