Designing the Ideal Cadence for Compaction and Snapshot Expiration

In previous posts, we explored how compaction and snapshot expiration keep Apache Iceberg tables performant and lean. But these actions aren’t one-and-done—they need to be scheduled strategically to balance compute cost, data freshness, and operational safety.

In this post, we’ll look at how to design a cadence for compaction and snapshot expiration based on your workload patterns, data criticality, and infrastructure constraints.

Why Cadence Matters

Without a thoughtful schedule:

Over-optimization can waste compute and create unnecessary load
Under-optimization leads to performance degradation and metadata bloat
Poor coordination can cause clashes with ingestion or query jobs

You need a cadence that fits your data’s lifecycle and your platform’s SLAs.

Key Factors to Consider

1. Ingestion Rate and Pattern

Streaming data? Expect high file churn. Compact frequently (hourly or near-real-time).
Batch jobs? Compact after each large load or on a daily schedule.
Hybrid? Monitor ingestion metrics and trigger compaction based on thresholds.

2. Query Frequency and Latency Expectations

High query volume tables benefit from more frequent compaction to improve scan performance.
Low-usage tables can tolerate more infrequent optimization.

3. Storage Costs and File System Limits

Cloud storage costs can balloon with small files and lingering unreferenced data.
File system metadata limits may also be a concern at massive scale.

4. Retention and Governance Requirements

Snapshots may need to be retained longer for audit or rollback policies.
Balance expiration with compliance needs.

Suggested Cadence Models

Use Case	Compaction Cadence	Snapshot Expiration
High-volume streaming pipeline	Hourly or event-based	Daily, keep 1–3 days
Daily batch ingestion	Post-batch or nightly	Weekly, keep 7–14 days
Low-latency analytics	Hourly	Daily, keep 3–5 days
Regulatory or audited data	Weekly or on-demand	Monthly, retain 30–90 days

Use metadata queries (e.g., from files, manifests, snapshots) to drive dynamic policies.

Automating the Schedule

You can use orchestration tools like:

Airflow / Dagster / Prefect: Schedule and monitor compaction and expiration tasks
dbt Cloud: Use post-run hooks or scheduled jobs to optimize models backed by Iceberg
Flink / Spark Streaming: Trigger compaction inline or via micro-batch jobs

Tip: Tag critical jobs with priorities and isolate them from ingestion workloads where needed.

Coordinating Between Compaction and Expiration

Ideally:

Compact first, then expire snapshots
This ensures snapshots written by compaction are retained at least temporarily
Avoid expiring snapshots too soon after compaction to prevent data loss

Example Workflow:

Run metadata scan to detect small file bloat
Trigger compaction on affected partitions
Delay snapshot expiration by a few hours
Run snapshot expiration with a safety buffer

Monitoring and Adjusting Over Time

Cadence isn’t static—adjust based on:

Changing ingestion rates
New query patterns
Storage trends
Platform feedback (slow queries, GC delays, etc.)

Use logs, metadata tables, and query performance dashboards to guide adjustments.

Summary

An effective compaction and snapshot expiration cadence keeps your Iceberg tables fast, lean, and cost-effective. Your schedule should:

Match your workload patterns
Respect operational and governance needs
Be flexible and monitorable

In the next post, we’ll look at how to use Iceberg’s metadata tables to dynamically determine when optimization is needed—so you can make it event-driven instead of fixed-schedule.