DEV Community

Cover image for Apache Iceberg Table Optimization #9: Managing Large-Scale Optimizations — Parallelism, Checkpointing, and Fail Recovery
Alex Merced
Alex Merced

Posted on • Edited on

Apache Iceberg Table Optimization #9: Managing Large-Scale Optimizations — Parallelism, Checkpointing, and Fail Recovery

Managing Large-Scale Optimizations — Parallelism, Checkpointing, and Fail Recovery

When working with Apache Iceberg at scale, optimization jobs can become heavy and time-consuming. Rewriting thousands of files, scanning massive partitions, and coordinating metadata updates requires careful execution planning—especially in environments with limited compute or strict SLAs.

In this post, we’ll look at strategies for making compaction and metadata cleanup operations scalable, resilient, and efficient, including:

  • Tuning parallelism
  • Using partition pruning
  • Applying checkpointing for long-running jobs
  • Handling failures safely and automatically

Why Scaling Optimization Matters

As your Iceberg tables grow:

  • File counts increase
  • Partition cardinality rises
  • Manifest files balloon
  • Compaction jobs touch terabytes of data

Without scaling strategies:

  • Jobs may fail due to timeouts or memory errors
  • Optimization may lag behind ingestion
  • Query performance continues to degrade despite efforts

1. Leveraging Partition Pruning

Partition pruning ensures that only the parts of the table that need compaction are touched.

Use metadata tables to target only problem areas:

SELECT partition
FROM my_table.files
GROUP BY partition
HAVING COUNT(*) > 20 AND AVG(file_size_in_bytes) < 100000000;
Enter fullscreen mode Exit fullscreen mode

You can then pass this list to a compaction job to limit the scope of the rewrite.

2. Tuning Parallelism in Spark or Flink

Large optimization jobs should run with enough parallel tasks to distribute I/O and computation.

In Spark:
Use spark.sql.shuffle.partitions to increase default parallelism.

Tune executor memory and cores to handle larger partitions.

Use .option("partial-progress.enabled", true) for better resilience in Iceberg actions.

spark.conf.set("spark.sql.shuffle.partitions", "200")

Actions.forTable(spark, table)
  .rewriteDataFiles()
  .option("min-input-files", "5")
  .option("partial-progress.enabled", "true")
  .execute()
Enter fullscreen mode Exit fullscreen mode

In Flink:

  • Use fine-grained task managers

  • Enable incremental compaction and checkpointing

3. Incremental and Windowed Compaction

Don’t try to compact the entire table at once. Instead:

  • Group partitions into batches

  • Use rolling windows (e.g., compact N partitions per hour)

  • Resume from the last successfully compacted partition on failure

  • You can build this logic into orchestration tools like Airflow or Dagster.

4. Checkpointing and Partial Progress

Iceberg supports partial progress mode in Spark:

.option("partial-progress.enabled", "true")
Enter fullscreen mode Exit fullscreen mode

This allows successfully compacted partitions to commit, even if others fail—making retries cheaper and safer.

In Flink, this is handled more granularly via stateful streaming checkpointing.

5. Retry and Failover Strategies

Wrap compaction logic in robust retry mechanisms:

  • Use exponential backoff

  • Separate retries by partition

  • Alert on repeated failures for human intervention

For example, in Airflow:

PythonOperator(
    task_id="compact_partition",
    python_callable=run_compaction,
    retries=3,
    retry_delay=timedelta(minutes=5)
)
Enter fullscreen mode Exit fullscreen mode

Also consider:

  • Writing logs to object storage for audit

  • Emitting metrics to Prometheus/Grafana for observability

6. Monitoring Job Health

Track:

  • Job duration

  • Files rewritten vs skipped

  • Failed partitions

  • Number of manifests reduced

  • Snapshot size pre- and post-job

  • These metrics help tune parameters and detect regressions over time.

Summary

Scaling Iceberg optimization jobs requires thoughtful execution planning:

  • Use metadata to limit scope

  • Tune parallelism to avoid resource waste

  • Use partial progress and checkpointing to survive failure

  • Automate retries and monitor outcomes

In the final post of this series, we’ll bring it all together—showing how to build a fully autonomous optimization pipeline using orchestration, metadata triggers, and smart defaults.

Warp.dev image

Warp is the #1 coding agent.

Warp outperforms every other coding agent on the market, and gives you full control over which model you use. Get started now for free, or upgrade and unlock 2.5x AI credits on Warp's paid plans.

Download Warp

Top comments (0)

Gen AI apps are built with MongoDB Atlas

Gen AI apps are built with MongoDB Atlas

MongoDB Atlas is the developer-friendly database for building, scaling, and running gen AI & LLM apps—no separate vector DB needed. Enjoy native vector search, 115+ regions, and flexible document modeling. Build AI faster, all in one place.

Start Free

👋 Kindness is contagious

Explore this insightful write-up, celebrated by our thriving DEV Community. Developers everywhere are invited to contribute and elevate our shared expertise.

A simple "thank you" can brighten someone’s day—leave your appreciation in the comments!

On DEV, knowledge-sharing fuels our progress and strengthens our community ties. Found this useful? A quick thank you to the author makes all the difference.

Okay