Expert Checklist for Apache Iceberg on AWS: From the Essentials to Advanced Optimization
here is an enhanced, precise, and actionable checklist tailored for Apache Iceberg implementations on AWS. This list captures core principles, best practices, and advanced techniques to maximize performance and efficiency in real-world scenarios.
Published Jan 15, 2025
- Define Use Cases and Data Patterns:
- Identify if the workload involves transactional data lakes, time travel for historical analytics, or incremental updates for near-real-time needs.
- Plan for concurrent read/write scenarios using Iceberg’s atomic operations.
- Select the Deployment Environment:
- Use Amazon S3 for scalable storage and native Iceberg compatibility.
- Ensure the table format version (prefer v2) matches your requirements for advanced features like row-level updates and deletes.
- Partitioning Strategy:
- Choose hidden partitioning for high-cardinality columns like user IDs or timestamps.
- For low-cardinality fields (e.g., region or product category), explicit partitions offer better control.
- Use dynamic partition evolution to adapt as the dataset grows and query patterns change.
- Batch and Streaming:
- Leverage Spark Structured Streaming or Flink for real-time ingestion.
- Optimize streaming ingestion with merge-on-read mode, deferring compactions until necessary.
- Data Deduplication:
- Use SQL-like
MERGE INTO
for deduplication and handling upserts efficiently.
- Row-Level Operations:
- Implement row-level deletes using Iceberg’s built-in operations to maintain transactional integrity.
- Predicate Pushdown:
- Write queries to take advantage of Iceberg’s metadata filtering (e.g., by partition, file statistics, or min/max column values).
- Partition Pruning:
- Ensure query engines like Spark or Athena are configured to prune unnecessary partitions automatically.
- Time Travel Queries:
- Query historical data using:
VERSION AS OF snapshot_id
TIMESTAMP AS OF yyyy-MM-dd
for point-in-time analysis.
- Incremental Reads:
- Fetch newly added records using snapshot boundaries (
start-snapshot-id
andend-snapshot-id
).
- Compaction:
- Regularly run
RewriteDataFiles
to consolidate small files and improve read efficiency. - Use
ExpireSnapshots
to clean up obsolete metadata while preserving necessary snapshots for compliance.
- File Size Configuration:
- Optimize file sizes to at least 100 MB for Parquet or ORC files to avoid performance bottlenecks.
- Use Iceberg's
write.target-file-size-bytes
to control target file sizes dynamically.
- Metadata Cleaning:
- Run
RemoveOrphanFiles
to clean up unreferenced files left from aborted writes or older snapshots.
- AWS Glue:
- Use Glue as the catalog for schema and metadata management.
- Enable Glue’s locking mechanism to avoid concurrent update conflicts in multi-writer scenarios.
- Amazon EMR:
- Run Iceberg on Spark with optimized cluster scaling for cost efficiency.
- Configure
spark.sql.extensions
andspark.sql.catalog.<catalog_name>
for seamless Iceberg integration.
- Amazon Athena:
- Query Iceberg tables serverlessly with full support for time travel and partition pruning.
- Optimize query performance by periodically compacting small files.
- AWS Lake Formation:
- Use Lake Formation to manage fine-grained access control and ensure secure data access.
- Compression:
- Use Zstandard (ZSTD) for efficient compression without compromising on query performance.
- Caching:
- Cache frequently accessed metadata files to reduce latency for repeated queries.
- Optimize Query Plans:
- Analyze query plans using Spark UI or Athena query logs to detect inefficiencies.
- Parallelism:
- Configure appropriate parallelism for ingestion and query tasks to match cluster resources.
- Data Encryption:
- Encrypt data at rest using server-side encryption (e.g., AWS KMS).
- Ensure TLS is enabled for data in transit.
- Audit Logging:
- Enable audit logging for Iceberg table operations to comply with data governance standards.
- Access Control:
- Implement fine-grained role-based access policies using AWS Identity and Access Management (IAM) or Lake Formation.
- Real-Time Monitoring:
- Use Amazon CloudWatch to track ingestion rates, snapshot sizes, and query latencies.
- Monitor file pruning and metadata growth for potential inefficiencies.
- Alerts:
- Set up alerts for storage thresholds, query failures, or high-latency operations.
- Periodic Maintenance:
- Schedule compactions, snapshot expiration, and orphan file removal as part of routine maintenance.
- Schema Evolution:
- Use Iceberg's schema evolution to seamlessly handle changes like adding, dropping, or renaming columns.
- Hybrid Table Migration:
- Migrate legacy Hive or Parquet tables to Iceberg using in-place conversion or re-ingestion strategies.
- Multi-Engine Queries:
- Test cross-engine compatibility with Spark, Trino, Presto, and Athena for diverse workload support.
- S3 Lifecycle Policies:
- Transition old data to S3 Glacier or Intelligent-Tiering to save storage costs.
- Cluster Autoscaling:
- Enable autoscaling on EMR to dynamically match resources to workload demands.
- Query Cost Analysis:
- Regularly analyze Athena or Spark query costs to identify optimization opportunities.
This checklist combines strategic planning, integration with AWS services, and Iceberg-specific optimizations for building scalable, high-performance, and cost-effective data lakes. Let me know if you’d like further refinement or a deeper dive into any of the steps!