Apache Hudi for Incremental Processing

Traditional data lakes excel at storing vast amounts of immutable data but struggle with record-level updates and incremental changes, forcing engineers to rewrite entire datasets. Apache Hudi solves this by introducing transactional guarantees, upsert capabilities, and incremental processing directly on your data lake storage. By managing these core functions, Hudi transforms static object stores into dynamic, high-performance data lakes that can power near-real-time analytics and efficient downstream pipelines.

Core Concepts: Upserts and Table Types

At its heart, Hudi enables upsert operations (update + insert) on data lakes. Instead of rewriting a massive Parquet file to change one record, Hudi manages this elegantly. When you perform an upsert, Hudi compares incoming records with existing ones using a primary key (like record_key). It then determines which records are new inserts and which are updates to existing data.

Hudi implements this through two primary table types, each with distinct performance trade-offs. The copy-on-write (CoW) table type is the simpler model. When an update arrives, Hudi rewrites the entire original data file containing that record, creating a new version. This means readers always see the latest data in the most efficient columnar format (like Parquet). CoW offers excellent read performance because queries read directly from optimized base files, but write operations are more expensive due to the file rewriting.

In contrast, the merge-on-read (MoR) table type uses a hybrid approach. Updates and inserts are first written to a more efficient row-based log file (like Avro). A background compaction process later merges these log files with the base columnar files. This design provides very fast write latency, as only the small log file is appended. However, reads can be more expensive because queries might need to merge the base file with the log file on-the-fly to get the latest snapshot. You choose CoW for read-heavy, batch-focused workloads and MoR for write-heavy, near-real-time ingestion.

Enabling Efficient Pipelines with Incremental Queries

One of Hudi's most powerful features is its support for incremental queries. Instead of querying an entire dataset every time, downstream consumers can pull only the data that has changed since their last check. Hudi achieves this by maintaining a precise timeline of all actions performed on the dataset (commits, compactions, cleans).

This timeline is the source of truth for incremental processing. An application can query Hudi for all changes (upserts and deletes) that occurred after a specific commit timestamp. For example, an hourly job can fetch only the records changed in the last hour and feed them into a downstream dashboard or machine learning model. This drastically reduces compute and I/O overhead compared to full table scans, enabling efficient streaming architectures built on batch data.

Managing Performance: Compaction and Timeline

To maintain performance, especially for MoR tables, Hudi employs automated compaction strategies. Compaction is the process of merging the row-based delta log files with the base columnar files to produce new, optimized base files. Without compaction, log files would grow indefinitely, making reads progressively slower. Hudi allows you to configure compaction schedules (e.g., after every 5 commits) and strategies (e.g., prioritizing partitions with the most logs) to balance write latency and read performance.

The timeline management system is central to Hudi's operations. Stored in the .hoodie metadata directory, the timeline is a chronological record of all actions. Each action (like a commit for new data or a compaction request) is saved as a file. This provides atomicity and consistency, allowing Hudi to roll back failed writes and give readers a consistent view of the data. It also enables time travel queries, where you can query the dataset exactly as it was at a previous point in time by referencing an earlier commit on the timeline.

Choosing Hudi: Comparison with Delta Lake and Iceberg

Apache Hudi, Delta Lake, and Apache Iceberg are the three leading open-source table formats for data lakes. Your choice depends heavily on your primary update pattern requirements and architectural priorities.

Hudi is particularly strong for use cases requiring high-frequency upserts and incremental processing streams. Its built-in support for incremental queries via the timeline is a first-class feature. It shines in scenarios like change data capture (CDC) from databases or real-time event processing where you need to mutate a large-scale lake efficiently.

Delta Lake, integrated deeply with the Apache Spark ecosystem, excels at providing ACID transactions and robust schema evolution for large-scale batch ETL workloads. Its strength is in reliability and performance for massive batch writes and updates. Iceberg is designed for correctness at scale, with sophisticated partitioning and hidden partitioning features that prevent common data layout errors. It is often chosen for its superb performance on complex analytical queries and its engine-agnostic design (working well with Spark, Trino, Flink, etc.).

In summary, if your primary need is to build incremental pipelines with low-latency upserts on a data lake, Hudi's architecture is purpose-built for this. If your focus is on giant, reliable batch operations within Spark, consider Delta. If you need maximum query performance and partition flexibility across multiple query engines, evaluate Iceberg.

Common Pitfalls

1. Defaulting to Merge-on-Read for All Scenarios: While MoR offers fast writes, using it for a primarily batch-analytical workload can lead to unexpectedly high query costs and latency. The need for real-time merge during reads can burden your query engine. Correction: Use Copy-on-Write for batch-dominated, read-heavy pipelines. Reserve Merge-on-Read for true low-latency ingestion pipelines where fast writes are the critical requirement.

1. Neglecting Compaction Tuning: In an MoR setup, letting compaction run with default settings or ignoring it can cause performance to degrade over time as delta logs accumulate. Correction: Actively monitor the size and number of log files. Configure compaction to run on a schedule that matches your data ingestion rate and SLA requirements, ensuring log files are merged into base files before they impact read performance.

1. Misunderstanding "Incremental" in Queries: A common mistake is attempting to perform incremental processing by filtering on a data field (like last_updated_date) instead of using Hudi's native incremental query API. This can miss deletes and lead to data correctness issues. Correction: Always use Hudi's provided readers (like .readChanges() in Spark or the HudiIncrementalPuller tool) that leverage the timeline to get a guaranteed correct set of changes.

1. Ignoring File Sizing: Writing very small base files or log files creates excessive metadata overhead and hurts query performance due to poor I/O patterns. Correction: Tune write operations to target optimal file sizes (e.g., 128MB - 1GB for Parquet files). Use Hudi's built-in features like clustering to reorganize small files into larger ones.

Summary

• Apache Hudi transforms static data lakes by enabling efficient upsert operations and incremental processing directly on cloud storage.
• Choose the copy-on-write table type for superior read performance in batch scenarios, and the merge-on-read type for low-latency write performance, remembering to manage compaction.
• Leverage incremental queries against Hudi's timeline to build efficient downstream pipelines that process only changed data, eliminating costly full-table scans.
• Select Hudi over alternatives like Delta Lake or Iceberg when your primary architectural driver is high-frequency record-level updates and building incremental change streams on a data lake.