Apache Hudi

What Is Apache Hudi?

Core Features of Apache Hudi

• ACID Compliance and Transactional Integrity: Hudi brings ACID (Atomicity, Consistency, Isolation, Durability) compliance to data lakes, ensuring transactional integrity and enabling complex data transformations and rollback capabilities.

• Efficient Upserts and Deletes: Unlike traditional data lakes where modifying data can be cumbersome and slow, Hudi enables efficient upserts (updating or inserting) and deletes. This feature is crucial for maintaining large datasets where changes are frequent.

• Incremental Processing: Hudi supports incremental data processing, which allows for processing only the data that has changed, rather than reprocessing entire datasets. This method significantly reduces the computational load and speeds up data ingestion cycles.

• Scalability: Designed for large-scale operations, Hudi efficiently manages vast amounts of data, demonstrated by its deployment in organizations like ByteDance and Uber, where it handles multiple petabytes of data.

Apache Hudi Architecture

Apache Hudi's architecture can be summarized as follows:

• Write Client: An API for writing batch or streaming data to Hudi tables, enabling record-level insertions, updates, and deletions.
• Delta Streamer: An independent service for merging new changes from various sources like Kafka or AWS S3 into existing datasets in Hudi tables, accommodating both batch and streaming data.
• Hudi Table: Represents datasets managed by Apache Hudi, consisting of multiple versions created through different write operations.
• Indexing: Accelerates data reads and writes by efficiently locating data records.
• Query Engine: Provides SQL query capabilities and native integrations with frameworks like Delta Lake, Apache Spark, and Presto.
• Timeline Server: Manages Hudi dataset metadata to offer a consistent view for optimizing query performance.

Table Types

 

Copy-On-Write (COW): In this table type, every change to a dataset results in rewriting the file, which ensures that data reads are quick and simple as each read fetches the latest snapshot of the file. This model is ideal for workloads with heavier read operations and fewer write operations.

• For Copy-On-Write Tables, when users update data, the file containing the data is rewritten. This results in high write amplification, but zero read amplification, making it suitable for scenarios with low write and high read frequency.

Copy-On-Write

Merge-On-Read (MOR): Merge On Read is optimized for workloads requiring frequent updates. It stores data in a combination of columnar and row-based formats, allowing for faster writes by logging only the changes. These changes are periodically merged with the main dataset, ensuring efficient reads.

• For Merge-On-Read Tables, the overall structure is somewhat similar to an LSM-Tree. User write operations initially go into delta data, which is stored in a row-based format. This delta data can be manually merged into the existing files, organized into a columnar structure like Parquet.
  
  
  Merge-On-Read (MOR)

 

Query Types

• Snapshot Queries: These provide a view of the table state at a specific point in time, including all the data up to a particular commit. This is useful for audits and historical analysis.

• Incremental Queries: Essential for real-time data processing, these queries fetch only the data that has changed since the last commit, facilitating efficient data syncing.

• Read Optimized Queries: These focus on delivering the fastest read performance by accessing only the compacted and cleaned up base files, which represent the latest snapshots of the data.

Benefits of Apache Hudi

• Data Consistency: In environments where large volumes of data are processed, Apache Hudi ensures data is accurate, complete, and current. It achieves this through ACID transactions, which maintain data integrity, and mechanisms for resolving conflicts during concurrent updates. This aspect is vital for maintaining reliable data states across large-scale systems.

• Data Integrity: Apache Hudi prioritizes data integrity, especially when dealing with large-scale data. It employs techniques like checksum verification to prevent data corruption during transfers and updates. Additionally, its data partitioning feature helps manage large datasets by dividing them into manageable segments, thereby mitigating risks of corruption and loss.

• Schema Flexibility: Hudi supports schema evolution, which is a critical feature for organizations dealing with evolving data models. This flexibility allows them to modify their data schema without disrupting existing processes, facilitating adaptability in dynamic data environments.

• Performance: In the realm of large datasets, performance is key. Apache Hudi enhances data query and ingestion performance by using indexing for quick data retrieval and incremental data updates. This approach minimizes processing time and resource usage, making data handling more efficient.

• Integration: Hudi integrates seamlessly with popular big data solutions, enhancing its utility in diverse environments. It supports a variety of storage layers like Amazon S3, Apache HDFS, and Google Cloud Storage, and works well with query engines such as Apache Hive, Presto, and Apache Spark. This makes it a versatile choice for building high-performance, scalable data lakes.

Apache Hudi Best Practices

• Understand Your Data Model and Access Patterns: Before implementing Hudi, it's essential to understand your data model and how your application will access the data. This understanding helps you select the right table design and configuration options for your use case.

• Use the Appropriate File Format: Hudi supports various file formats like Parquet, ORC, and Avro. Choose the file format based on your use case and performance requirements. For instance, columnar formats like Parquet or ORC are better for low-latency read performance.

• Optimize the Storage Layer: Hudi's performance heavily relies on the underlying storage layer. Optimizing this layer can significantly improve Hudi's performance. Tips include choosing the right storage medium (e.g., SSDs), optimizing block size and replication factors, and using compression.

• Monitor Hudi Performance Metrics: Keep an eye on Hudi performance metrics such as query latency, ingestion rate, and storage usage to identify potential bottlenecks and areas for improvement.

• Partition Your Data: Data partitioning can significantly enhance Hudi's performance. It allows you to filter out unnecessary data based on your query patterns and filter predicates, thus improving query performance. Partitioning also helps maintain a balanced distribution of data across the storage layer and leverages parallelism for faster queries.

• Use DeltaStreamer for Data Ingestion: Hudi offers a tool called DeltaStreamer that helps efficiently move existing data into Hudi tables. DeltaStreamer can process large volumes of data in parallel and supports various data sources and target formats.

• Design Hudi Table Primary Keys Carefully: Choosing the right primary key is crucial for setting up Hudi tables. It determines how data is partitioned and how upsert and delete operations are performed. A good design should avoid hotspots and single points of failure, and support high-performance queries and updates.

• Optimize Write Performance: Write performance is one of the key performance indicators for Hudi. Optimization includes choosing the right writing tool (e.g., Spark or Flink), adjusting batch sizes and parallelism, using Hive metadata caching, etc.

• Use Hudi Tables for Incremental Computations: Hudi tables support incremental computation, making them highly suitable for real-time computation and stream processing scenarios. By using Hudi APIs, you can easily write incremental computation logic and update results directly back to the tables.

Apache Hudi vs. Apache Iceberg vs. Delta Lake

StarRocks + Apache Hudi = The Modern Open Data Lake