Apache Hudi

Core concepts and architecture

Hudi organizes data into logical tables that map to paths in the underlying storage. Each Hudi table consists of:

• File groups: Collections of files containing related records
• File slices: Groups of base files and log files that contain record versions
• Timeline: Metadata tracking all changes and operations on the table

Table types and storage options

Hudi supports two main table types:

1. Copy-on-Write (CoW): Optimized for read-heavy workloads

   • Updates create new file versions
   • Provides snapshot isolation
   • Higher storage cost but better query performance

2. Merge-on-Read (MoR): Optimized for write-heavy workloads

   • Updates stored in delta files
   • Supports real-time and batch views
   • Lower storage cost but requires merge during reads

Integration with data ecosystems

Hudi integrates with popular data processing frameworks:

• Apache Spark for data processing and queries
• Apache Flink for streaming ingestion
• Presto and Trino for interactive queries
• Apache Hive for batch processing

This ecosystem integration enables Hudi to serve as a bridge between traditional data lakes and modern lakehouse architecture.

Key features and capabilities

Incremental processing

Hudi's incremental processing capability allows applications to:

• Efficiently process only changed data
• Track modifications using internal timestamps
• Support incremental data pipelines

ACID transactions

Hudi ensures data consistency through:

• Atomic writes and updates
• Concurrent reader isolation
• Rollback capabilities for failed operations

Data optimization

The framework provides several optimization features:

• Automatic file sizing and compaction
• Record-level index for efficient updates
• Clustering for optimal data layout

Comparison with other frameworks

Hudi is often compared with other data lake frameworks:

While Delta Lake and Apache Iceberg offer similar capabilities, Hudi distinguishes itself through:

• First-class support for upserts and deletes
• Built-in record-level indexing
• Flexible storage options with CoW and MoR tables

Best practices and considerations

When implementing Hudi, consider:

1. Table type selection based on workload patterns
2. Indexing strategy for update-heavy scenarios
3. Cleaning and compaction policies
4. Partition strategy optimization
5. Resource allocation for different operations

These considerations help ensure optimal performance and resource utilization while maintaining data consistency and accessibility.