Apache Hudi: Capabilities, Architecture, Use Cases, and Future Outlook

1. Hudi Capabilities and Positioning

Apache Hudi is a next‑generation streaming data‑lake platform built on a database kernel, supporting insert, update, delete, and incremental processing, enabling efficient enterprise‑grade data lakes.

2. Overall Structure

Hudi can be deployed on cloud storage or HDFS, supporting Parquet, ORC, HFile, and Avro formats and offering rich APIs such as Spark DataFrame, RDD, Flink SQL, and Flink DataStream. It integrates with engines like Presto, Trino, Hive, StarRocks, and Doris.

3. Basic Concepts

Hudi’s core concepts are Timeline (commits like DELTA_COMMIT, CLEAN, ROLLBACK) and File Layout (FileGroup → FileSlice → Base + Log files). It provides MOR (Merge‑On‑Read) and COW (Copy‑On‑Write) table types, each with different write‑latency and query characteristics.

4. Application Scenarios

• CDC data ingestion: near‑real‑time capture of database changes using Debezium/Maxwell, feeding Hudi tables for minute‑level freshness.

• Minute‑level real‑time data warehouse: unified storage eliminates double‑write, enabling low‑latency OLAP queries via Presto, Trino, Spark, StarRocks, etc.

• Streaming PV/UV counting: custom Payload classes (e.g., RecordCountAvroPayload) implement deduplication and counting without state explosion.

• Multi‑stream joining (wide tables): Hudi merges LogFiles and BaseFiles, handling out‑of‑order and late events with optimistic concurrency control.

• Real‑time ad attribution: Flink SQL streams click and user data into Hudi, then batch jobs merge for attribution results with sub‑15‑minute latency.

• Stream‑to‑batch (flow‑to‑batch): progress flags stored in Hudi commit metadata trigger downstream batch jobs once data is complete.

5. Future Outlook

Hudi continues to evolve with support for advanced CDC, query optimization, and integration with emerging compute engines, aiming to provide a unified, low‑latency data‑lake solution.