Apache Hudi

Apache Hudi
Name	Apache Hudi
Developer	Apache Software Foundation
Initial release	2016
Stable release	0.11.1
Repo	apache/hudi
Programming language	Java, Scala
License	Apache License 2.0

Apache Hudi is an open-source data management framework for large-scale distributed data lake storage and stream/batch processing. It provides versioned datasets, transactional semantics, and near-real-time ingestion for analytical engines on top of object storage. Hudi bridges batch systems like Apache Hadoop and Apache Spark with query engines such as Apache Hive, Presto, and Trino while supporting cloud platforms like Amazon Web Services, Microsoft Azure, and Google Cloud Platform.

Overview

Hudi was created to address operational challenges encountered by teams using Apache Hadoop-based stacks, Apache Spark streaming, and object stores such as Amazon S3 and Azure Data Lake Storage. It implements ACID semantics on immutable storage by coordinating file-level operations for datasets managed as tables compatible with Apache Hive metastore and Delta Lake-style transactional tables used by diverse analytics workloads. The project evolved within the ecosystem of the Apache Software Foundation alongside sister projects like Apache Parquet, Apache ORC, Apache Avro, and Apache Arrow.

Architecture

Hudi’s architecture centers on an embedded indexing and commit timeline managed by a write client and a timeline server, coordinating with cluster managers such as YARN, Kubernetes, and Apache Mesos. Writes are performed via two main writers: the Copy-on-Write (COW) writer and the Merge-on-Read (MOR) writer, which interact with columnar formats like Parquet and row formats like Avro for storage. The system exposes metadata through the Hive Metastore and integrates with catalog services such as AWS Glue and Apache Iceberg registries. For compute, Hudi leverages Apache Spark jobs and can be accessed by ad-hoc engines including Presto, Trino, and Apache Impala via snapshot or incremental queries.

Data Models and Storage

Hudi organizes data into tables with a timeline of commits, incremental checkpoints, and rollback information; it supports record-level upserts and deletes using primary keys and partitioning schemes familiar to users of Amazon Redshift, Snowflake, and Google BigQuery. Physical storage uses formats like Apache Parquet and Apache Avro stored on object stores such as Amazon S3, Google Cloud Storage, and Azure Data Lake Storage. Schema evolution follows patterns compatible with Apache Avro and is designed to interoperate with serialization frameworks used by projects like Apache Kafka and Confluent Platform.

Key Features and Capabilities

Hudi provides transactional commits, snapshot isolation, and support for incremental pulls enabling Change Data Capture (CDC) workflows with systems such as Debezium and Apache Kafka. It supports point-in-time queries, time travel, and rollbacks similar to features in Delta Lake and Apache Iceberg. Data skipping, bloom filters, and embedded indexing improve read performance for selective queries used by Apache Hive and Presto. Hudi also supports asynchronous compaction and clustering to optimize file layout for engines like Druid and ClickHouse when exporting transformed datasets.

Integrations and Ecosystem

The project integrates broadly across the modern data ecosystem: ingestion tools like Apache Kafka, Apache Flink, and NiFi; orchestration systems such as Apache Airflow, Dagster, and Apache Oozie; catalogs and governance platforms like AWS Glue, Apache Atlas, and LinkedIn WhereHows-style registries; and analytics suites including Tableau, Looker, and Superset. Cloud vendor integrations include Amazon EMR, Google Dataproc, and Azure Synapse Analytics. Hudi coexists with table formats and management layers such as Apache Iceberg and Delta Lake in multi-format lake architectures and participates in benchmarking exercises and interoperability efforts with projects like TPC and Speculative Benchmarking Consortium.

Use Cases and Adoption

Organizations adopt Hudi for streaming ingestion at scale, CDC-based synchronization from transactional databases like MySQL, PostgreSQL, and Oracle Database into analytic lakes, and for GDPR-style point-in-time data recovery needs found in enterprises like Netflix, Uber, and LinkedIn that operate large event-driven platforms. Common uses include near-real-time analytics for marketing stacks used by companies comparable to Airbnb and Lyft, ML feature stores similar to Feast deployments, and data engineering pipelines orchestrated by Airflow or Luigi. Adoption in cloud-native environments is prominent among users of AWS Redshift Spectrum and Google BigQuery federated query patterns.

Performance and Scalability

Hudi is engineered to scale across clusters managed by YARN and Kubernetes and to handle terabyte- to petabyte-scale datasets stored on Amazon S3 and similar object stores. Performance characteristics depend on writer modes (COW vs MOR), file sizing strategies, partitioning akin to patterns in Apache Hive and Delta Lake, and compaction policies tuned for workload profiles seen in high-throughput systems like Apache Kafka and Apache Flink. Benchmarking comparisons often involve Presto, Trino, Apache Spark SQL, and columnar file formats like Parquet and ORC under realistic ETL and interactive query workloads. Queueing, resource isolation, and autoscaling strategies from Kubernetes and cloud platforms influence end-to-end latency and throughput in production deployments.

Category:Big data