[FLINK-36429] [runtime-web] Enhancing Flink History Server File Storage and Retrieval with RocksDB

[Shawnsuun]

What is the purpose of the change

Follow up PR of #25838

Currently, when a Flink job finishes, it writes an archive as a single file that maps paths to JSON files. Flink History Server (FHS) job archives are pulled locally to where the FHS is running. This process creates a local directory structure that scales inefficiently as the number of jobs increases.

Key Problems

• High inode usage in the file system due to nested directories for job archives.
• Slower data retrieval and bottlenecks in job archive navigation at scale.
• Challenges due to limited file system scalability.

Proposed Solution

Integrating RocksDB, a high-performance embedded database, as an alternative storage backend for job archives. RocksDB provides:

• Faster job data retrieval.
• Reduced inode consumption.
• Enhanced scalability, especially in containerized environments.

The integration of RocksDB is implemented as a pluggable backend. The current file system storage remains intact, while RocksDB serves as an optional alternative for efficient storage and retrieval of job archives.

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Brief Change Log

1. KVStore Interface

• Introduced KVStore as an abstraction for key-value storage systems to enable flexible storage backends.
• Added basic CRUD operations and advanced capabilities for managing job archives.

2. RocksDB Integration

• Implemented HistoryServerRocksDBKVStore as the RocksDB-based implementation of the KVStore interface.
• Mapped the hierarchical file-based job archive structure into key-value pairs for efficient storage and retrieval.

3. ArchiveFetcher Abstraction and Improvements

• Introduced ArchiveFetcher as an abstract class to support multiple backends for job archive fetching.
• Updated HistoryServerArchiveFetcher for file-based systems.
• Created HistoryServerKVStoreArchiveFetcher to fetch job archives using RocksDB.

4. ServerHandler Abstraction and Improvements

• Designed HistoryServerServerHandler as an abstract base class for handling HTTP requests, supporting pluggable backends.
• Updated HistoryServerStaticFileServerHandler for file-based job archive serving.
• Implemented HistoryServerKVStoreServerHandler to serve job data from RocksDB via REST APIs.

5. HistoryServer Updates

• Modified HistoryServer to integrate the KVStore interface and support RocksDB as a pluggable backend.
• Added configuration options in HistoryServerOptions to toggle between file-based and RocksDB storagen:
• Add the following configuration options in your flink-conf.yaml file to enable RocksDB as the storage backend for the History Server.

  historyserver.storage.backend: kvstore

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Verifying this change

This change added tests and can be verified as follows:

1. Testing

• Unit Tests:

  • Added FhsRocksDBKVStoreTest to validate CRUD operations and resource cleanup for RocksDB.
  • Added HistoryServerKVStoreArchiveFetcherTest to ensure correct fetching and processing of job archives from RocksDB.

• Integration Tests:

  • Built a Flink binary and configured flink-conf.yaml to test both file-based and RocksDB backends.
  • Verified archive retrieval via the History Server web UI and ensured backward compatibility with the file-based backend.

• End-to-End Tests:

  • Conducted tests in a Kubernetes cluster with both RocksDB and file-based storage backends.
  • Verified correct behavior of the History Server in processing and displaying job archives for both storage backends in a real-world setup.

2. Performance Enhancements

• Faster Archive Retrieval: Achieved a 4.25x improvement in fetching and processing archives with RocksDB compared to the traditional file system (tested in a production environment).
  • File system: 17 minutes for 100 archives.
  • RocksDB: 4 minutes for 100 archives.
• Reduced Inode Usage: Reduced inode consumption by over 99.99%.
  • File system: Over 20 million inodes.
  • RocksDB: Only 79 inodes.
• Lower Storage Usage: Achieved a 95.6% reduction in storage usage.
  • File system: 48 GB for 100 archives.
  • RocksDB: 2.1 GB for 100 archives.

These enhancements significantly improve scalability, reduce resource overhead, and make the History Server more responsive for large-scale deployments.

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Does this pull request potentially affect one of the following parts:

• Dependencies: No (using existing RocksDB dependency).
• Public API: No.
• Serializers: No.
• Performance-sensitive code paths: Yes (job archive storage and retrieval).
• Deployment or recovery: Yes (affects FHS deployment with the RocksDB backend option).
• File system connectors: No.

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Documentation

• Does this pull request introduce a new feature? (yes)
• If yes, how is the feature documented? (not documented)

[flinkbot]

CI report:

• 2f8fbaf UNKNOWN
• c7ebd7e Azure: FAILURE

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