In large‑scale Flink double‑stream joins, terabyte‑sized RocksDB state caused severe compaction latency and CPU spikes, but enabling RocksDB BlobDB KV‑separation (and an inner‑compaction patch) dramatically shrank SST files, reduced read/write latencies to sub‑millisecond levels, and cut CPU spikes by about half.
This article explains Bilibili's design and implementation of a remote state backend for Flink, detailing the motivations, pain points of the existing RocksDBStateBackend, the architecture of TaishanStateBackend, and the performance optimizations applied to achieve storage‑compute separation and faster rescaling.
This article explains how to optimize Flink’s RocksDB state backend for large‑scale streaming jobs, covering state types, enabling latency tracking, incremental checkpoints, predefined options, and advanced memory and thread settings, with practical configuration examples and performance comparisons.
This article describes the motivation, challenges, design, and performance optimizations of a remote state backend for Flink that leverages Bilibili's Taishan distributed KV store to achieve storage‑compute separation, lighter checkpoints, faster rescaling, and improved resource utilization in large‑scale streaming jobs.
Bilibili’s FlinkSQL team has enhanced the Flink engine—now based on 1.11 with back‑ported 1.15 features—by adding Delay‑Join, table‑valued functions, projection‑push‑down, UDF and object reuse, automatic mini‑batch/two‑phase aggregation, key‑group skew fixes, connector slot‑groups, real‑time projection with Hudi, and RocksDB state‑performance tweaks, while planning remote state backends and deeper stream‑batch integration.
Flink 1.13 introduces passive scaling that lets users adjust parallelism to resize jobs, adds visual tools such as load/back‑pressure charts, CPU flame graphs, and state‑backend metrics for deeper performance insight, and includes numerous community optimizations for easier upgrades and operation.
This article presents a detailed collection of 2021 Flink interview questions covering checkpoint mechanisms, watermarks, state backends, join types, fault tolerance, resource configuration, and recent Flink 1.10 features, providing concise explanations and code examples for each topic.
This article explains Flink's checkpoint mechanism, outlines key performance metrics, discusses interval configuration, external state storage choices, resource allocation, and task-local recovery strategies to improve checkpoint speed and reliability in large‑scale state scenarios.
This article describes how Kuaishou leverages Apache Flink for large‑scale real‑time multi‑dimensional analytics, details the architecture of its analytics platform using Kudu storage and KwaiBI, and introduces SlimBase—a lightweight, embedded shared state backend that replaces RocksDB to reduce I/O, latency, and CPU overhead.
This article presents Kuaishou's extensive use of Apache Flink for real-time multi-dimensional analytics, detailing the platform's architecture, cluster scale, data processing pipelines, the design of a shared state storage engine called SlimBase, and performance improvements achieved through replacing RocksDB with a customized HBase‑based solution.
This article explains a small‑file‑merging technique for Apache Flink checkpoints that reuses FSDataOutputStreams to combine multiple state files into a single HDFS file, detailing design considerations such as concurrent checkpoint support, reference‑counted deletion, space amplification reduction, fault handling, compatibility, and observed production performance gains.
This article explains how Flink checkpoints persist state, compares the three built‑in state backends (MemoryStateBackend, FsStateBackend, RocksDBStateBackend), discusses their configurations, advantages, limitations, and provides guidance on selecting the appropriate backend for different big‑data streaming scenarios.
This article explains Flink's checkpoint mechanism, its execution flow, common configuration options, and the benefits and considerations of incremental checkpoints using the RocksDB state backend, providing practical code examples and YAML settings for reliable stream processing.
This guide provides a detailed overview of Apache Flink covering its core streaming engine, APIs (DataSet, DataStream, Table), architectural components, comparison with Spark Streaming, partitioning, parallelism, restart strategies, state backends, time semantics, watermarks, SQL processing, fault‑tolerance mechanisms, memory management, serialization, RPC framework, back‑pressure handling, operator chaining, and practical tips for interview preparation.
This article describes the motivation, technology selection, implementation details, and practical challenges of building a real‑time data warehouse using Flink, covering stream ingestion, data cleaning, dimension‑table joins, state backend choices, and operational lessons for large‑scale streaming pipelines.
This article describes the motivation, technology selection, implementation details, and encountered challenges of building a real‑time data warehouse using Flink, covering streaming computation, code examples, dimension‑table caching, state backend choices, and best practices for production deployment.
This article explains the concept of stateful computation in stream processing, highlights the shortcomings of traditional systems, and details how Apache Flink provides rich state access, various state backends, and robust checkpointing mechanisms to achieve scalable, fault‑tolerant real‑time analytics.
