Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
This article explains what Flink’s Exactly‑Once semantics actually guarantee, why it does not mean each event is processed only once, how checkpointing and two‑phase commit sinks enable end‑to‑end exactly‑once, and the three safeguards needed for true exactly‑once computation.
This guide explains how to optimize Paimon primary‑key and Append‑Scalable tables in Flink by adjusting sink and source parallelism, checkpoint intervals, making small‑file merges fully asynchronous, changing file formats, and applying ordering strategies to improve both write and read performance.
This article explains how to monitor and interpret Full GC in Flink TaskManagers, detect and address data skew through proper data distribution and parallelism settings, and recommends aligning consumer parallelism with Kafka partitions, while also providing practical tips for using tools like Prometheus and Arthas.
This article presents Tencent's real‑time lakehouse architecture, detailing its three‑layer design of compute, management and storage, and explains the six components of the Intelligent Optimization Service—including Compaction, Index, Clustering, and AutoEngine—along with scenario‑based capabilities, migration strategies, and future optimization directions.
The article argues that while solid SQL skills are essential for data development and interview preparation, investing excessive effort to master advanced SQL yields diminishing returns, especially as AI tools automate routine queries, and recommends focusing on newer, more impactful technologies instead.
This article explains how Adaptive Query Execution (AQE) in Apache Spark 4.0 dynamically optimizes query plans through features such as join reordering, partition pruning, skew handling and coalescing, delivering significant performance gains, resource efficiency and reduced manual tuning across real‑world big‑data workloads.
This article outlines Douyin Group's approach to handling petabyte‑scale data, addressing metric inconsistencies, and improving data product agility through a four‑layer Volcano Engine platform, systematic indicator production‑management‑consumption cycles, organizational design, automation, and future plans for large‑model‑driven metric splitting.
This article provides a comprehensive walkthrough of Doris's SQL execution pipeline, covering the query optimizer's parsing, rewriting, and plan generation, the scheduler's fragment distribution, and the executor's fragment processing, including code examples of expression rewrite rules, join strategies, and data flow between FE and BE nodes.
This article details Ximalaya Live's transition from an offline‑centric data warehouse to a real‑time lakehouse using Flink, Paimon, and StarRocks, covering business background, architectural challenges, technology evaluation, implementation steps, encountered issues, performance gains, and future expansion plans.
The article explains Paimon's changelog‑producer capability, detailing its purpose, storage format, and the four generation modes—None, Input, Lookup, and Full Compaction—while comparing their costs, implementation details, and suitability for different data sources such as CDC.
