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DataFunTalk
DataFunTalk
Sep 15, 2022 · Big Data

Bilibili Offline Platform: Migration from Hive to Spark and Large‑Scale Optimizations

This article details Bilibili's evolution of its offline computing platform from Hadoop‑based Hive to Spark, describing the migration process, automated SQL conversion, result verification, stability and performance enhancements, meta‑store optimizations, and future work on remote shuffle and vectorized execution.

Data SkippingHiveMetaStore
0 likes · 28 min read
Bilibili Offline Platform: Migration from Hive to Spark and Large‑Scale Optimizations
DataFunTalk
DataFunTalk
Aug 29, 2021 · Big Data

Building and Optimizing the Offline Computing Platform at Autohome: Challenges, Solutions, and Future Plans

This article details the evolution of Autohome's offline computing platform from a 50‑node cluster in 2013 to a multi‑thousand‑node Hadoop ecosystem, describing performance and stability challenges, multi‑tenant operational issues, low resource utilization, and the comprehensive technical solutions and future roadmap implemented to address them.

AI on HadoopHadoopMetaStore
0 likes · 11 min read
Building and Optimizing the Offline Computing Platform at Autohome: Challenges, Solutions, and Future Plans
Big Data Technology Architecture
Big Data Technology Architecture
Apr 13, 2021 · Big Data

Hive Metadata Migration and Merging Tool for Consolidating Multiple Hive Metastores

This article describes how NetEase developed a Hive metadata migration and merging tool that consolidates metadata from multiple independent Hive clusters into a single Hive metastore without moving HDFS data, detailing the challenges, ID handling, database operations, and step‑by‑step migration process.

HiveMetaStoreMyBatis
0 likes · 12 min read
Hive Metadata Migration and Merging Tool for Consolidating Multiple Hive Metastores
Big Data Technology Architecture
Big Data Technology Architecture
Mar 11, 2021 · Big Data

Challenges and Optimizations of Hive MetaStore at Kuaishou

This article details how Kuaishou tackled performance, scalability, and stability challenges of Hive MetaStore by introducing a BeaconServer hook architecture, read‑write separation, API refinements, traffic control, and federation designs, resulting in significant query efficiency and service reliability improvements.

FederationHiveMetaStore
0 likes · 14 min read
Challenges and Optimizations of Hive MetaStore at Kuaishou
DataFunTalk
DataFunTalk
Mar 10, 2021 · Big Data

Hive MetaStore Challenges and Optimizations at Kuaishou

At Kuaishou, the Hive MetaStore service, which stores metadata for Hive, faced scalability and performance challenges due to massive dynamic partitions and high query volume, leading to a series of architectural optimizations—including read‑write separation, API enhancements, traffic control, and federation—to improve stability and efficiency.

HiveKuaishouMetaStore
0 likes · 15 min read
Hive MetaStore Challenges and Optimizations at Kuaishou