Adaptive Execution

DataFunTalk

Nov 12, 2023 · Big Data

MaxCompute Incremental Update Architecture, Intelligent Materialized Views, and Adaptive Execution Optimizations

This article presents a comprehensive overview of MaxCompute's near‑real‑time incremental update and processing architecture, the design of Transactional Table 2.0, intelligent materialized view evolution and recommendation, as well as multi‑level adaptive execution optimizations for the SQL engine, illustrating how these innovations improve efficiency, cost, and scalability for large‑scale data workloads.

Adaptive ExecutionMaxComputeSQL engine

0 likes · 20 min read

MaxCompute Incremental Update Architecture, Intelligent Materialized Views, and Adaptive Execution Optimizations

DataFunTalk

Aug 29, 2023 · Big Data

MaxCompute Incremental Update, Processing Architecture, and Intelligent Data Warehouse Optimizations

This article presents a comprehensive overview of MaxCompute's incremental update and processing architecture, the design of intelligent materialized views, and the engine's adaptive execution optimizations, detailing the integrated near‑real‑time and batch pipelines, transactional table 2.0, and practical Q&A.

Adaptive ExecutionData WarehouseMaxCompute

0 likes · 21 min read

MaxCompute Incremental Update, Processing Architecture, and Intelligent Data Warehouse Optimizations

JD Retail Technology

Apr 14, 2023 · Big Data

Understanding Data Skew and Its Mitigation in Hive and Spark

This article explains the concept of data skew, its symptoms such as slow tasks and OOM errors, and provides comprehensive mitigation techniques and configuration examples for Hive and Spark, including custom partitioning, map joins, adaptive execution, and key detection methods.

Adaptive ExecutionHiveSQL optimization

0 likes · 15 min read

Understanding Data Skew and Its Mitigation in Hive and Spark

Big Data Technology Architecture

Apr 1, 2021 · Big Data

Spark Adaptive Execution: Dynamic Shuffle Partition, Broadcast Join, and Skew Handling

The article explains the limitations of static shuffle partitions, execution‑plan estimation, and data skew in Spark SQL, and describes how Spark Adaptive Execution can automatically adjust shuffle partition numbers, switch join strategies, and mitigate skew through configurable parameters and code examples.

Adaptive ExecutionBroadcast JoinShuffle Partition

0 likes · 11 min read

Spark Adaptive Execution: Dynamic Shuffle Partition, Broadcast Join, and Skew Handling