This article compares PostgreSQL and SQL Server data access methods—including sequential scans, index scans, bitmap heap scans, index‑only scans, and parallel query execution—highlighting PostgreSQL's lack of clustered indexes and the performance implications of each approach.
This article explains how to implement a custom MyBatis-Plus pagination interceptor that parallelizes COUNT and data queries using CompletableFuture and a configurable thread pool in Spring Boot, suitable for large single-table and complex multi-table pagination scenarios, and provides detailed configuration and code examples.
GaiaDB 4.0 introduces cloud‑native parallel query execution, allowing the same MySQL‑compatible cluster to handle both OLTP and OLAP workloads with up to fourteen‑fold speedup on complex queries, configurable via global parameters or query hints, and adds columnar indexes for further analytical acceleration.
GaiaDB 4.0 introduces a zero‑ETL parallel query engine that leverages multi‑core CPUs to run OLTP and OLAP workloads on a single cluster, dramatically improving complex query performance while offering configurable parallelism and a rich set of parallel operators.
PostgreSQL 16 introduces enhanced parallel query capabilities, incremental sorting, SIMD acceleration, improved logical replication, and new monitoring tools like pg_stat_io, offering notable performance gains while still leaving some long‑awaited features such as XID‑64 absent.
This transcript details the design, implementation, and performance evaluation of TDSQL‑C's parallel query feature, covering product background, workload challenges, execution plans, Amdahl's law analysis, task scheduling, data partitioning, and future enhancements for cloud‑native database systems.
This presentation details the design, implementation, and performance evaluation of parallel query in Tencent's cloud‑native database TDSQL‑C, covering product overview, workload challenges, query‑level concurrency techniques, underlying execution models, scheduling framework, and future enhancements.
This article explains how cloud databases use cost‑based parallel query engines to overcome I/O bottlenecks, select tables for parallel scans, implement parallel joins and aggregations, and achieve linear speedups on TPCH benchmarks, highlighting the key techniques and architectural components.
This article details how to improve performance of massive MySQL tables on Alibaba Cloud by redesigning schemas and indexes, migrating to PolarDB with read‑write separation, partitioning historical data using the X‑Engine storage engine, and leveraging PolarDB's parallel query capabilities for faster analytics.
The article explains how Citus enables sharding and MapReduce‑style parallel query execution in PostgreSQL, showing performance gains, example bucket algorithms, and how standard SQL can replace custom MapReduce code for large‑scale data analytics.
This article reviews advanced SQL optimization techniques—including using indexes to avoid sorting, reasons an optimizer may skip indexes, hash‑based access methods, and parallel query execution—while summarizing a four‑part series on DBMS‑agnostic performance tuning.
This article explains Greenplum's MPP architecture, master‑segment design, high‑availability, interconnect network, rich management tools, parallel query planning, data loading techniques, and additional capabilities such as LDAP authentication and resource queues, demonstrating why it is a strong next‑generation big‑data query engine.
This article explains why frequent small commits are slower than a single bulk SQL operation, demonstrates how to rewrite PL/SQL procedures to use bulk inserts, adjust UNDO settings, and enable parallel execution, achieving up to six‑fold performance gains on large data loads.
SQL Server’s parallel query execution leverages multiple CPUs to accelerate CPU‑bound workloads, and this article explains the underlying hardware prerequisites, scheduler/task/worker model, serial vs parallel plans, exchange operators, parallel joins, CXPACKET waits, and configuration settings such as cost threshold and max degree of parallelism.
This article explains Oracle's parallel query feature, covering its benefits, resource costs, required conditions, various data‑distribution methods such as broadcast, replicate and hash, how to read parallel execution plans, and practical monitoring techniques to avoid performance pitfalls.
