Vectorized Execution

JD Retail Technology

Apr 8, 2025 · Databases

ClickHouse Architecture and Core Technologies Overview

ClickHouse is an open‑source, massively parallel, column‑oriented OLAP database that integrates its own columnar storage, vectorized batch processing, pre‑sorted data, diverse table engines, extensive data types, sharding with replication, sparse primary‑key and skip indexes, and a multithreaded query engine, delivering high‑throughput real‑time analytics on massive datasets.

Big DataClickHouseOLAP

0 likes · 15 min read

ClickHouse Architecture and Core Technologies Overview

Kuaishou Tech

Sep 13, 2024 · Big Data

Blaze: Kuaishou’s Rust‑Based Vectorized Execution Engine for Spark SQL

Blaze is a Rust‑implemented, DataFusion‑based vectorized execution engine created by Kuaishou to accelerate Spark SQL queries, delivering up to 60% faster computation, 30% average compute‑power gains in production, and extensive architectural innovations such as native engine, protobuf protocol, JNI bridge, and Spark extension, while being open‑source and compatible with Spark 3.0‑3.5.

Big DataDataFusionPerformance Optimization

0 likes · 11 min read

Blaze: Kuaishou’s Rust‑Based Vectorized Execution Engine for Spark SQL

Youzan Coder

Jul 7, 2022 · Big Data

Optimizing Apache Doris Performance: A Case Study in Query Processing

Youzan replaced ClickHouse and Druid with Apache Doris, refined its vectorized engine by eliminating deserialization overhead in the merge‑aggregation phase, achieving roughly a 30 % query‑time boost, and validated compatibility through SQL rewriting and traffic replay, while planning further SIMD‑based optimizations and broader adoption.

Apache DorisClickHouseDruid

0 likes · 8 min read

Optimizing Apache Doris Performance: A Case Study in Query Processing

Python Programming Learning Circle

Jul 9, 2021 · Databases

Key Features of ClickHouse: DBMS Capabilities, Columnar Storage, Vectorized Execution, and Distributed Architecture

ClickHouse is an MPP column‑oriented DBMS that combines full DBMS functionality, advanced columnar storage with high compression, SIMD‑based vectorized execution, a rich relational SQL interface, diverse table engines, multi‑master clustering, and flexible sharding and distributed query capabilities, making it exceptionally fast for analytical workloads.

ClickHouseDBMSSQL

0 likes · 21 min read

Key Features of ClickHouse: DBMS Capabilities, Columnar Storage, Vectorized Execution, and Distributed Architecture

Big Data Technology Architecture

Jun 17, 2021 · Databases

Key Features of ClickHouse: DBMS Capabilities, Columnar Storage, Vectorized Execution, and Distributed Architecture

ClickHouse is a high‑performance MPP column‑store DBMS that combines complete DBMS functions, column‑oriented storage with aggressive compression, SIMD‑based vectorized execution, flexible table engines, multithreading, distributed processing, a multi‑master architecture, and SQL compatibility to deliver fast online analytical queries on massive data sets.

ClickHouseDBMSVectorized Execution

0 likes · 21 min read

Big Data Technology Architecture

Aug 8, 2020 · Big Data

Performance Comparison of SparkR with Vectorized Execution Using Apache Arrow

This article explains how SparkR’s performance compares to native Spark APIs, shows the slowdown caused by JVM‑R serialization, and demonstrates how enabling Apache Arrow’s vectorized execution in Spark 3.0 can accelerate SparkR operations by up to dozens of times.

Apache ArrowBig DataPerformance

0 likes · 7 min read

Performance Comparison of SparkR with Vectorized Execution Using Apache Arrow

DataFunTalk

Jul 18, 2020 · Databases

Core Features and Architecture of ClickHouse: An In‑Depth Overview

This article provides a comprehensive technical overview of ClickHouse, covering its complete DBMS capabilities, column‑oriented storage and compression, vectorized execution engine, relational SQL support, diverse table engines, multi‑master clustering, sharding, and the design philosophies that make it exceptionally fast for large‑scale analytical workloads.

ClickHouseColumnar DatabaseOLAP

0 likes · 29 min read

Core Features and Architecture of ClickHouse: An In‑Depth Overview

Big Data Technology Architecture

Jun 28, 2020 · Databases

Understanding OLAP Data Warehouse Types, Architectures, and Performance Optimizations

This article provides a comprehensive overview of OLAP data warehouses, covering classification by data volume and modeling, detailed explanations of MOLAP, ROLAP, HOLAP and HTAP, common open‑source implementations, and a deep dive into performance‑boosting techniques such as MPP architectures, cost‑based optimization, vectorized execution, dynamic code generation, storage compression, runtime filters and resource management.

Big DataCost-Based OptimizationDynamic Code Generation

0 likes · 25 min read

Understanding OLAP Data Warehouse Types, Architectures, and Performance Optimizations

Tencent Database Technology

Nov 7, 2019 · Databases

MonetDB: History, Storage Model, Execution Model, Architecture, and Key Technologies

This article provides a comprehensive overview of MonetDB, covering its origins at CWI, column‑oriented storage with BATs, memory‑mapped and vectorized execution, three‑layer system architecture, cache‑aware optimizations such as vector operations and radix‑partitioned hash joins, as well as its limitations and reference sources.

Cache OptimizationColumnar DatabaseMonetDB

0 likes · 10 min read

MonetDB: History, Storage Model, Execution Model, Architecture, and Key Technologies