This article details OceanBase's evolution from a distributed OLTP system to a unified HTAP database, covering its cost‑based optimizer, vectorized execution, integrated row‑column storage, bypass import, materialized views, external tables, full‑text search, and real‑world use cases for real‑time analytics.
This article explores ClickHouse’s high‑performance OLAP architecture, covering its MPP design, columnar storage, vectorized execution, pre‑sorting, table engines, data types, sharding and replication strategies, as well as index designs that together enable rapid analysis of massive datasets.
This article examines ClickHouse’s high‑performance OLAP design, covering its MPP architecture, columnar storage, vectorized execution, pre‑sorting, table engines, extensive data‑type system, sharding and replication strategies, as well as its sparse and skip‑index mechanisms that together enable ultra‑fast analytics on massive datasets.
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.
This article compares ClickHouse and MySQL by examining benchmark results, MPP architecture, columnar storage, compression techniques, vectorized execution, and index designs, showing why ClickHouse delivers dramatically higher query performance on massive data sets.
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.
TeleDB’s journey from a centralized MySQL/PostgreSQL‑based system to a native distributed HTAP database showcases innovations such as share‑nothing architecture, columnar storage, vectorized execution, Remote Data Access, global caching, and advanced dead‑lock detection, dramatically improving query performance, storage efficiency, and scalability.
This article explains how ClickHouse achieves high query performance by leveraging storage‑engine designs such as pre‑sorting, columnar layout, and block‑level compression, and by exploiting a vectorized compute engine while avoiding joins and using built‑in functions.
This article explains ClickHouse’s MPP columnar design, complete DBMS capabilities, columnar storage, vectorized execution, multi‑master architecture, real‑time queries, sharding, and performance‑focused hardware and algorithm choices that together deliver its superior speed.
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.
This article explains how StarRocks converts a SQL statement into an optimal distributed physical execution plan, schedules the plan across compute nodes, and runs it using MPP, pipeline parallelism, and vectorized execution to achieve near‑linear performance scaling.
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.
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.
This article explains how ClickHouse’s column‑oriented storage, vectorized execution engine, rich DBMS capabilities, flexible table engines, and carefully designed distributed architecture enable it to handle massive workloads with sub‑second query latency, making it a standout OLAP solution.
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.
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.
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.
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.
