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Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jan 19, 2026 · Databases

How Hologres Dynamic Table Accelerates Billion‑Row Data Refreshes

The article explains how Hologres Dynamic Table, a cloud‑native materialized‑view‑like feature, supports full and incremental refresh modes, enables minute‑level data freshness for billion‑row price tables, and provides join, aggregation, and partition capabilities while outlining its architecture, limitations, and real‑world performance gains.

Dynamic TableHologresIncremental Refresh
0 likes · 8 min read
How Hologres Dynamic Table Accelerates Billion‑Row Data Refreshes
Alibaba Cloud Observability
Alibaba Cloud Observability
Jan 5, 2026 · Cloud Native

How Materialized Views Turn Log Queries from Seconds to Milliseconds

Facing painfully slow log queries under high concurrency, the team enabled SLS materialized views and, through three real‑world cases, reduced query latency from thousands of milliseconds to sub‑second levels, achieving up to 89‑fold performance gains while keeping storage costs negligible.

Cloud NativeSLSlog query optimization
0 likes · 7 min read
How Materialized Views Turn Log Queries from Seconds to Milliseconds
Alibaba Cloud Native
Alibaba Cloud Native
Dec 30, 2025 · Cloud Native

How Materialized Views Cut Log Query Times from Seconds to Milliseconds

Backend developers often struggle with log queries that become unbearably slow at scale, causing timeouts and alerts; this article details how applying Alibaba Cloud Log Service materialized views transformed several real‑world cases—from high‑concurrency SDK calls to complex de‑duplication and latency‑comparison queries—cutting response times from seconds to milliseconds and delivering stable performance.

Cloud Nativelog querymaterialized view
0 likes · 7 min read
How Materialized Views Cut Log Query Times from Seconds to Milliseconds
Alibaba Cloud Native
Alibaba Cloud Native
Nov 15, 2025 · Cloud Native

How Materialized Views Supercharge Alibaba Cloud Log Service Queries

When log volumes explode from gigabytes to petabytes, Alibaba Cloud Log Service’s traditional on‑the‑fly querying becomes slow, resource‑hungry, and inaccurate, but materialized views pre‑compute and store results, delivering seconds‑level responses with far lower resource consumption.

Cloud NativeLog Analyticsmaterialized view
0 likes · 11 min read
How Materialized Views Supercharge Alibaba Cloud Log Service Queries
dbaplus Community
dbaplus Community
Oct 19, 2025 · Databases

How PostgreSQL 18 Made My Redis Cache Redundant (And What You Can Learn)

After disabling Redis in production and carefully testing on PostgreSQL 18, the author reduced p95 latency, simplified the system, and lowered operational overhead by replacing the cache with a covering index, a generated column, and a materialized view, while providing detailed query examples, configuration tweaks, and performance measurements.

Database Optimizationindexingmaterialized view
0 likes · 10 min read
How PostgreSQL 18 Made My Redis Cache Redundant (And What You Can Learn)
ITPUB
ITPUB
Oct 11, 2025 · Databases

How OceanBase Achieves Real‑Time HTAP: Inside Its Unified Storage and Vectorized Engine

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.

Columnar StorageHTAPOceanBase
0 likes · 12 min read
How OceanBase Achieves Real‑Time HTAP: Inside Its Unified Storage and Vectorized Engine
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Mar 26, 2025 · Big Data

Cutting Compute Costs with MaxCompute Materialized Views: Strategies and Results

This article details how MaxCompute leverages fuzzy materialized views, DAG scheduling adjustments, public layer mining, and FBI acceleration techniques to reduce compute resource consumption by up to 10%, improve task visibility, and achieve significant daily savings in large‑scale data warehouse environments.

Compute costMaxComputematerialized view
0 likes · 12 min read
Cutting Compute Costs with MaxCompute Materialized Views: Strategies and Results
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Feb 20, 2025 · Big Data

How Xiaohongshu Accelerated Data Warehouse Queries with Logical Datasets & Materialized Views

Xiaohongshu tackled low reuse of APP tables, limited scalability of single-table BI datasets, and poor dashboard query performance by introducing logical datasets and materialized views, which enable query pruning, reduce data redundancy, and accelerate BI queries, achieving up to 80% latency reduction and higher hit rates.

BIBig DataStarRocks
0 likes · 25 min read
How Xiaohongshu Accelerated Data Warehouse Queries with Logical Datasets & Materialized Views
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 25, 2024 · Big Data

Build a Low‑Cost, High‑Performance Game Player Profiling Platform with Alibaba Cloud EMR StarRocks

This tutorial walks you through using Alibaba Cloud EMR Serverless StarRocks and Apache Paimon to create a cost‑effective, high‑performance game player profiling and behavior analysis platform, covering data import, materialized view creation, DWD/ADS layer construction, and lakehouse integration.

Alibaba CloudData LakeGame Analytics
0 likes · 12 min read
Build a Low‑Cost, High‑Performance Game Player Profiling Platform with Alibaba Cloud EMR StarRocks
Ctrip Technology
Ctrip Technology
Nov 21, 2024 · Big Data

Performance Governance and Optimization of Ctrip's Nova Data Reporting Platform

This article details the performance challenges of Ctrip's Nova data reporting platform and describes a series of governance measures—including multi‑dimensional data caching, materialized view acceleration, query strategy optimization, and SQL quality improvements—that collectively reduced average query latency by over 50% and stabilized the system.

Data PlatformSQL Performancecaching
0 likes · 26 min read
Performance Governance and Optimization of Ctrip's Nova Data Reporting Platform
Shopee Tech Team
Shopee Tech Team
Oct 25, 2024 · Big Data

StarRocks at Shopee: Practical Use Cases and Performance Analysis

Shopee’s deployment of StarRocks across DataService, DataGo, and DataStudio demonstrates that its vectorized engine, cost‑based optimizer, and materialized‑view caching can query Hive, Iceberg, Delta Lake and Hudi up to 20,000× faster than Presto, cutting CPU usage and delivering consistently lower latency for complex analytics.

Data LakeMPPPresto
0 likes · 11 min read
StarRocks at Shopee: Practical Use Cases and Performance Analysis
StarRocks
StarRocks
Oct 16, 2024 · Big Data

How to Build a High‑Performance Lakehouse with StarRocks and Apache Hive

This guide walks through the core concepts of Apache Hive, its architecture and key features, then shows how to integrate Hive with StarRocks via the Hive Catalog, construct ODS/DWD/DWS/ADS tables, enable DataCache, use materialized views, and handle automatic partition detection for fast lakehouse analytics.

Apache HiveBig DataDataCache
0 likes · 17 min read
How to Build a High‑Performance Lakehouse with StarRocks and Apache Hive
Big Data Technology & Architecture
Big Data Technology & Architecture
Oct 16, 2024 · Databases

Kuaishou's Lakehouse‑Integrated OLAP Architecture with Apache Doris: Design, Migration, and Optimization

The article describes how Kuaishou transformed its high‑traffic OLAP system from a separated lake‑and‑warehouse architecture using Hive/Hudi and ClickHouse into a unified lakehouse solution powered by Apache Doris, detailing the challenges, design choices, caching and automatic materialization mechanisms, and the resulting performance and governance improvements.

Apache DorisBig DataData Caching
0 likes · 18 min read
Kuaishou's Lakehouse‑Integrated OLAP Architecture with Apache Doris: Design, Migration, and Optimization
DataFunSummit
DataFunSummit
Jul 9, 2024 · Big Data

Materialized Views in MaxCompute: Design, Implementation, and Best Practices

This article explains the concept, advantages, and drawbacks of materialized views, describes how MaxCompute implements them—including creation syntax, maintenance properties, automatic query rewrite, smart recommendation, and auto‑materialization—and shares performance results and future improvement plans.

Automatic RefreshBig DataMaxCompute
0 likes · 13 min read
Materialized Views in MaxCompute: Design, Implementation, and Best Practices
StarRocks
StarRocks
Jul 2, 2024 · Big Data

What’s New in StarRocks 3.3? Deep Dive into Lakehouse‑Optimized Performance and Features

StarRocks 3.3 introduces a comprehensive set of enhancements—including maturity levels, ARM‑optimized performance, advanced caching, materialized‑view rewrites, storage optimizations, and expanded lakehouse ecosystem support—that together boost stability, query speed, and usability for large‑scale analytics workloads.

Big DataLakehouseStarRocks
0 likes · 15 min read
What’s New in StarRocks 3.3? Deep Dive into Lakehouse‑Optimized Performance and Features
StarRocks
StarRocks
Apr 25, 2024 · Big Data

How StarRocks Beats Trino: 4.3× Faster Queries on Apache Paimon Lakehouse

This article explains how to build a high‑performance data‑lake analytics stack by combining StarRocks with Apache Paimon, covering direct queries, Data Cache acceleration, and asynchronous materialized views, and presents benchmark results that show StarRocks achieving up to 4.3× faster query speeds than Trino and significant latency reductions with caching and materialized views.

Apache PaimonData CacheData Lake
0 likes · 12 min read
How StarRocks Beats Trino: 4.3× Faster Queries on Apache Paimon Lakehouse
StarRocks
StarRocks
Apr 25, 2024 · Artificial Intelligence

How AI Boosts SQL Accuracy and Performance: Real‑World Demo & AutoMV Insights

The April 16 online meetup by Tencent Game Data and StarRocks explored AI‑generated SQL, tackled NL2SQL challenges, showcased a demo that lifted one‑shot accuracy to 89%, and introduced StarRocks AutoMV technology that automates materialized‑view recommendation and merging to accelerate data‑warehouse queries.

AutoMVNL2SQLSQL generation
0 likes · 9 min read
How AI Boosts SQL Accuracy and Performance: Real‑World Demo & AutoMV Insights
DataFunTalk
DataFunTalk
Apr 16, 2024 · Big Data

Materialized Views in MaxCompute: Design, Implementation, and Best Practices

This article explains how MaxCompute leverages materialized views as a query accelerator, covering their history, advantages and drawbacks, creation and maintenance details, automatic query rewriting, intelligent recommendation, auto‑materialization, and future enhancements for large‑scale data warehousing.

Automatic RefreshBig DataIntelligent Recommendation
0 likes · 13 min read
Materialized Views in MaxCompute: Design, Implementation, and Best Practices
DataFunSummit
DataFunSummit
Mar 25, 2024 · Big Data

Exploring Real-Time Data Lake Practices at Kangaroo Cloud

This article shares Kangaroo Cloud's exploration and practice of a real-time data lake, covering background, data lake concepts, challenges, solution architecture using the Shuzhan platform with Iceberg/Hudi, CDC ingestion, small file handling, cross-cluster ingestion, materialized view acceleration, and future development plans.

CDCCross-Cluster IngestionHudi
0 likes · 12 min read
Exploring Real-Time Data Lake Practices at Kangaroo Cloud
Big Data Technology & Architecture
Big Data Technology & Architecture
Mar 18, 2024 · Databases

Apache Doris 2.1.0 Release: Major Performance Boosts, New Data Types, Optimizer Enhancements and Operational Features

The Apache Doris 2.1.0 release introduces over 100% query performance improvements on TPC‑DS, up to 230% gains on ARM platforms, new Variant and IP data types, async materialized views, auto‑increment columns, auto‑partitioning, group commit, hardened workload groups, TopSQL monitoring, a built‑in job scheduler, and several behavior changes, all aimed at delivering faster, more flexible and more reliable OLAP processing.

ARM OptimizationApache Dorisdatabase
0 likes · 42 min read
Apache Doris 2.1.0 Release: Major Performance Boosts, New Data Types, Optimizer Enhancements and Operational Features
Big Data Technology & Architecture
Big Data Technology & Architecture
Jan 29, 2024 · Databases

Practical Experience of StarRocks Materialized Views at Didi

This article details Didi's evolution of OLAP systems, the adoption of StarRocks for high‑performance MPP analytics, and how materialized views, global dictionary mapping, and transparent acceleration were engineered to boost real‑time dashboard queries while outlining performance gains, challenges, and future optimization plans.

Big DataDidiOLAP
0 likes · 16 min read
Practical Experience of StarRocks Materialized Views at Didi
DataFunTalk
DataFunTalk
Jan 28, 2024 · Databases

Practical Experience of StarRocks Materialized Views at Didi

This article presents Didi's practical experience with StarRocks materialized views, covering the evolution of its OLAP architecture, the challenges of previous engines, the adoption of StarRocks, the design of materialized view acceleration for real‑time dashboards, and future optimization directions.

Big DataData PlatformOLAP
0 likes · 17 min read
Practical Experience of StarRocks Materialized Views at Didi
StarRocks
StarRocks
Dec 19, 2023 · Big Data

How WeChat Achieved Sub‑Second Real‑Time Analytics with StarRocks Lakehouse

WeChat transformed its data platform from Hadoop and ClickHouse to a StarRocks‑based lakehouse, tackling massive data volume, ultra‑low latency, and storage fragmentation by deploying lake‑on‑warehouse and warehouse‑lake fusion architectures, real‑time incremental materialized views, and unified SQL access, resulting in dramatic cost cuts and performance gains.

Big DataLakehouseStarRocks
0 likes · 15 min read
How WeChat Achieved Sub‑Second Real‑Time Analytics with StarRocks Lakehouse
DataFunTalk
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
StarRocks
StarRocks
Oct 31, 2023 · Databases

How Ctrip Accelerated Report Queries 10× with StarRocks: A Real‑World Lakehouse Migration

Ctrip migrated its Artnova reporting platform from Hive‑based queries to StarRocks, first loading data into OLAP tables and then using StarRocks as a lakehouse with Hive catalog, Data Cache and materialized views, achieving average query latency reductions from 20 seconds to 1.5 seconds, over 7× speed‑up versus Trino and up to 40× acceleration for complex workloads.

Big DataData CacheLakehouse
0 likes · 15 min read
How Ctrip Accelerated Report Queries 10× with StarRocks: A Real‑World Lakehouse Migration
Aikesheng Open Source Community
Aikesheng Open Source Community
Oct 18, 2023 · Databases

Diagnosing and Resolving USER_TAB_COLUMNS View Inconsistencies Between Oracle and OceanBase

This article investigates why Oracle and OceanBase return different results when querying the USER_TAB_COLUMNS view in stored procedures, demonstrates reproducible tests, analyzes system view behavior, and proposes workarounds such as using ALL_TAB_COLUMNS, creating synonyms, intermediate tables, or materialized views to ensure consistent table name retrieval.

Database ViewsOceanBaseOracle
0 likes · 19 min read
Diagnosing and Resolving USER_TAB_COLUMNS View Inconsistencies Between Oracle and OceanBase
Sohu Tech Products
Sohu Tech Products
Oct 11, 2023 · Industry Insights

How StarRocks Materialized Views Power Real‑Time Lakehouse Analytics

The article provides a deep technical overview of StarRocks 3.0’s data‑lake analysis capabilities, its unified Lakehouse architecture, Catalog integration, Trino compatibility, extensive I/O optimizations, materialized view features, resource isolation techniques, real‑world use cases, and future development directions.

AnalyticsData LakeLakehouse
0 likes · 22 min read
How StarRocks Materialized Views Power Real‑Time Lakehouse Analytics
StarRocks
StarRocks
Sep 6, 2023 · Big Data

How Paimon + StarRocks Revolutionize Lakehouse Analytics

This article reviews traditional Lambda and Kappa data‑warehouse architectures, then details four Paimon‑StarRocks lakehouse solutions—including a data‑lake center, accelerated query with materialized views, hot‑cold data separation, and the JNI connector—while also outlining StarRocks’ future roadmap for lakehouse analytics.

Big DataLakehousePaimon
0 likes · 11 min read
How Paimon + StarRocks Revolutionize Lakehouse Analytics
DataFunSummit
DataFunSummit
Aug 23, 2023 · Databases

An Overview of RisingWave: Design, Architecture, and Use Cases of an Open‑Source Distributed Streaming SQL Database

RisingWave is an open‑source distributed streaming SQL database that uses SQL to define tables and materialized views, offering low‑latency incremental query processing, a scalable architecture with separate compute and storage, robust consistency guarantees, and real‑time analytics demonstrated through several practical use cases.

Streaming Databasedistributed architecturematerialized view
0 likes · 24 min read
An Overview of RisingWave: Design, Architecture, and Use Cases of an Open‑Source Distributed Streaming SQL Database
Big Data Technology & Architecture
Big Data Technology & Architecture
Jun 20, 2023 · Big Data

Real‑time Data Lake Architecture with Flink and Hudi: Addressing Timeliness, Observability, and Cost Efficiency

The article presents a comprehensive big‑data solution that combines Flink and Apache Hudi to build a real‑time data lake, solving latency, observability, resource duplication, and data‑isolation challenges across DB ingestion, event tracking, BI reporting, and infrastructure optimization.

materialized view
0 likes · 20 min read
Real‑time Data Lake Architecture with Flink and Hudi: Addressing Timeliness, Observability, and Cost Efficiency
StarRocks
StarRocks
Apr 7, 2023 · Databases

StarRocks 3.0 Highlights: Storage‑Compute Separation, New RBAC, and Lakehouse Features

StarRocks 3.0 introduces a storage‑compute separation architecture, a full‑featured RBAC permission framework, enhanced materialized views, Trino‑compatible query dialect, richer Primary‑Key update/delete syntax, automatic partition creation, and numerous performance optimizations, marking a major step from OLAP to lakehouse analytics.

LakehouseRBACStarRocks
0 likes · 10 min read
StarRocks 3.0 Highlights: Storage‑Compute Separation, New RBAC, and Lakehouse Features
DataFunTalk
DataFunTalk
Dec 6, 2022 · Databases

Performance Optimization of Apache Doris for A/B Experiment Queries at Xiaomi

This article analyzes the performance bottlenecks of A/B experiment report queries on Apache Doris at Xiaomi, presents data-driven insights on query latency, field usage, and experiment ID matching, and details a series of optimizations—including pre‑aggregation, materialized views, bitmap deduplication, and schema redesign—that reduced query times by up to 60× and lowered cluster load.

A/B testingApache DorisBitmap
0 likes · 17 min read
Performance Optimization of Apache Doris for A/B Experiment Queries at Xiaomi
StarRocks
StarRocks
Nov 15, 2022 · Databases

How StarRocks 2.5 Improves Materialized Views for Real‑Time and Offline Queries

This article analyzes the requirements, design choices, and implementation details of materialized views in StarRocks, covering demand analysis, synchronous and asynchronous refresh solutions, partition binding, task scheduling, partition‑refresh maintenance, Insert‑Overwrite mechanics, view invalidation handling, and the upcoming features planned for version 2.5.

Async RefreshInsert OverwriteQuery Rewrite
0 likes · 14 min read
How StarRocks 2.5 Improves Materialized Views for Real‑Time and Offline Queries
Aikesheng Open Source Community
Aikesheng Open Source Community
Sep 13, 2022 · Databases

Using ClickHouse Materialized Views: Creation, Testing, and Time‑Zone Issue Resolution

This article explains how to create a ClickHouse materialized view that aggregates per‑minute data from a per‑second table, demonstrates insertion and query tests, investigates an unexpected 1970‑01‑01 timestamp caused by time‑zone handling, and provides the corrected view definition aligning field names.

Time Zoneclickhousedatabase
0 likes · 8 min read
Using ClickHouse Materialized Views: Creation, Testing, and Time‑Zone Issue Resolution
DataFunSummit
DataFunSummit
Aug 11, 2022 · Big Data

Huya Data Platform: Cost Reduction and SLA Strategies

This article presents Huya's big data platform evolution, detailing cost‑saving measures, SLA practices, multi‑datacenter architecture, containerized resources, metadata‑driven intelligence, and future directions such as hybrid‑engine materialized views to improve efficiency and service reliability.

Cost OptimizationData PlatformSLA
0 likes · 15 min read
Huya Data Platform: Cost Reduction and SLA Strategies
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jul 19, 2022 · Big Data

Real-Time & Offline Data Warehouse Integration: New Capabilities Explained

This article provides an overview of real-time and offline integrated data warehousing, tracing its evolution from early offline warehouses to modern cloud-native solutions, and details the latest capabilities—including multi-engine computation, data sharing between MaxCompute and Hologres, progressive computing, materialized views, and practical use cases such as telecom analytics and connected‑car scenarios.

HologresMaxComputecloud-native
0 likes · 16 min read
Real-Time & Offline Data Warehouse Integration: New Capabilities Explained
HomeTech
HomeTech
Nov 3, 2021 · Big Data

Real‑time Materialized View Practices with Apache Flink: System Analysis, Algorithm Design, and Implementation

This article presents Car Home's experience building a real‑time materialized view system on Apache Flink, detailing system analysis, problem decomposition, a global‑version‑based CDC algorithm, its implementation as a Flink connector, practical deployment results, and remaining challenges such as clock dependency and state size.

CDCFlinkalgorithm
0 likes · 17 min read
Real‑time Materialized View Practices with Apache Flink: System Analysis, Algorithm Design, and Implementation
DataFunSummit
DataFunSummit
Oct 16, 2021 · Databases

Practical Use Cases of Materialized Views and Indexes in Doris

This article shares practical experiences with Doris, covering materialized view concepts, typical use cases, index principles, performance optimizations, and real‑world scenarios such as order analysis, PV/UV aggregation, and detailed queries, while also providing operational tips and Q&A insights.

Big DataOLAPdoris
0 likes · 16 min read
Practical Use Cases of Materialized Views and Indexes in Doris
JD Retail Technology
JD Retail Technology
Oct 15, 2021 · Big Data

How JD’s Activity Cockpit Supercharges Mega‑Sale Performance with Optimize Table, BitMap, and Materialized Views

The article explains how JD’s Activity Cockpit tackles mega‑sale challenges by monitoring the consumer golden‑link, applying Optimize Table, BitMap, and materialized view techniques to reduce data volume, accelerate queries, and enable precise real‑time marketing for brands.

Big Databitmap indexinge-commerce analytics
0 likes · 6 min read
How JD’s Activity Cockpit Supercharges Mega‑Sale Performance with Optimize Table, BitMap, and Materialized Views
DataFunTalk
DataFunTalk
Sep 23, 2021 · Databases

Practical Use Cases of Materialized Views and Indexes in Doris

This article shares practical experiences with Doris, covering materialized view concepts, typical use cases, advantages, creation syntax, prefix index principles, performance‑boosting scenarios such as order analysis, PV/UV counting, detail queries, and operational tips for high‑throughput and low‑latency workloads.

Big DataOLAPdoris
0 likes · 18 min read
Practical Use Cases of Materialized Views and Indexes in Doris
Python Crawling & Data Mining
Python Crawling & Data Mining
Sep 12, 2021 · Databases

How ClickHouse Projections Supercharge Query Performance

The article explains ClickHouse's new Projection feature, how it overcomes MergeTree's single‑sort limitation and materialized view drawbacks, provides step‑by‑step commands to create, materialize, and query projections, demonstrates massive performance gains, and outlines the rules for automatic projection selection.

Database OptimizationProjectionclickhouse
0 likes · 12 min read
How ClickHouse Projections Supercharge Query Performance
dbaplus Community
dbaplus Community
Sep 8, 2020 · Databases

Achieving Billion‑Row Second‑Level Queries with ClickHouse Real‑Time Engine

JD’s Algorithmic Intelligence team built a ClickHouse‑based real‑time analytics engine that ingests Kafka and offline data, uses MergeTree tables with strategic partitioning and sorting, and employs batch writes, materialized views, and monitoring to achieve second‑level queries over billions of rows.

Big DataMergeTreeReal-time analytics
0 likes · 17 min read
Achieving Billion‑Row Second‑Level Queries with ClickHouse Real‑Time Engine
Tencent Cloud Developer
Tencent Cloud Developer
Aug 6, 2020 · Big Data

ClickHouse Real‑Time Analytics at QQ Music: Challenges, Solutions, and Tencent Cloud Best Practices

QQ Music replaced its slow Hive warehouse with a massive ClickHouse cluster, achieving sub‑second to ten‑second query latency on petabyte‑scale data, enabling real‑time analytics for non‑technical users, and following five operational best practices—ZooKeeper planning, idempotent writes, sensible partitions, read‑write separation, and localized joins—while leveraging Tencent Cloud’s managed ClickHouse service.

Database OptimizationOLAPclickhouse
0 likes · 24 min read
ClickHouse Real‑Time Analytics at QQ Music: Challenges, Solutions, and Tencent Cloud Best Practices
dbaplus Community
dbaplus Community
Jul 8, 2019 · Big Data

How to Use ClickHouse Sampling and Materialized Views for Real‑Time Monitoring of Billion‑Scale Ad Traffic

This article explains how to handle high‑volume advertising monitoring by storing raw request logs in ClickHouse, enabling sampling and materialized views, and using TP999 metrics, aggregating tables, and Grafana queries to achieve fast, flexible, and low‑impact real‑time analytics on billions of events.

Samplingbig-dataclickhouse
0 likes · 10 min read
How to Use ClickHouse Sampling and Materialized Views for Real‑Time Monitoring of Billion‑Scale Ad Traffic
dbaplus Community
dbaplus Community
Mar 16, 2016 · Databases

How to Supercharge SELECT COUNT(*) in Oracle: 6 Proven Optimization Tricks

Discover six powerful techniques—including B‑tree and bitmap indexes, materialized views, result‑set caching, and clever query rewrites—to dramatically reduce logical reads and accelerate Oracle's SELECT COUNT(*) performance, with step‑by‑step examples and measurable improvements.

OracleResult Cacheindexes
0 likes · 6 min read
How to Supercharge SELECT COUNT(*) in Oracle: 6 Proven Optimization Tricks