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Wuming AI
Wuming AI
May 10, 2026 · Artificial Intelligence

Can Large Models Really Understand 1 M Tokens? Lessons from the RULER Benchmark

The article examines why a model’s advertised context window (e.g., 128 K or 1 M tokens) does not guarantee effective long‑context reasoning, summarizing the RULER framework that breaks long‑context ability into retrieval, interference resistance, multi‑hop tracking, aggregation, and multi‑answer recall, and offering practical guidance for evaluating and using such models.

LLMRULERaggregation
0 likes · 16 min read
Can Large Models Really Understand 1 M Tokens? Lessons from the RULER Benchmark
Big Data Tech Team
Big Data Tech Team
Feb 12, 2026 · Big Data

Mastering the DWS Layer: Core Strategies for Scalable Data Warehouses

This article provides a comprehensive, business‑driven analysis of the Data Warehouse Service (DWS) layer, covering its core positioning, design goals, modeling and aggregation tactics, storage optimizations, typical challenges with practical solutions, and best‑practice recommendations for building efficient, cost‑effective data services.

DWS LayerData WarehousePerformance Optimization
0 likes · 8 min read
Mastering the DWS Layer: Core Strategies for Scalable Data Warehouses
Ray's Galactic Tech
Ray's Galactic Tech
Jan 4, 2026 · Fundamentals

Master Elasticsearch: From Core Concepts to Advanced Search and Scaling

This guide introduces Elasticsearch’s fundamental architecture, explains core concepts such as inverted indexes, analyzers, and mapping, demonstrates essential query types, aggregation techniques, performance optimizations, distributed design, and real‑world use cases like blog and e‑commerce search, while also covering monitoring and advanced features.

ElasticsearchQuery DSLaggregation
0 likes · 9 min read
Master Elasticsearch: From Core Concepts to Advanced Search and Scaling
Selected Java Interview Questions
Selected Java Interview Questions
Dec 11, 2025 · Backend Development

Master Maven Multi‑Module Design: Inheritance, Aggregation, and Private Repositories

This guide explains how to split a large Java project into Maven modules, use parent‑project inheritance and aggregation to share configurations, manage dependency versions with dependencyManagement, and configure a private repository for releasing SNAPSHOT and RELEASE artifacts, complete with practical code snippets and step‑by‑step instructions.

InheritanceJavaMaven
0 likes · 11 min read
Master Maven Multi‑Module Design: Inheritance, Aggregation, and Private Repositories
Big Data Tech Team
Big Data Tech Team
Nov 24, 2025 · Big Data

Avoid the 5 Common DWS Design Traps and Build Scalable Data Warehouses

This article analyzes the five typical pitfalls when designing DWS aggregation tables—from chimney‑style schemas to performance blind spots—explains their consequences, and provides concrete, production‑ready recommendations, code examples, and design principles to create reusable, efficient data‑warehouse layers.

DWS DesignData WarehousePerformance Optimization
0 likes · 10 min read
Avoid the 5 Common DWS Design Traps and Build Scalable Data Warehouses
Java Tech Enthusiast
Java Tech Enthusiast
Nov 9, 2025 · Databases

Why Setting MySQL Columns to NOT NULL Boosts Performance and Saves Space

This article explains how defining MySQL table columns as NOT NULL—rather than allowing NULL—can improve query speed, reduce storage consumption, simplify indexing and aggregation, make application code cleaner, and enhance data consistency, while also noting scenarios where NULL is appropriate.

Database OptimizationNOT NULLStorage Efficiency
0 likes · 9 min read
Why Setting MySQL Columns to NOT NULL Boosts Performance and Saves Space
Mike Chen's Internet Architecture
Mike Chen's Internet Architecture
Oct 11, 2025 · Cloud Native

Exploring Microservice Architecture Patterns: Aggregation, Sharing, Proxy, and Async Messaging

This article outlines four key microservice architecture patterns—aggregation, shared database, proxy (API gateway), and asynchronous messaging—detailing their advantages, drawbacks, and typical application scenarios to help architects choose the right approach for scalable, resilient systems.

Proxyaggregationarchitecture
0 likes · 5 min read
Exploring Microservice Architecture Patterns: Aggregation, Sharing, Proxy, and Async Messaging
Sohu Tech Products
Sohu Tech Products
Oct 9, 2025 · Databases

When to Use SELECT DISTINCT vs GROUP BY in MySQL: Performance & Use Cases

This article compares MySQL’s SELECT DISTINCT and GROUP BY clauses, explaining their syntax, functional differences, performance implications, and ideal scenarios through detailed examples, index usage analysis, and a feature comparison table, helping developers choose the right approach for deduplication or aggregation tasks.

DeduplicationGROUP BYSELECT DISTINCT
0 likes · 10 min read
When to Use SELECT DISTINCT vs GROUP BY in MySQL: Performance & Use Cases
Senior Brother's Insights
Senior Brother's Insights
Sep 27, 2025 · Databases

When to Use SELECT DISTINCT vs GROUP BY in MySQL: Features, Performance, and Examples

This article explains the functional differences, performance considerations, and practical use cases of MySQL's SELECT DISTINCT and GROUP BY clauses, providing syntax details, example tables, query results, and a comparison table to help developers choose the right approach for deduplication or aggregation tasks.

DeduplicationGROUP BYPerformance
0 likes · 8 min read
When to Use SELECT DISTINCT vs GROUP BY in MySQL: Features, Performance, and Examples
Linux Cloud Computing Practice
Linux Cloud Computing Practice
Sep 26, 2025 · Databases

Master MongoDB: From Basics to Advanced Practices

This comprehensive MongoDB tutorial covers its core concepts, suitable scenarios, basic CRUD operations, document structure, query syntax, indexing, aggregation framework, advanced features like MapReduce and transactions, Python integration, real‑world case studies, and provides command summaries with practice exercises for beginners to intermediate users.

DatabaseMongoDBNoSQL
0 likes · 5 min read
Master MongoDB: From Basics to Advanced Practices
High Availability Architecture
High Availability Architecture
Sep 10, 2025 · Big Data

How Ctrip Business Travel Built a Near‑Real‑Time Lakehouse with Flink CDC & Paimon

This article details Ctrip Business Travel’s implementation of a near‑real‑time data warehouse using Flink CDC and the Paimon lakehouse engine, covering order wide‑table construction, ticket refund alerts, ad attribution, batch‑stream integration, and practical lessons on Partial Update, Aggregation, and Tag‑based incremental processing.

?=Batch-Stream IntegrationFlink
0 likes · 17 min read
How Ctrip Business Travel Built a Near‑Real‑Time Lakehouse with Flink CDC & Paimon
DevOps Operations Practice
DevOps Operations Practice
Jun 30, 2025 · Big Data

Master Elasticsearch: Core Features, Basic Operations, and Advanced Search Techniques

Elasticsearch, built on Lucene, is a distributed search and analytics engine offering full-text search, log and metric analysis, real-time data processing, and recommendation capabilities; the guide explains its core functions, basic index and document management, and advanced query and aggregation features with practical API examples.

APIElasticsearchFull-Text Search
0 likes · 5 min read
Master Elasticsearch: Core Features, Basic Operations, and Advanced Search Techniques
Lin is Dream
Lin is Dream
May 25, 2025 · Databases

Master MySQL Row-to-Column Pivot: From Basics to Advanced Aggregations

This article walks through using MySQL to pivot rows into columns, covering storage concepts, practical CASE‑WHEN aggregation techniques, and detailed SQL examples for sum, count, average and max calculations, while also discussing performance considerations for medium‑scale and large‑scale data workloads.

Data WarehouseOLAPOLTP
0 likes · 7 min read
Master MySQL Row-to-Column Pivot: From Basics to Advanced Aggregations
Mike Chen's Internet Architecture
Mike Chen's Internet Architecture
Dec 26, 2024 · Backend Development

Four Main Microservice Architecture Patterns Explained

This article introduces and compares four common microservice architecture patterns—aggregation, shared database, proxy (including sidecar), and asynchronous messaging—detailing their designs, advantages, disadvantages, and typical use cases for large‑scale systems.

Proxyaggregationasynchronous messaging
0 likes · 6 min read
Four Main Microservice Architecture Patterns Explained
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Oct 19, 2024 · Databases

Understanding the HAVING Clause in SQL: Concepts, Examples, and Best Practices

This article explains the purpose and proper use of the SQL HAVING clause, contrasts it with WHERE, and provides multiple practical examples—including counting groups, detecting missing IDs, calculating mode and median, and filtering fully‑submitted records—while highlighting common pitfalls and performance considerations.

DatabaseGROUP BYHAVING
0 likes · 13 min read
Understanding the HAVING Clause in SQL: Concepts, Examples, and Best Practices
Test Development Learning Exchange
Test Development Learning Exchange
Aug 16, 2024 · Fundamentals

Advanced Pandas Techniques: Grouping, Aggregation, Window Functions, and More

This article demonstrates eleven practical Pandas examples covering grouping aggregation, conditional filtering, rolling windows, multi-indexing, melting, broadcasting, concatenation, merging, time-series creation, missing-value handling, and custom function application, each accompanied by complete Python code and expected output.

Data Analysisaggregationdata manipulation
0 likes · 7 min read
Advanced Pandas Techniques: Grouping, Aggregation, Window Functions, and More
Python Programming Learning Circle
Python Programming Learning Circle
May 23, 2024 · Artificial Intelligence

Comprehensive Collection of Aggregation Functions for Feature Engineering in Python

This article presents a detailed compilation of pandas built‑in aggregation methods and a wide range of custom Python functions for time‑series feature engineering, providing ready‑to‑use code snippets that cover statistical descriptors, drawdown metrics, peak detection, and more for data science practitioners.

PythonTime-seriesaggregation
0 likes · 17 min read
Comprehensive Collection of Aggregation Functions for Feature Engineering in Python
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 4, 2024 · Big Data

Elasticsearch Deep Dive: Features, Mapping & Zero‑Downtime Reindexing

This article provides a comprehensive overview of Elasticsearch, covering its distributed architecture, key features such as JSON RESTful APIs and multi‑tenant support, core functionalities like full‑text search and aggregations, comparisons with Solr, advanced mapping techniques, various query DSLs, suggestion mechanisms, and practical zero‑downtime reindexing strategies.

DSLElasticsearchSearch
0 likes · 13 min read
Elasticsearch Deep Dive: Features, Mapping & Zero‑Downtime Reindexing
dbaplus Community
dbaplus Community
Jan 21, 2024 · Databases

How to Use ES|QL in Elasticsearch 8.11: DSL vs SQL Queries Compared

This guide demonstrates how Elasticsearch 8.11's new ES|QL query language matches the traditional DSL syntax, covering basic searches, sorting, wildcard matching, aggregations, REST API nuances, and practical examples that show equivalent DSL and ES|QL statements with visual results.

DSLES|QLElasticsearch
0 likes · 6 min read
How to Use ES|QL in Elasticsearch 8.11: DSL vs SQL Queries Compared
Full-Stack Internet Architecture
Full-Stack Internet Architecture
Dec 22, 2023 · Databases

Understanding NULL Values in MySQL

This article explains the nature of NULL in MySQL, demonstrates why comparisons with = or != fail, shows the correct IS NULL/IS NOT NULL syntax, and discusses how NULL behaves with DISTINCT, GROUP BY, ORDER BY, and aggregate functions.

DatabaseNULLSQL
0 likes · 6 min read
Understanding NULL Values in MySQL
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Oct 30, 2023 · Databases

Easy-Es Tutorial: Simplify Elasticsearch Operations with a Java ORM Framework

This article introduces Easy-Es, a Java ORM framework built on Elasticsearch's RestHighLevelClient that simplifies query construction and CRUD operations, provides step‑by‑step setup including Maven dependencies, configuration, entity and mapper definitions, controller examples, and advanced features such as condition builders, index management, logging, and aggregation queries.

CRUDEasy-EsElasticsearch
0 likes · 8 min read
Easy-Es Tutorial: Simplify Elasticsearch Operations with a Java ORM Framework
ITPUB
ITPUB
Oct 4, 2023 · Backend Development

How to Speed Up Slow Elasticsearch Aggregations with execution_hint "map"

In a high‑traffic e‑commerce system, sharding makes cross‑shop queries inefficient, and adding terms aggregations in Elasticsearch caused queries to take dozens of seconds, but using the "execution_hint":"map" option dramatically reduces aggregation latency.

Big DataElasticsearchPerformance Optimization
0 likes · 7 min read
How to Speed Up Slow Elasticsearch Aggregations with execution_hint "map"
StarRocks
StarRocks
Aug 22, 2023 · Databases

How StarRocks Query Cache Supercharges High‑Concurrency Aggregations

StarRocks introduces a Query Cache that stores intermediate aggregation results in memory, enabling reuse across semantically equivalent, partition‑overlapping, or append‑only queries, which can boost query performance by 3‑17× in high‑concurrency scenarios while reducing CPU and disk load.

MPP databasePerformance OptimizationStarRocks
0 likes · 13 min read
How StarRocks Query Cache Supercharges High‑Concurrency Aggregations
dbaplus Community
dbaplus Community
Aug 17, 2023 · Databases

Mastering Redis Set Operations for Scalable Statistics and Aggregations

This article explains how to leverage Redis data structures such as Set, Sorted Set, Bitmap, and HyperLogLog to perform aggregation, sorted, binary‑state, and cardinality statistics efficiently in large‑scale applications, while addressing performance considerations and practical implementation details.

HyperLogLogRedisSet
0 likes · 10 min read
Mastering Redis Set Operations for Scalable Statistics and Aggregations
IT Services Circle
IT Services Circle
Jul 16, 2023 · Databases

Using MySQL Window Functions for Ranking, Aggregation, and Data Analysis

This article explains how MySQL 8.x window functions such as OVER, PARTITION BY, and ORDER BY can simplify complex ranking and aggregation queries, demonstrates creating a sample scores table, and provides practical examples of functions like ROW_NUMBER, RANK, DENSE_RANK, NTILE, LAG, and LEAD with their results.

OVERPartitionSQL
0 likes · 18 min read
Using MySQL Window Functions for Ranking, Aggregation, and Data Analysis
Su San Talks Tech
Su San Talks Tech
Jun 29, 2023 · Backend Development

Master Redis Set Operations for Scalable Statistics and Analytics

This article explains how to use Redis sets, sorted sets, lists, bitmaps, and HyperLogLog to perform aggregation, ordered queries, binary state tracking, and cardinality estimation for large‑scale applications such as sign‑in systems, e‑commerce comments, and social‑network friend lists.

HyperLogLogRedisSet
0 likes · 10 min read
Master Redis Set Operations for Scalable Statistics and Analytics
Python Programming Learning Circle
Python Programming Learning Circle
May 4, 2023 · Big Data

Advanced Pandas Data Manipulation Techniques

This article provides a comprehensive guide to using Pandas for complex queries, data type conversion, sorting, adding and modifying data, advanced filtering, iteration, and functional operations, offering numerous code examples that illustrate how to efficiently clean, transform, and analyze tabular data in Python.

PythonSortingaggregation
0 likes · 14 min read
Advanced Pandas Data Manipulation Techniques
ITPUB
ITPUB
Apr 23, 2023 · Databases

Why SQL Still Dominates Data Analysis: From Relational Algebra to Modern OLAP

This article explains how SQL, built on relational algebra, became the standard analysis language for OLAP engines, covering its history, data models, syntax, functions, aggregation techniques, window functions, subqueries, and practical optimization considerations for modern data warehouses.

Data AnalysisDatabaseOLAP
0 likes · 46 min read
Why SQL Still Dominates Data Analysis: From Relational Algebra to Modern OLAP
Top Architect
Top Architect
Jan 11, 2023 · Big Data

Comprehensive Elasticsearch Query Guide with Java High Level REST Client Examples

This tutorial provides a thorough overview of Elasticsearch 7.13.2 query techniques—including term, terms, range, prefix, wildcard, boolean, filter, and aggregation queries—showing both raw DSL syntax and equivalent Java High Level REST Client code, enabling developers to quickly master search operations on large datasets.

ElasticsearchSearchaggregation
0 likes · 16 min read
Comprehensive Elasticsearch Query Guide with Java High Level REST Client Examples
政采云技术
政采云技术
Aug 23, 2022 · Backend Development

Understanding Elasticsearch Document Scoring and Aggregation Techniques

This article explains the underlying principles of Elasticsearch scoring, covering Boolean model queries, TF/IDF, field length normalization, the vector space model, and detailed aggregation examples with code snippets to illustrate practical search and analytics usage.

ElasticsearchScoringSearch
0 likes · 19 min read
Understanding Elasticsearch Document Scoring and Aggregation Techniques
Selected Java Interview Questions
Selected Java Interview Questions
Apr 9, 2022 · Databases

Comprehensive SQL Basics, Interview Questions, and Advanced Query Techniques

This article provides a thorough guide to SQL fundamentals—including SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY, LIMIT, aggregate functions, joins, set operations, and Oracle-specific objects—followed by a collection of common interview questions with detailed solutions, top‑N queries, and row‑to‑column transformations, while also containing promotional notes for related resources.

DatabaseJoinsOracle
0 likes · 25 min read
Comprehensive SQL Basics, Interview Questions, and Advanced Query Techniques
Architecture Digest
Architecture Digest
Feb 28, 2022 · Fundamentals

Object‑Oriented Modeling: Concepts, Code Examples, and Design Principles

This article explains the principles of object‑oriented modeling, contrasting object and data models, illustrating with a simple account example, discussing composition versus aggregation, and providing Java code snippets for modeling processes such as eating, demonstrating how to design and implement domain objects and services.

JavaObject-Orientedaggregation
0 likes · 12 min read
Object‑Oriented Modeling: Concepts, Code Examples, and Design Principles
Ops Development Stories
Ops Development Stories
Feb 24, 2022 · Big Data

Master Elasticsearch: Core Concepts, APIs, Mapping, and Performance Tuning

This comprehensive guide explains Elasticsearch fundamentals—including documents, indices, nodes, clusters, REST and Document APIs, query DSL, relevance scoring, distributed architecture, real‑time indexing, search execution, pagination, scroll, aggregations, data modeling, mapping options, parent/child relationships, reindexing, and practical cluster and write/read performance optimizations.

Cluster TuningElasticsearchaggregation
0 likes · 58 min read
Master Elasticsearch: Core Concepts, APIs, Mapping, and Performance Tuning
Big Data Technology & Architecture
Big Data Technology & Architecture
Feb 23, 2022 · Big Data

Understanding Mini‑Batch Streaming Aggregation in Flink SQL

This article explains Flink SQL’s streaming aggregation Mini‑Batch feature, covering its purpose, configuration parameters, underlying optimizer rules, operator implementations, watermark handling, buffer processing, and the optional Local‑Global two‑phase aggregation optimization for improving throughput and reducing state overhead in large‑scale data pipelines.

Big DataFlinkMini-Batch
0 likes · 10 min read
Understanding Mini‑Batch Streaming Aggregation in Flink SQL
dbaplus Community
dbaplus Community
Jan 26, 2022 · Big Data

Why Does Elasticsearch Aggregate Faster with Fewer Terms? Uncover the Secrets

This article examines a real‑world Elasticsearch cluster handling hundreds of terabytes, explains why high‑cardinality aggregations can be slower, and shows how setting execution_hint=map and tuning doc_values dramatically improves aggregation performance for ultra‑high‑concurrency workloads.

Big DataData AnalyticsElasticsearch
0 likes · 12 min read
Why Does Elasticsearch Aggregate Faster with Fewer Terms? Uncover the Secrets
Code Ape Tech Column
Code Ape Tech Column
Jan 19, 2022 · Databases

Choosing Appropriate Redis Data Structures for Large‑Scale Statistics: Cardinality, Sorting, and Aggregation

This article explains how to select Redis data structures such as Bitmap, HyperLogLog, Set, List, Sorted Set, and Hash to efficiently handle massive statistical scenarios like user login status, UV counting, ranking, and set aggregation, while providing concrete command examples and best‑practice recommendations.

CardinalityData StructuresHyperLogLog
0 likes · 11 min read
Choosing Appropriate Redis Data Structures for Large‑Scale Statistics: Cardinality, Sorting, and Aggregation
Top Architect
Top Architect
Dec 16, 2021 · Fundamentals

Object‑Oriented Modeling: From Data Model to Object Model, Composition, Aggregation and Practical Java Examples

This article explains the fundamentals of object‑oriented modeling, contrasting data‑model and object‑model designs, illustrating attribute‑method composition, composition versus aggregation, and providing concrete Java and SQL examples such as a simple Account domain, an eating‑process model, and an open‑source e‑commerce reference.

DatabaseObject-Orientedaggregation
0 likes · 14 min read
Object‑Oriented Modeling: From Data Model to Object Model, Composition, Aggregation and Practical Java Examples
Top Architect
Top Architect
Dec 1, 2021 · Fundamentals

Object‑Oriented Modeling: Concepts, Java Implementations, and Composition vs. Aggregation

This article explains the fundamentals of object‑oriented modeling, contrasts data‑model and object‑model designs with Java code examples such as a simple Account entity and an eating‑process simulation, and clarifies the difference between composition and aggregation while also touching on application services and a Spring‑Boot e‑commerce project.

JavaObject-Orientedaggregation
0 likes · 12 min read
Object‑Oriented Modeling: Concepts, Java Implementations, and Composition vs. Aggregation
Architects Research Society
Architects Research Society
Sep 19, 2021 · Fundamentals

Understanding UML Associations, Aggregations, Compositions, Generalization and Specialization

This article explains UML relationship types—including association, aggregation, composition, generalization and specialization—by describing their definitions, visual notations, multiplicity, role names, and real‑world examples, helping readers distinguish each concept and apply them in software modeling.

AssociationGeneralizationSpecialization
0 likes · 7 min read
Understanding UML Associations, Aggregations, Compositions, Generalization and Specialization
Architect
Architect
Sep 16, 2021 · Fundamentals

Object Modeling: Comparing Object and Data Models, OOP Principles, and Composition vs Aggregation

This article explains the philosophical basis of object‑oriented thinking, distinguishes objects from things, discusses attributes and methods, compares object‑oriented and data‑model designs with Java and SQL examples, and clarifies composition and aggregation through real‑world and code illustrations.

Domain-Driven DesignJavaObject-Oriented Design
0 likes · 13 min read
Object Modeling: Comparing Object and Data Models, OOP Principles, and Composition vs Aggregation
Open Source Linux
Open Source Linux
Aug 8, 2021 · Databases

MongoDB Essentials: 52 Common Questions Answered

This comprehensive guide covers everything from MongoDB's core concepts, data model, and architecture to practical commands for creating databases, collections, indexes, sharding, replication, aggregation, and troubleshooting, providing clear answers to over fifty frequently asked questions.

DatabaseIndexesMongoDB
0 likes · 27 min read
MongoDB Essentials: 52 Common Questions Answered
MaGe Linux Operations
MaGe Linux Operations
Aug 4, 2021 · Databases

What Is MongoDB? Features, Commands, and Best Practices Explained

This article provides a comprehensive overview of MongoDB, covering its definition, document‑oriented storage, key features, differences from relational databases, sharding and replication mechanisms, common commands, data types, indexing, aggregation, and practical usage scenarios for modern applications.

DatabaseMongoDBNoSQL
0 likes · 23 min read
What Is MongoDB? Features, Commands, and Best Practices Explained
Python Crawling & Data Mining
Python Crawling & Data Mining
Apr 22, 2021 · Databases

MongoDB Mastery: Install, Configure, and Perform CRUD

This comprehensive tutorial walks you through installing MongoDB on Windows, configuring data and log directories, setting environment variables, creating and managing databases, collections, indexes, aggregation pipelines, backup and restore procedures, monitoring tools, advanced query operators, user management, and using a visual tool like Navicat for MongoDB.

CRUDDatabaseInstallation
0 likes · 15 min read
MongoDB Mastery: Install, Configure, and Perform CRUD
Programmer DD
Programmer DD
Apr 8, 2021 · Databases

Master SQL HAVING: Filter Groups Like a Pro

This article explains the purpose and proper use of the SQL HAVING clause, demonstrates how it works with GROUP BY to filter aggregated groups, shows examples for finding groups with specific counts, missing IDs, mode, median, and non‑NULL values, and clarifies best practices for query performance and semantics.

GROUP BYHAVINGSQL
0 likes · 13 min read
Master SQL HAVING: Filter Groups Like a Pro
DevOps Cloud Academy
DevOps Cloud Academy
Nov 7, 2020 · Databases

How to Use Elasticsearch Search Templates for Batch Terms Queries

This article explains why a terms query with an array fails inside an Elasticsearch search template, demonstrates the correct Mustache‑based template syntax, and provides step‑by‑step code examples for defining the index, inserting data, creating the template, and executing the query.

DSLElasticsearchSearch Template
0 likes · 8 min read
How to Use Elasticsearch Search Templates for Batch Terms Queries
Programmer DD
Programmer DD
Sep 28, 2020 · Fundamentals

UML Relationship Cheat Sheet: From Dependency to Realization

This article explains the six core UML relationship types—Dependency, Association, Aggregation, Composition, Generalization, and Realization—detailing their semantics, visual notations, code representations in Java/C++, and practical distinctions such as lifecycle ownership and usage contexts.

Object-OrientedUMLaggregation
0 likes · 13 min read
UML Relationship Cheat Sheet: From Dependency to Realization
Big Data Technology & Architecture
Big Data Technology & Architecture
Aug 30, 2020 · Big Data

Kylin Cube Construction Principles and Optimization Techniques

This article explains the fundamentals of Kylin Cube construction—including dimensions, measures, Cuboid generation, layer-by-layer and in‑memory building algorithms, storage mechanisms, and various optimization strategies such as derived dimensions, aggregation groups, row‑key design, and concurrency granularity—providing a comprehensive guide for big‑data OLAP practitioners.

Big DataCubeKylin
0 likes · 14 min read
Kylin Cube Construction Principles and Optimization Techniques
Programmer DD
Programmer DD
Aug 27, 2020 · Fundamentals

UML: Dependency, Association, Aggregation, Composition, Generalization, Realization

This article explains the six fundamental UML relationship types—Dependency, Association, Aggregation, Composition, Generalization, and Realization—detailing their semantics, visual notations, code examples in Java/C++, and the distinctions among them, helping developers model object interactions accurately.

AssociationObject-OrientedUML
0 likes · 11 min read
UML: Dependency, Association, Aggregation, Composition, Generalization, Realization
Sohu Tech Products
Sohu Tech Products
Oct 9, 2019 · Databases

MongoDB Aggregation Framework: Stages, Pipelines, and Examples

This article provides an in‑depth overview of MongoDB’s aggregation framework, explaining the concepts of pipelines and stages such as $match, $group, $project, $lookup, $unwind, and $out, and includes practical code examples, syntax details, and comparisons to SQL aggregation.

DatabaseMapReduceMongoDB
0 likes · 25 min read
MongoDB Aggregation Framework: Stages, Pipelines, and Examples
Aikesheng Open Source Community
Aikesheng Open Source Community
Jun 27, 2019 · Databases

Understanding MongoDB Sort Memory Limits and Index Optimization

This article analyzes MongoDB sort operation memory limits, explains why queries may exceed the 32 MB in‑memory sort threshold, demonstrates how to inspect and adjust server parameters, create appropriate compound indexes, and use allowDiskUse in aggregation pipelines to avoid sort‑related errors.

MemoryLimitMongoDBPerformance
0 likes · 14 min read
Understanding MongoDB Sort Memory Limits and Index Optimization
dbaplus Community
dbaplus Community
Aug 20, 2018 · Databases

Unlock PostgreSQL’s Powerful SQL Features: CTEs, UPSERT, Sampling, and More

This article explores PostgreSQL’s advanced SQL capabilities—including Common Table Expressions (WITH), batch inserts, the RETURNING clause, UPSERT with ON CONFLICT, TABLESAMPLE methods, aggregation functions like string_agg and array_agg, and a range of window functions—providing practical examples, performance insights, and guidance for real‑world use.

CTEPostgreSQLSQL
0 likes · 17 min read
Unlock PostgreSQL’s Powerful SQL Features: CTEs, UPSERT, Sampling, and More
ITFLY8 Architecture Home
ITFLY8 Architecture Home
Jul 15, 2018 · Fundamentals

Mastering UML: The Six Core Relationships Every Developer Should Know

UML class diagrams feature six primary relationships—Generalization, Realization, Dependency, Association, Aggregation, and Composition—each with distinct visual notations and semantics, and this guide explains their definitions, code representations in Java, and practical examples to help developers model system structures accurately.

AssociationInheritanceUML
0 likes · 8 min read
Mastering UML: The Six Core Relationships Every Developer Should Know
ITPUB
ITPUB
Jul 4, 2018 · Databases

What’s New in MongoDB 4.0? Transactions, Aggregation, and Platform Updates

MongoDB 4.0 has been officially released, bringing multi‑document ACID transactions, new aggregation operators, driver compatibility updates, enhanced security features, deprecation of the MMAPv1 storage engine, and broader platform support, while also offering free cloud monitoring for standalone and replica set deployments.

4.0MongoDBaggregation
0 likes · 5 min read
What’s New in MongoDB 4.0? Transactions, Aggregation, and Platform Updates
dbaplus Community
dbaplus Community
Nov 12, 2017 · Databases

MongoDB 3.6 RC3 Release Highlights & Common Pitfalls Explained

This article provides a concise overview of MongoDB 3.6 RC3 release notes, covering security defaults, new aggregation operators, array update enhancements, shell command additions, JSON schema support, replica set authentication, sharding parameters, and practical tips to avoid common pitfalls.

3.6MongoDBRelease Notes
0 likes · 7 min read
MongoDB 3.6 RC3 Release Highlights & Common Pitfalls Explained
Liulishuo Tech Team
Liulishuo Tech Team
Sep 24, 2016 · Backend Development

Developing Custom Presto SQL Functions (UDF) with Java Plugins

This tutorial explains how to create, register, and deploy custom scalar, aggregation, and window functions for the Presto distributed query engine using Java annotations, the Presto plugin mechanism, and code examples that illustrate UDF development, plugin packaging, and state handling for aggregation functions.

JavaPrestoSQL
0 likes · 11 min read
Developing Custom Presto SQL Functions (UDF) with Java Plugins
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Sep 21, 2016 · Databases

10 Powerful MongoDB Performance Tricks You Need to Know

This guide shares ten practical MongoDB performance tips—including proper index ordering, using explain() and hint(), avoiding costly query operators, leveraging capped collections, upsert, sorting limits, aggregation, drop vs remove, batch inserts, and optimal indexing for arrays and range queries—to help developers boost database efficiency.

Database OptimizationMongoDBPerformance
0 likes · 5 min read
10 Powerful MongoDB Performance Tricks You Need to Know
ITPUB
ITPUB
Dec 3, 2015 · Databases

Choosing the Right Time‑Series Database: Types, Queries, and Performance Trade‑offs

Time‑series data, defined by a timestamp field, appears everywhere, and the article explains how to choose an appropriate time‑series database by comparing two schema models, their query patterns, performance trade‑offs, and why modern solutions like Elasticsearch, columnar stores, and Druid excel at real‑time massive aggregation.

Big DataDatabaseElasticsearch
0 likes · 9 min read
Choosing the Right Time‑Series Database: Types, Queries, and Performance Trade‑offs
21CTO
21CTO
Aug 31, 2015 · Databases

Optimizing Time-Series Storage: Files, LSM Trees, and B‑Tree Strategies

This article examines the evolution and challenges of time‑series storage, compares file‑based, LSM‑tree, and B‑tree approaches, and proposes a vector‑based method to efficiently handle writes, reads, query semantics, dimensions, and aggregation for modern big‑data applications.

LSMTime-seriesaggregation
0 likes · 8 min read
Optimizing Time-Series Storage: Files, LSM Trees, and B‑Tree Strategies