This article explains the logical execution order of SQL clauses (FROM, WHERE, GROUP BY, HAVING, SELECT, ORDER BY, LIMIT), why SELECT aliases aren't visible in WHERE, how HAVING differs from WHERE, JOIN/ON placement, LEFT JOIN pitfalls, and how to verify with EXPLAIN, distinguishing logical vs. physical execution order.
This article explains why SQL NULL represents an unknown state rather than a value, how three-valued logic (TRUE, FALSE, UNKNOWN) breaks equality comparisons, and demonstrates practical pitfalls with WHERE clauses, NOT IN subqueries, aggregate functions, and MySQL's NULL-safe operator.
This article explains ten advanced SQL concepts including CTEs, recursive queries, temporary functions, pivoting with CASE WHEN, EXCEPT vs NOT IN, self-joins, ranking functions, delta calculations with LEAD/LAG, running totals, and datetime manipulation, with code examples for data science interview preparation.
This article explains the difference between SQL's written clause order and its logical execution order, detailing why WHERE cannot use column aliases, how HAVING filters groups, the impact of JOIN/ON placement in LEFT JOINs, and how to verify execution plans with EXPLAIN.
A comprehensive guide to integrating Apache Ignite 2.16.x with Spring Boot 3.x, covering distributed caching, SQL querying, affinity collocation, compute grid patterns, native persistence, high-availability configurations, and performance tuning for production deployments.
This article benchmarks PostgreSQL JSON and JSONB types on 1 million rows, revealing JSONB delivers 6-9x faster queries and 26% less storage despite 31% slower writes, with practical guidance on when to choose each type and how to leverage GIN indexes for optimal performance.
This article explains how placing filter conditions in ON versus WHERE clauses affects MySQL JOIN results, demonstrating with concrete examples that INNER JOIN behaves equivalently while LEFT JOIN preserves driving table rows only when conditions are in ON, with SQL code and result comparisons.
This tutorial explains MySQL table joins—including inner, left, right, full outer, and exclusion joins—using Venn diagrams, sample tables, and SQL queries to illustrate each join type's result set.
This tutorial demonstrates four methods for multi-table updates in MySQL—INNER JOIN, LEFT JOIN, subquery, and direct multi-table UPDATE—using order and product tables, showing how each handles non-matching rows differently with concrete SQL examples and result comparisons.
This article explains how StarRocks queries Paimon Variant data, covering the differences between regular Parquet JSON and Variant, the three-phase read architecture, Shredding optimization for hot paths, practical SQL examples using get_variant_* functions, and a decision framework for choosing between JSON, Plain Variant, Shredded Variant, and formal typed columns.
Hologres evolves from a real-time data warehouse into an AI data infrastructure, integrating multimodal data via Object Tables and Dynamic Tables, providing AI Functions for hybrid search within SQL, an AI Assistant for automated warehouse operations, and a long-term memory service for cross-session agent recall.
Many developers write messy SQL or generate slow reports because they don’t know built‑in functions; this guide compiles the most frequently used string, date, math, and conversion functions—complete with ready‑to‑copy examples—to handle 99% of everyday data‑processing scenarios.
The article systematically dissects common data‑preparation failure patterns in performance testing, explains root‑cause tracing techniques such as SQL log sampling and dynamic timestamp generation, and proposes a three‑layer, engineering‑driven pipeline—including JSON‑Schema validation, Drools rule checks, and read‑only API verification—to ensure reliable, reproducible test data.
The article recounts how an API that originally used OFFSET pagination suffered 2‑3 seconds response time, and after rewriting the query to Keyset (Seek) pagination—adding a secondary sort key for stability and exploring cursor pagination, index‑only OFFSET, and materialized views—the same endpoint now responds in under 200 ms, with detailed benchmark comparisons.
This concluding tutorial walks beginners through SQL Server stored procedures, highlighting their speed, reusability, and automation advantages over single statements and views, and demonstrates creating, invoking (with and without parameters), altering, and dropping procedures while sharing naming conventions and common pitfalls.
This article explains how database views act as virtual tables that store only query logic, allowing developers to replace repetitive, multi‑join SQL statements with a single, reusable query while improving consistency, hiding sensitive fields, and boosting development efficiency.
The article explains how database transactions ensure atomicity, consistency, isolation, and durability, showing why they are essential for preventing data inconsistency, loss, and reconciliation errors, and provides step‑by‑step SQL examples of successful and rolled‑back transactions along with practical tips for safe usage.
DBX is a lightweight ~20 MB client that natively supports Easysearch and Elasticsearch, offering quick Windows installation, graphical connection setup, SQL and native DSL query editors, and a visual filter panel, making index inspection and data retrieval fast and convenient.
This guide explains why subqueries are essential after learning JOINs, outlines their core characteristics, common use cases, two main types, step‑by‑step examples—including single‑row, multi‑row, aggregate, and derived‑table queries—and lists five key rules to avoid pitfalls while showing when to prefer subqueries over JOINs.
NL2SQL transforms natural language queries into executable SQL, enabling non‑technical users to retrieve data by simply speaking, and the article explains its workflow, evolution from rule‑based to large‑model approaches, current performance on the Spider benchmark, remaining challenges, and real‑world use cases.
This article explains the four fundamental SQL JOIN types, shows when to use each in real business scenarios, and provides a step‑by‑step example that creates tables, inserts data, and demonstrates LEFT and INNER JOIN queries with practical tips for readability.
The article explains what a database transaction is, breaks down the ACID properties, details MySQL’s four isolation levels with their appropriate use‑cases and pitfalls, and provides step‑by‑step SQL and SpringBoot code to reproduce and resolve dirty reads, non‑repeatable reads, and phantom reads.
This article clarifies the difference between SQL DELETE and DROP TABLE, showing that DELETE removes rows while preserving table structure (and requires a WHERE clause), whereas DROP TABLE eliminates the entire table, and provides practical scenarios and code examples for each.
The article presents a systematic, three‑layer approach to uncovering root causes of database performance test failures, illustrating each step with real‑world financial and e‑commerce case studies, key metrics to monitor, reproducible fault injection techniques, and a baseline‑driven change‑gate process.
The article explains what MySQL slow queries are, why they must be detected, when to enable slow‑query logging, step‑by‑step commands to configure the log, how to simulate and analyze problematic SQL with EXPLAIN, and practical optimization techniques—including index creation and Spring Boot integration—to eliminate performance bottlenecks.
This article explains how the SQL UPDATE statement modifies existing rows, emphasizes the necessity of a WHERE clause to avoid full‑table changes, provides single‑row, batch, and multi‑column examples, and advises verifying data with SELECT before executing updates in production.
This article demystifies databases by contrasting them with Excel, explains core concepts such as tables, rows, columns, primary and foreign keys, compares relational and NoSQL systems, introduces SQL CRUD operations, ACID properties, and provides a quick MySQL hands‑on guide, helping readers decide when to adopt a database.
When query results appear unordered, the SQL ORDER BY clause lets you instantly arrange rows by ascending or descending values—such as timestamps, amounts, or multiple fields—providing clean, organized output for latest‑first displays, statistical sorting, and tidy list presentations.
When traffic spikes, a multi‑table JOIN can hold database connections far longer than separate single‑table queries, quickly exhausting the connection pool and slowing the entire system, as demonstrated by concrete timing examples and a practical workaround.
This article explains MySQL indexes with a simple book‑catalog analogy, shows why missing or mis‑used indexes cause severe latency and CPU issues, outlines when to create or avoid indexes, and provides a step‑by‑step SpringBoot demo that measures query time dropping from 80‑200 ms to under 5 ms.
SELECT is the most frequently used SQL command, handling about 80% of data‑related work; this guide explains its core role, basic operations such as selecting all rows, specific columns, distinct values, and row limits, shows practical code examples, and advises against using SELECT * for better performance.
This comprehensive DuckDB tutorial explains why loading large CSV files into Pandas is inefficient, demonstrates how DuckDB can query CSV, Parquet, and JSON directly, walks through SQL basics, advanced features like window functions and CTEs, compares performance against Pandas on ten‑million‑row datasets, and provides practical tips for tuning, common pitfalls, and when to choose DuckDB for data analysis.
This tutorial explains the purpose of the SQL INSERT statement, compares column‑specified and full‑field syntax, shows single‑row and batch‑row examples using a UserInfo table, and highlights common pitfalls such as column‑value ordering, quoting rules, and auto‑increment IDs.
This tutorial walks SQL newcomers through the three essential database commands—creating a database, listing all databases, switching to a specific one, and safely dropping a database—providing copy‑paste code, practical scenarios, and beginner‑focused pitfalls.
This lesson walks through relational database normalization using a messy enrollment table, showing how to derive functional dependencies, identify candidate keys, detect partial, transitive and BCNF violations, and systematically decompose the schema into student, major, course, teacher and enrollment tables while ensuring lossless joins and dependency preservation.
The article explains that in a LEFT JOIN the ON clause determines how rows are matched but never filters out rows from the left table, while a WHERE clause is applied after the join and can remove left‑table rows, illustrated with concrete SQL examples and step‑by‑step analysis.
The paper introduces Sonar‑TS, a training‑free neural‑symbolic system that tackles the newly defined NLQ4TSDB problem—natural‑language queries over database‑scale time‑series—by converting shape intents into searchable symbols and verifying candidates with executable code, achieving up to 3.8× higher scores than the strongest Text‑to‑SQL baseline while highlighting remaining challenges in shape understanding.
This article walks MySQL developers through the essential architectural, schema, data‑type, SQL‑dialect, transaction, vacuum, indexing, and extension differences when moving to PostgreSQL, providing concrete examples, code snippets, and best‑practice recommendations to avoid common pitfalls.
This article walks through setting up ClickHouse 24.8‑alpine, explains the MergeTree storage model, demonstrates data import and ADS queries, and then directly compares ClickHouse’s performance and semantics with Apache Doris on an identical orders CSV dataset, offering practical selection guidance and troubleshooting tips.
The article analyzes the newly released AI Function suite in Alibaba Cloud EMR Serverless StarRocks, detailing multimodal embedding, AI‑driven aggregation, semantic filtering, mixed vector‑full‑text search, architectural advantages such as SQL‑native execution, async pipelines, bounded resources, and real‑world use cases in advertising, gaming, and finance.
The article walks through the pain of manually creating, validating, and cleaning test data for a user‑registration feature and shows how the test-data-manager Skill automates the entire workflow—producing dependency lists, SQL/API scripts, verification steps, and cleanup commands in minutes instead of hours.
Manticore Search, a C++‑based open‑source search engine forked from Sphinx, claims to outperform Elasticsearch by up to 15× in various scenarios, offers modern multithreaded architecture, SQL compatibility, extensive client libraries, and easy Docker deployment, positioning itself as a fast, lightweight, full‑text search solution.
The article analyzes two emerging RAG strategies—SAG, which rebuilds relational structure with dynamic SQL hyperedges, and OpenViking, which treats agent context as a virtual file system—detailing their architectures, benchmarks, limitations, and guidance on when to adopt each.
The article identifies eight easy-to-overlook SQL habits—such as using SELECT *, applying functions on indexed columns, careless LIKE patterns, improper IN/OR logic, deep offset pagination, unconditional UPDATE/DELETE, excessive indexing, and ignoring execution plans—that can cause slow queries, lock tables, index failures, and even data loss, and provides concrete examples and safer alternatives.
Most slow SQL statements stem from poor query writing—such as index‑breaking functions, implicit type casts, leading wildcards, unnecessary column selection, improper joins, sorting, grouping, and pagination—rather than merely the size of the data, and the article shows how to diagnose and fix each issue.
The article explains that IN is fine for small constant lists, but NOT IN can produce unexpected empty results when NULL values appear, while EXISTS (or NOT EXISTS) often better expresses existence checks; it also stresses the decisive role of proper indexing and using EXPLAIN to verify performance.
The article analyzes how MySQL executes different COUNT() forms—count(*), count(1), count(primary‑key), and count(column)—showing that count(*) and count(1) have identical performance, count(column) is the slowest, and offers indexing and approximation tips for large tables.
This article reveals that most slow SQL queries aren't caused by large data volumes but by poor query writing, such as index‑killing functions, implicit type casts, leading wildcards, SELECT *, missing LIMIT, and inefficient JOINs, and offers a five‑step method to diagnose and fix them.
The article outlines a practical framework for assessing data‑centric AI agents, highlighting challenges such as nondeterminism, black‑box behavior, and error amplification, and proposes concrete dimensions—result correctness, semantic consistency, query quality, security, and explainability—to ensure zero‑tolerance accuracy and reproducibility.
The article argues that SQL, once the core moat for data analysts, is losing its protective power as AI can instantly generate queries and BI tools enable self‑service analytics, forcing analysts to shift from pure data extraction to business‑level interpretation and decision‑making.
This article demonstrates how Hologres AI Function enables end‑to‑end text classification directly in the database using a single SQL call, covering data preparation, prompt engineering, batch inference, accuracy evaluation (up to 95%), and cost analysis with KV‑Cache optimization that reduces token charges to as low as 0.11 CNY for 200 reviews.
The article systematically breaks down pagination challenges in billion‑row sharded databases, compares common sharding strategies and middleware architectures, analyzes the performance drawbacks of a naïve global‑query approach, and presents several practical alternatives—including keyset pagination, two‑stage queries, index‑table tricks, and external search or NewSQL solutions—while highlighting their trade‑offs for interview discussions.
The article explains how Hologres AI Function and Skills transform traditional, slow, and fragmented ad‑creative production into a fully automated, SQL‑driven workflow that handles multimodal data ingestion, AI‑based labeling, video generation, and real‑time performance analysis in a single closed‑loop system.
This article walks through diagnosing slow MySQL queries with EXPLAIN, applying index and join optimizations, using FORCE INDEX, monitoring index usage, preventing unsafe MyBatis patterns, and scaling massive tables with sharding, presenting concrete SQL examples and step‑by‑step guidance.
This article examines eight common SQL pitfalls—including misuse of LIMIT offsets, implicit type conversion, sub‑query updates, mixed ordering, EXISTS clauses, condition push‑down, early limiting, and intermediate result set handling—showing how each can degrade performance and providing rewritten queries with execution‑plan evidence that dramatically improve speed.
The author argues that in most companies you only need to write simple, single‑table or limited‑join queries and focus on index tuning and basic MySQL concepts such as InnoDB, locks, and execution flow, while complex SQL work often signals a poorly organized team.
Apache Spark 4.2 introduces a lightweight Spark Connect architecture, native AI integration, enhanced Metrics View for unified semantics, Arrow‑first performance gains, advanced SQL extensions like vector search and QUALIFY, robust geospatial support, and a revamped streaming engine with auto CDC and sub‑millisecond state cleanup.
The article explains that the classic N+1 query anti‑pattern, harmful on client‑server databases like MySQL, is irrelevant for SQLite because its embedded architecture eliminates network round‑trips, turning hundreds of queries into cheap function calls, and examines the performance data and trade‑offs behind this claim.
Dolt is an open‑source, MySQL‑compatible database that embeds Git’s version‑control model, letting you commit, branch, merge and audit table data just like source code, with simple installation, CLI commands, SQL stored procedures, GitHub remote support, and CI integration.
The article examines how Xiaohongshu, Kuaishou and Meituan adopted Yunqi Lakehouse’s General Incremental Computing and Single‑Engine architecture to achieve near‑real‑time data warehouses, cutting resource usage to as low as 1/20 of full‑batch jobs, reducing data latency from days to minutes, and improving query performance.
SAG (SQL‑Retrieval Augmented Generation) introduces a hypergraph‑based event‑entity data model that combines SQL joins, vector similarity, and hyperedge reasoning to achieve 79%‑88% Recall@2‑5 with second‑level latency on a 500 M‑row corpus, outperforming GraphRAG and HippoRAG in multi‑hop tasks.
This article details how LoongSuite‑Pilot captures heterogeneous AI coding agent events and leverages Alibaba Cloud Log Service (SLS) SQL dashboards to provide end‑to‑end, organization‑wide metrics—covering individual usage, team adoption, token consumption, skill and tool utilization—enabling R&D teams to quantify the real‑world effectiveness of AI coding assistants.
Chat2DB is a free open‑source multi‑database client that adds AI capabilities such as natural‑language‑to‑SQL, SQL‑to‑natural‑language, and performance suggestions; this guide walks through downloading, installing, configuring OpenAI keys, using its four main menus, and evaluating its SQL generation, explanation, and optimization functions.
When two ex‑employees were terminated, one twin's account stayed active, allowing him to delete 96 federal databases within an hour, query AI on log‑clearing, steal thousands of records, and expose how a missed account revocation can lead to massive insider‑threat damage.
The article compares paid database clients such as Navicat, DBeaver, TablePlus and DataGrip, highlights their cost and usability issues, and presents Beekeeper Studio—a cross‑platform, open‑source tool with a sleek UI, tab persistence, smart SQL completion, extensive database support, and an AI Shell for automatic query generation—while also outlining its strengths, limitations, and when to choose the free Community edition versus the paid Ultimate edition.
This guide lists the most frequently used MySQL commands for checking version, connections, process lists, killing queries, global status, configuration variables, lock waits, slow‑query settings, table sizes, index information, execution plans, and replication status, each with practical usage scenarios.
The article explains how to capture event‑level AI coding agent data using LoongSuite‑Pilot, align it with the LoongSuite GenAI semantic conventions, store it in Alibaba Cloud Log Service (SLS), and construct a multi‑layered SQL dashboard that turns personal usage signals into organization‑wide metrics for informed decision‑making.
This article walks through three common data‑masking approaches—SQL queries using string functions, a Java‑based "sensitive‑plus" plugin, and the mybatis‑mate‑sensitive‑jackson extension—showing configuration, code examples, and how each method masks phone numbers, ID cards and other personal fields.
This article explains why using LIKE% for fuzzy searches in MySQL is inefficient, introduces InnoDB full‑text search (available since MySQL 5.6), describes inverted indexes, shows how to create and query full‑text indexes with natural language, boolean, and query‑expansion modes, discusses relevance calculation, stopwords, token‑size parameters, and provides the syntax for dropping full‑text indexes.
This article provides a comprehensive, step‑by‑step reference of essential MySQL commands—including connection, database and table creation, data manipulation, schema alteration, and query optimization with EXPLAIN—complete with code examples, parameter explanations, and performance warnings for safe production use.
EMR Serverless Spark now supports scalar‑vector hybrid search via DLF Global Index, allowing a single Spark SQL statement to perform vector similarity and scalar filtering together, eliminating data movement, reducing latency, and boosting performance for scenarios such as autonomous driving, e‑commerce, and knowledge‑base retrieval.
This article details Fresha's real‑world integration of StarRocks with Apache Iceberg, covering metadata planning, distributed execution, adaptive metadata retrieval, hot‑cold data layering, missing statistics handling, catalog configuration, and performance optimizations that together demonstrate how federated queries can be efficiently executed over data‑lake tables.
The article explains why developers should move beyond MySQL to PostgreSQL, illustrating the productivity gains of native window functions, MVCC concurrency, rich indexing, JSONB support, and extensive extensions through detailed examples, installation guides, performance comparisons, and real‑world use cases.
The article analyzes the challenges of AI‑generated SQL in a data‑warehouse environment—context loss, unstable rule enforcement, and token overflow—and presents a five‑layer Harness architecture that persists constraints, injects hooks, uses subagents, and refactors SKILL files, raising SQL compliance from 70‑80% to over 95% while reducing context compacting.
The article argues that a 100% accuracy claim for Text2SQL is misleading without considering stability, coverage, and pass‑rate metrics, and it details a deterministic NLQ pipeline that converts natural language to a verifiable intermediate format before rule‑based SQL compilation.
Even though the phone and email columns each have a single‑column index, an OR condition forces MySQL's cost‑based optimizer to choose a full table scan because the estimated cost of index merge (random I/O and possible sort‑union) exceeds the cost of a sequential scan, and the article explains the underlying mechanics and practical workarounds.
After two decades of causing trigger failures during cascaded foreign‑key updates, MySQL Bug #11472 was finally resolved in March 2026, ending a long‑running saga that sparked countless community jokes, developer debates, and detailed reproduction steps.
This article explains why inefficient SQL queries cause most database bottlenecks and presents eleven concrete rules—covering indexes, SELECT *, LIMIT, WHERE clause tuning, join strategies, execution‑plan analysis, subqueries, DISTINCT, ORDER BY/GROUP BY, UNION vs UNION ALL, and query decomposition with materialized views—to help developers systematically improve SQL execution speed on MySQL and Oracle.
Just five minutes after being terminated, twin brothers with prior fraud convictions used SQL commands to drop 96 U.S. government databases, queried AI on log‑clearing techniques, and exposed critical failures in the company's off‑boarding process, leading to a high‑profile federal investigation and legal fallout.
This guide compiles the most frequent interview pitfalls for data warehouse roles, covering SQL join and aggregation errors, window function misuse, subquery versus CTE performance myths, dimensional modeling mistakes, SCD implementation traps, layered design issues, data quality handling, ETL traps, Hive and Spark performance questions, real‑time warehousing considerations, and effective interview strategies.
This article demonstrates how to leverage StarRocks AI Function on EMR Serverless to perform sentiment analysis, intelligent classification, information extraction, and PII redaction on financial text entirely within SQL, eliminating data export, reducing latency, and ensuring compliance while providing concrete code examples, performance benchmarks, and best‑practice recommendations.
The article analyzes why a MySQL LIMIT OFFSET query on a 9.5 million‑row table takes 16 seconds, demonstrates how moving the filter into a sub‑query that returns only primary‑key IDs and joining back reduces execution to 0.35 seconds, and validates the theory by measuring InnoDB buffer‑pool page accesses.
This article showcases a collection of impressive SQL queries—including a PostgreSQL Tetris implemented with a recursive CTE, window‑function session analysis, a ClickHouse real‑time funnel, dynamic WHERE clause generation, and a recursive employee hierarchy—while discussing performance tips and engine choices.
Apache Doris 4.1 introduces a systematic evolution for AI and search workloads, adding low‑cost massive vector storage, unified structured, full‑text and vector search, 100 MB JSON document support, Segment V3 metadata decoupling, sparse column optimizations, lakehouse lifecycle management, and a suite of performance‑boosting features such as aggregate push‑down, condition cache, and spill‑to‑disk, all backed by detailed benchmark results.
This article presents thirty practical SQL optimization tips, covering index usage, avoiding full‑table scans caused by operators like !=, NULL checks, OR, LIKE, IN, functions, and expressions, as well as best practices for temporary tables, cursors, and transaction size to improve database performance.
This article walks through a real‑world MySQL slow‑query case, showing how to identify the bottleneck with EXPLAIN, design covering and composite indexes, rewrite the SQL, tune InnoDB parameters, and safely deploy the changes, ultimately shrinking execution time from seconds to a few milliseconds.
The article analyzes the inherent shortcomings of black‑box Text2SQL solutions—highlighting benchmark collapses, lack of auditability, and unacceptable error rates—and proposes a white‑box approach with a human‑readable intermediate language that enables deterministic, enterprise‑grade SQL generation.
The article explains why major tech companies discourage using NULL in MySQL, covering its meaning as an unknown state, three-valued logic pitfalls such as NOT IN subqueries, index inefficiencies, aggregate function quirks, storage overhead, Java handling issues, and offers practical alternatives like NOT NULL constraints with sensible defaults.
Alibaba’s Java Development Manual mandates that any SQL involving more than three tables must be avoided, a rule that stems from the exponential cost of multi‑table joins in a single‑instance database, prompting engineers to rethink data modeling, adopt denormalization, wide tables, materialized views, CQRS or application‑level assembly instead of relying on complex joins.
The article enumerates eight common MySQL query patterns—such as large‑offset LIMIT, implicit type conversion, sub‑query updates, mixed ordering, unnecessary EXISTS, poor condition push‑down, early range reduction, and inefficient intermediate result handling—and shows rewritten SQL that reduces execution time from seconds to milliseconds.
This article walks through the rationale, common functions, and real‑world case studies of storing and querying JSON columns in a relational database, exposing issues with null handling, batch updates, and dynamic SQL generation, and presents step‑by‑step debugging and robust solutions.
The SCALE April 2026 report adds four new models—DeepSeek‑V4‑Pro, DeepSeek‑V4‑Flash, GPT‑5.5 and Claude Opus 4.7—to its SQL capability leaderboard, evaluates them across SQL understanding, optimization and dialect conversion, and highlights each model’s strengths, weaknesses, and recommended deployment scenarios.
The article walks through a real-world e-commerce incident, explains why a seemingly simple slow order-query SQL can cripple an entire high-traffic system, and presents a complete automated workflow—from detection and analysis to optimization, deployment, verification, and regression monitoring—to achieve sustainable full-stack SQL performance.
This article presents 160 high‑frequency MySQL interview questions, covering fundamentals such as SQL basics, MySQL vs. Oracle vs. SQL Service, normalization rules, permission tables, and more, with a free PDF of the full list for interview preparation.
This article explains why MySQL deadlocks occur, shows how to enable and read deadlock logs, walks through three real‑world case studies, and provides practical SQL, configuration, and architectural solutions to resolve and prevent deadlocks in production environments.
This article presents ten practical AI prompt templates that help programmers efficiently handle requirement clarification, unit test generation, code explanation, refactoring, exception troubleshooting, performance tuning, SQL creation, knowledge documentation, design review, and cross‑language translation, each illustrated with concrete examples and usage tips.
This article presents a practical collection of high‑frequency tools—including Python, Shell, SQL, Mermaid, Pandoc, curl, ImageMagick, and PlantUML—that can be wrapped as Agent Skills to give AI agents real execution power, illustrated with concrete prompts and scripts that cut manual work from hours to seconds.
The article walks through a real production incident caused by a massive IN‑list query, then presents a complete methodology for designing, implementing, and tuning an enterprise‑grade MyBatis persistence layer—including core execution chain, caching strategies, batch processing, read/write splitting, sharding, observability, and deployment best practices.
The article experimentally compares MySQL JOIN and IN queries across small, medium, and large data sets, showing that JOIN generally outperforms IN for modest data, while IN suffers from long‑list limitations and higher overhead as data grows.
This article outlines a comprehensive, risk‑controlled SOP for migrating business data—including model changes, storage shifts, incremental dual‑write, back‑filling, full and incremental consistency checks, read‑switching, and final decommissioning—backed by concrete SQL examples and visual diagrams.
