Best of SQL — December 2025

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    Article
    Avatar of freecodecampfreeCodeCamp·22w

    freeCodeCamp's New Relational Databases Certification is Now Live

    freeCodeCamp launched a new Relational Databases certification covering Bash scripting, SQL, and Git. The certification includes interactive lessons, workshops, labs, and quizzes across multiple modules, with five required certification projects. Students must pass a 50-question exam using an open-source exam environment that runs on laptops/desktops. The certification is free and verified, with anti-cheating measures including randomized questions, weekly attempt limits, and manual review. Exams are closed-book and timed, with one attempt per week allowed. The certification replaces portions of the previous Full Stack Developer certification.

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    Article
    Avatar of v7balm8y0o32yjz1hhf5aFabian Letsch·23w

    Colleague today: "I wiped all orphans on playground"

    A developer humorously shared that they cleaned up orphaned database records (child rows without parent references) in a development environment. The post highlights the amusing out-of-context phrasing that made the team laugh, while explaining the technical concept of orphaned rows that result from incomplete deletion operations.

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    Article
    Avatar of freecodecampfreeCodeCamp·22w

    Christmas gifts for you from the freeCodeCamp community: Learn Python, SQL, Spanish, and more

    freeCodeCamp launched Version 10 of its curriculum with four new free certifications: Python, JavaScript, Responsive Web Design, and Relational Database/SQL. Each certification includes dozens of projects and a final exam. Two additional certifications (Front End Libraries and Back End Development) will launch in 2026. The platform also released English for Developers certifications (A2 and B1 levels) and beta A1 level courses for Spanish and Mandarin Chinese. The community published 129 video courses, 45 books, 452 tutorials, and merged over 4,000 commits to their open source platform in 2025.

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    Video
    Avatar of primeagenThePrimeTime·21w

    They did what to SQL?

    A satirical commentary on TailwindSQL, an absurd open-source project that proposes writing database queries using CSS class syntax similar to Tailwind. The piece humorously critiques the concept of executing SQL queries from the client side using HTML classes, highlighting the obvious security implications and questioning the practicality of build-time query execution. The commentary uses heavy sarcasm to mock the trend of over-engineering simple solutions and the proliferation of questionable developer tools.

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    Article
    Avatar of programmingdigestProgramming Digest·23w

    A modern guide to SQL JOINs

    SQL JOINs are best understood by starting with LEFT JOIN and focusing on the N:1 case (primary key on the right side). Always use ID equality in ON conditions and move other filters to WHERE clauses. The 1:N case of LEFT JOIN produces mixed results that combine data and metadata awkwardly. INNER JOIN is a filtered Cartesian product and is symmetrical, but maintaining discipline in syntax helps when switching between JOIN types. Self-joins work identically to regular joins but require table aliasing. Understanding these patterns prevents common performance issues and logic errors.

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    Article
    Avatar of jetbrainsJetBrains·23w

    Query Consoles Are Coming Back

    JetBrains is reverting a controversial workflow change in DataGrip 2025.3 that replaced query consoles with query files. The redesign caused issues with global data sources and disrupted user workflows. Version 2025.3.1, releasing this week, will restore the original query console behavior. Users who created query files during the migration can delete them, convert them to consoles, or keep them for a planned improved workflow early next year. The team acknowledges failing their zero-regression standard and commits to more careful, flexible updates going forward.

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    Article
    Avatar of cybertec_postgresqlCYBERTEC PostgreSQL·23w

    Comparing stats! PostgreSQL 18 against 17

    PostgreSQL 18 introduces several new statistics columns for performance monitoring. The pg_stat_all_tables table adds four time-tracking columns for operations. VACUUM/ANALYZE now reports WAL, CPU, and read statistics. The pg_stat_io table gains three new byte-level I/O columns (read_bytes, write_bytes, extend_bytes) while removing the generic op_bytes column. Additionally, pg_stat_statements now tracks parallel worker activity with two new columns for launched and planned parallel workers.

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    Article
    Avatar of clickhouseClickHouse·24w

    Introducing pg_clickhouse: A Postgres extension for querying ClickHouse

    ClickHouse released pg_clickhouse v0.1.0, an Apache 2-licensed PostgreSQL extension that enables transparent execution of analytics queries on ClickHouse directly from PostgreSQL. Built on the foundation of clickhouse_fdw, the extension addresses the challenge of migrating analytical queries when moving workloads from PostgreSQL to ClickHouse. Key features include advanced query pushdown capabilities, support for ordered-set aggregates like percentile_cont(), SEMI JOIN pushdown, and transparent conversion of PostgreSQL aggregate FILTER expressions to ClickHouse combinators. Testing with TPC-H benchmarks shows 21 of 22 queries execute efficiently with 12 achieving full pushdown. The roadmap includes completing pushdown coverage for all analytic workloads, supporting all ClickHouse data types, and adding DML features.

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    Article
    Avatar of arstechnicaArs Technica·25w

    In comedy of errors, men accused of wiping gov databases turned to an AI tool

    Two federal contractors were arrested for allegedly deleting 96 government databases and sensitive records minutes after being fired. The defendants, previously convicted of similar crimes in 2015, attempted to cover their tracks by using an AI chatbot to learn how to clear SQL server logs and Windows event logs. Despite their efforts to destroy evidence, including wiping their laptops three days later, prosecutors obtained sufficient records to charge them with conspiracy to destroy government databases.

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    Article
    Avatar of postgresPostgreSQL·23w

    pg_ai_query v0.1.0 — First stable release with multi-model AI for PostgreSQL

    pg_ai_query v0.1.0 is now stable, bringing AI-powered query development directly into PostgreSQL. The extension generates SQL from natural language, provides AI-interpreted EXPLAIN ANALYZE results, and offers automated index and rewrite recommendations. It supports multiple AI providers including OpenAI, Anthropic, Google Gemini, OpenAI-compatible APIs like OpenRouter, and local models through Ollama. The extension works with PostgreSQL 14+ on Linux and macOS, enabling developers to choose between cloud models or fully local inference while staying within the Postgres environment.

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    Article
    Avatar of hnHacker News·23w

    stoolap/stoolap: A Modern Embedded SQL Database written in Rust

    Stoolap is an embedded SQL database written in Rust that supports both in-memory and persistent storage with ACID compliance. It features MVCC transactions with two isolation levels, time-travel queries for historical data access, multiple index types (B-tree, Hash, Bitmap), window functions, CTEs including recursive queries, and a cost-based query optimizer. The database includes 100+ built-in functions across string, math, date/time, JSON, aggregate, and window categories. It uses write-ahead logging with periodic snapshots for durability and can be used as a library or via command-line interface.

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    Article
    Avatar of crunchydataCrunchy Data·25w

    Postgres Scan Types in EXPLAIN Plans

    Explains the different scan types that appear in Postgres EXPLAIN plans and when each is used. Covers sequential scans (reading entire tables row-by-row), index scans (using B-tree indexes to locate data), bitmap index scans (hybrid approach creating in-memory bitmaps), parallel scans (using multiple workers concurrently), and index-only scans (retrieving all data from the index without touching the main table). Includes practical examples showing when each scan type is optimal and how to identify them in query plans.

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