'postgres' 검색 결과

Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.

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Use this skill for planning, testing, and safely executing PostgreSQL schema migrations — especially when working with production data or shared databases. **Trigger when user asks to:** - Test a schema migration before applying it to production - Add, remove, or rename columns safely on a live table - Change a column's data type without downtime - Add or drop indexes, constraints, or foreign keys on large tables - Understand which ALTER TABLE operations lock the table - Roll back a failed migration - Plan a zero-downtime migration strategy - Fork a database to test a migration safely **Keywords:** migration, schema change, ALTER TABLE, add column, drop column, rename column, change type, zero downtime, lock, AccessExclusiveLock, concurrent index, forking, rollback, backfill, deploy Covers: lock-level reference for every common DDL operation, safe migration patterns, fork-based testing, zero-downtime column changes, index creation, constraint addition, backfill strategies, pre/post-migration validation, and rollback planning.

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Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features

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SQL database migrations with zero-downtime strategies for PostgreSQL, MySQL, SQL Server

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Read and write a Supabase Postgres database over PostgREST: list tables, query, insert, update, and delete rows. Trigger phrases: supabase, supabase table, query supabase.

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Use when doing ANY task involving Supabase. Triggers: Supabase products (Database, Auth, Edge Functions, Realtime, Storage, Vectors, Cron, Queues); client libraries and SSR integrations (supabase-js, @supabase/ssr) in Next.js, React, SvelteKit, Astro, Remix; auth issues (login, logout, sessions, JWT, cookies, getSession, getUser, getClaims, RLS); Supabase CLI or MCP server; schema changes, migrations, declarative schemas, security audits, Postgres extensions (pg_graphql, pg_cron, pg_vector); debugging and troubleshooting errors or unexpected behavior on Supabase projects (HTTP errors, Postgres errors, RLS surprises, permission denied, schema cache issues, timeouts, Edge Function crashes, Realtime drops, Storage failures) and reading or querying logs (Logs Explorer, ClickHouse).

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Use CloudBase document database Web SDK only for confirmed NoSQL collection work. Query, create, update, and delete document data; if the task mentions PostgreSQL / CloudBase PG / app.rdb(), route to postgresql-development instead.

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CloudBase PostgreSQL access-pattern and slow-query quality guidance. Use when designing how tables are read and written, eliminating per-row database calls, adding indexes for filters or joins, reviewing high-traffic data access, or explaining a slow SQL query. Not for first-time PG SDK setup, login UI, or NoSQL collections.

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[Deprecated] This is the required documentation for agents operating on the CloudBase Relational Database through MCP. It defines the canonical SQL management flow with `queryMysqlDatabase`, `manageMysqlDatabase`, `queryPermissions`, and `managePermissions`, including destroy flow, async status checks, safe query execution, schema initialization, and permission updates. MySQL provisioning is no longer available through MCP; new environments should use PostgreSQL — see postgresql-development skill instead.

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[Deprecated] Use when building frontend Web apps that talk to CloudBase Relational Database via @cloudbase/js-sdk – provides the canonical init pattern so you can then use Supabase-style queries from the browser. New environments should use PostgreSQL with app.rdb() — see postgresql-development skill instead.

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Optimizes PostgreSQL queries, configures replication, implements advanced features including EXPLAIN analysis, JSONB operations, extension usage, and VACUUM tuning.

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Expert knowledge for Azure Database for MySQL development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when planning MySQL Flexible Server tiers, HA/DR, automated deployments, backups/replication, or secure app connectivity, and other Azure Database for MySQL related development tasks. Not for Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), Azure Database for PostgreSQL (use azure-database-postgresql).

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Enforce multi-tenant isolation and PostgreSQL Row Level Security in NestJS. Use when enforcing tenant isolation or PostgreSQL RLS in NestJS multi-tenant apps.

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PostgreSQL database patterns for query optimization, schema design, indexing, and security. Based on Supabase best practices.

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用于火山引擎（Volcengine）数据库（MySQL、veDB-MySQL、PostgreSQL、SQL Server、MongoDB、Redis）和公网自建数据库（MySQL和PostgreSQL系列）的元数据管理、数据分析、开发变更、运维诊断、巡检。覆盖实例列表查询、实例下数据库列表查询、表列表查询、表结构查询、数据查询、数据分析与可视化报告（含跨数据源/文件联合分析）、数据治理（资产盘点/数据画像/数据质量/敏感数据识别）、慢查询诊断、死锁与锁等待分析、事务与活跃会话排查、错误日志查询、表空间分析、健康巡检、监控指标查询、变更工单申请等场景。不支持字节云（ByteCloud）数据库，如 ByteRDS / ByteDoc / ByteRedis。

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Comprehensive guide to PostgreSQL migrations - common errors, generated columns, full-text search, indexes, idempotent migrations, and best practices for database schema changes

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Consult PostgreSQL's parser and grammar (gram.y) to understand SQL syntax, DDL statement structure, and parsing rules when implementing pgschema features

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Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features

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Database specialist covering PostgreSQL, MongoDB, Redis, and advanced data patterns for modern applications

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Comprehensive guide for migrating PostgreSQL tables to TimescaleDB hypertables with optimal configuration and performance validation

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Use when doing ANY task involving Supabase. Triggers: Supabase products (Database, Auth, Edge Functions, Realtime, Storage, Vectors, Cron, Queues); client libraries and SSR integrations (supabase-js, @supabase/ssr) in Next.js, React, SvelteKit, Astro, Remix; auth issues (login, logout, sessions, JWT, cookies, getSession, getUser, getClaims, RLS); Supabase CLI or MCP server; schema changes, migrations, declarative schemas, security audits, Postgres extensions (pg_graphql, pg_cron, pg_vector); debugging and troubleshooting errors or unexpected behavior on Supabase projects (HTTP errors, Postgres errors, RLS surprises, permission denied, schema cache issues, timeouts, Edge Function crashes, Realtime drops, Storage failures) and reading or querying logs (Logs Explorer, ClickHouse).

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Databricks Lakebase Postgres: projects, scaling, connectivity, Lakebase synced tables, and Data API. Use when asked about Lakebase databases, OLTP storage, or connecting apps to Postgres on Databricks.

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Optimize slow PostgreSQL inserts in Python using psycopg2. Use when: (1) Row-by-row inserts are taking too long over network, (2) executemany() isn't providing speedup, (3) Migrating large datasets to PostgreSQL, (4) Network latency making individual INSERT statements impractical. The key is using execute_values() from psycopg2.extras instead of executemany() or individual execute() calls.

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You are an expert in Drizzle ORM, the lightweight TypeScript ORM that maps directly to SQL. You help developers write type-safe database queries that look like SQL (not a new query language), generate migrations from schema changes, and deploy to serverless environments with zero overhead — supporting Postgres, MySQL, SQLite, Turso, Neon, PlanetScale, and Cloudflare D1.

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