“postgresql”的搜索结果

Azure PostgreSQL Flexible Server SDK for .NET. Database management for PostgreSQL Flexible Server deployments.

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Azure PostgreSQL Flexible Server SDK for .NET. Database management for PostgreSQL Flexible Server deployments.

中文简介正在准备中。当前显示原文说明。

Azure PostgreSQL Flexible Server SDK for .NET. Database management for PostgreSQL Flexible Server deployments.

中文简介正在准备中。当前显示原文说明。

Schema design, normalization decisions, indexing strategies, and migration safety for relational databases. Use when designing tables, choosing between normalize vs denormalize, selecting index types, planning zero-downtime migrations, or setting up connection pooling. Activate on "schema design", "normalization", "denormalization", "foreign key", "index strategy", "migration", "connection pooling", "composite key", "surrogate key", "soft delete", "polymorphic association". NOT for PostgreSQL-specific query tuning or EXPLAIN analysis (use postgresql-optimization), NoSQL or document database design, or cloud database provisioning.

中文简介正在准备中。当前显示原文说明。

Azure PostgreSQL Flexible Server SDK for .NET. Database management for PostgreSQL Flexible Server deployments.

中文简介正在准备中。当前显示原文说明。

在PostgreSQL中格式化数字显示，去除小数点后的尾随零，并在小数点后无数字时去除小数点。

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Specialized skill for containerized database testing using Testcontainers. Use when testing real database behavior, using SQL Server/PostgreSQL/MySQL containers, testing EF Core/Dapper. Covers container startup, database migrations, test isolation, container sharing. Keywords: testcontainers, container testing, database testing, MsSqlContainer, PostgreSqlContainer, MySqlContainer, EF Core testing, Dapper testing, Testcontainers.MsSql, Testcontainers.PostgreSql, GetConnectionString, IAsyncLifetime, CollectionFixture

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Plan and implement OpenMeld PostgreSQL business persistence, schemas and migrations, transactions, Hyperdrive access, and data cutover. Use for the shared relational database transition or PostgreSQL work; not for unrelated D1 performance fixes or OpenConnector's upstream database.

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提供使用Kotlin和JetBrains Exposed实现数据库查询与增删改的模式，涵盖连接复用、结构变更管理、事务、数据访问分离及测试。

• 实现数据库查询与更新
• 配置数据库连接复用
• 管理数据库结构变更

Set up Python APIs with FastAPI using organized project templates. Build routes, asynchronous database operations, authentication, and error handling, with examples for sharing dependencies and testing endpoints through a separate test database.

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• Set up a new FastAPI project
• Build a Python API with asynchronous database access
• Organize authentication and shared dependencies

Create a Kotlin Spring Boot project with database connections, API documentation, and local services managed through Docker Compose. Set up dependencies and configuration files, then run Gradle tests to check the initial project.

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• Starting a Kotlin Spring Boot project
• Setting up local Redis, PostgreSQL, and MongoDB services
• Adding API documentation and architecture test dependencies

Write complex SQL, improve slow queries, and design database schemas for transaction processing and analytics. Uses schema details and query execution plans to guide indexing, validate results, and assess performance with realistic data volumes.

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• Investigate and improve slow analytical queries
• Design a database schema for a SaaS application
• Write customer retention and cohort analysis queries

Investigate database slowdowns and plan measurable improvements to queries, indexes, caching, and scaling. The skill covers SQL and NoSQL systems, with guidance on monitoring, load testing, migrations, and balancing performance against cost.

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• Analyze slow SQL queries and execution plans
• Resolve repeated database calls in APIs
• Plan indexes and caching for high traffic

Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.

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• Investigate AI application problems using execution traces
• Evaluate answer quality across a test dataset
• Compare prompts and models through experiments

Turn a plain-language data request into SQL tailored to your database. Build queries that combine tables, filter records, and calculate summaries, with explanations of each step, performance considerations, and suggestions for adjusting dates or other conditions.

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• Create SQL for order counts by status
• Analyze retention by signup month
• Query large tables with date-based partitions

Turn plain language requirements into SQL, investigate slow queries, document database structures, and prepare migration templates with rollback plans. Support everyday database development and ORM use, where libraries connect application code to database tables.

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• Writing SQL for reports and data retrieval
• Investigating slow queries and repeated database calls
• Preparing schema changes and rollback plans

Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.

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• Investigate AI application problems using execution traces
• Evaluate answer quality across a test dataset
• Compare prompts and models through experiments

Investigate slow SQL, write complex joins and summaries, and design database structures. Uses execution plans, which show how queries run, to guide index choices and query rewrites, with before-and-after performance measurements.

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• Investigate a slow SQL query
• Write complex joins and grouped calculations
• Design tables, keys, and constraints

Audit database migration changes before a Bifrost release for lock contention, deadlocks, and delayed application startup. Receive pass, warning, or failure results, a release recommendation, and concrete remediation steps without modifying files.

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• Review migration safety before a release
• Check index creation risks on large log tables
• Find migration work that delays application startup

Assess web applications and APIs for SQL injection, where input changes database queries. The skill covers finding vulnerable inputs, testing different database systems, automating checks, and documenting findings with remediation suggestions.

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• Assessing web application inputs for SQL injection
• Reviewing GraphQL and WebSocket injection risks
• Combining SQLmap automation with manual verification

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 to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. **Trigger when user asks to:** - Migrate or convert PostgreSQL tables to hypertables - Execute hypertable migration with minimal downtime - Plan blue-green migration for large tables - Validate hypertable migration success - Configure compression after migration **Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed) **Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup Step-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.

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Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. **Trigger when user asks to:** - Analyze database tables for hypertable conversion potential - Identify time-series or event tables in an existing schema - Evaluate if a table would benefit from Timescale/TimescaleDB - Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData - Score or rank tables for hypertable candidacy **Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables Provides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.

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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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