'sql' 검색 결과

Expert knowledge for Azure Cosmos DB development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Cosmos DB NoSQL/Mongo/Cassandra APIs, change feed, multi-region HA, vector search, or Cosmos DB for PostgreSQL, and other Azure Cosmos DB related development tasks. Not for Azure Table Storage (use azure-table-storage), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), Azure Virtual Machines (use azure-virtual-machines).

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Relational database implementation across Python, Rust, Go, and TypeScript. Use when building CRUD applications, transactional systems, or structured data storage. Covers PostgreSQL (primary), MySQL, SQLite, ORMs (SQLAlchemy, Prisma, SeaORM, GORM), query builders (Drizzle, sqlc, SQLx), migrations, connection pooling, and serverless databases (Neon, PlanetScale, Turso).

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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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数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schema 异常检测) / 数据治理编目与血缘 (DataHub/Amundsen/OpenMetadata/Unity Catalog + 列级血缘 + PII 分类 + 访问控制 + GDPR) / DataOps 与可靠性 (数据 CI/CD + 转换版本控制 + 环境隔离 + 幂等重处理 + 数据 SLA/SLO + 计算存储 FinOps) / 数据架构范式 (现代数据栈 + lakehouse + data mesh + data fabric + 去中心化 vs 中心化所有权) / 分析工程角色 (dbt 时代连接数据工程与分析的桥) — 不含 数据科学/ML 建模本身 (是下游消费者) / BI 仪表盘制作 (serving 下游) / 数据分析报表为终点 / 'data engineer = 跑 Hadoop 的' 过时窄化 / 通用后端应用开发 (平行学科) (Data Engineering — the cognitive operating system of practitioners who design, build, and operate the data platform: moving data from source systems into reliable, queryable, trustworthy form for analytics / ML / products, covering (a) the data engineering lifecycle (generation → ingestion → storage → transformation → serving, with the undercurrents security / data management / DataOps / data architecture / orchestration / software engineering — Reis & Housley framing), (b) ingestion & integration (batch + CDC change-data-capture with Debezium, EL tools Fivetran / Airbyte / Meltano / dlt, Kafka Connect, API + file + database sources, schema drift handling), (c) storage & file/table formats (object storage data lakes, columnar formats Parquet / ORC / Arrow / Avro, open table formats Apache Iceberg / Delta Lake / Apache Hudi, lakehouse architecture, partitioning / compaction / Z-ordering), (d) transformation & modeling (ELT with dbt / SQLMesh, Spark, dimensional modeling Kimball, Inmon CIF, Data Vault, One Big Table / wide tables, normalization vs denormalization, slowly changing dimensions, incremental models, the semantic / metrics layer), (e) orchestration & workflow (Apache Airflow, Dagster, Prefect, Mage, Kestra, Apache DolphinScheduler, DAGs, idempotency, backfills, data-aware / asset-based scheduling), (f) batch vs streaming & real-time (Apache Kafka, Apache Flink, Spark Structured Streaming, Kinesis / Pulsar / Redpanda, the Lambda vs Kappa debate, watermarks / windowing / exactly-once, streaming SQL Materialize / RisingWave, real-time OLAP ClickHouse / Apache Druid / Apache Pinot / StarRocks / Apache Doris), (g) warehouses & query engines (Snowflake, BigQuery, Redshift, Databricks SQL, Trino / Presto, DuckDB, Polars, decoupled storage & compute, MPP), (h) data quality, testing & observability (dbt tests, Great Expectations, Soda, data contracts, Monte Carlo / data downtime, freshness / volume / schema anomaly detection, unit / integration testing of pipelines), (i) data governance, catalog & lineage (DataHub, Amundsen, OpenMetadata, Unity Catalog, column-level lineage, PII / data classification, access control, GDPR / data privacy), (j) DataOps & reliability (CI/CD for data, version control of transformations, environments, idempotent reprocessing, SLAs / SLOs for data, cost / FinOps for compute & storage), (k) data architecture paradigms (modern data stack, data lakehouse, data mesh, data fabric, decentralized vs centralized ownership), (l) the analytics engineering role (the dbt-era bridge between data engineering and analysis); N

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A source-backed ASE skill for Beekeeper Studio, the SQL editor and database manager for Linux, macOS, and Windows. It fits workflows that need a real client for querying, browsing tables, and working across PostgreSQL, MySQL, SQLite, SQL Server, and other supported databases.

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Flask-specific delta on top of python-foundation:sqlalchemy-patterns: Flask-SQLAlchemy extension setup, db.Model declarative models (3.x Mapped style and 2.x legacy db.Column), synchronous db.session lifecycle bound to the app context, Flask-Migrate integration. Used by flask-architect (model definitions) and flask-migrate-specialist (column finalization and migration). Activated automatically by flask-plugin/stack.md. Use this skill to: - Set up the SQLAlchemy and Migrate extensions with the app-factory pattern. - Write Flask-SQLAlchemy models with the db.Model base. - Query with db.session and manage the request-scoped session lifecycle. - Integrate Flask-Migrate for Alembic-based migrations managed via flask db commands. Do NOT use this skill for: - Framework-agnostic model, column, relationship, and querying rules — see python-foundation:sqlalchemy-patterns (load it first). - Flask routing and template/API patterns — see flask-plugin:flask-conventions. - Migration execution (flask db migrate, flask db upgrade) — that's flask-migrate-specialist's job.

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Write Prisma Next queries for Postgres, SQLite, or Mongo — pick a lane (Postgres/SQLite `db.orm.<Model>` + `db.sql.<table>`; Mongo `db.orm.<root>` + `db.query.from(...)` pipeline builder), filter / project / sort / paginate, eager-load with `.include(...)`, Postgres/SQLite `db.transaction(...)`, Postgres/SQLite ORM `.aggregate(...)`, Mongo aggregations via query builder, namespace-aware accessors (`db.orm.<ns>.<Model>`, `db.sql.<ns>.<table>`). Triggers: query, where, match, select, project, orderBy, take, skip, include, lookup, first, all, count, aggregate, group, create, update, delete, upsert, returning, transaction, db.close, script teardown, variant, polymorphism, drizzle-style, kysely-style. Notes: `.all()` is a Thenable (just `await` it), iterators are single-use (`RUNTIME.ITERATOR_CONSUMED`), Postgres `count` is `number` while sum/avg/min/max are `number | null`, ranges use chained `.where()` or `and(...)` (no `.between(...)`).

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Universal SQL performance optimization assistant for comprehensive query tuning, indexing strategies, and database performance analysis across all SQL databases (MySQL, PostgreSQL, SQL Server, Oracle). Provides execution plan analysis, pagination optimization, batch operations, and performance monitoring guidance. Use when a query is slow, an index strategy is unclear, or database performance needs analysis.

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Reviews SQL for security (injection, privileges), performance, maintainability, and quality across MySQL, PostgreSQL, SQL Server, Oracle, and other dialects. Use when reviewing a query, migration, stored procedure, or the SQL in a pull request.

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PostgreSQL-specific code review assistant focusing on PostgreSQL best practices, anti-patterns, and unique quality standards. Covers JSONB operations, array usage, custom types, schema design, function optimization, and PostgreSQL-exclusive security features like Row Level Security (RLS). Use when reviewing SQL, schema, or functions written for PostgreSQL.

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This skill should be used when the user asks to "test for SQL injection vulnerabilities", "perform SQLi attacks", "bypass authentication using SQL injection", "extract database information through injection", "detect SQL injection flaws", or "exploit database query vulnerabilities". It provides comprehensive techniques for identifying, exploiting, and understanding SQL injection attack vectors across different database systems.

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SQL injection — error-based, blind boolean, time-based ve UNION tekniklerini kapsar; CTF'te login bypass, veri çekme ve flag okumak için tam otomatik exploit şablonları içerir

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Detecting and exploiting SQL injection vulnerabilities using sqlmap to extract database contents during authorized penetration tests.

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Tests APIs for injection vulnerabilities including SQL injection, NoSQL injection, OS command injection, LDAP injection, and Server-Side Request Forgery (SSRF) through API parameters, headers, and request bodies. The tester crafts malicious payloads targeting different backend technologies and injection contexts to extract data, execute commands, or access internal services. Maps to OWASP API8:2023 Security Misconfiguration and API7:2023 SSRF. Activates for requests involving API injection testing, SQLi in APIs, NoSQL injection, SSRF testing, or API input validation assessment.

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Spring Boot의 데이터베이스 설계와 조회를 개선합니다. JPA/Hibernate의 데이터 구조, 관계, 쿼리와 트랜잭션을 검토하고 불필요한 조회를 줄입니다.

• 데이터 구조와 관계 설계
• 관련 데이터 조회의 SQL 수 줄이기
• 수정 처리와 변경 기록 검토

운영 데이터베이스의 열, 인덱스와 데이터 변경을 설계합니다. 대규모 테이블의 분할 갱신, 단계적 구조 전환과 복구 계획을 포함해 마이그레이션 절차를 정리합니다.

• 대규모 테이블에 열과 인덱스 추가
• 중단을 피하는 단계적 열 이름 변경
• 기존 데이터의 분할 보완

Check a Twenty pull request through real browser scenarios derived from its changes. Verify database effects and server logs alongside visible results, then produce screenshots, a structured verdict, and a report of any failures or uncertainty.

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• Validate user flows before or after merging a Twenty pull request
• Check database writes and timeline activity after saving records
• Investigate backend errors hidden behind a successful screen

Manage SiYuan notebooks, documents, and blocks, the individual pieces of note content, from the terminal. Search your knowledge base, create or update notes, inspect document structure, and export documents as Markdown through its local API.

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• Organizing SiYuan notebooks and document structures
• Creating documents from Markdown content
• Finding notes with full-text search or SQL

Build Python code for Azure Cosmos DB's NoSQL API to create, read, update, delete, and query documents. Covers authentication, containers for storing data, partition keys for distributing records, throughput settings, asynchronous operations, and error handling.

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• Implementing document operations in Python
• Writing parameterized queries for filtered searches
• Configuring containers, partition keys, and throughput

Implement Azure Cosmos DB document operations in Rust using its NoSQL SDK. The skill covers authentication, database and container clients, creating and reading records, replacing or patching fields, and deleting items with partition keys.

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• Connecting a Rust application to Azure Cosmos DB
• Creating and reading documents
• Replacing documents or patching selected fields

Coordinate authorized penetration testing with tools for port discovery, vulnerability scanning, web testing, and password hash recovery. Use MCP servers to connect agents to security tools, record reproducible evidence, and support reporting within the approved scope.

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• Identifying open ports, services, and subdomains
• Scanning authorized targets for known vulnerabilities
• Validating web application security findings

Turn application requirements into an Azure Cosmos DB NoSQL data model. Gather read and write patterns, workload volumes, and relationships, then document container structures, partition keys for distributing data, indexes, and design trade-offs in two Markdown files.

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• Designing a data model for a new application
• Choosing how to group and partition related data
• Reviewing database costs for high-volume workloads

Design a database layer from business requirements, access patterns, and expected growth. Compare database technologies, define tables and relationships, and plan indexes, caching, scaling, migrations, backups, and recovery with clear reasons and trade-offs.

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• Choosing a database for a new service
• Designing tables, relationships, and indexes
• Planning database migrations and rollback procedures

Connect a company SQL database, support ticket exports, and documents in cognee, linking shared people, teams, and projects so questions about ownership, decisions, and ongoing work can draw on several sources together.

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• Finding owners across company databases, tickets, and documents
• Tracing decisions across multiple company sources
• Checking who is handling a support issue