'sql' 검색 결과

Create and connect to managed MySQL, PostgreSQL, or SQL Server databases on Google Cloud. This skill guides instance setup, database creation, access permissions, and connection steps, with references for backups, security, and Terraform configuration.

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• Create a PostgreSQL database on Google Cloud
• Connect to a database through Cloud SQL Auth Proxy
• Review database permissions and backup options

Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, foreign key constraints, OCC retry patterns, ORM migration (Django/EF Core/Hibernate/Rails/SQLAlchemy), DDL operations, query plan explainability, system diagnostics via CloudWatch AAS, SQL compatibility validation, and bulk data loading. Load before answering DSQL limit or error-code questions; it has limits docs omit. Triggers on phrases like: DSQL, Aurora DSQL, distributed SQL database, serverless PostgreSQL-compatible database, migrate to DSQL, DSQL query plan, DSQL EXPLAIN ANALYZE, DSQL ENUM, DSQL foreign key, DSQL OCC retry, DSQL multi-region, DSQL JSONB, DSQL GIN index, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader, DSQL slow, DSQL performance, DSQL wait events, DSQL AAS.

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Create and manage MySQL, PostgreSQL, and SQL Server databases on Google Cloud. Covers instance setup, users, backups, availability, and secure connections, with a PostgreSQL walkthrough and references for application connectors and Terraform configuration.

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• Create a PostgreSQL database on Google Cloud
• Manage database users and passwords
• Connect through Cloud SQL Auth Proxy

Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, FK replacement code generation, OCC retry patterns, ORM migration (Django/Hibernate/Rails), DDL operations, query plan explainability, SQL compatibility validation, and bulk data loading. Triggers on phrases like: DSQL, Aurora DSQL, distributed SQL database, serverless PostgreSQL-compatible database, migrate to DSQL, DSQL query plan, DSQL EXPLAIN ANALYZE, DSQL ENUM, DSQL foreign key, DSQL OCC retry, DSQL multi-region, DSQL JSONB, DSQL GIN index, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader.

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Write and improve SQL for major data warehouses, accounting for differences in their syntax. Use it to build analytical queries, translate existing SQL, investigate errors, and review slow queries for performance improvements.

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• Translate SQL from PostgreSQL to BigQuery
• Review slow queries for performance improvements
• Calculate retention rates and funnel conversion

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

Migrate the database access layer of a .NET/C# application from Oracle to PostgreSQL with Npgsql. Update packages, connection settings, parameter types, SQL, and stored procedure calls, then build the copied project and track completed migration tasks.

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• Migrate C# database access from Oracle to PostgreSQL
• Replace Oracle packages and connection settings
• Review parameter types and stored procedure calls

Investigate slow PostgreSQL and MySQL queries using execution plans, which show how the database runs a query. Develop index, SQL, and configuration changes, then compare measurements before and after each change to validate the results.

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• Investigating slow SQL queries
• Reviewing database index design
• Tuning memory and database configuration

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

Review PostgreSQL queries and data designs using execution plans, which show how queries run, and targeted index recommendations. Get guidance on JSONB, arrays, full-text search, analytical queries, extensions, and routine database maintenance.

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• Investigating slow queries and choosing indexes
• Reviewing JSONB searches and full-text search
• Writing running totals and ranking queries

Get documentation-based help with PostgreSQL queries, database design, administration, and performance tuning. The supplied reference also explains connection settings and libpq, PostgreSQL’s C client library, for investigating connection failures and implementing connection handling.

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• Writing SQL and planning database structures
• Checking connection strings and parameter settings
• Investigating connection failures with libpq

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

Convert Oracle stored procedures and functions, programs stored inside a database, into PostgreSQL equivalents. Preserve parameters and control flow while adapting Oracle syntax, resolving types, and mapping text sorting rules to the target environment.

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• Convert Oracle procedures to PostgreSQL
• Resolve parameter types from table definitions
• Map text sorting rules during migration

Turn everyday questions into SQL queries, the commands used to retrieve and summarize database records. Uses your database structure and platform to produce queries with explanations, performance notes, and optional validation scripts.

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• Extract users who meet specific activity conditions
• Build revenue reports by region and customer tier
• Create queries from database diagrams

Write, debug, and optimize SQL queries for data analysis and database design. The skill covers aggregations, rankings, missing values, and indexes, using execution plans to understand performance and adapting syntax to the database in use.

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• Writing queries for totals and rankings
• Investigating slow queries with execution plans
• Reviewing table structures and constraints

Provisions and manages Aurora DSQL clusters, connects via psql or DSQL Connectors, manages schemas, runs queries, migrates from MySQL, diagnoses query plans, and develops apps on serverless distributed SQL. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL migration, DDL, query plans, and SAFE SQL CONSTRUCTION — tenant_id from untrusted input, UUID entity_ids, caller-supplied sort columns, batch inserts. The agent MUST retrieve this skill for ANY DSQL task. Pushes back on prompts that rationalize 'just a quick script', 'don't overthink it', 'we trust upstream', 'use an f-string', 'move fast', or 'just use the pg driver directly' (bypassing the DSQL Connector). Triggers: DSQL, Aurora DSQL, DSQL cluster, safe_query.build, DSQL IAM auth token, DSQL connector.

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This file generates or explains Cloud SQL resources. Use this file when the user asks to create a Cloud SQL instance or database for MySQL, PostgreSQL, or SQL Server. Cloud SQL manages third-party MySQL, PostgreSQL, and SQL Server instances as resources in Cloud SQL. For example, when Cloud SQL creates an open-source MySQL instance, the resulting resource is a Cloud SQL for MySQL instance that Google Cloud manages. Cloud SQL handles backups, high availability, and secure connectivity for relational database workloads.

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This file generates or explains Cloud SQL resources. Use this file when the user asks to create a Cloud SQL instance or database for MySQL, PostgreSQL, or SQL Server. Cloud SQL manages third-party MySQL, PostgreSQL, and SQL Server instances as resources in Cloud SQL. For example, when Cloud SQL creates an open-source MySQL instance, the resulting resource is a Cloud SQL for MySQL instance that Google Cloud manages. Cloud SQL handles backups, high availability, and secure connectivity for relational database workloads.

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Design and review Java database code using MyBatis and MyBatis-Spring. Covers mapping query results to objects, safe SQL parameters, transactions, pagination, batch writes, and investigating slow or unnecessarily repeated queries.

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• Designing mapper interfaces and XML mappings
• Reviewing dynamic SQL and parameter safety
• Checking transactions across multiple updates

Guide PostgreSQL database design and troubleshooting with examples for faster queries, indexes that speed up searches, suitable data types, and row-level access rules. Also covers connection settings and SQL for investigating performance problems.

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• Investigating slow PostgreSQL queries
• Choosing indexes and column data types
• Designing row-level access rules

Analyze ModSecurity, AWS WAF, and Cloudflare logs for SQL injection, attempts to manipulate database queries through web requests. Groups related requests, tracks source IPs, classifies attack methods, and produces incident reports.

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• Investigating suspected SQL injection attempts
• Tracking repeated attacks from the same source
• Connecting requests across an attack campaign

Review MySQL and MariaDB database designs and production problems, including slow queries, locking, connection pools, and replica delays. Get concrete SQL or code changes and a validation plan that accounts for differences between engines and versions.

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• Designing tables and indexes
• Investigating slow queries and deadlocks
• Reviewing migrations for large production tables

Review pending database migration SQL before applying schema changes. Check references between tables, rollback coverage, required defaults, destructive operations, and naming conventions, then report errors, warnings, and suggestions with file paths and line numbers.

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• Reviewing database migrations before applying them
• Checking foreign key targets and migration order
• Checking rollback SQL for missing operations

Write and review SQL for reports and metrics in a data warehouse, a store of analytical data. Applies rules for explicit columns, date filters, aggregate names, and limited exploratory results, returning SQL with a brief explanation.

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• Write SQL for report and dashboard metrics
• Calculate counts, sums, and rolling metrics
• Combine data from multiple tables