'cassandra' 검색 결과

Write Apache Cassandra distributed tests that simulate multiple nodes within one JVM, the Java runtime. The guide covers cluster setup, queries, node restarts, message dropping for failure scenarios, and troubleshooting problems caused by isolated class loading.

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• Writing read and write tests across multiple nodes
• Testing timeouts by dropping messages between nodes
• Verifying node replacement and restarts

Apache Cassandra is a distributed NoSQL database designed for high availability and linear scalability. Learn CQL (Cassandra Query Language), data modeling with partition keys, replication strategies, and integration with Node.js using the DataStax driver.

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Provides authoritative compatibility checks, pricing estimates, connection troubleshooting, pre-warming guidance, and infrastructure mutations for Amazon Keyspaces (for Apache Cassandra). Covers LWT/batch operations, secondary indexes, materialized views, capacity modes, TTL, PITR, CDC, auto-scaling, multi-region keyspaces, UDTs, nodetool diagnostics parsing, SQL-to-Cassandra migration, and Cassandra-to-Keyspaces migration scenarios. Agents frequently produce incomplete or incorrect answers about Keyspaces feature support without this skill loaded.

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Use when working with Cassandra — apache Cassandra keyspace analysis, compaction strategies, repair status, nodetool operations, and cluster health monitoring.

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Model distributed systems and concurrent algorithms in TLA+ or PlusCal, then check possible states with TLC. Use the results to investigate deadlocks, broken rules, and differences between specifications and implementation code.

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• Model a distributed protocol before implementation
• Create a specification from existing concurrent code
• Investigate deadlocks and race conditions

Analyze bug-fix commits in a Git repository to uncover recurring failure patterns. Produce individual bug analyses and a reusable pattern guide that removes project-specific details, helping inform future code reviews.

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• Extract recurring bug patterns from past fixes
• Build a reusable guide for code reviews
• Analyze fixes within a chosen date range or path

Turn bug reports into small, runnable tests or scripts with clear failure checks. Verify that they expose the reported problem, then reduce inputs and setup while preserving the same failure.

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• Turn a bug report into a runnable test
• Reduce a complex test to its essential trigger
• Record failure rates for intermittent bugs

Analyze Git history to find frequently and recently changed files and lines, then rank review targets. Use the results to focus pull request reviews, bug investigations, or security audits on specific code regions and risks.

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• Prioritizing files during pull request reviews
• Finding starting points for bug investigations
• Narrowing security audits to frequently changed code

Review medium to large code changes using checks selected for the actual patch. Analyze the change and surrounding code, assign focused reviews by function, file, or feature, and combine findings with locations, confidence levels, and supporting evidence.

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• Reviewing patches of roughly 50 to 1,000 lines
• Checking risks specific to a changed feature
• Reviewing changes alongside callers and related implementations

Run a quick first review of code changes with six specialist agents working in parallel. They check logic, input and output, shared state, resources, missing behavior, and API completeness, then combine duplicate findings into a report ranked by confidence.

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• Running a quick first review of a patch
• Screening diffs to prioritize further investigation
• Checking for missing cleanup and handlers

Review changes of 1,000 lines or more by dividing work across files, focused areas, and commits. Combine several review methods, check consistency across subsystems, verify bug fixes, and report validated findings alongside a checklist showing review coverage.

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• Reviewing feature branches with 1,000 or more changed lines
• Checking a large change before merging
• Finding inconsistencies across commits and subsystems

Explain code structure and behavior with text diagrams, including before-and-after views of changes. Trace data flow and state changes, clarify design choices, and examine assumptions, possible failures, and interactions between tasks running at the same time.

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• Understanding a patch through before-and-after diagrams
• Tracing a class's behavior and state changes
• Mapping component relationships across a codebase

Review selected files for bugs by reading full source code, callers, and related implementations. Apply detailed checklists and investigate suspected issues, then report evidence, confidence levels, and suggested fixes.

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• Review selected files in a critical code change
• Investigate concerns raised by an initial review
• Check complex state transitions and object lifecycles

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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Design relational database schemas (and choose when to go NoSQL) that stay maintainable and fast. Use this skill whenever the user mentions tables, DDL, entities and relationships, normalization (1NF/2NF/3NF), primary and foreign keys, UUID vs bigint IDs, indexes (B-tree, composite, covering, partial), EXPLAIN, constraints (CHECK, UNIQUE, exclusion), transactions and isolation levels, migrations (Alembic, Prisma, Flyway, expand-contract, backfilling), or SQL vs NoSQL (MongoDB, DynamoDB, Cassandra, graph databases). Also trigger for "design the database", "model this domain", "which database should I use", or writing ORM models and migration files for PostgreSQL, MySQL, SQLite, or SQL Server.

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MongoDB, Redis, Cassandra, DynamoDB, and distributed database patterns for scalable applications

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Expert guidance for distributed NoSQL databases (Cassandra, DynamoDB). Focuses on mental models, query-first modeling, single-table design, and avoiding hot partitions in high-scale systems.

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