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risingwaverisingwave 命令行

Agent Skill

risingwave 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

384

周安装

16

GitHub Stars

7

下载量

128
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:risingwave(risingwave 命令行)
来源仓库:https://github.com/risingwavelabs/agent-skills
仓库路径:skills/risingwave
安装命令:
npx skills add https://github.com/risingwavelabs/agent-skills --skill risingwave
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/risingwavelabs/agent-skills --skill risingwave

简介

用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。

  • 适合围绕仓库状态、代码变更或协作事项进行整理。
  • 可结合来源仓库和安装命令核验具体用法。
  • 安装前建议确认权限范围及是否会触发联网或文件读写。
  • risingwave 属于开发类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

RisingWave

RisingWave is a streaming SQL database implementing the PostgreSQL wire protocol. The core pipeline is: Source (ingest) → Materialized View (continuous compute) → Sink (output).

Core Principles

  1. Port 4566, not 5432 — RisingWave listens on 4566 for SQL connections. Dashboard is at 5691.
  2. SOURCE ≠ TABLECREATE SOURCE connects a stream but doesn't persist data. CREATE TABLE persists. For CDC (Debezium, Maxwell, Canal), you MUST use CREATE TABLE... FROM source.
  3. Watermarks unlock window closing — Without WATERMARK FOR col AS col - INTERVAL '...', EMIT ON WINDOW CLOSE won't work; use it on the source or table definition. In RisingWave 2.8+, watermarks on TABLE require APPEND ONLY — use CREATE SOURCE for non-append-only streams that need watermarks.
  4. EMIT ON WINDOW CLOSE vs default — Default emit-on-update sends partial results after each checkpoint. Use EMIT ON WINDOW CLOSE for final, immutable window results.
  5. Large backfills need BACKGROUND_DDL — Creating an MV over a large table blocks without SET BACKGROUND_DDL = true. Monitor with SELECT * FROM rw_catalog.rw_ddl_progress.
  6. snapshot = false on sinks — Adding a sink to an existing MV without this flag replays all historical data into the sink.
  7. Verify docs — RisingWave evolves rapidly. When unsure, check docs.risingwave.com or query SHOW CREATE on existing objects.

Connection

# Default local connection
psql -h localhost -p 4566 -d dev -U root

# Docker (single-node)
docker run -it --pull=always -p 4566:4566 -p 5691:5691 \
  risingwavelabs/risingwave:latest single_node
ParameterDefault
Hostlocalhost
Port4566
Databasedev
Userroot
Password(none)

Connection string: postgresql://root:@localhost:4566/dev

Any PostgreSQL-compatible client works: psql, JDBC, psycopg2, pgx, SQLAlchemy, dbt, Grafana.

MCP Server Setup

RisingWave has an official MCP server with 100+ tools (query execution, schema exploration, streaming job monitoring, CDC progress, Kafka lag, Hummock storage analysis).

git clone https://github.com/risingwavelabs/risingwave-mcp.git
cd risingwave-mcp && pip install -r requirements.txt

Configure via environment variable:

RISINGWAVE_CONNECTION_STR=postgresql://root:@localhost:4566/dev

Add to your agent's MCP config (Claude Code: ~/.claude/claude_desktop_config.json, VS Code: .vscode/mcp.json):

{
  "mcpServers": {
    "risingwave": {
      "type": "stdio",
      "command": "python",
      "args": ["/path/to/risingwave-mcp/src/main.py"],
      "env": {
        "RISINGWAVE_CONNECTION_STR": "postgresql://root:@localhost:4566/dev"
      }
    }
  }
}

The Pipeline Pattern

Stream data → SOURCE → MATERIALIZED VIEW(s) → SINK(s)
Static/CDC data → TABLE ─────────────────────────────╯

Step 1: Source (streaming, no persistence)

CREATE SOURCE user_events (
    user_id     INT,
    action      VARCHAR,
    event_time  TIMESTAMP,
    WATERMARK FOR event_time AS event_time - INTERVAL '5 SECOND'
)
WITH (
    connector = 'kafka',
    topic = 'user-events',
    properties.bootstrap.server = 'kafka:9092',
    scan.startup.mode = 'latest'
)
FORMAT PLAIN ENCODE JSON;

Step 2: Materialized View (continuous compute)

-- Windowed aggregation with final results on window close
CREATE MATERIALIZED VIEW active_users_per_minute AS
SELECT
    action,
    COUNT(DISTINCT user_id) AS unique_users,
    window_start,
    window_end
FROM TUMBLE(user_events, event_time, INTERVAL '1 MINUTE')
GROUP BY action, window_start, window_end
EMIT ON WINDOW CLOSE;

-- Plain aggregation (emit on every update)
CREATE MATERIALIZED VIEW user_action_counts AS
SELECT user_id, action, COUNT(*) AS cnt
FROM user_events
GROUP BY user_id, action;

Step 3: Sink (output)

CREATE SINK alerts_to_kafka FROM active_users_per_minute
WITH (
    connector = 'kafka',
    topic = 'user-alerts',
    properties.bootstrap.server = 'kafka:9092',
    snapshot = false   -- skip historical backfill
)
FORMAT PLAIN ENCODE JSON;

CDC Pattern (Database Replication)

CDC requires a two-step setup: shared connection source + per-table TABLE.

-- Step 1: shared CDC source connection
CREATE SOURCE pg_cdc WITH (
    connector = 'postgres-cdc',
    hostname = 'postgres-host',
    port = '5432',
    username = 'replicator',
    password = '<your-password>',
    database.name = 'mydb',
    slot.name = 'rw_slot'
);

-- Step 2: per-table ingestion (TABLE, not SOURCE)
CREATE TABLE orders (
    id          INT PRIMARY KEY,
    customer_id INT,
    total       DECIMAL,
    created_at  TIMESTAMP
)
FROM pg_cdc TABLE 'public.orders';

Time Windows

All three window types add window_start and window_end columns.

-- TUMBLE: non-overlapping fixed windows
FROM TUMBLE(table, time_col, INTERVAL '5 MINUTES')

-- HOP (sliding): overlapping windows
-- hop_size = slide interval, window_size = total duration
FROM HOP(table, time_col, INTERVAL '1 MINUTE', INTERVAL '5 MINUTES')
Note: SESSION windows are only supported in batch mode in RisingWave 2.x. For streaming, use TUMBLE or HOP.

Pattern: Always group by window_start, window_end and add EMIT ON WINDOW CLOSE when using watermarks.

Useful System Catalog Queries

-- All materialized views with definitions
SELECT name, definition FROM rw_catalog.rw_materialized_views;

-- DDL progress during MV creation / backfill
SELECT ddl_id, ddl_statement, progress FROM rw_catalog.rw_ddl_progress;

-- Active sources and their connectors
SELECT name, connector FROM rw_catalog.rw_sources;

-- Sink info
SELECT name, sink_type, connector FROM rw_catalog.rw_sinks;

-- CDC backfill progress
SELECT job_id, split_total_count, split_backfilled_count, split_completed_count
FROM rw_catalog.rw_cdc_progress;

-- Cluster nodes
SELECT id, host, type, state FROM rw_catalog.rw_worker_nodes;

-- Inspect object definition
SHOW CREATE MATERIALIZED VIEW my_mv;
SHOW CREATE SOURCE my_source;
SHOW CREATE SINK my_sink;

Useful Session Settings

-- Large backfills: non-blocking DDL
SET BACKGROUND_DDL = true;

-- Share Kafka source across multiple MVs (v2.1+)
SET streaming_use_shared_source = true;

Troubleshooting

MCP server not connecting

  • Verify RISINGWAVE_CONNECTION_STR is set and RisingWave is running on port 4566
  • Test the connection first: psql -h localhost -p 4566 -d dev -U root
  • Check Python version: python --version (requires Python 3.8+)

EMIT ON WINDOW CLOSE produces no output

  • The source or table must have WATERMARK FOR col AS col - INTERVAL '...' defined
  • Confirm watermark is advancing: insert rows with recent timestamps, not historical data
  • Check MV definition: SHOW CREATE MATERIALIZED VIEW my_mv

MV creation hangs / session times out

  • Use SET BACKGROUND_DDL = true before CREATE MATERIALIZED VIEW
  • Monitor progress: SELECT ddl_id, ddl_statement, progress FROM rw_catalog.rw_ddl_progress

CDC table not receiving updates

  • Verify PostgreSQL has wal_level = logical and the replication user has REPLICATION role
  • Check CDC lag: SELECT * FROM rw_catalog.rw_cdc_progress
  • Ensure slot.name in CREATE SOURCE is unique and does not already exist on the upstream DB

Sink sending duplicate historical data

  • Add snapshot = false to the sink WITH clause to skip backfilling existing MV data

References

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.93%
按下载量换算45

Claude

29.32%
按下载量换算38

Cursor

16.97%
按下载量换算22

Gemini CLI

9.9%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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