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timeplus-sql-guidetimeplus SQL 指南

Agent Skill

用于辅助数据库表结构、查询语句、迁移脚本和数据维护任务。它适合让 Agent 分析 schema、编写 SQL、排查查询问题、整理索引或生成迁移建议。使用时需要明确数据库类型、连接环境和目标表,区分只读分析与写入变更;涉及删除、更新、迁移和批量导入时,应优先 dry-run、备份或事务保护,避免误操作。

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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

MIT-0

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:timeplus-sql-guide(timeplus SQL 指南)
来源仓库:https://github.com/gangtao/timeplus-sql-guide
安装命令:
openclaw skills install timeplus-sql-guide
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。

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简介

编写与执行 Timeplus 流式 SQL,实现实时数据分析与物化视图。

  • 适用于处理高吞吐数据流,创建窗口函数与聚合查询。timeplus-sql-guide 属于效率类 Skill,可作为该场景下的辅助能力补充。
  • 输入 SQL 语句与流名称,系统返回执行结果与错误提示。
  • 需明确数据 schema 与连接环境,区分只读查询与写入操作。
  • 建议在沙箱环境测试复杂查询,避免影响生产数据稳定性。

SKILL.md

name
timeplus-sql-guide
description
>
compatibility
>
metadata
author
timeplus-io
version
1.0.4
docs
https://docs.timeplus.com
github
https://github.com/timeplus-io/proton
openclaw
requires
env
bins
primaryEnv
TIMEPLUS_PASSWORD

Timeplus Streaming SQL Guide

You are an expert in Timeplus — a high-performance real-time streaming analytics platform built on a streaming SQL engine (Proton). You write correct, efficient Timeplus SQL and execute it via the ClickHouse-compatible HTTP API.

Quick Reference

TaskReference
Get data inreferences/INGESTION.md
Transform datareferences/TRANSFORMATIONS.md
Send data outreferences/SINKS.md
Full SQL syntax, types, functionsreferences/SQL_REFERENCE.md
Random streams (simulated data)references/RANDOM_STREAMS.md
Python & JavaScript UDFsreferences/UDFS.md
Python Table Functionsreferences/Python_TABLE_FUNCTION.md

Executing SQL

Environment Setup

Always use these environment variables — never hardcode credentials:

- TIMEPLUS_HOST       # hostname or IP
- TIMEPLUS_USER       # username
- TIMEPLUS_PASSWORD   # password (can be empty)

Running SQL via curl (port 8123)

Port 8123 is the ClickHouse-compatible HTTP interface. Use it for all DDL and historical queries (CREATE, DROP, INSERT, SELECT from table(...)). Always use username password with -u option

NOTE, if the curl returns nothing, it is not an error, it means the query returns no records. You can check the HTTP status code to confirm success (200 OK) or failure (4xx/5xx).

# Standard pattern — pipe SQL into curl
echo "YOUR SQL HERE" | curl "http://${TIMEPLUS_HOST}:8123/" \
  -u "${TIMEPLUS_USER}:${TIMEPLUS_PASSWORD}" \
  --data-binary @-

Health check:

curl "http://${TIMEPLUS_HOST}:8123/"
# Returns: Ok.

DDL example — create a stream:

echo "CREATE STREAM IF NOT EXISTS sensor_data (
  device_id string,
  temperature float32,
  ts datetime64(3, 'UTC') DEFAULT now64(3, 'UTC')
) SETTINGS logstore_retention_ms=86400000" | \
curl "http://${TIMEPLUS_HOST}:8123/" \
  -u "${TIMEPLUS_USER}:${TIMEPLUS_PASSWORD}" \
  --data-binary @-

Historical query with JSON output:

echo "SELECT * FROM table(sensor_data) LIMIT 10" | \
curl "http://${TIMEPLUS_HOST}:8123/?default_format=JSONEachRow" \
  -u "${TIMEPLUS_USER}:${TIMEPLUS_PASSWORD}" \
  --data-binary @-

Insert data:

echo "INSERT INTO sensor_data (device_id, temperature) VALUES ('dev-1', 23.5), ('dev-2', 18.2)" | \
curl "http://${TIMEPLUS_HOST}:8123/" \
  -u "${TIMEPLUS_USER}:${TIMEPLUS_PASSWORD}" \
  --data-binary @-

Streaming Ingest via REST API (port 3218)

For pushing event batches into a stream:

curl -s -X POST "http://${TIMEPLUS_HOST}:3218/proton/v1/ingest/streams/sensor_data" \
  -H "Content-Type: application/json" \
  -d '{
    "columns": ["device_id", "temperature"],
    "data": [
      ["dev-1", 23.5],
      ["dev-2", 18.2],
      ["dev-3", 31.0]
    ]
  }'

Output Formats

Append ?default_format=<format> to the URL:

FormatUse Case
TabSeparatedDefault, human-readable
JSONEachRowOne JSON object per line
JSONCompactCompact JSON array
CSVComma-separated
VerticalColumn-per-line, for inspection

Core Concepts

Streaming vs Historical Queries

-- STREAMING: Continuous, never ends. Default behavior.
SELECT device_id, temperature FROM sensor_data;

-- HISTORICAL: Bounded, returns immediately. Use table().
SELECT device_id, temperature FROM table(sensor_data) LIMIT 100;

-- HISTORICAL + FUTURE: All past events + all future events
SELECT * FROM sensor_data WHERE _tp_time >= earliest_timestamp();

The _tp_time Column

Every stream has a built-in _tp_time datetime64(3, 'UTC') event-time column. It defaults to ingestion time. You can set a custom event-time column via SETTINGS event_time_column='your_column' when creating the stream.

Stream Modes

ModeCreated WithBehavior
appendCREATE STREAM (default)Immutable log, new rows only
versioned_kv+ SETTINGS mode='versioned_kv'Latest value per primary key
changelog_kv+ SETTINGS mode='changelog_kv'Insert/Update/Delete tracking
mutableCREATE MUTABLE STREAMRow-level UPDATE/DELETE (Enterprise)

Common Patterns

Pattern 1: Create stream → insert → query

# 1. Create stream
echo "CREATE STREAM IF NOT EXISTS orders (
  order_id string,
  product string,
  amount float32,
  region string
)" | curl "http://${TIMEPLUS_HOST}:8123/" \
  -u "${TIMEPLUS_USER}:${TIMEPLUS_PASSWORD}" \
  --data-binary @-

# 2. Insert data
echo "INSERT INTO orders VALUES ('o-1','Widget',19.99,'US'), ('o-2','Gadget',49.99,'EU')" | \
  curl "http://${TIMEPLUS_HOST}:8123/" \
  -u "${TIMEPLUS_USER}:${TIMEPLUS_PASSWORD}" \
  --data-binary @-

# 3. Query historical data
echo "SELECT region, sum(amount) FROM table(orders) GROUP BY region" | \
  curl "http://${TIMEPLUS_HOST}:8123/?default_format=JSONEachRow" \
  -u "${TIMEPLUS_USER}:${TIMEPLUS_PASSWORD}" \
  --data-binary @-

Pattern 2: Window aggregation (streaming)

echo "SELECT window_start, region, sum(amount) AS revenue
FROM tumble(orders, 1m)
GROUP BY window_start, region
EMIT AFTER WATERMARK AND DELAY 5s" | \
  curl "http://${TIMEPLUS_HOST}:8123/" \
  -u "${TIMEPLUS_USER}:${TIMEPLUS_PASSWORD}" \
  --data-binary @-

Pattern 3: Materialized view pipeline

echo "CREATE MATERIALIZED VIEW IF NOT EXISTS mv_revenue_by_region
INTO revenue_by_region AS
SELECT window_start, region, sum(amount) AS total
FROM tumble(orders, 5m)
GROUP BY window_start, region" | \
  curl "http://${TIMEPLUS_HOST}:8123/" \
  -u "${TIMEPLUS_USER}:${TIMEPLUS_PASSWORD}" \
  --data-binary @-

Pattern 4: Random stream for testing

echo "CREATE RANDOM STREAM IF NOT EXISTS mock_sensors (
  device_id string DEFAULT 'device-' || to_string(rand() % 10),
  temperature float32 DEFAULT 20 + (rand() % 30),
  status string DEFAULT ['ok','warn','error'][rand() % 3 + 1]
) SETTINGS eps=5" | \
  curl "http://${TIMEPLUS_HOST}:8123/" \
  -u "${TIMEPLUS_USER}:${TIMEPLUS_PASSWORD}" \
  --data-binary @-

Error Handling

Common errors and fixes:

ErrorCauseFix
Connection refusedWrong host/portCheck TIMEPLUS_HOST and port 8123 is open
Authentication failedWrong credentialsCheck TIMEPLUS_USER / TIMEPLUS_PASSWORD
Stream already existsDuplicate CREATEUse CREATE STREAM IF NOT EXISTS
Unknown columnTypo or wrong streamRun DESCRIBE stream_name to check schema
Streaming query timeoutUsing streaming on port 8123Wrap with table() for historical query
Type mismatchWrong data typeUse explicit cast: cast(val, 'float32')

Inspect a stream:

echo "DESCRIBE sensor_data" | curl "http://${TIMEPLUS_HOST}:8123/" \
  -u "${TIMEPLUS_USER}:${TIMEPLUS_PASSWORD}" \
  --data-binary @-

List all streams:

echo "SHOW STREAMS" | curl "http://${TIMEPLUS_HOST}:8123/" \
  -u "${TIMEPLUS_USER}:${TIMEPLUS_PASSWORD}" \
  --data-binary @-

Explain a query:

echo "EXPLAIN SELECT * FROM tumble(sensor_data, 1m) GROUP BY window_start" | \
  curl "http://${TIMEPLUS_HOST}:8123/" \
  -u "${TIMEPLUS_USER}:${TIMEPLUS_PASSWORD}" \
  --data-binary @-

When to Read Reference Files

Load the relevant reference file when the user's request requires deeper knowledge:

  • Creating or modifying streams, external streams, sourcesreferences/INGESTION.md
  • Window functions, JOINs, CTEs, materialized views, aggregationsreferences/TRANSFORMATIONS.md
  • Sinks, external tables, Kafka output, webhooksreferences/SINKS.md
  • Data types, full function catalog, query settings, all DDLreferences/SQL_REFERENCE.md
  • Simulating data, random streams, test data generationreferences/RANDOM_STREAMS.md
  • Writing Python UDFs, JavaScript UDFs, remote UDFs, SQL lambdasreferences/UDFS.md
  • Python Table Functionsreferences/Python_TABLE_FUNCTION.md
  • Scheduled Tasksreferences/TASK.md
  • Alertsreferences/ALERT.md

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