timeplus-sql-guide

name	timeplus-sql-guide
description	Write and execute Timeplus streaming SQL for real-time analytics. Use this skill when the user wants to create streams, run streaming queries, build materialized views, ingest data, send data to sinks, write UDFs, or simulate data with random streams. Executes SQL via the ClickHouse-compatible HTTP interface on port 8123 using environment variables TIMEPLUS_HOST, TIMEPLUS_USER, and TIMEPLUS_PASSWORD. Covers full Timeplus SQL syntax including window functions, JOINs, CTEs, UDFs, data types, aggregations, and all DDL/DML statements.s
compatibility	Requires curl. Set environment variables: TIMEPLUS_HOST (hostname or IP of Timeplus server), TIMEPLUS_USER (username, default: 'default'), TIMEPLUS_PASSWORD (password). Port 8123 must be accessible. For streaming ingest, port 3218 must also be accessible.
metadata	{"author":"timeplus-io","version":"1.0.6","docs":"https://docs.timeplus.com","github":"https://github.com/timeplus-io/proton","openclaw":{"requires":{"env":["TIMEPLUS_HOST","TIMEPLUS_USER","TIMEPLUS_PASSWORD"],"bins":["curl"]},"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

Task	Reference
Get data in	references/INGESTION.md
Transform data	references/TRANSFORMATIONS.md
Send data out	references/SINKS.md
Full SQL syntax, types, functions	references/SQL_REFERENCE.md
Random streams (simulated data)	references/RANDOM_STREAMS.md
Python & JavaScript UDFs	references/UDFS.md
Python Table Functions	references/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:

Format	Use Case
TabSeparated	Default, human-readable
JSONEachRow	One JSON object per line
JSONCompact	Compact JSON array
CSV	Comma-separated
Vertical	Column-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();

ORDER BY and LIMIT on Streaming Queries

A streaming query result is unbounded — new rows arrive forever — so the familiar ORDER BY / LIMIT semantics from batch SQL do not apply directly:

• ORDER BY is not allowed on a non-windowed streaming query. Sorting requires a finite input. You can only use ORDER BY when the upstream is bounded: a table(...) historical query, or a windowed aggregation (tumble/hop/session) where each window closure produces a finite batch that gets sorted per window.
• LIMIT n on a streaming query terminates the query after n rows. It is not "top n by some order" — it is a hard stop. Pair LIMIT with a window (LIMIT n BY window_start) or use streaming-native top-k aggregates (top_k, min_k, max_k) for per-window ranking.
• For historical queries (SELECT ... FROM table(stream)), ORDER BY and LIMIT work the normal way.

See references/TRANSFORMATIONS.md → Top-N per Window for the recommended patterns.

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

Mode	Created With	Behavior
append	CREATE 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
mutable	CREATE MUTABLE STREAM	Row-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:

Error	Cause	Fix
Connection refused	Wrong host/port	Check TIMEPLUS_HOST and port 8123 is open
Authentication failed	Wrong credentials	Check TIMEPLUS_USER / TIMEPLUS_PASSWORD
Stream already exists	Duplicate CREATE	Use CREATE STREAM IF NOT EXISTS
Unknown column	Typo or wrong stream	Run DESCRIBE stream_name to check schema
Streaming query timeout	Using streaming on port 8123	Wrap with table() for historical query
Type mismatch	Wrong data type	Use explicit cast: cast(val, 'float32')
Watermark with delay is only supported with streaming window function	EMIT ... WITH DELAY on a plain GROUP BY	Drop the EMIT clause (plain GROUP BY already emits on update), or wrap the source in tumble()/hop()/session()

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, sources → references/INGESTION.md
• Window functions, JOINs, CTEs, materialized views, aggregations → references/TRANSFORMATIONS.md
• Sinks, external tables, Kafka output, webhooks → references/SINKS.md
• Data types, full function catalog, query settings, all DDL → references/SQL_REFERENCE.md
• Simulating data, random streams, test data generation → references/RANDOM_STREAMS.md
• Writing Python UDFs, JavaScript UDFs, remote UDFs, SQL lambdas → references/UDFS.md
• Python Table Functions → references/Python_TABLE_FUNCTION.md
• Scheduled Tasks → references/TASK.md
• Alerts → references/ALERT.md