chcli — ClickHouse CLI

chcli is a lightweight ClickHouse command-line client. Use it to run SQL queries, explore schemas, and extract data from ClickHouse databases.

Running chcli

Prefer bunx if Bun is available, otherwise use npx:

bunx @obsessiondb/chcli -q "SELECT 1"
npx @obsessiondb/chcli -q "SELECT 1"

Or install globally:

bun install -g chcli
chcli -q "SELECT 1"

Connection

Set connection details via environment variables (preferred for agent use) or CLI flags.

Flag	Env Var	Alt Env Var	Default
--host	CLICKHOUSE_HOST		localhost
--port	CLICKHOUSE_PORT		8123
-u, --user	CLICKHOUSE_USER	CLICKHOUSE_USERNAME	default
--password	CLICKHOUSE_PASSWORD		(empty)
-d, --database	CLICKHOUSE_DATABASE	CLICKHOUSE_DB	default
-s, --secure	CLICKHOUSE_SECURE		false
(none)	CLICKHOUSE_URL		(none)

CLICKHOUSE_URL accepts a full URL (e.g. https://host:8443) and is parsed into host, port, secure, and password as a fallback when the individual env vars are not set.

Resolution Order

CLI flag > Individual env var > CLICKHOUSE_URL (parsed) > Default value

For agent workflows, prefer setting env vars in a .env file (Bun loads .env automatically) or using a secrets manager like Doppler so every invocation uses the same connection without repeating flags.

See references/connection.md for detailed connection examples.

Query Patterns

Inline query (most common for agents):

bunx @obsessiondb/chcli -q "SELECT count() FROM events"

From a SQL file:

bunx @obsessiondb/chcli -f query.sql

Via stdin pipe:

echo "SELECT 1" | bunx @obsessiondb/chcli

Output Formats

Always use -F json or -F csv when the output will be parsed by an agent. The default format (pretty) is for human display and is difficult to parse programmatically.

# JSON — best for structured parsing
bunx @obsessiondb/chcli -q "SELECT * FROM events LIMIT 5" -F json

# CSV — good for tabular data
bunx @obsessiondb/chcli -q "SELECT * FROM events LIMIT 5" -F csv

# JSONL (one JSON object per line) — good for streaming/large results
bunx @obsessiondb/chcli -q "SELECT * FROM events LIMIT 100" -F jsonl

Available format aliases: json, jsonl/ndjson, jsoncompact, csv, tsv, pretty, vertical, markdown, sql. Any native ClickHouse format name also works.

See references/formats.md for the full format reference.

Common Workflows

Schema Discovery

# List all databases
bunx @obsessiondb/chcli -q "SHOW DATABASES" -F json

# List tables in current database
bunx @obsessiondb/chcli -q "SHOW TABLES" -F json

# List tables in a specific database
bunx @obsessiondb/chcli -q "SHOW TABLES FROM analytics" -F json

# Describe table schema
bunx @obsessiondb/chcli -q "DESCRIBE TABLE events" -F json

# Show CREATE TABLE statement
bunx @obsessiondb/chcli -q "SHOW CREATE TABLE events"

Data Exploration

# Row count
bunx @obsessiondb/chcli -q "SELECT count() FROM events" -F json

# Sample rows
bunx @obsessiondb/chcli -q "SELECT * FROM events LIMIT 10" -F json

# Column statistics
bunx @obsessiondb/chcli -q "SELECT uniq(user_id), min(created_at), max(created_at) FROM events" -F json

Data Extraction

# Extract to CSV file
bunx @obsessiondb/chcli -q "SELECT * FROM events WHERE date = '2024-01-01'" -F csv > export.csv

# Extract as JSON
bunx @obsessiondb/chcli -q "SELECT * FROM events LIMIT 1000" -F json > export.json

Additional Flags

Flag	Description
-t, --time	Print execution time to stderr
-v, --verbose	Print query metadata (format, elapsed time) to stderr
--help	Show help text
--version	Print version

Best Practices for Agents

1. Always specify -F json or -F csv — never rely on the default format, which varies by TTY context.
2. Always use LIMIT on SELECT queries unless you know the table is small. ClickHouse tables can contain billions of rows.
3. Start with schema discovery — run SHOW TABLES and DESCRIBE TABLE before querying unfamiliar databases.
4. Use -t for timing — helps gauge whether queries are efficient.
5. Prefer env vars for connection — set them once in .env or via a secrets manager like Doppler rather than repeating flags on every command.
6. Use count() first — before extracting data, check how many rows match to avoid overwhelming output.