clickhouse/agent-skills

chdb-sql

- Use when the user wants to run SQL — especially analytical SQL — on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server.

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chdb SQL — ClickHouse in Your Python Process

Run ClickHouse SQL directly in Python — no server needed. Query local files, remote databases, and cloud storage with full ClickHouse SQL power.

bash
pip install chdb

Decision Tree: Pick the Right API

1. One-off query on files or databases → chdb.query()
2. Multi-step analysis with tables      → Session
3. DB-API 2.0 connection                → chdb.connect()
4. Pandas-style DataFrame operations    → Use chdb-datastore skill instead

chdb.query() — One Line, Any Data

python
import chdb

chdb.query("SELECT * FROM file('data.parquet', Parquet) WHERE price > 100 LIMIT 10")       # local files
chdb.query("SELECT * FROM mysql('db:3306', 'shop', 'orders', 'root', 'pass')")              # databases
chdb.query("SELECT * FROM s3('s3://bucket/data.parquet', NOSIGN) LIMIT 10")                 # cloud storage
chdb.query("SELECT * FROM deltaLake('s3://bucket/delta/table', NOSIGN) LIMIT 10")           # data lakes

# Cross-source join
chdb.query("""
    SELECT u.name, o.amount FROM mysql('db:3306', 'crm', 'users', 'root', 'pass') AS u
    JOIN file('orders.parquet', Parquet) AS o ON u.id = o.user_id ORDER BY o.amount DESC
""")

data = {"name": ["Alice", "Bob"], "score": [95, 87]}
chdb.query("SELECT * FROM Python(data) ORDER BY score DESC")                                # Python data
df = chdb.query("SELECT * FROM numbers(10)", "DataFrame")                                   # output formats
chdb.query("SELECT toDate({d:String}) + number FROM numbers({n:UInt64})",
    "DataFrame", params={"d": "2025-01-01", "n": 30})                                      # parametrized

Table functions → table-functions.md | SQL functions → sql-functions.md | Full API → api-reference.md

Session — Stateful Analysis Pipelines

python
from chdb import session as chs
sess = chs.Session("./analytics_db")   # persistent; Session() for in-memory

sess.query("CREATE TABLE users ENGINE=MergeTree() ORDER BY id AS SELECT * FROM mysql('db:3306','crm','users','root','pass')")
sess.query("CREATE TABLE events ENGINE=MergeTree() ORDER BY (ts,user_id) AS SELECT * FROM s3('s3://logs/events/*.parquet',NOSIGN)")
sess.query("""
    SELECT u.country, count() AS cnt, uniqExact(e.user_id) AS users
    FROM events e JOIN users u ON e.user_id = u.id
    WHERE e.ts >= today() - 7 GROUP BY u.country ORDER BY cnt DESC
""", "Pretty").show()
sess.close()

Connection API (DB-API 2.0)

python
from chdb import dbapi
conn = dbapi.connect()
cur = conn.cursor()
cur.execute("SELECT * FROM file('data.parquet', Parquet) WHERE value > 100")
print(cur.fetchall())
cur.close()
conn.close()

Troubleshooting

ProblemFix
ImportError: No module named 'chdb'pip install chdb
DB::Exception: FILE_NOT_FOUNDCheck file path; use absolute path or verify cwd
DB::Exception: Unknown table functionCheck function name spelling (e.g., deltaLake not deltalake)
Connection refused to remote DBCheck host:port format; ensure remote DB allows connections
Environment checkRun python scripts/verify_install.py (from skill directory)

References

Note: This skill teaches how to use chdb SQL. For pandas-style operations, use the chdb-datastore skill. For contributing to chdb source code, see CLAUDE.md in the project root.
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Altri Skills

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clickhouse
Ufficiale

chdb-datastore

- Use when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas. Provides chDB DataStore — same pandas API, ClickHouse engine underneath. Also handles reading from S3, MySQL, PostgreSQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake as DataFrames and joining across sources. TRIGGER when: user mentions DataFrame, parquet, csv, "fast pandas", "speed up pandas", or cross-source DataFrame joins; user imports chdb.datastore or from datastore import DataStore. SKIP this skill for raw SQL syntax (use chdb-sql instead), ClickHouse server administration, or non-Python DataStore API work.

installazioni
1
GitHub Stars
536
Aggiornato
10 set
clickhouse
Ufficiale

clickhouse-best-practices

MUST USE when reviewing ClickHouse schemas, queries, or configurations. Contains 31 rules that MUST be checked before providing recommendations. Always read relevant rule files and cite specific rules in responses.

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1
GitHub Stars
536
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10 set
clickhouse
Ufficiale

clickstack-otel-collector

Use when a user wants to wire an OpenTelemetry collector into a Managed ClickStack service on ClickHouse Cloud, either by deploying a new local collector (Docker run or Docker Compose) or by configuring their own existing collector, then send rich synthetic telemetry and verify it is visible in ClickStack.

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1
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536
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10 set
clickhouse
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clickhouse-architecture-advisor

MUST USE when designing ClickHouse architectures, selecting between ingestion or modeling patterns, or translating best practices into workload-specific system designs. Complements clickhouse-best-practices with decision frameworks and explicit provenance labels.

installazioni
2
GitHub Stars
536
Aggiornato
10 set