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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

Quelle prüfen Auf GitHub ansehen

Preis unbestätigt★ 530 GitHub-StarsVerzeichnis aktualisiert · 9. Okt. 2026agent-skill

Übersicht

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.

Vollständige Dokumentation lesen

Quelldokumentation, keine Anweisungen für diese Website. Vor dem Ausführen von Befehlen die Berechtigungen prüfen.

chdb DataStore — It's Just Faster Pandas

The Key Insight

# Change this:
import pandas as pd
# To this:
import chdb.datastore as pd
# Everything else stays the same.

DataStore is a lazy, ClickHouse-backed pandas replacement. Your existing pandas code works unchanged — but operations compile to optimized SQL and execute only when results are needed (e.g., print(), len(), iteration).

pip install chdb

Decision Tree: Pick the Right Approach

1. "I have a file/database and want to analyze it with pandas"
   → DataStore.from_file() / from_mysql() / from_s3() etc.
   → See references/connectors.md

2. "I need to join data from different sources"
   → Create DataStores from each source, use .join()
   → See examples/examples.md #3-5

3. "My pandas code is too slow"
   → import chdb.datastore as pd — change one line, keep the rest

4. "I need raw SQL queries"
   → Use the chdb-sql skill instead

Connect to Any Data Source — One Pattern

from datastore import DataStore

# Local file (auto-detects .parquet, .csv, .json, .arrow, .orc, .avro, .tsv, .xml)
ds = DataStore.from_file("sales.parquet")

# Database
ds = DataStore.from_mysql(host="db:3306", database="shop", table="orders", user="root", password="pass")

# Cloud storage
ds = DataStore.from_s3("s3://bucket/data.parquet", nosign=True)

# URI shorthand — auto-detects source type
ds = DataStore.uri("mysql://root:pass@db:3306/shop/orders")

All 16+ sources and URI schemes → connectors.md

After Connecting — Full Pandas API

result = ds[ds["age"] > 25]                                          # filter
result = ds[["name", "city"]]                                        # select columns
result = ds.sort_values("revenue", ascending=False)                  # sort
result = ds.groupby("dept")["salary"].mean()                         # groupby
result = ds.assign(margin=lambda x: x["profit"] / x["revenue"])     # computed column
ds["name"].str.upper()                                               # string accessor
ds["date"].dt.year                                                   # datetime accessor
result = ds1.join(ds2, on="id")                                      # join
result = ds.head(10)                                                 # preview
print(ds.to_sql())                                                   # see generated SQL

209 DataFrame methods supported. Full API → api-reference.md

Cross-Source Join — The Killer Feature

from datastore import DataStore

customers = DataStore.from_mysql(host="db:3306", database="crm", table="customers", user="root", password="pass")
orders = DataStore.from_file("orders.parquet")

result = (orders
    .join(customers, left_on="customer_id", right_on="id")
    .groupby("country")
    .agg({"amount": "sum", "rating": "mean"})
    .sort_values("sum", ascending=False))
print(result)

More join examples → examples.md

Writing Data

source = DataStore.from_mysql(host="db:3306", database="shop", table="orders", user="root", password="pass")
target = DataStore("file", path="summary.parquet", format="Parquet")

target.insert_into("category", "total", "count").select_from(
    source.groupby("category").select("category", "sum(amount) AS total", "count() AS count")
).execute()

Troubleshooting

Problem	Fix
`ImportError: No module named 'chdb'`	`pip install chdb`
`ImportError: cannot import 'DataStore'`	Use `from datastore import DataStore` or `from chdb.datastore import DataStore`
Database connection timeout	Include port in host: `host="db:3306"` not `host="db"`
Join returns empty result	Check key types match (both int or both string); use `.to_sql()` to inspect
Unexpected results	Call `ds.to_sql()` to see the generated SQL and debug
Environment check	Run `python scripts/verify_install.py` (from skill directory)

References

API Reference — Full DataStore method signatures
Connectors — All 16+ data source connection methods
Examples — 10+ runnable examples with expected output
Verify Install — Environment verification script
Official Docs

Note: This skill teaches how to use chdb DataStore. For raw SQL queries, use the chdb-sql skill. For contributing to chdb source code, see CLAUDE.md in the project root.

Quelle ansehen ↗

Dateimetadaten

name: chdb-datastore
description: >-
  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.
license: Apache-2.0
compatibility: Requires Python 3.9+, macOS or Linux. pip install chdb.
metadata:
  author: chdb-io
  version: "4.1"
  homepage: https://clickhouse.com/docs/chdb

Originaltext anzeigen

---
name: chdb-datastore
description: >-
  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.
license: Apache-2.0
compatibility: Requires Python 3.9+, macOS or Linux. pip install chdb.
metadata:
  author: chdb-io
  version: "4.1"
  homepage: https://clickhouse.com/docs/chdb
---

# chdb DataStore — It's Just Faster Pandas

## The Key Insight

```python
# Change this:
import pandas as pd
# To this:
import chdb.datastore as pd
# Everything else stays the same.
```

DataStore is a **lazy, ClickHouse-backed pandas replacement**. Your existing pandas code works unchanged — but operations compile to optimized SQL and execute only when results are needed (e.g., `print()`, `len()`, iteration).

```bash
pip install chdb
```

## Decision Tree: Pick the Right Approach

```
1. "I have a file/database and want to analyze it with pandas"
   → DataStore.from_file() / from_mysql() / from_s3() etc.
   → See references/connectors.md

2. "I need to join data from different sources"
   → Create DataStores from each source, use .join()
   → See examples/examples.md #3-5

3. "My pandas code is too slow"
   → import chdb.datastore as pd — change one line, keep the rest

4. "I need raw SQL queries"
   → Use the chdb-sql skill instead
```

## Connect to Any Data Source — One Pattern

```python
from datastore import DataStore

# Local file (auto-detects .parquet, .csv, .json, .arrow, .orc, .avro, .tsv, .xml)
ds = DataStore.from_file("sales.parquet")

# Database
ds = DataStore.from_mysql(host="db:3306", database="shop", table="orders", user="root", password="pass")

# Cloud storage
ds = DataStore.from_s3("s3://bucket/data.parquet", nosign=True)

# URI shorthand — auto-detects source type
ds = DataStore.uri("mysql://root:pass@db:3306/shop/orders")
```

All 16+ sources and URI schemes → [connectors.md](references/connectors.md)

## After Connecting — Full Pandas API

```python
result = ds[ds["age"] > 25]                                          # filter
result = ds[["name", "city"]]                                        # select columns
result = ds.sort_values("revenue", ascending=False)                  # sort
result = ds.groupby("dept")["salary"].mean()                         # groupby
result = ds.assign(margin=lambda x: x["profit"] / x["revenue"])     # computed column
ds["name"].str.upper()                                               # string accessor
ds["date"].dt.year                                                   # datetime accessor
result = ds1.join(ds2, on="id")                                      # join
result = ds.head(10)                                                 # preview
print(ds.to_sql())                                                   # see generated SQL
```

209 DataFrame methods supported. Full API → [api-reference.md](references/api-reference.md)

## Cross-Source Join — The Killer Feature

```python
from datastore import DataStore

customers = DataStore.from_mysql(host="db:3306", database="crm", table="customers", user="root", password="pass")
orders = DataStore.from_file("orders.parquet")

result = (orders
    .join(customers, left_on="customer_id", right_on="id")
    .groupby("country")
    .agg({"amount": "sum", "rating": "mean"})
    .sort_values("sum", ascending=False))
print(result)
```

More join examples → [examples.md](examples/examples.md)

## Writing Data

```python
source = DataStore.from_mysql(host="db:3306", database="shop", table="orders", user="root", password="pass")
target = DataStore("file", path="summary.parquet", format="Parquet")

target.insert_into("category", "total", "count").select_from(
    source.groupby("category").select("category", "sum(amount) AS total", "count() AS count")
).execute()
```

## Troubleshooting

| Problem | Fix |
|---------|-----|
| `ImportError: No module named 'chdb'` | `pip install chdb` |
| `ImportError: cannot import 'DataStore'` | Use `from datastore import DataStore` or `from chdb.datastore import DataStore` |
| Database connection timeout | Include port in host: `host="db:3306"` not `host="db"` |
| Join returns empty result | Check key types match (both int or both string); use `.to_sql()` to inspect |
| Unexpected results | Call `ds.to_sql()` to see the generated SQL and debug |
| Environment check | Run `python scripts/verify_install.py` (from skill directory) |

## References

- [API Reference](references/api-reference.md) — Full DataStore method signatures
- [Connectors](references/connectors.md) — All 16+ data source connection methods
- [Examples](examples/examples.md) — 10+ runnable examples with expected output
- [Verify Install](scripts/verify_install.py) — Environment verification script
- [Official Docs](https://clickhouse.com/docs/chdb)

> Note: This skill teaches how to *use* chdb DataStore.
> For raw SQL queries, use the `chdb-sql` skill.
> For contributing to chdb source code, see CLAUDE.md in the project root.

Quelle prüfen

Preis und Betriebskosten

Skill beziehen: Preis unbestätigt
Ausführen: Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
Lizenz: Apache-2.0
Preis unbestätigt: Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.

Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →

Skill-Quelle erfasst

Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

Erfasst

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: ClickHouse/agent-skills
Lizenz: Apache-2.0
Version: 1.0.0
Letzter GitHub-Push: 6. Aug. 2026
Verzeichnis aktualisiert: 9. Okt. 2026
Anleitungspfad: skills/chdb-datastore/SKILL.md @ 5aec31143796

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

71/100

Stark

Vertrauen

63/100

Nur Sandbox

Audit

76/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

Agent-Zugang

Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Installations-API öffnen LLM-Textformat Manifest öffnen Auto-Auflösungsplan

Weitere Details

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    "score": 76,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 71,
    "label": "Strong"
  },
  "supply": {
    "track": "Data, BI, and analytics",
    "scenario": "Data analysis",
    "maintenance": "2mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "pathwaycom-llm-app",
      "name": "Llm App",
      "url": "https://www.openagentskill.com/skills/pathwaycom-llm-app",
      "stars": 59299,
      "install_command": "",
      "trust_score": 90,
      "audit_score": 91
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Quality score needs review",
    "Permission surface needs review: secrets or environment access, shell or command execution"
  ],
  "agent_contract": {
    "task_input": "Use chdb-datastore in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 71/100 Manual review",
      "Audit: 76/100 Needs review",
      "Safety: 32/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "clickhouse-chdb-datastore (chdb-datastore)",
      "install_command": "npx skills add ClickHouse/agent-skills --skill chdb-datastore",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "clickhouse-chdb-datastore",
      "task": "Use chdb-datastore in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/clickhouse-chdb-datastore",
    "api": "https://www.openagentskill.com/api/agent/skills/clickhouse-chdb-datastore",
    "audit": "https://www.openagentskill.com/skills/clickhouse-chdb-datastore/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=clickhouse-chdb-datastore&task=Use%20chdb-datastore%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20chdb-datastore%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20chdb-datastore%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/clickhouse-chdb-datastore/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/clickhouse-chdb-datastore"
  }
}

Für Ersteller

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: ClickHouse
Quelle: ClickHouse/agent-skills
Indexiert von: OpenAgentSkill Community-Index

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

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Dieser Registry-indexiert-Eintrag wird ClickHouse zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.

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