{
    "app": {
        "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.",
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            "provider": "deepseek",
            "model": "deepseek-chat",
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    "instructions": "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 # 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.",
    "variables": [],
    "opening_statement": "你好，我是 chdb-datastore，Use when the user has tabular data (pandas DataFra...",
    "suggested_questions": [],
    "source": "DeepseekModel",
    "source_url": "https://deepseekmodel.com/skill?id=clickhouse-agent-skills-skills-chdb-datastore-skill-md"
}