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ClickHouse engineering resources

  • How to read a Feather file from the command line

    Read a .feather file and query it with SQL from your terminal using clickhouse local. Feather is the Arrow IPC format, so you read it with FORMAT Arrow. No server, no schema declaration, no import step.

    Last updated: Jun 15, 2026

  • How to read a BSON file in Python

    Read a .bson export (mongoexport / mongodump) into a DataFrame with chDB. Work with it using the pandas API you already know, running on ClickHouse's engine, with no MongoDB server and no document-by-document decode loop.

    Last updated: Jun 15, 2026

  • How to read an Arrow file in Python (faster than pandas)

    Read an Arrow IPC file into a DataFrame with chDB, a drop-in replacement for pandas. Change one import line and your existing pandas code runs on ClickHouse's engine, so it stays fast as files grow.

    Last updated: Jun 15, 2026

  • How to query a BSON file

    Query a BSON file (MongoDB's binary JSON, the format mongoexport produces) directly from your terminal with clickhouse local. The schema, including nested sub-documents, is auto-detected. No server, no import step.

    Last updated: Jun 15, 2026

  • How to convert Parquet to Arrow

    Convert a Parquet file to the Arrow IPC (Feather) format in one command with clickhouse-local — columnar in, columnar out, types preserved, no upload and no server.

    Last updated: Jun 15, 2026

  • How to convert BSON to JSON

    Convert a BSON file to JSON from your terminal with clickhouse-local — the document schema is auto-inferred and nested fields are preserved, with no upload and no server.

    Last updated: Jun 15, 2026

  • Convert BSON to CSV

    Convert a BSON file to CSV from your terminal with clickhouse-local — one command, no upload and no server, with nested sub-documents flattened into real columns.

    Last updated: Jun 15, 2026

  • How to convert Arrow to Parquet

    Convert an Arrow IPC file to Parquet with one clickhouse-local command — types carry across exactly, nothing is uploaded, and files larger than RAM stream straight through.

    Last updated: Jun 15, 2026

  • How to read an ORC file in Python (faster than pandas)

    Read an ORC file into a DataFrame with chDB, a drop-in replacement for pandas. Change one import line and your existing pandas code runs on ClickHouse's engine, so it stays fast as files grow.

    Last updated: Jun 15, 2026

  • How to read an ORC file from the command line

    Open and query an ORC file from your terminal with one command using clickhouse-local. The schema is read from the file's footer, so there's no server and no CREATE TABLE.

    Last updated: Jun 6, 2026

  • How to parse a log file with regex in SQL

    Turn unstructured log lines into typed columns and query them with SQL, using a regex and clickhouse local. One pattern maps the line layout, then ordinary SQL answers the questions.

    Last updated: Jun 6, 2026

  • ORC file format

    ORC (Optimized Row Columnar) is an open, columnar, compressed storage format for tabular data. This page explains its stripes, indexes and footer, and proves the structure by reading a real ORC file.

    Last updated: Jun 6, 2026

  • How to convert ORC to Parquet

    Convert an ORC file to Parquet with a single command using clickhouse-local — the schema is read from the ORC footer and carried into Parquet, with no server and no upload.

    Last updated: Jun 6, 2026

  • How to convert ORC to CSV

    Convert an ORC file to CSV with one clickhouse-local command — the schema is read from the ORC footer, no server and no upload, and it streams files larger than RAM.

    Last updated: Jun 6, 2026

  • How to convert JSON to CSV

    Convert JSON to CSV from your terminal with clickhouse-local — one command, schema auto-inferred, with the nested fields flattened into flat CSV columns.

    Last updated: Jun 6, 2026

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