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ClickHouse engineering resources

  • 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: 6 juin 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: 6 juin 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: 6 juin 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: 6 juin 2026

  • How to convert CSV to ORC

    Convert a CSV file to columnar ORC with one clickhouse-local command — no upload and no server, the schema is inferred from the CSV and the types are carried into the ORC file.

    Last updated: 8 juin 2026

  • How to convert CSV to JSON

    Convert a CSV file to JSON from your terminal with clickhouse-local — one object per line or a single JSON array, schema auto-inferred and types preserved, with no upload and no server.

    Last updated: 8 juin 2026

  • What is a Parquet file?

    A Parquet file is an open, column-oriented, compressed storage format for tabular data, built for analytics. This page explains its internal structure and proves it by reading a real file's metadata.

    Last updated: 8 juin 2026

  • What is NDJSON / JSON Lines?

    NDJSON (newline-delimited JSON), also called JSON Lines, stores one JSON object per line. This page explains the format, how it differs from a single JSON array, and proves it by reading a real file with clickhouse local.

    Last updated: 8 juin 2026

  • What is clickhouse-local? Run SQL on any file or data source, no server

    clickhouse-local is a small, fast, standalone build of ClickHouse that runs full ClickHouse SQL over local and remote files (and external databases) from a single binary, with no server to install or start.

    Last updated: 8 juin 2026

  • What is chDB? In-process ClickHouse for Python

    chDB is an in-process SQL OLAP engine for Python: an embedded build of ClickHouse you pip install and call directly, running full ClickHouse SQL over files, DataFrames, and remote sources with no server to start.

    Last updated: 8 juin 2026

  • What is a TSV file?

    A TSV file is a plain-text table whose columns are separated by tab characters, optionally with a header row. This page explains the format, how it differs from CSV, and proves it by reading a real TSV with clickhouse local.

    Last updated: 8 juin 2026

  • How to run SQL on a JSONL file

    Query a .jsonl file (one JSON object per line) with SQL straight from your terminal using clickhouse local. Keys become columns, types are inferred, no server and no import step.

    Last updated: 8 juin 2026

  • How to query a JSON file with SQL

    Query a JSON file with SQL directly from your terminal using clickhouse local. Reads line-delimited JSON or a top-level array, infers nested objects and arrays, with no server and no import step.

    Last updated: 8 juin 2026

  • How to run SQL on a CSV file

    Query a CSV file with SQL directly from your terminal using clickhouse-local — header and column types are auto-detected, with no database and no import step.

    Last updated: 8 juin 2026

  • How to read a TSV file in Python (faster than pandas)

    Read a tab-separated 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: 8 juin 2026

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