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ClickHouse engineering resources

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

    Read a JSONL 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 8, 2026

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

    Read an NDJSON or JSON 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 8, 2026

  • How to read a file with a custom delimiter

    Query a file with any field or row separator using clickhouse local and the CustomSeparated format. Set the delimiter, query with SQL, no import step and no preprocessing.

    Last updated: Jun 8, 2026

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

    Read a CSV 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 8, 2026

  • How to query nested JSON with SQL

    Query deeply nested JSON with SQL from your terminal using clickhouse local — dot access into objects, ARRAY JOIN to explode arrays, and the JSON type for irregular keys, with no server and no import step.

    Last updated: Jun 8, 2026

  • How to query an NDJSON file with SQL

    Run SQL on an NDJSON file from your terminal with one command, using clickhouse local. The schema is inferred from the data, with no server and no import step.

    Last updated: Jun 8, 2026

  • How to query a JSON Lines file

    Query a JSON Lines (JSONL) file with SQL straight from your terminal using clickhouse local. One object per line, schema inferred, no server and no import step.

    Last updated: Jun 8, 2026

  • How to query a compressed file (gzip, zstd) from the command line

    Query a gzipped CSV or a zstd-compressed Parquet file directly with clickhouse local. The codec is detected from the file, decompressed on the fly, with no unzip step and no flag.

    Last updated: Jun 8, 2026

  • How to read a TSV file

    Read and query a tab-separated (TSV) file with SQL from your terminal using clickhouse local. The header and column types are auto-detected, with no database and no import step.

    Last updated: Jun 8, 2026

  • How to query a Parquet file from the command line

    Open and query a Parquet file from your terminal with one command, using clickhouse local. No server, no schema declaration, no import step.

    Last updated: Jun 8, 2026

  • How to flatten nested JSON in Python

    Flatten JSON with nested objects and arrays-of-objects into a tidy pandas DataFrame using chDB. Change one import line and the pandas code you already write keeps working on large files.

    Last updated: Jun 8, 2026

  • How to convert TSV to Parquet

    Convert a tab-separated file to Parquet from your terminal with clickhouse-local — schema inferred from the TSV, types carried into Parquet, zstd compression by default. No upload, no server.

    Last updated: Jun 8, 2026

  • How to convert TSV to CSV

    Convert a tab-separated file to comma-separated from your terminal with clickhouse-local — one command, header preserved, types inferred, no upload and no row limit.

    Last updated: Jun 8, 2026

  • How to convert Parquet to TSV

    Convert a Parquet file to tab-separated values with one clickhouse-local command. Types are read from the Parquet footer, nothing is uploaded, and files larger than RAM stream through.

    Last updated: Jun 8, 2026

  • How to convert Parquet to NDJSON

    Convert a Parquet file to NDJSON from your terminal with clickhouse-local — one command, no server and no upload, with the Parquet schema read straight from the file footer.

    Last updated: Jun 8, 2026

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