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ClickHouse engineering resources

  • 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: 2026年6月8日

  • 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: 2026年6月8日

  • How to read a semicolon-separated file

    Query a semicolon-delimited file (European-style CSV) with SQL from your terminal using clickhouse local — set the field delimiter to ";", with no database and no import step.

    Last updated: 2026年6月8日

  • How to read a pipe-delimited file

    Query a pipe-delimited (|) file with SQL from your terminal using clickhouse local. Set the field delimiter to a pipe, let the header and types be detected, and run.

    Last updated: 2026年6月8日

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

    Read a Parquet 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: 2026年6月8日

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

    Read an NDJSON 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: 2026年6月8日

  • 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: 2026年6月8日

  • 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: 2026年6月8日

  • 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: 2026年6月8日

  • 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: 2026年6月8日

  • 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: 2026年6月8日

  • 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: 2026年6月8日

  • 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: 2026年6月8日

  • 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: 2026年6月8日

  • 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: 2026年6月8日

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