datasqrl/sqrl/handle-large-data-files

Claude Code and agent skill by datasqrl/sqrl

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Use when the workspace contains a large data file, or when a compile/test run is blocked with LARGE_DATA_BLOCKED. Used to sample the large files down or exclude them so that DataSQRL compiles seamlessly.

Install

npx vskill@latest install datasqrl/sqrl/handle-large-data-files

SKILL.md

Handle large data files

DataSQRL copies every recognized data file (.csv, .csv.gz, .json, .jsonl, .parquet, .orc, .avro, ...) found anywhere in the project into Flink's build/ data directory on every compile and test — with no cache. A large file (tens of MB or more) therefore makes every run slow and can exceed the test timeout. compile/test is blocked while such a file is present (LARGE_DATA_BLOCKED).

Renaming a file to *.bak removes it from the copy: the compiler recognizes files by data extension, and .bak is not one, so a .bak file is skipped — and it no longer triggers the block.

For each large file, first invoke the /data-observation skill and observe it (so you understand its schema and values), then classify and act.

Steps to follow

(a) The file is data a connector reads

Replace it with a small, coherent sample and point the connector at the sample.

  1. Produce a small sample (<1000 rows), keeping the same format so the connector's 'format' still matches:
  • For .jsonl / .csv files, use head -1000 big.jsonl > sample.jsonl command
  • For .csv.gz files, use zcat big.csv.gz | head -1000 | gzip > sample.csv.gz command
  • A CSV sample must contain only data rows, because the header data only lives in the CREATE TABLE schema. If the original starts with a header row, note the column names and skip it while sampling: tail -n +2 big.csv | head -1000 > sample.csv, or zcat big.csv.gz | tail -n +2 | head -1000 | gzip > sample.csv.gz
  • For .parquet files, use duckdb -c "COPY (SELECT * FROM 'big.parquet' LIMIT 1000) TO 'sample.parquet' (FORMAT PARQUET)" command
  1. Sample coherently across related files. When files reference each other (foreign keys), sample a consistent set of keys so references still resolve — otherwise the sampled test data has orphaned references. Pick a set of parent keys first, then keep only the child rows for those keys.
  2. Point the connector's path at the sample file. If you sampled into a different format than the original (e.g. parquet → jsonl), also update the connector's 'format' (and any format-specific options) to match — the 'format' key is independent of the file extension.
  3. Rename the original large file to *.bak so it is not copied, and delete any copy already written under build/.

(b) The file is only reference/sample material (no connector reads it)

You still need to observe it, but it must not be copied.

  1. Invoke the /data-observation skill and observe a bounded prefix to learn the schema and values.
  2. Rename the file to *.bak so it is not copied, and delete any copy already written under build/.

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