Akshat Shenoi created SPARK-57419:
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Summary: [SQL] Support read & schema inference of JSON files
inside tar archives
Key: SPARK-57419
URL: https://issues.apache.org/jira/browse/SPARK-57419
Project: Spark
Issue Type: New Feature
Components: SQL
Affects Versions: 4.3.0
Reporter: Akshat Shenoi
Assignee: Akshat Shenoi
Fix For: 4.3.0
SPARK-57135 added opt-in reading of CSV files packaged in tar archives (.tar,
.tar.gz, .tgz), but only with an explicit schema — schema inference was out of
scope, and inferring without a schema errors (UNABLE_TO_INFER_SCHEMA).
This follow-up adds schema inference for tar archives, so
spark.read.csv("data.tar") (with spark.sql.files.archive.reader.enabled=true)
infers a schema instead of erroring, matching how a directory of the same CSV
files is inferred:
- CSVDataSource.inferSchema streams each archive's entries through the existing
ArchiveReader (never unpacking to disk), tokenizes each entry like a standalone
CSV file (dropping its header row when header=true), and feeds all entries'
rows into a single CSVInferSchema pass keyed on the first entry's header — the
same first-header, type-widening model used for a multi-file CSV read.
- When archives and loose CSV files are read together, the two inferred schemas
are merged positionally with CSV-aware type widening.
- ignoreCorruptFiles / ignoreMissingFiles are honored at archive granularity,
matching the loose-file path.
- Reuses the spark.sql.files.archive.reader.enabled config from SPARK-57135; no
new config.
Scope: CSV over tar, building on SPARK-57135. Inference for other file formats
(JSON, text, XML) follows their respective read support.
Tests: directory parity, all archive formats agree, corrupt-archive skip among
good ones, cross-entry type widening, and mixed archive + loose-file inference.
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