Max Gekk created SPARK-57828:
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Summary: Add vectorized Parquet reader support for
nanosecond-precision timestamp types
Key: SPARK-57828
URL: https://issues.apache.org/jira/browse/SPARK-57828
Project: Spark
Issue Type: Sub-task
Components: SQL
Affects Versions: 4.3.0
Reporter: Max Gekk
This sub-task is part of the umbrella SPARK-56822 (timestamps with nanosecond
precision).
h2. Problem
Nanosecond Parquet READ only works via the row-based reader.
{{TimestampNanosParquetOps.isBatchReadSupported}}
(datasources/parquet/TimestampNanosParquetOps.scala ~L54-58) stays at the trait
default {{false}}, and {{ParquetUtils.isBatchReadSupportedForSchema}} requires
every column to support batch read, so any nanosecond column forces the whole
file onto {{ParquetRowConverter}} even when vectorized reading is enabled.
{{ParquetVectorUpdaterFactory}} (~L140-161) only has MICROS/MILLIS timestamp
updaters. Correct but slow; Parquet WRITE + non-vectorized READ are already
done (SPARK-57102).
h2. Goal
Read {{TIMESTAMP(NANOS)}} columns through the vectorized path, materializing
the 16-byte {{TimestampNanosVal}} (epoch micros + nanosecond remainder) into
the columnar batch.
h2. Scope
Implement a nanosecond updater in {{ParquetVectorUpdaterFactory}} /
{{VectorizedColumnReader}}; flip {{isBatchReadSupported}} for the nanosecond
ops; ensure the columnar layout matches the nanosecond {{ColumnVector}} (added
under SPARK-57100).
h2. Acceptance criteria
* Nanosecond Parquet files read correctly with
{{spark.sql.parquet.enableVectorizedReader=true}}, matching the row-reader
results; no fallback for nanosecond-only schemas.
h2. Testing
{{ParquetTimestampNanosSuite}} (with the vectorized reader on),
{{ParquetQuerySuite}}.
h2. Dependencies
None - independent (columnar layout resolved in SPARK-57100).
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