Add batch/columnar write API for Arrow VectorSchemaRoot (Java parity with C++/Python)

Inizia leggendo il modulo parquet-arrow e le API indicate PageWriteStore, PageWriter, BytesInput e ColumnChunkPageWriteStore. Il lavoro proposto comprende cinque fasi di implementazione, iniziando dalle scritture zero-copy per colonne PLAIN a larghezza fissa non nullable; il completamento dovrebbe essere valutato in base alla fase scelta, mentre l'ambito completo include il supporto per colonne nullable, a larghezza variabile e per i dizionari.

Problem

parquet-java has no public API to write Arrow VectorSchemaRoot to Parquet files. Callers must construct row objects and feed them through ParquetWriter<T>.write(T) one at a time. Arrow C++ and PyArrow support this natively via parquet::WriteTable() / pq.write_table().

Related prior discussion:

• #2264 (open since 2019)
• #3353

Motivation

Several downstream Java projects work with Arrow-columnar data internally and must materialize row objects solely to satisfy ParquetWriter's input API:

• Apache Iceberg (FileAppender<Record>) — iceberg#17748
• Apache Fluss (Arrow-native streaming storage) — fluss#4047
• Apache Paimon (worked around this by building their own paimon-arrow writer that bypasses parquet-java's row API entirely)

Key Insight

For PLAIN-encoded, fixed-width columns, Arrow's in-memory format (contiguous little-endian values) is identical to Parquet's PLAIN page encoding. A zero-copy path is possible by wrapping the Arrow data buffer directly as a BytesInput and passing it to PageWriter.writePage().

For nullable columns, Arrow's validity bitmap can be scanned for contiguous non-null runs, with each run bulk-copied and definition levels encoded as RLE runs of the same value — O(null_transitions) instead of O(N).

Proposed Design

A new ArrowParquetWriter in the parquet-arrow module that writes pages directly to PageWriter rather than going through RecordConsumer/ColumnWriter:

ArrowParquetWriter.writeBatch(VectorSchemaRoot)
    → per column: selects optimal write strategy
    → writes pages to PageWriter via getPageWriter(ColumnDescriptor)
    → manages row groups via ParquetFileWriter

Per-column strategy selection (best to worst):

1. Zero-copy (non-null + fixed-width + PLAIN): wrap Arrow buffer as BytesInput directly. RL/DL as single-value RLE runs. Stats from sequential buffer scan.
2. Bulk-copy with nulls (nullable + fixed-width + PLAIN): scan validity bitmap for non-null runs, bulk-copy each, emit DL as RLE runs per null-transition.
3. Variable-width rewrite (PLAIN + string/binary): single-pass transformation of Arrow offset+data buffers to Parquet's length-prefixed format.
4. Dictionary: map Arrow's DictionaryEncodedVector to Parquet dictionary pages, or build dictionary from plain vector.
5. Fallback: per-value through ValuesWriter (same cost as today, for unsupported encoding/type combos).

All verified public APIs exist for this:

• PageWriteStore.getPageWriter(ColumnDescriptor) — get column page writer directly
• PageWriter.writePage(BytesInput, valueCount, rowCount, stats, encodings) — write pre-encoded pages
• BytesInput.from(ByteBuffer) — zero-copy buffer wrapping
• RunLengthBitPackingHybridEncoder — encode RL/DL levels
• ColumnChunkPageWriteStore.flushToFileWriter() — flush pages to file

Why Not Extend ParquetWriter?

ParquetWriter.write(T) calls InternalParquetRecordWriter.write() which increments recordCount by 1 per call and checks row-group boundaries based on that count. This is incompatible with batch semantics. The writer must manage ParquetFileWriter and row groups directly.

Scope

• Parquet file format unchanged
• Existing ParquetWriter<T> API unchanged
• parquet-arrow gains parquet-hadoop as a compile dependency (for ParquetFileWriter, ColumnChunkPageWriteStore)
• No Hadoop runtime dependency for uncompressed output (compression requires caller-supplied CompressionCodecFactory)
• Flat schemas (primitive columns) first; nested types deferred

Implementation Phases

1. Core infrastructure + zero-copy for non-null fixed-width PLAIN columns
2. Nullable column support (validity bitmap scanning + run-based bulk copy)
3. Variable-width types (string/binary offset rewriting)
4. Dictionary encoding support
5. Bloom filter, nested types, advanced features