voonhous commented on code in PR #18961:
URL: https://github.com/apache/hudi/pull/18961#discussion_r3828457638
##########
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/io/storage/HoodieSparkFileWriterFactory.java:
##########
@@ -56,6 +62,43 @@ public HoodieSparkFileWriterFactory(HoodieStorage storage) {
protected HoodieFileWriter newParquetFileWriter(
String instantTime, StoragePath path, HoodieConfig config, HoodieSchema
schema,
TaskContextSupplier taskContextSupplier) throws IOException {
+ // The row write support resolves its HoodieSchema from the config
(hoodie.write.schema /
+ // hoodie.avro.schema), not the schema argument, so inferable columns are
detected on that
+ // config schema (the one the splice below targets) intersected with the
schema argument's
+ // (the shape of the rows being written, which the samples come from). The
argument is
+ // checked first because it is already parsed: native log, delete and CDC
writers share this
+ // factory with schemas that have no top-level variant, and they must not
pay a config-schema
Review Comment:
Dropped native log from the list; the data writer there does carry the
variant. The claim now covers delete and CDC writers only.
##########
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/io/storage/row/HoodieRowParquetWriteSupport.java:
##########
@@ -902,16 +908,25 @@ private Type convertField(HoodieSchema fieldSchema,
StructField structField, Typ
.named(MAP_REPEATED_NAME))
.named(structField.name());
} else if (dataType instanceof StructType) {
+ StructType nestedStruct = (StructType) dataType;
Types.GroupBuilder<GroupType> groupBuilder =
Types.buildGroup(repetition);
- Arrays.stream(((StructType) dataType).fields()).forEach(field -> {
+ Arrays.stream(nestedStruct.fields()).forEach(field -> {
// Note: Cannot use HoodieSchemaField::schema method reference due to
Java 17 compilation ambiguity
HoodieSchema nestedFieldSchema = Option.ofNullable(resolvedSchema)
.flatMap(s -> s.getField(field.name()))
.map(f -> f.schema())
.orElse(null);
groupBuilder.addField(convertField(nestedFieldSchema, field));
});
- return groupBuilder.named(structField.name());
+ // A shredded variant column reaches here as a marked struct (the
VariantType was replaced by
+ // its {metadata, value, typed_value} shredding schema). Tag the parquet
group with the VARIANT
+ // logical type so external readers recognize it as a Variant, matching
Spark's native shredded
+ // parquet schema. No-op on Spark 4.0/3.x (the annotation only exists in
parquet 1.16+).
+ Types.GroupBuilder<GroupType> taggedBuilder =
Review Comment:
Updated the Impact section to cover the row-path shredded groups too.
##########
hudi-common/src/main/java/org/apache/hudi/common/avro/VariantSchemaUtils.java:
##########
@@ -123,6 +143,235 @@ private static HoodieSchema
stripVariantShreddingAt(HoodieSchema schema) {
return wasNullable ? HoodieSchema.createNullable(replacement) :
replacement;
}
+ /**
+ * Strips {@code typed_value} from top-level fields that have the variant
SHAPE but lost the
+ * variant logical type, i.e. plain records of {@code {metadata: bytes,
value: [nullable]
+ * bytes, typed_value}} (see {@link #isShreddedVariantShape}).
Parquet-footer-derived schemas
+ * come back this way (the converter does not attach the variant logical
type), so
+ * {@link #stripVariantShredding} alone cannot see them. Used by the
table-schema footer
+ * fallback only; returns {@code schema} as-is when nothing matches.
+ *
+ * <p>Unlike {@link #isShreddedVariantTarget}, the match here has NO
requested-side anchor:
+ * the footer fallback runs precisely when no table schema is available to
anchor on, so a
+ * plain user struct that happens to have exactly this shape is stripped too
(a documented,
+ * accepted false positive: {@code metadata} plus {@code typed_value} is the
variant spec's
+ * vocabulary). Top-level fields only, matching the scope of
+ * {@link #getInferableVariantColumns}: inference never shreds a nested
variant, and
+ * {@code HoodieAvroWriteSupport.applyForcedShreddingSchema} walks top-level
fields only. The row
+ * writer can force-shred at depth (see {@link #swapShreddedVariantFields}),
a test-only
Review Comment:
Reworded both spots: the javadoc now says the row writer shreds at any depth
its write schema asks, forced DDL or not, and the resolver comment notes the
shape fallback is top-level only, so a nested shredded column can still surface.
##########
hudi-common/src/main/java/org/apache/hudi/common/config/HoodieStorageConfig.java:
##########
@@ -298,6 +298,23 @@ public class HoodieStorageConfig extends HoodieConfig {
+ "The provider parses variant binary data and populates typed_value
columns. "
+ "When not set, the provider is auto-detected from the classpath.");
+ public static final ConfigProperty<Boolean>
PARQUET_VARIANT_SHREDDING_SCHEMA_INFERENCE_ENABLED = ConfigProperty
+ .key("hoodie.parquet.variant.shredding.schema.inference.enabled")
+ .defaultValue(false)
+ .sinceVersion("1.3.0")
+ .withDocumentation("When enabled, the shredding schema for variant
columns without an explicit "
+ + "typed_value in the write schema is inferred automatically per
parquet file from a sample of "
+ + "the records written to that file, mirroring Spark 4.1's "
+ + "spark.sql.variant.inferShreddingSchema. Requires Spark 4.1+ on
the writer classpath; "
+ + "writes stay unshredded otherwise (Spark 4.0, Flink, Java
engines). Applies to every "
+ + "parquet file the writer produces: base files and, on table
version 10+, the native "
+ + "parquet log files of MOR tables (each infers its own schema);
legacy Avro log blocks "
+ + "stay unshredded and shred at compaction. Up to 4096 records or
64MB are buffered per "
+ + "open file writer before the writer is created, on top of
parquet's own row-group "
+ + "buffer, so size executor memory for concurrently open handles
accordingly. Ignored when "
+ + "hoodie.parquet.variant.force.shredding.schema.for.test is set or
when write shredding "
Review Comment:
Added the internal-schema gate to the config doc.
##########
hudi-common/src/main/java/org/apache/hudi/core/io/storage/VariantShreddingInferenceFileWriter.java:
##########
@@ -0,0 +1,328 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.hudi.core.io.storage;
+
+import org.apache.hudi.common.avro.VariantShreddingSchemaInferrer;
+import
org.apache.hudi.common.avro.VariantShreddingSchemaInferrer.VariantSample;
+import org.apache.hudi.common.model.HoodieKey;
+import org.apache.hudi.common.model.HoodieRecord;
+import org.apache.hudi.common.schema.HoodieSchema;
+import org.apache.hudi.common.util.CloseableUtils;
+import org.apache.hudi.common.util.DefaultSizeEstimator;
+import org.apache.hudi.common.util.SizeEstimator;
+import org.apache.hudi.exception.HoodieIOException;
+
+import lombok.extern.slf4j.Slf4j;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Properties;
+
+/**
+ * A {@link HoodieFileWriter} decorator that infers a per-file variant
shredding schema from the
+ * first records before opening the real parquet writer.
+ *
+ * <p>Records are buffered (and their variant binaries sampled) until a
threshold is reached or
+ * the writer closes, the sampled binaries are fed to a {@link
VariantShreddingSchemaInferrer},
+ * the real writer is created against the schema with the inferred typed_value
spliced in, and
+ * the buffer is replayed in arrival order. Replay reproduces each call
exactly (write vs
+ * writeWithMetadata), so commit seqnos, bloom filters and min/max record keys
come out
+ * identical to a non-buffered write. Buffering thresholds mirror Spark's
+ * {@code ParquetOutputWriterWithVariantShredding} (4096 rows / 64MB).
+ *
+ * <p>Buffered records are {@link HoodieRecord#copy() copied} because Spark
iterators reuse row
+ * instances, then handed to {@link VariantSampleExtractor#prepare}, which
serves two purposes:
+ * an extractor that has to materialize the record to sample it (the Avro one
deserializes
+ * payload-backed records) returns the materialized form for buffering so the
replay does not
+ * repeat that work, and (since copy() returns the caller's own wrapper for
every record type
+ * today) both shipped extractors return a wrapper the caller does not hold,
so a handle that
+ * deflates its record right after the write call cannot blank a buffered one.
Records with
+ * nothing to materialize (delete payloads) are buffered as they are, so
replay still relies on
+ * writer-level records being freshly allocated per record, which holds today;
variant samples
+ * are extracted eagerly into immutable byte arrays so inference itself never
depends on it.
+ *
+ * <p>Inference failures never fail the write: the file falls back to
unshredded variants. This
+ * deliberately diverges from Spark (which propagates inference failures)
because a throwing
+ * inference would fail compaction. Writer-creation or replay failures,
however, are latched and
+ * rethrown from every subsequent call including {@link #close()}, so a task
cannot silently
+ * drop buffered records that the handle already counted as written.
+ *
+ * <p>{@link #writeRow} carries neither a {@link HoodieRecord} nor a schema to
sample from, so the
+ * first such call materializes the real writer with whatever has been sampled
so far (unshredded
+ * when nothing has) and passes the row straight through. Footer metadata
added before
+ * materialization is queued and handed to the real writer once it exists;
parquet only consumes
+ * it at close, so nothing is lost.
+ *
+ * <p>Single-threaded by contract, same as the writers it wraps.
+ *
+ * <p>See https://github.com/apache/hudi/issues/18937.</p>
+ *
+ * @param <T> the engine-native record type of the wrapped writer
+ */
+@Slf4j
+public class VariantShreddingInferenceFileWriter<T> implements
HoodieFileWriter<T> {
+
+ /** Buffer caps mirroring Spark's ParquetOutputWriterWithVariantShredding. */
+ public static final int MAX_BUFFERED_RECORDS = 4096;
+ public static final long MAX_BUFFERED_BYTES = 64L * 1024 * 1024;
+ private static final int SIZE_ESTIMATE_INTERVAL = 100;
+
+ /**
+ * Extracts the variant binaries of the inferable columns from a record.
Bound to the writer
+ * schema and column set by the creating factory; must defensively copy the
bytes.
+ */
+ @FunctionalInterface
+ public interface VariantSampleExtractor {
+ VariantSample[] extract(HoodieRecord record, HoodieSchema schema,
Properties props) throws IOException;
+
+ /**
+ * Returns the record to buffer for replay; {@link #extract} is then
called with that record.
+ * An extractor that must materialize the record to sample it returns the
materialized form,
+ * so the replay does not redo the work. Defaults to the record itself.
+ */
+ default HoodieRecord prepare(HoodieRecord record, HoodieSchema schema,
Properties props) throws IOException {
+ return record;
+ }
+
+ /**
+ * Bytes of state that every buffered record references but that is shared
across them, so a
+ * deep object-size walk of one record counts it in full: the Avro {@code
Schema} graph of an
+ * Avro record, the {@code StructType} of a Spark row. Subtracted from
each record's size
+ * estimate so the byte cap budgets record payload rather than the schema
times the record
+ * count (the HUDI-9499 class of over-estimate, which would shrink the
inference sample to a
+ * fraction of the intended 4096 rows). Defaults to 0.
+ */
+ default long sharedSizeEstimate(HoodieSchema schema) {
+ return 0;
+ }
+ }
+
+ /** Creates the real file writer once the inferred typed_value schemas are
known. */
+ @FunctionalInterface
+ public interface InferredWriterFactory<T> {
+ HoodieFileWriter<T> create(Map<String, HoodieSchema> inferredTypedValues)
throws IOException;
+ }
+
+ private final List<String> variantColumns;
+ private final VariantSampleExtractor extractor;
+ private final VariantShreddingSchemaInferrer inferrer;
+ private final InferredWriterFactory<T> writerFactory;
+ private final long maxBufferedBytes;
+ private final SizeEstimator<HoodieRecord> sizeEstimator = new
DefaultSizeEstimator<>();
+
+ private final List<BufferedWrite> buffer = new ArrayList<>();
+ private final List<VariantSample[]> samples = new ArrayList<>();
+ private final Map<String, String> pendingFooterMetadata = new
LinkedHashMap<>();
+ private long estimatedRecordSize = 0;
+ private long bufferedBytes = 0;
+ private HoodieFileWriter<T> delegate;
+ private IOException fatalFailure;
+ private boolean closed = false;
+
+ public VariantShreddingInferenceFileWriter(List<String> variantColumns,
+ VariantSampleExtractor extractor,
+ VariantShreddingSchemaInferrer
inferrer,
+ InferredWriterFactory<T>
writerFactory,
+ long maxFileSize) {
+ this.variantColumns = variantColumns;
+ this.extractor = extractor;
+ this.inferrer = inferrer;
+ this.writerFactory = writerFactory;
+ this.maxBufferedBytes = Math.min(MAX_BUFFERED_BYTES, Math.max(1,
maxFileSize));
+ }
+
+ @Override
+ public boolean canWrite() {
+ // Nothing has been physically written while buffering, so size-based
rollover cannot apply yet.
+ return delegate == null || delegate.canWrite();
+ }
+
+ @Override
+ public void writeWithMetadata(HoodieKey key, HoodieRecord record,
HoodieSchema schema, Properties props) throws IOException {
+ rethrowIfFailed();
+ if (delegate != null) {
+ delegate.writeWithMetadata(key, record, schema, props);
+ } else {
+ buffer(true, key, null, record, schema, props);
+ }
+ }
+
+ @Override
+ public void write(String recordKey, HoodieRecord record, HoodieSchema
schema, Properties props) throws IOException {
+ rethrowIfFailed();
+ if (delegate != null) {
+ delegate.write(recordKey, record, schema, props);
+ } else {
+ buffer(false, null, recordKey, record, schema, props);
+ }
+ }
+
+ @Override
+ public void writeRow(String recordKey, T record) throws IOException {
+ rethrowIfFailed();
+ // No HoodieRecord or schema to sample from: materialize with what has
been sampled so far and
+ // pass the row through. Today only the native log-format delete writer
takes this path, and its
+ // delete schema has no variant column to infer for.
+ materialize();
+ delegate.writeRow(recordKey, record);
+ }
+
+ @Override
+ public void addFooterMetadata(Map<String, String> footerMetadata) {
+ if (delegate != null) {
+ delegate.addFooterMetadata(footerMetadata);
+ } else {
+ // Footer metadata is only consumed at close, so it can wait for the
real writer.
+ pendingFooterMetadata.putAll(footerMetadata);
+ }
+ }
+
+ @Override
+ public void close() throws IOException {
+ if (closed) {
+ return;
+ }
+ closed = true;
+ boolean delegateClosed = false;
+ try {
+ rethrowIfFailed();
+ // Materialize even with an empty buffer: handles expect the file to
exist at close.
+ materialize();
+ // Mark before close() so a throwing delegate.close() surfaces, not
retried in the catch.
+ delegateClosed = true;
+ delegate.close();
+ } catch (IOException | RuntimeException e) {
Review Comment:
Widened both decorators to catch Error, with a test each pinning that the
delegate gets closed.
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