JingsongLi commented on a change in pull request #1237:
URL: https://github.com/apache/iceberg/pull/1237#discussion_r461402934
##########
File path:
flink/src/main/java/org/apache/iceberg/flink/data/FlinkParquetReaders.java
##########
@@ -19,64 +19,714 @@
package org.apache.iceberg.flink.data;
+import java.math.BigDecimal;
+import java.math.BigInteger;
+import java.nio.ByteBuffer;
import java.util.List;
-import org.apache.flink.types.Row;
+import java.util.Map;
+import org.apache.commons.lang3.ArrayUtils;
+import org.apache.flink.table.data.ArrayData;
+import org.apache.flink.table.data.DecimalData;
+import org.apache.flink.table.data.GenericRowData;
+import org.apache.flink.table.data.MapData;
+import org.apache.flink.table.data.RawValueData;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.data.StringData;
+import org.apache.flink.table.data.TimestampData;
import org.apache.iceberg.Schema;
-import org.apache.iceberg.data.parquet.BaseParquetReaders;
+import org.apache.iceberg.parquet.ParquetSchemaUtil;
import org.apache.iceberg.parquet.ParquetValueReader;
import org.apache.iceberg.parquet.ParquetValueReaders;
+import org.apache.iceberg.parquet.TypeWithSchemaVisitor;
+import org.apache.iceberg.relocated.com.google.common.collect.ImmutableList;
+import org.apache.iceberg.relocated.com.google.common.collect.ImmutableMap;
+import org.apache.iceberg.relocated.com.google.common.collect.Lists;
+import org.apache.iceberg.relocated.com.google.common.collect.Maps;
import org.apache.iceberg.types.Types;
+import org.apache.parquet.column.ColumnDescriptor;
+import org.apache.parquet.io.api.Binary;
+import org.apache.parquet.schema.GroupType;
+import
org.apache.parquet.schema.LogicalTypeAnnotation.DecimalLogicalTypeAnnotation;
import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType;
import org.apache.parquet.schema.Type;
-public class FlinkParquetReaders extends BaseParquetReaders<Row> {
+public class FlinkParquetReaders {
+ private FlinkParquetReaders() {
+ }
- private static final FlinkParquetReaders INSTANCE = new
FlinkParquetReaders();
+ public static ParquetValueReader<RowData> buildReader(Schema expectedSchema,
MessageType fileSchema) {
+ return buildReader(expectedSchema, fileSchema, ImmutableMap.of());
+ }
- private FlinkParquetReaders() {
+ @SuppressWarnings("unchecked")
+ public static ParquetValueReader<RowData> buildReader(Schema expectedSchema,
+ MessageType fileSchema,
+ Map<Integer, ?>
idToConstant) {
+ if (ParquetSchemaUtil.hasIds(fileSchema)) {
+ return (ParquetValueReader<RowData>)
+ TypeWithSchemaVisitor.visit(expectedSchema.asStruct(), fileSchema,
+ new ReadBuilder(fileSchema, idToConstant));
+ } else {
+ return (ParquetValueReader<RowData>)
+ TypeWithSchemaVisitor.visit(expectedSchema.asStruct(), fileSchema,
+ new FallbackReadBuilder(fileSchema, idToConstant));
+ }
+ }
+
+ private static class FallbackReadBuilder extends ReadBuilder {
+ FallbackReadBuilder(MessageType type, Map<Integer, ?> idToConstant) {
+ super(type, idToConstant);
+ }
+
+ @Override
+ public ParquetValueReader<?> message(Types.StructType expected,
MessageType message,
+ List<ParquetValueReader<?>>
fieldReaders) {
+ // the top level matches by ID, but the remaining IDs are missing
+ return super.struct(expected, message, fieldReaders);
+ }
+
+ @Override
+ public ParquetValueReader<?> struct(Types.StructType ignored, GroupType
struct,
+ List<ParquetValueReader<?>>
fieldReaders) {
+ // the expected struct is ignored because nested fields are never found
when the
+ List<ParquetValueReader<?>> newFields =
Lists.newArrayListWithExpectedSize(
+ fieldReaders.size());
+ List<Type> types =
Lists.newArrayListWithExpectedSize(fieldReaders.size());
+ List<Type> fields = struct.getFields();
+ for (int i = 0; i < fields.size(); i += 1) {
+ Type fieldType = fields.get(i);
+ int fieldD = type().getMaxDefinitionLevel(path(fieldType.getName())) -
1;
+ newFields.add(ParquetValueReaders.option(fieldType, fieldD,
fieldReaders.get(i)));
+ types.add(fieldType);
+ }
+
+ return new RowDataReader(types, newFields);
+ }
+ }
+
+ private static class ReadBuilder extends
TypeWithSchemaVisitor<ParquetValueReader<?>> {
+ private final MessageType type;
+ private final Map<Integer, ?> idToConstant;
+
+ ReadBuilder(MessageType type, Map<Integer, ?> idToConstant) {
+ this.type = type;
+ this.idToConstant = idToConstant;
+ }
+
+ @Override
+ public ParquetValueReader<?> message(Types.StructType expected,
MessageType message,
+ List<ParquetValueReader<?>>
fieldReaders) {
+ return struct(expected, message.asGroupType(), fieldReaders);
+ }
+
+ @Override
+ public ParquetValueReader<?> struct(Types.StructType expected, GroupType
struct,
+ List<ParquetValueReader<?>>
fieldReaders) {
+ // match the expected struct's order
+ Map<Integer, ParquetValueReader<?>> readersById = Maps.newHashMap();
+ Map<Integer, Type> typesById = Maps.newHashMap();
+ List<Type> fields = struct.getFields();
+ for (int i = 0; i < fields.size(); i += 1) {
+ Type fieldType = fields.get(i);
+ int fieldD = type.getMaxDefinitionLevel(path(fieldType.getName())) - 1;
+ if (fieldType.getId() != null) {
+ int id = fieldType.getId().intValue();
+ readersById.put(id, ParquetValueReaders.option(fieldType, fieldD,
fieldReaders.get(i)));
+ typesById.put(id, fieldType);
+ }
+ }
+
+ List<Types.NestedField> expectedFields = expected != null ?
+ expected.fields() : ImmutableList.of();
+ List<ParquetValueReader<?>> reorderedFields =
Lists.newArrayListWithExpectedSize(
+ expectedFields.size());
+ List<Type> types =
Lists.newArrayListWithExpectedSize(expectedFields.size());
+ for (Types.NestedField field : expectedFields) {
+ int id = field.fieldId();
+ if (idToConstant.containsKey(id)) {
+ // containsKey is used because the constant may be null
+
reorderedFields.add(ParquetValueReaders.constant(idToConstant.get(id)));
+ types.add(null);
+ } else {
+ ParquetValueReader<?> reader = readersById.get(id);
+ if (reader != null) {
+ reorderedFields.add(reader);
+ types.add(typesById.get(id));
+ } else {
+ reorderedFields.add(ParquetValueReaders.nulls());
+ types.add(null);
+ }
+ }
+ }
+
+ return new RowDataReader(types, reorderedFields);
+ }
+
+ @Override
+ public ParquetValueReader<?> list(Types.ListType expectedList, GroupType
array,
+ ParquetValueReader<?> elementReader) {
+ GroupType repeated = array.getFields().get(0).asGroupType();
+ String[] repeatedPath = currentPath();
+
+ int repeatedD = type.getMaxDefinitionLevel(repeatedPath) - 1;
+ int repeatedR = type.getMaxRepetitionLevel(repeatedPath) - 1;
+
+ Type elementType = repeated.getType(0);
+ int elementD = type.getMaxDefinitionLevel(path(elementType.getName())) -
1;
+
+ return new ArrayReader<>(repeatedD, repeatedR,
ParquetValueReaders.option(elementType, elementD, elementReader));
+ }
+
+ @Override
+ public ParquetValueReader<?> map(Types.MapType expectedMap, GroupType map,
+ ParquetValueReader<?> keyReader,
+ ParquetValueReader<?> valueReader) {
+ GroupType repeatedKeyValue = map.getFields().get(0).asGroupType();
+ String[] repeatedPath = currentPath();
+
+ int repeatedD = type.getMaxDefinitionLevel(repeatedPath) - 1;
+ int repeatedR = type.getMaxRepetitionLevel(repeatedPath) - 1;
+
+ Type keyType = repeatedKeyValue.getType(0);
+ int keyD = type.getMaxDefinitionLevel(path(keyType.getName())) - 1;
+ Type valueType = repeatedKeyValue.getType(1);
+ int valueD = type.getMaxDefinitionLevel(path(valueType.getName())) - 1;
+
+ return new MapReader<>(repeatedD, repeatedR,
+ ParquetValueReaders.option(keyType, keyD, keyReader),
+ ParquetValueReaders.option(valueType, valueD, valueReader));
+ }
+
+ @Override
+ public ParquetValueReader<?>
primitive(org.apache.iceberg.types.Type.PrimitiveType expected,
+ PrimitiveType primitive) {
+ ColumnDescriptor desc = type.getColumnDescription(currentPath());
+
+ if (primitive.getOriginalType() != null) {
+ switch (primitive.getOriginalType()) {
+ case ENUM:
+ case JSON:
+ case UTF8:
+ return new StringReader(desc);
+ case INT_8:
+ case INT_16:
+ case INT_32:
+ case TIME_MICROS:
Review comment:
Yes, unfortunately Flink can only store time in milliseconds. (Although
the time type can be defined to nanoseconds, the internal implementation is
only milliseconds).
Compared with behavior that throwing exception when encountering a non-zero
micro value (If there is a piece of dirty data, it will make the program very
dangerous), I prefer just ignore micro part. Though it's not friendly, the user
is most convenient to use.
We need to document this behavior.
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