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 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
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package org.apache.iceberg.spark.data;

import java.io.IOException;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import org.apache.avro.LogicalType;
import org.apache.avro.LogicalTypes;
import org.apache.avro.Schema;
import org.apache.avro.io.DatumReader;
import org.apache.avro.io.Decoder;
import org.apache.avro.io.DecoderFactory;
import org.apache.avro.io.ResolvingDecoder;
import org.apache.iceberg.avro.AvroSchemaWithTypeVisitor;
import org.apache.iceberg.avro.ValueReader;
import org.apache.iceberg.avro.ValueReaders;
import org.apache.iceberg.exceptions.RuntimeIOException;
import org.apache.iceberg.relocated.com.google.common.collect.ImmutableMap;
import org.apache.iceberg.relocated.com.google.common.collect.MapMaker;
import org.apache.iceberg.types.Type;
import org.apache.iceberg.types.Types;
import org.apache.spark.sql.catalyst.InternalRow;

public class SparkAvroReader implements DatumReader<InternalRow> {

  private static final ThreadLocal<Map<Schema, Map<Schema, ResolvingDecoder>>> DECODER_CACHES =
      ThreadLocal.withInitial(() -> new MapMaker().weakKeys().makeMap());

  private final Schema readSchema;
  private final ValueReader<InternalRow> reader;
  private Schema fileSchema = null;

  public SparkAvroReader(org.apache.iceberg.Schema expectedSchema, Schema readSchema) {
    this(expectedSchema, readSchema, ImmutableMap.of());

  public SparkAvroReader(org.apache.iceberg.Schema expectedSchema, Schema readSchema, Map<Integer, ?> constants) {
    this.readSchema = readSchema;
    this.reader = (ValueReader<InternalRow>) AvroSchemaWithTypeVisitor
        .visit(expectedSchema, readSchema, new ReadBuilder(constants));

  public void setSchema(Schema newFileSchema) {
    this.fileSchema = Schema.applyAliases(newFileSchema, readSchema);

  public InternalRow read(InternalRow reuse, Decoder decoder) throws IOException {
    ResolvingDecoder resolver = resolve(decoder);
    InternalRow row = reader.read(resolver, reuse);
    return row;

  private ResolvingDecoder resolve(Decoder decoder) throws IOException {
    Map<Schema, Map<Schema, ResolvingDecoder>> cache = DECODER_CACHES.get();
    Map<Schema, ResolvingDecoder> fileSchemaToResolver = cache
        .computeIfAbsent(readSchema, k -> new HashMap<>());

    ResolvingDecoder resolver = fileSchemaToResolver.get(fileSchema);
    if (resolver == null) {
      resolver = newResolver();
      fileSchemaToResolver.put(fileSchema, resolver);


    return resolver;

  private ResolvingDecoder newResolver() {
    try {
      return DecoderFactory.get().resolvingDecoder(fileSchema, readSchema, null);
    } catch (IOException e) {
      throw new RuntimeIOException(e);

  private static class ReadBuilder extends AvroSchemaWithTypeVisitor<ValueReader<?>> {
    private final Map<Integer, ?> idToConstant;

    private ReadBuilder(Map<Integer, ?> idToConstant) {
      this.idToConstant = idToConstant;

    public ValueReader<?> record(Types.StructType expected, Schema record, List<String> names,
                                 List<ValueReader<?>> fields) {
      return SparkValueReaders.struct(fields, expected, idToConstant);

    public ValueReader<?> union(Type expected, Schema union, List<ValueReader<?>> options) {
      return ValueReaders.union(options);

    public ValueReader<?> array(Types.ListType expected, Schema array, ValueReader<?> elementReader) {
      return SparkValueReaders.array(elementReader);

    public ValueReader<?> map(Types.MapType expected, Schema map,
                              ValueReader<?> keyReader, ValueReader<?> valueReader) {
      return SparkValueReaders.arrayMap(keyReader, valueReader);

    public ValueReader<?> map(Types.MapType expected, Schema map, ValueReader<?> valueReader) {
      return SparkValueReaders.map(SparkValueReaders.strings(), valueReader);

    public ValueReader<?> primitive(Type.PrimitiveType expected, Schema primitive) {
      LogicalType logicalType = primitive.getLogicalType();
      if (logicalType != null) {
        switch (logicalType.getName()) {
          case "date":
            // Spark uses the same representation
            return ValueReaders.ints();

          case "timestamp-millis":
            // adjust to microseconds
            ValueReader<Long> longs = ValueReaders.longs();
            return (ValueReader<Long>) (decoder, ignored) -> longs.read(decoder, null) * 1000L;

          case "timestamp-micros":
            // Spark uses the same representation
            return ValueReaders.longs();

          case "decimal":
            ValueReader<byte[]> inner;
            switch (primitive.getType()) {
              case FIXED:
                inner = ValueReaders.fixed(primitive.getFixedSize());
              case BYTES:
                inner = ValueReaders.bytes();
                throw new IllegalArgumentException(
                    "Invalid primitive type for decimal: " + primitive.getType());

            LogicalTypes.Decimal decimal = (LogicalTypes.Decimal) logicalType;
            return SparkValueReaders.decimal(inner, decimal.getScale());

          case "uuid":
            return SparkValueReaders.uuids();

            throw new IllegalArgumentException("Unknown logical type: " + logicalType);

      switch (primitive.getType()) {
        case NULL:
          return ValueReaders.nulls();
        case BOOLEAN:
          return ValueReaders.booleans();
        case INT:
          return ValueReaders.ints();
        case LONG:
          return ValueReaders.longs();
        case FLOAT:
          return ValueReaders.floats();
        case DOUBLE:
          return ValueReaders.doubles();
        case STRING:
          return SparkValueReaders.strings();
        case FIXED:
          return ValueReaders.fixed(primitive.getFixedSize());
        case BYTES:
          return ValueReaders.bytes();
        case ENUM:
          return SparkValueReaders.enums(primitive.getEnumSymbols());
          throw new IllegalArgumentException("Unsupported type: " + primitive);