[HUDI-3982] Comprehensive schema evolution in flink when read/batch/cow/snapshot #5443
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.hudi.table.format; | ||
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| import org.apache.avro.Schema; | ||
| import org.apache.flink.annotation.VisibleForTesting; | ||
| import org.apache.flink.table.data.DecimalData; | ||
| import org.apache.flink.table.data.binary.BinaryStringData; | ||
| import org.apache.flink.table.types.DataType; | ||
| import org.apache.flink.table.types.logical.DecimalType; | ||
| import org.apache.flink.table.types.logical.LogicalType; | ||
| import org.apache.flink.table.types.logical.LogicalTypeRoot; | ||
| import org.apache.flink.util.Preconditions; | ||
| import org.apache.hudi.internal.schema.InternalSchema; | ||
| import org.apache.hudi.internal.schema.Type; | ||
| import org.apache.hudi.internal.schema.Types; | ||
| import org.apache.hudi.internal.schema.convert.AvroInternalSchemaConverter; | ||
| import org.apache.hudi.internal.schema.utils.InternalSchemaUtils; | ||
| import org.apache.hudi.util.AvroSchemaConverter; | ||
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| import java.io.Serializable; | ||
| import java.math.BigDecimal; | ||
| import java.time.LocalDate; | ||
| import java.util.Collection; | ||
| import java.util.HashMap; | ||
| import java.util.List; | ||
| import java.util.Map; | ||
| import java.util.stream.Collectors; | ||
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| import static org.apache.flink.table.types.logical.LogicalTypeRoot.BIGINT; | ||
| import static org.apache.flink.table.types.logical.LogicalTypeRoot.DATE; | ||
| import static org.apache.flink.table.types.logical.LogicalTypeRoot.DECIMAL; | ||
| import static org.apache.flink.table.types.logical.LogicalTypeRoot.DOUBLE; | ||
| import static org.apache.flink.table.types.logical.LogicalTypeRoot.FLOAT; | ||
| import static org.apache.flink.table.types.logical.LogicalTypeRoot.INTEGER; | ||
| import static org.apache.flink.table.types.logical.LogicalTypeRoot.VARCHAR; | ||
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| /** | ||
| * CastMap is responsible for type conversion when full schema evolution enabled. | ||
| */ | ||
| public final class CastMap implements Serializable { | ||
| // Maps position (column number) to corresponding cast | ||
| private final Map<Integer, Cast> castMap = new HashMap<>(); | ||
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| /** | ||
| * Creates CastMap by comparing two schemes. Cast of a specific column is created if its type has changed. | ||
| */ | ||
| public static CastMap of(String tableName, InternalSchema querySchema, InternalSchema actualSchema) { | ||
| DataType queryType = internalSchemaToDataType(tableName, querySchema); | ||
| DataType actualType = internalSchemaToDataType(tableName, actualSchema); | ||
| CastMap castMap = new CastMap(); | ||
| InternalSchemaUtils.collectTypeChangedCols(querySchema, actualSchema).entrySet() | ||
| .stream() | ||
| .filter(e -> !isSameType(e.getValue().getLeft(), e.getValue().getRight())) | ||
| .forEach(e -> { | ||
| int pos = e.getKey(); | ||
| LogicalType target = queryType.getChildren().get(pos).getLogicalType(); | ||
| LogicalType actual = actualType.getChildren().get(pos).getLogicalType(); | ||
| castMap.add(pos, actual, target); | ||
| }); | ||
| return castMap; | ||
| } | ||
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| public Object castIfNeed(int pos, Object val) { | ||
| Cast cast = castMap.get(pos); | ||
| if (cast == null) { | ||
| return val; | ||
| } | ||
| return cast(val, cast.from(), cast.to()); | ||
| } | ||
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| private Object cast(Object val, LogicalType fromType, LogicalType toType) { | ||
| LogicalTypeRoot from = fromType.getTypeRoot(); | ||
| LogicalTypeRoot to = toType.getTypeRoot(); | ||
| switch (to) { | ||
| case BIGINT: { | ||
| // Integer => Long | ||
|
Contributor
There was a problem hiding this comment. What is the philosophy of these mappings ?
Contributor
Author
There was a problem hiding this comment. Assume schema evolution DDL alter table t1 alter column val type bigintwhich changes type of val from int to bigint We want to be able to read old data. To do it we need to cast val from int to long otherwise, an exception will be thrown java.lang.ClassCastException: java.lang.Integer cannot be cast to java.lang.Long
at org.apache.flink.table.data.GenericRowData.getLong(GenericRowData.java:154)This class is an analogue of |
||
| if (from == INTEGER) { | ||
| return ((Number) val).longValue(); | ||
| } | ||
| break; | ||
| } | ||
| case FLOAT: { | ||
| // Integer => Float | ||
| // Long => Float | ||
| if (from == INTEGER || from == BIGINT) { | ||
| return ((Number) val).floatValue(); | ||
| } | ||
| break; | ||
| } | ||
| case DOUBLE: { | ||
| // Integer => Double | ||
| // Long => Double | ||
| if (from == INTEGER || from == BIGINT) { | ||
| return ((Number) val).doubleValue(); | ||
| } | ||
| // Float => Double | ||
| if (from == FLOAT) { | ||
| return Double.parseDouble(val.toString()); | ||
| } | ||
| break; | ||
| } | ||
| case DECIMAL: { | ||
| // Integer => Decimal | ||
| // Long => Decimal | ||
| // Double => Decimal | ||
| if (from == INTEGER || from == BIGINT || from == DOUBLE) { | ||
| return toDecimalData((Number) val, toType); | ||
| } | ||
| // Float => Decimal | ||
| if (from == FLOAT) { | ||
| return toDecimalData(Double.parseDouble(val.toString()), toType); | ||
| } | ||
| // String => Decimal | ||
| if (from == VARCHAR) { | ||
| return toDecimalData(Double.parseDouble(val.toString()), toType); | ||
| } | ||
| // Decimal => Decimal | ||
| if (from == DECIMAL) { | ||
| return toDecimalData(((DecimalData) val).toBigDecimal(), toType); | ||
| } | ||
| break; | ||
| } | ||
| case VARCHAR: { | ||
| // Integer => String | ||
| // Long => String | ||
| // Float => String | ||
| // Double => String | ||
| // Decimal => String | ||
| if (from == INTEGER | ||
| || from == BIGINT | ||
| || from == FLOAT | ||
| || from == DOUBLE | ||
| || from == DECIMAL) { | ||
| return new BinaryStringData(String.valueOf(val)); | ||
| } | ||
| // Date => String | ||
| if (from == DATE) { | ||
| return new BinaryStringData(LocalDate.ofEpochDay(((Integer) val).longValue()).toString()); | ||
| } | ||
| break; | ||
| } | ||
| case DATE: { | ||
| // String => Date | ||
| if (from == VARCHAR) { | ||
| return (int) LocalDate.parse(val.toString()).toEpochDay(); | ||
| } | ||
| break; | ||
| } | ||
| default: | ||
| } | ||
| return val; | ||
| } | ||
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| public boolean containsAnyPos(Collection<Integer> positions) { | ||
| return positions.stream().anyMatch(castMap.keySet()::contains); | ||
| } | ||
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| public CastMap rearrange(List<Integer> oldIndexes, List<Integer> newIndexes) { | ||
| Preconditions.checkArgument(oldIndexes.size() == newIndexes.size()); | ||
| CastMap newCastMap = new CastMap(); | ||
| for (int i = 0; i < oldIndexes.size(); i++) { | ||
| Cast cast = castMap.get(oldIndexes.get(i)); | ||
| if (cast != null) { | ||
| newCastMap.add(newIndexes.get(i), cast.from(), cast.to()); | ||
| } | ||
| } | ||
| return newCastMap; | ||
| } | ||
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| @VisibleForTesting | ||
| void add(int pos, LogicalType from, LogicalType to) { | ||
| castMap.put(pos, new Cast(from, to)); | ||
| } | ||
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| private DecimalData toDecimalData(Number val, LogicalType decimalType) { | ||
| BigDecimal valAsDecimal = BigDecimal.valueOf(val.doubleValue()); | ||
| return toDecimalData(valAsDecimal, decimalType); | ||
| } | ||
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| private DecimalData toDecimalData(BigDecimal valAsDecimal, LogicalType decimalType) { | ||
| return DecimalData.fromBigDecimal( | ||
| valAsDecimal, | ||
| ((DecimalType) decimalType).getPrecision(), | ||
| ((DecimalType) decimalType).getScale()); | ||
| } | ||
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| private static boolean isSameType(Type left, Type right) { | ||
| if (left instanceof Types.DecimalType && right instanceof Types.DecimalType) { | ||
| return left.equals(right); | ||
| } | ||
| return left.typeId().equals(right.typeId()); | ||
| } | ||
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| private static DataType internalSchemaToDataType(String tableName, InternalSchema internalSchema) { | ||
| Schema schema = AvroInternalSchemaConverter.convert(internalSchema, tableName); | ||
| return AvroSchemaConverter.convertToDataType(schema); | ||
| } | ||
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| private static final class Cast implements Serializable { | ||
| private final LogicalType from; | ||
| private final LogicalType to; | ||
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| Cast(LogicalType from, LogicalType to) { | ||
| this.from = from; | ||
| this.to = to; | ||
| } | ||
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| LogicalType from() { | ||
| return from; | ||
| } | ||
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| LogicalType to() { | ||
| return to; | ||
| } | ||
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| @Override | ||
| public String toString() { | ||
| return from + " => " + to; | ||
| } | ||
| } | ||
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| @Override | ||
| public String toString() { | ||
| return castMap.entrySet().stream() | ||
| .map(e -> e.getKey() + ": " + e.getValue()) | ||
| .collect(Collectors.joining(", ", "{", "}")); | ||
| } | ||
| } | ||
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Why introduces the spark dependency in flink pom ?
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To prepare test data. Currently, only Spark engine provides way to change schema and write new data after that.
I think when full support of schema evolution is implemented, we can remove this dependency by rewriting test to pure Flink