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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.
*/
package org.apache.hudi.internal.schema.action;
import org.apache.hudi.common.util.StringUtils;
import org.apache.hudi.internal.schema.InternalSchema;
import org.apache.hudi.internal.schema.Type;
import org.apache.hudi.internal.schema.Types;
import java.util.ArrayList;
import java.util.List;
/**
* Auxiliary class.
* help to merge file schema and query schema to produce final read schema for avro/parquet file
*/
public class InternalSchemaMerger {
private final InternalSchema fileSchema;
private final InternalSchema querySchema;
// now there exist some bugs when we use spark update/merge api,
// those operation will change col nullability from optional to required which is wrong.
// Before that bug is fixed, we need to do adapt.
// if mergeRequiredFiledForce is true, we will ignore the col's required attribute.
private final boolean ignoreRequiredAttribute;
// Whether to use column Type from file schema to read files when we find some column type has changed.
// spark parquetReader need the original column type to read data, otherwise the parquetReader will failed.
// eg: current column type is StringType, now we changed it to decimalType,
// we should not pass decimalType to parquetReader, we must pass StringType to it; when we read out the data, we convert data from String to Decimal, everything is ok.
// for log reader
// since our reWriteRecordWithNewSchema function support rewrite directly, so we no need this parameter
// eg: current column type is StringType, now we changed it to decimalType,
// we can pass decimalType to reWriteRecordWithNewSchema directly, everything is ok.
private boolean useColumnTypeFromFileSchema = true;
// deal with rename
// Whether to use column name from file schema to read files when we find some column name has changed.
// spark parquetReader need the original column name to read data, otherwise the parquetReader will read nothing.
// eg: current column name is colOldName, now we rename it to colNewName,
// we should not pass colNewName to parquetReader, we must pass colOldName to it; when we read out the data.
// for log reader
// since our reWriteRecordWithNewSchema function support rewrite directly, so we no need this parameter
// eg: current column name is colOldName, now we rename it to colNewName,
// we can pass colNewName to reWriteRecordWithNewSchema directly, everything is ok.
private boolean useColNameFromFileSchema = true;
public InternalSchemaMerger(InternalSchema fileSchema, InternalSchema querySchema, boolean ignoreRequiredAttribute, boolean useColumnTypeFromFileSchema, boolean useColNameFromFileSchema) {
this.fileSchema = fileSchema;
this.querySchema = querySchema;
this.ignoreRequiredAttribute = ignoreRequiredAttribute;
this.useColumnTypeFromFileSchema = useColumnTypeFromFileSchema;
this.useColNameFromFileSchema = useColNameFromFileSchema;
}
public InternalSchemaMerger(InternalSchema fileSchema, InternalSchema querySchema, boolean ignoreRequiredAttribute, boolean useColumnTypeFromFileSchema) {
this(fileSchema, querySchema, ignoreRequiredAttribute, useColumnTypeFromFileSchema, true);
}
/**
* Create final read schema to read avro/parquet file.
*
* @return read schema to read avro/parquet file.
*/
public InternalSchema mergeSchema() {
Types.RecordType record = (Types.RecordType) mergeType(querySchema.getRecord(), 0);
return new InternalSchema(record);
}
/**
* Create final read schema to read avro/parquet file.
* this is auxiliary function used by mergeSchema.
*/
private Type mergeType(Type type, int currentTypeId) {
switch (type.typeId()) {
case RECORD:
Types.RecordType record = (Types.RecordType) type;
List newTypes = new ArrayList<>();
for (Types.Field f : record.fields()) {
Type newType = mergeType(f.type(), f.fieldId());
newTypes.add(newType);
}
return Types.RecordType.get(buildRecordType(record.fields(), newTypes));
case ARRAY:
Types.ArrayType array = (Types.ArrayType) type;
Type newElementType;
Types.Field elementField = array.fields().get(0);
newElementType = mergeType(elementField.type(), elementField.fieldId());
return buildArrayType(array, newElementType);
case MAP:
Types.MapType map = (Types.MapType) type;
Type newValueType = mergeType(map.valueType(), map.valueId());
return buildMapType(map, newValueType);
default:
return buildPrimitiveType((Type.PrimitiveType) type, currentTypeId);
}
}
private List buildRecordType(List oldFields, List newTypes) {
List newFields = new ArrayList<>();
for (int i = 0; i < newTypes.size(); i++) {
Type newType = newTypes.get(i);
Types.Field oldField = oldFields.get(i);
int fieldId = oldField.fieldId();
String fullName = querySchema.findfullName(fieldId);
if (fileSchema.findField(fieldId) != null) {
if (fileSchema.findfullName(fieldId).equals(fullName)) {
// maybe col type changed, deal with it.
newFields.add(Types.Field.get(oldField.fieldId(), oldField.isOptional(), oldField.name(), newType, oldField.doc()));
} else {
// find rename, deal with it.
newFields.add(dealWithRename(fieldId, newType, oldField));
}
} else {
// buildFullName
fullName = normalizeFullName(fullName);
if (fileSchema.findField(fullName) != null) {
newFields.add(Types.Field.get(oldField.fieldId(), oldField.isOptional(), oldField.name() + "suffix", oldField.type(), oldField.doc()));
} else {
// find add column
// now there exist some bugs when we use spark update/merge api, those operation will change col optional to required.
if (ignoreRequiredAttribute) {
newFields.add(Types.Field.get(oldField.fieldId(), true, oldField.name(), newType, oldField.doc()));
} else {
newFields.add(Types.Field.get(oldField.fieldId(), oldField.isOptional(), oldField.name(), newType, oldField.doc()));
}
}
}
}
return newFields;
}
private Types.Field dealWithRename(int fieldId, Type newType, Types.Field oldField) {
Types.Field fieldFromFileSchema = fileSchema.findField(fieldId);
String nameFromFileSchema = fieldFromFileSchema.name();
String nameFromQuerySchema = querySchema.findField(fieldId).name();
String finalFieldName = useColNameFromFileSchema ? nameFromFileSchema : nameFromQuerySchema;
Type typeFromFileSchema = fieldFromFileSchema.type();
// Current design mechanism guarantees nestedType change is not allowed, so no need to consider.
if (newType.isNestedType()) {
return Types.Field.get(oldField.fieldId(), oldField.isOptional(),
finalFieldName, newType, oldField.doc());
} else {
return Types.Field.get(oldField.fieldId(), oldField.isOptional(),
finalFieldName, useColumnTypeFromFileSchema ? typeFromFileSchema : newType, oldField.doc());
}
}
private String normalizeFullName(String fullName) {
// find parent rename, and normalize fullName
// eg: we renamed a nest field struct(c, d) to aa, the we delete a.d and add it back later.
String[] nameParts = fullName.split("\\.");
String[] normalizedNameParts = new String[nameParts.length];
System.arraycopy(nameParts, 0, normalizedNameParts, 0, nameParts.length);
for (int j = 0; j < nameParts.length - 1; j++) {
StringBuilder sb = new StringBuilder();
for (int k = 0; k <= j; k++) {
sb.append(nameParts[k]);
}
String parentName = sb.toString();
int parentFieldIdFromQuerySchema = querySchema.findIdByName(parentName);
String parentNameFromFileSchema = fileSchema.findfullName(parentFieldIdFromQuerySchema);
if (parentNameFromFileSchema.isEmpty()) {
break;
}
if (!parentNameFromFileSchema.equalsIgnoreCase(parentName)) {
// find parent rename, update nameParts
String[] parentNameParts = parentNameFromFileSchema.split("\\.");
System.arraycopy(parentNameParts, 0, normalizedNameParts, 0, parentNameParts.length);
}
}
return StringUtils.join(normalizedNameParts, ".");
}
private Type buildArrayType(Types.ArrayType array, Type newType) {
Types.Field elementField = array.fields().get(0);
int elementId = elementField.fieldId();
if (elementField.type() == newType) {
return array;
} else {
return Types.ArrayType.get(elementId, elementField.isOptional(), newType);
}
}
private Type buildMapType(Types.MapType map, Type newValue) {
Types.Field valueFiled = map.fields().get(1);
if (valueFiled.type() == newValue) {
return map;
} else {
return Types.MapType.get(map.keyId(), map.valueId(), map.keyType(), newValue, map.isValueOptional());
}
}
private Type buildPrimitiveType(Type.PrimitiveType typeFromQuerySchema, int currentPrimitiveTypeId) {
Type typeFromFileSchema = fileSchema.findType(currentPrimitiveTypeId);
if (typeFromFileSchema == null) {
return typeFromQuerySchema;
} else {
return useColumnTypeFromFileSchema ? typeFromFileSchema : typeFromQuerySchema;
}
}
}
