Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* Licensed 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.hadoop.hive.ql.io.parquet.read;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.ListIterator;
import java.util.Map;
import java.util.Set;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hive.conf.HiveConf;
import org.apache.hadoop.hive.ql.io.IOConstants;
import org.apache.hadoop.hive.ql.io.parquet.convert.DataWritableRecordConverter;
import org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe;
import org.apache.hadoop.hive.ql.metadata.VirtualColumn;
import org.apache.hadoop.hive.ql.optimizer.FieldNode;
import org.apache.hadoop.hive.ql.optimizer.NestedColumnFieldPruningUtils;
import org.apache.hadoop.hive.serde2.ColumnProjectionUtils;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.typeinfo.ListTypeInfo;
import org.apache.hadoop.hive.serde2.typeinfo.StructTypeInfo;
import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo;
import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory;
import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils;
import org.apache.hadoop.io.ArrayWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.util.StringUtils;
import org.apache.parquet.hadoop.api.InitContext;
import org.apache.parquet.hadoop.api.ReadSupport;
import org.apache.parquet.io.api.RecordMaterializer;
import org.apache.parquet.schema.GroupType;
import org.apache.parquet.schema.MessageType;
import org.apache.parquet.schema.OriginalType;
import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
import org.apache.parquet.schema.Type;
import org.apache.parquet.schema.Type.Repetition;
import org.apache.parquet.schema.Types;
/**
*
* A MapWritableReadSupport
*
* Manages the translation between Hive and Parquet
*
*/
public class DataWritableReadSupport extends ReadSupport {
public static final String HIVE_TABLE_AS_PARQUET_SCHEMA = "HIVE_TABLE_SCHEMA";
public static final String PARQUET_COLUMN_INDEX_ACCESS = "parquet.column.index.access";
private TypeInfo hiveTypeInfo;
/**
* From a string which columns names (including hive column), return a list
* of string columns
*
* @param columns comma separated list of columns
* @return list with virtual columns removed
*/
public static List getColumnNames(final String columns) {
return (List) VirtualColumn.
removeVirtualColumns(StringUtils.getStringCollection(columns));
}
/**
* Returns a list of TypeInfo objects from a string which contains column
* types strings.
*
* @param types Comma separated list of types
* @return A list of TypeInfo objects.
*/
public static List getColumnTypes(final String types) {
return TypeInfoUtils.getTypeInfosFromTypeString(types);
}
/**
* Searchs for a fieldName into a parquet GroupType by ignoring string case.
* GroupType#getType(String fieldName) is case sensitive, so we use this method.
*
* @param groupType Group of field types where to search for fieldName
* @param fieldName The field what we are searching
* @return The Type object of the field found; null otherwise.
*/
private static Type getFieldTypeIgnoreCase(GroupType groupType, String fieldName) {
for (Type type : groupType.getFields()) {
if (type.getName().equalsIgnoreCase(fieldName)) {
return type;
}
}
return null;
}
/**
* Searchs column names by name on a given Parquet schema, and returns its corresponded
* Parquet schema types.
*
* @param schema Group schema where to search for column names.
* @param colNames List of column names.
* @param colTypes List of column types.
* @return List of GroupType objects of projected columns.
*/
private static List getProjectedGroupFields(GroupType schema, List colNames, List colTypes) {
List schemaTypes = new ArrayList();
ListIterator columnIterator = colNames.listIterator();
while (columnIterator.hasNext()) {
TypeInfo colType = colTypes.get(columnIterator.nextIndex());
String colName = columnIterator.next();
Type fieldType = getFieldTypeIgnoreCase(schema, colName);
if (fieldType == null) {
schemaTypes.add(Types.optional(PrimitiveTypeName.BINARY).named(colName));
} else {
schemaTypes.add(getProjectedType(colType, fieldType));
}
}
return schemaTypes;
}
private static Type getProjectedType(TypeInfo colType, Type fieldType) {
switch (colType.getCategory()) {
case STRUCT:
List groupFields = getProjectedGroupFields(
fieldType.asGroupType(),
((StructTypeInfo) colType).getAllStructFieldNames(),
((StructTypeInfo) colType).getAllStructFieldTypeInfos()
);
Type[] typesArray = groupFields.toArray(new Type[0]);
return Types.buildGroup(fieldType.getRepetition())
.addFields(typesArray)
.named(fieldType.getName());
case LIST:
TypeInfo elemType = ((ListTypeInfo) colType).getListElementTypeInfo();
if (elemType.getCategory() == ObjectInspector.Category.STRUCT) {
Type subFieldType = fieldType.asGroupType().getType(0);
if (!subFieldType.isPrimitive()) {
String subFieldName = subFieldType.getName();
Text name = new Text(subFieldName);
if (name.equals(ParquetHiveSerDe.ARRAY) || name.equals(ParquetHiveSerDe.LIST)) {
subFieldType = new GroupType(Repetition.REPEATED, subFieldName,
getProjectedType(elemType, subFieldType.asGroupType().getType(0)));
} else {
subFieldType = getProjectedType(elemType, subFieldType);
}
return Types.buildGroup(Repetition.OPTIONAL).as(OriginalType.LIST).addFields(
subFieldType).named(fieldType.getName());
}
}
break;
default:
}
return fieldType;
}
/**
* Searches column names by name on a given Parquet message schema, and returns its projected
* Parquet schema types.
*
* @param schema Message type schema where to search for column names.
* @param colNames List of column names.
* @param colTypes List of column types.
* @return A MessageType object of projected columns.
*/
public static MessageType getSchemaByName(MessageType schema, List colNames, List colTypes) {
List projectedFields = getProjectedGroupFields(schema, colNames, colTypes);
Type[] typesArray = projectedFields.toArray(new Type[0]);
return Types.buildMessage()
.addFields(typesArray)
.named(schema.getName());
}
/**
* Searches column names by indexes on a given Parquet file schema, and returns its corresponded
* Parquet schema types.
*
* @param schema Message schema where to search for column names.
* @param colNames List of column names.
* @param colIndexes List of column indexes.
* @return A MessageType object of the column names found.
*/
public static MessageType getSchemaByIndex(MessageType schema, List colNames, List colIndexes) {
List schemaTypes = new ArrayList();
for (Integer i : colIndexes) {
if (i < colNames.size()) {
if (i < schema.getFieldCount()) {
schemaTypes.add(schema.getType(i));
} else {
//prefixing with '_mask_' to ensure no conflict with named
//columns in the file schema
schemaTypes.add(
Types.optional(PrimitiveTypeName.BINARY).named("_mask_" + colNames.get(i)));
}
}
}
return new MessageType(schema.getName(), schemaTypes);
}
/**
* Generate the projected schema from colIndexes and nested column paths. If the column is
* contained by colIndex, it will be added directly, otherwise it will build a group type which
* contains all required sub types using nestedColumnPaths.
* @param schema original schema
* @param colNames
* @param colIndexes the index of needed columns
* @param nestedColumnPaths the paths for nested columns
* @return
*/
public static MessageType getProjectedSchema(
MessageType schema,
List colNames,
List colIndexes,
Set nestedColumnPaths) {
List schemaTypes = new ArrayList();
Map prunedCols = getPrunedNestedColumns(nestedColumnPaths);
for (Integer i : colIndexes) {
if (i < colNames.size()) {
if (i < schema.getFieldCount()) {
Type t = schema.getType(i);
String tn = t.getName().toLowerCase();
if (!prunedCols.containsKey(tn)) {
schemaTypes.add(schema.getType(i));
} else {
if (t.isPrimitive()) {
// For primitive type, add directly.
schemaTypes.add(t);
} else {
// For group type, we need to build the projected group type with required leaves
List g =
projectLeafTypes(Arrays.asList(t), Arrays.asList(prunedCols.get(tn)));
if (!g.isEmpty()) {
schemaTypes.addAll(g);
}
}
}
} else {
//prefixing with '_mask_' to ensure no conflict with named
//columns in the file schema
schemaTypes.add(Types.optional(PrimitiveTypeName.BINARY).named("_mask_" + colNames.get(i)));
}
}
}
return new MessageType(schema.getName(), schemaTypes);
}
/**
* Return the columns which contains required nested attribute level
* E.g., given struct a: while 'x' is required and 'y' is not, the method will return
* a pruned struct for 'a' which only contains the attribute 'x'
*
* @param nestedColPaths the paths for required nested attribute
* @return a map from the column to its selected nested column paths, of which the keys are all lower-cased.
*/
private static Map getPrunedNestedColumns(Set nestedColPaths) {
Map resMap = new HashMap<>();
if (nestedColPaths.isEmpty()) {
return resMap;
}
for (String s : nestedColPaths) {
String c = StringUtils.split(s, '.')[0].toLowerCase();
if (!resMap.containsKey(c)) {
FieldNode f = NestedColumnFieldPruningUtils.addNodeByPath(null, s);
resMap.put(c, f);
} else {
resMap.put(c, NestedColumnFieldPruningUtils.addNodeByPath(resMap.get(c), s));
}
}
return resMap;
}
private static GroupType buildProjectedGroupType(
GroupType originalType,
List types) {
if (types == null || types.isEmpty()) {
return null;
}
return new GroupType(originalType.getRepetition(), originalType.getName(), types);
}
private static List projectLeafTypes(
List types,
List nodes) {
List res = new ArrayList<>();
if (nodes.isEmpty()) {
return res;
}
Map fieldMap = new HashMap<>();
for (FieldNode n : nodes) {
fieldMap.put(n.getFieldName().toLowerCase(), n);
}
for (Type type : types) {
String tn = type.getName().toLowerCase();
if (fieldMap.containsKey(tn)) {
FieldNode f = fieldMap.get(tn);
if (f.getNodes().isEmpty()) {
// no child, no need for pruning
res.add(type);
} else {
if (type instanceof GroupType) {
GroupType groupType = type.asGroupType();
List ts = projectLeafTypes(groupType.getFields(), f.getNodes());
GroupType g = buildProjectedGroupType(groupType, ts);
if (g != null) {
res.add(g);
}
} else {
throw new RuntimeException(
"Primitive type " + f.getFieldName() + "should not " + "doesn't match type" + f
.toString());
}
}
}
}
return res;
}
/**
* It creates the readContext for Parquet side with the requested schema during the init phase.
*
* @param context
* @return the parquet ReadContext
*/
@Override
public org.apache.parquet.hadoop.api.ReadSupport.ReadContext init(InitContext context) {
Configuration configuration = context.getConfiguration();
MessageType fileSchema = context.getFileSchema();
String columnNames = configuration.get(IOConstants.COLUMNS);
Map contextMetadata = new HashMap();
boolean indexAccess = configuration.getBoolean(PARQUET_COLUMN_INDEX_ACCESS, false);
if (columnNames != null) {
List columnNamesList = getColumnNames(columnNames);
String columnTypes = configuration.get(IOConstants.COLUMNS_TYPES);
List columnTypesList = getColumnTypes(columnTypes);
MessageType tableSchema =
getRequestedSchemaForIndexAccess(indexAccess, columnNamesList, columnTypesList, fileSchema);
contextMetadata.put(HIVE_TABLE_AS_PARQUET_SCHEMA, tableSchema.toString());
contextMetadata.put(PARQUET_COLUMN_INDEX_ACCESS, String.valueOf(indexAccess));
this.hiveTypeInfo = TypeInfoFactory.getStructTypeInfo(columnNamesList, columnTypesList);
return new ReadContext(getRequestedPrunedSchema(columnNamesList, tableSchema, configuration),
contextMetadata);
} else {
contextMetadata.put(HIVE_TABLE_AS_PARQUET_SCHEMA, fileSchema.toString());
return new ReadContext(fileSchema, contextMetadata);
}
}
/**
* It's used for vectorized code path.
* @param indexAccess
* @param columnNamesList
* @param columnTypesList
* @param fileSchema
* @param configuration
* @return
*/
public static MessageType getRequestedSchema(
boolean indexAccess,
List columnNamesList,
List columnTypesList,
MessageType fileSchema,
Configuration configuration) {
MessageType tableSchema =
getRequestedSchemaForIndexAccess(indexAccess, columnNamesList, columnTypesList, fileSchema);
List indexColumnsWanted = ColumnProjectionUtils.getReadColumnIDs(configuration);
//TODO Duplicated code for init method since vectorization reader path doesn't support Nested
// column pruning so far. See HIVE-15156
if (!ColumnProjectionUtils.isReadAllColumns(configuration) && !indexColumnsWanted.isEmpty()) {
return DataWritableReadSupport
.getSchemaByIndex(tableSchema, columnNamesList, indexColumnsWanted);
} else {
return tableSchema;
}
}
private static MessageType getRequestedSchemaForIndexAccess(
boolean indexAccess,
List columnNamesList,
List columnTypesList,
MessageType fileSchema) {
if (indexAccess) {
List indexSequence = new ArrayList();
// Generates a sequence list of indexes
for (int i = 0; i < columnNamesList.size(); i++) {
indexSequence.add(i);
}
return getSchemaByIndex(fileSchema, columnNamesList, indexSequence);
} else {
return getSchemaByName(fileSchema, columnNamesList, columnTypesList);
}
}
private static MessageType getRequestedPrunedSchema(
List columnNamesList,
MessageType fileSchema,
Configuration configuration) {
Set groupPaths = ColumnProjectionUtils.getNestedColumnPaths(configuration);
List indexColumnsWanted = ColumnProjectionUtils.getReadColumnIDs(configuration);
if (!ColumnProjectionUtils.isReadAllColumns(configuration) && !indexColumnsWanted.isEmpty()) {
return getProjectedSchema(fileSchema, columnNamesList, indexColumnsWanted, groupPaths);
} else {
return fileSchema;
}
}
/**
*
* It creates the hive read support to interpret data from parquet to hive
*
* @param configuration // unused
* @param keyValueMetaData
* @param fileSchema // unused
* @param readContext containing the requested schema and the schema of the hive table
* @return Record Materialize for Hive
*/
@Override
public RecordMaterializer prepareForRead(final Configuration configuration,
final Map keyValueMetaData, final MessageType fileSchema,
final org.apache.parquet.hadoop.api.ReadSupport.ReadContext readContext) {
final Map metadata = readContext.getReadSupportMetadata();
if (metadata == null) {
throw new IllegalStateException("ReadContext not initialized properly. " +
"Don't know the Hive Schema.");
}
String key = HiveConf.ConfVars.HIVE_PARQUET_TIMESTAMP_SKIP_CONVERSION.varname;
if (!metadata.containsKey(key)) {
metadata.put(key, String.valueOf(HiveConf.getBoolVar(
configuration, HiveConf.ConfVars.HIVE_PARQUET_TIMESTAMP_SKIP_CONVERSION)));
}
return new DataWritableRecordConverter(readContext.getRequestedSchema(), metadata, hiveTypeInfo);
}
}
