[
https://issues.apache.org/jira/browse/HIVE-25975?focusedWorklogId=735272&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-735272
]
ASF GitHub Bot logged work on HIVE-25975:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 02/Mar/22 12:42
Start Date: 02/Mar/22 12:42
Worklog Time Spent: 10m
Work Description: szlta commented on a change in pull request #3060:
URL: https://github.com/apache/hive/pull/3060#discussion_r817654079
##########
File path:
iceberg/iceberg-handler/src/main/java/org/apache/iceberg/mr/hive/GenericUDFIcebergBucket.java
##########
@@ -0,0 +1,209 @@
+/*
+ * 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.iceberg.mr.hive;
+
+import java.nio.ByteBuffer;
+import org.apache.hadoop.hive.ql.exec.Description;
+import org.apache.hadoop.hive.ql.exec.UDFArgumentException;
+import org.apache.hadoop.hive.ql.exec.UDFArgumentLengthException;
+import org.apache.hadoop.hive.ql.exec.UDFArgumentTypeException;
+import org.apache.hadoop.hive.ql.metadata.HiveException;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDF;
+import org.apache.hadoop.hive.serde2.io.HiveDecimalWritable;
+import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
+import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector;
+import
org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorConverter;
+import
org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory;
+import
org.apache.hadoop.hive.serde2.objectinspector.primitive.WritableConstantIntObjectInspector;
+import org.apache.hadoop.hive.serde2.typeinfo.DecimalTypeInfo;
+import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils;
+import org.apache.hadoop.io.BytesWritable;
+import org.apache.hadoop.io.DoubleWritable;
+import org.apache.hadoop.io.FloatWritable;
+import org.apache.hadoop.io.IntWritable;
+import org.apache.hadoop.io.LongWritable;
+import org.apache.iceberg.transforms.Transform;
+import org.apache.iceberg.transforms.Transforms;
+import org.apache.iceberg.types.Type;
+import org.apache.iceberg.types.Types;
+
+/**
+ * GenericUDFIcebergBucket - UDF that wraps around Iceberg's bucket transform
function
+ */
+@Description(name = "iceberg_bucket",
+ value = "_FUNC_(value, bucketCount) - " +
+ "Returns the bucket value calculated by Iceberg bucket transform
function ",
+ extended = "Example:\n > SELECT _FUNC_('A bucket full of ice!', 5);\n 4")
+//@VectorizedExpressions({StringLength.class})
+public class GenericUDFIcebergBucket extends GenericUDF {
+ private final IntWritable result = new IntWritable();
+ private transient PrimitiveObjectInspector argumentOI;
+ private transient PrimitiveObjectInspectorConverter.StringConverter
stringConverter;
+ private transient PrimitiveObjectInspectorConverter.BinaryConverter
binaryConverter;
+ private transient PrimitiveObjectInspectorConverter.IntConverter
intConverter;
+ private transient PrimitiveObjectInspectorConverter.LongConverter
longConverter;
+ private transient PrimitiveObjectInspectorConverter.HiveDecimalConverter
decimalConverter;
+ private transient PrimitiveObjectInspectorConverter.FloatConverter
floatConverter;
+ private transient PrimitiveObjectInspectorConverter.DoubleConverter
doubleConverter;
+ private transient Type.PrimitiveType icebergType;
+ private int numBuckets = -1;
+
+ @Override
+ public ObjectInspector initialize(ObjectInspector[] arguments) throws
UDFArgumentException {
+ if (arguments.length != 2) {
+ throw new UDFArgumentLengthException(
+ "ICEBERG_BUCKET requires 2 argument, got " + arguments.length);
+ }
+
+ if (arguments[0].getCategory() != ObjectInspector.Category.PRIMITIVE) {
+ throw new UDFArgumentException(
+ "ICEBERG_BUCKET first argument takes primitive types, got " +
argumentOI.getTypeName());
+ }
+ argumentOI = (PrimitiveObjectInspector) arguments[0];
+
+ PrimitiveObjectInspector.PrimitiveCategory inputType =
argumentOI.getPrimitiveCategory();
+ ObjectInspector outputOI = null;
+ switch (inputType) {
+ case CHAR:
+ case VARCHAR:
+ case STRING:
+ icebergType = Types.StringType.get();
+ stringConverter = new
PrimitiveObjectInspectorConverter.StringConverter(argumentOI);
+ break;
+
+ case BINARY:
+ icebergType = Types.BinaryType.get();
+ binaryConverter = new
PrimitiveObjectInspectorConverter.BinaryConverter(argumentOI,
+ PrimitiveObjectInspectorFactory.writableBinaryObjectInspector);
+ break;
+
+ case INT:
+ icebergType = Types.IntegerType.get();
+ intConverter = new
PrimitiveObjectInspectorConverter.IntConverter(argumentOI,
+ PrimitiveObjectInspectorFactory.writableIntObjectInspector);
+ break;
+
+ case LONG:
+ icebergType = Types.LongType.get();
+ longConverter = new
PrimitiveObjectInspectorConverter.LongConverter(argumentOI,
+ PrimitiveObjectInspectorFactory.writableLongObjectInspector);
+ break;
+
+ case DECIMAL:
+ DecimalTypeInfo decimalTypeInfo = (DecimalTypeInfo)
TypeInfoUtils.getTypeInfoFromObjectInspector(argumentOI);
+ icebergType = Types.DecimalType.of(decimalTypeInfo.getPrecision(),
decimalTypeInfo.getScale());
+
+ decimalConverter = new
PrimitiveObjectInspectorConverter.HiveDecimalConverter(argumentOI,
+
PrimitiveObjectInspectorFactory.writableHiveDecimalObjectInspector);
+ break;
+
+ case FLOAT:
+ icebergType = Types.FloatType.get();
+ floatConverter = new
PrimitiveObjectInspectorConverter.FloatConverter(argumentOI,
+ PrimitiveObjectInspectorFactory.writableFloatObjectInspector);
+ break;
+
+ case DOUBLE:
+ icebergType = Types.DoubleType.get();
+ doubleConverter = new
PrimitiveObjectInspectorConverter.DoubleConverter(argumentOI,
+ PrimitiveObjectInspectorFactory.writableDoubleObjectInspector);
+ break;
+
+
+ default:
+ throw new UDFArgumentException(
+ " ICEBERG_BUCKET() only takes
STRING/CHAR/VARCHAR/BINARY/INT/LONG/DECIMAL/FLOAT/DOUBLE" +
+ " types as first argument, got " + inputType);
+ }
+
+ argumentOI = (PrimitiveObjectInspector) arguments[1];
+ if (!isValid2ndArgumentType(arguments[1])) {
+ throw new UDFArgumentTypeException(1,
+ "ICEBERG_BUCKET() second argument only takes an int types, got " +
arguments[1].getTypeName());
+ }
+ numBuckets = ((WritableConstantIntObjectInspector)
argumentOI).getWritableConstantValue().get();
+
+ outputOI = PrimitiveObjectInspectorFactory.writableIntObjectInspector;
+ return outputOI;
+ }
+
+ private boolean isValid2ndArgumentType(ObjectInspector arg) {
+ if (arg.getCategory() != ObjectInspector.Category.PRIMITIVE) {
+ return false;
+ } else {
+ PrimitiveObjectInspector.PrimitiveCategory inputType =
argumentOI.getPrimitiveCategory();
+ if (inputType != PrimitiveObjectInspector.PrimitiveCategory.INT) {
+ return false;
+ }
+ }
+ return true;
+ }
+
+ @Override
+ public Object evaluate(DeferredObject[] arguments) throws HiveException {
+
+ DeferredObject argument = arguments[0];
+ if (argument == null) {
+ return null;
+ }
+
+ if (Types.StringType.get().equals(icebergType)) {
Review comment:
Discussed offline - will be refactoring
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Issue Time Tracking
-------------------
Worklog Id: (was: 735272)
Time Spent: 5h 10m (was: 5h)
> Optimize ClusteredWriter for bucketed Iceberg tables
> ----------------------------------------------------
>
> Key: HIVE-25975
> URL: https://issues.apache.org/jira/browse/HIVE-25975
> Project: Hive
> Issue Type: Improvement
> Reporter: Ádám Szita
> Assignee: Ádám Szita
> Priority: Major
> Labels: pull-request-available
> Time Spent: 5h 10m
> Remaining Estimate: 0h
>
> The first version of the ClusteredWriter in Hive-Iceberg will be lenient for
> bucketed tables: i.e. the records do not need to be ordered by the bucket
> values, the writer will just close its current file and open a new one for
> out-of-order records.
> This is suboptimal for the long-term due to creating many small files. Spark
> uses a UDF to compute the bucket value for each record and therefore it is
> able to order the records by bucket values, achieving optimal clustering.
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