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https://issues.apache.org/jira/browse/NIFI-1663?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15344498#comment-15344498
]
ASF GitHub Bot commented on NIFI-1663:
--------------------------------------
Github user markap14 commented on a diff in the pull request:
https://github.com/apache/nifi/pull/477#discussion_r68074457
--- Diff:
nifi-nar-bundles/nifi-hive-bundle/nifi-hive-processors/src/main/java/org/apache/nifi/util/orc/OrcUtils.java
---
@@ -0,0 +1,443 @@
+/*
+ * 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.nifi.util.orc;
+
+import org.apache.avro.Schema;
+import org.apache.commons.lang3.StringUtils;
+import org.apache.commons.lang3.mutable.MutableInt;
+import org.apache.hadoop.hive.ql.exec.vector.BytesColumnVector;
+import org.apache.hadoop.hive.ql.exec.vector.ColumnVector;
+import org.apache.hadoop.hive.ql.exec.vector.DoubleColumnVector;
+import org.apache.hadoop.hive.ql.exec.vector.ListColumnVector;
+import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector;
+import org.apache.hadoop.hive.ql.exec.vector.MapColumnVector;
+import org.apache.hadoop.hive.ql.exec.vector.UnionColumnVector;
+import org.apache.orc.TypeDescription;
+
+import java.nio.ByteBuffer;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+
+/**
+ * Utility methods for ORC support (conversion from Avro, conversion to
Hive types, e.g.
+ */
+public class OrcUtils {
+
+ public static void putToRowBatch(ColumnVector col, MutableInt
vectorOffset, int rowNumber, Schema fieldSchema, Object o) {
+ Schema.Type fieldType = fieldSchema.getType();
+
+ if (fieldType == null) {
+ throw new IllegalArgumentException("Field type is null");
+ }
+
+ if (Schema.Type.INT.equals(fieldType)) {
+ if (o == null) {
+ col.isNull[rowNumber] = true;
+ } else {
+ ((LongColumnVector) col).vector[rowNumber] = (int) o;
+ }
+ } else if (Schema.Type.LONG.equals(fieldType)) {
+ if (o == null) {
+ col.isNull[rowNumber] = true;
+ } else {
+ ((LongColumnVector) col).vector[rowNumber] = (long) o;
+ }
+ } else if (Schema.Type.BOOLEAN.equals(fieldType)) {
+ if (o == null) {
+ col.isNull[rowNumber] = true;
+ } else {
+ ((LongColumnVector) col).vector[rowNumber] = ((boolean) o)
? 1 : 0;
+ }
+ } else if (Schema.Type.BYTES.equals(fieldType)) {
+ if (o == null) {
+ col.isNull[rowNumber] = true;
+ } else {
+ ByteBuffer byteBuffer = ((ByteBuffer) o);
+ int size = byteBuffer.remaining();
+ byte[] buf = new byte[size];
+ byteBuffer.get(buf, 0, size);
+ ((BytesColumnVector) col).setVal(rowNumber, buf);
+ }
+ } else if (Schema.Type.DOUBLE.equals(fieldType)) {
+ if (o == null) {
+ col.isNull[rowNumber] = true;
+ } else {
+ ((DoubleColumnVector) col).vector[rowNumber] = (double) o;
+ }
+ } else if (Schema.Type.FLOAT.equals(fieldType)) {
+ if (o == null) {
+ col.isNull[rowNumber] = true;
+ } else {
+ ((DoubleColumnVector) col).vector[rowNumber] = (float) o;
+ }
+ } else if (Schema.Type.STRING.equals(fieldType) ||
Schema.Type.ENUM.equals(fieldType)) {
+ if (o == null) {
+ col.isNull[rowNumber] = true;
+ } else {
+ ((BytesColumnVector) col).setVal(rowNumber,
o.toString().getBytes());
+ }
+ } else if (Schema.Type.UNION.equals(fieldType)) {
+ // If the union only has one non-null type in it, it was
flattened in the ORC schema
+ if (col instanceof UnionColumnVector) {
+ UnionColumnVector union = ((UnionColumnVector) col);
+ Schema.Type avroType =
OrcUtils.getAvroSchemaTypeOfObject(o);
+ // Find the index in the union with the matching Avro type
+ int unionIndex = -1;
+ List<Schema> types = fieldSchema.getTypes();
+ final int numFields = types.size();
+ for (int i = 0; i < numFields && unionIndex == -1; i++) {
+ if (avroType.equals(types.get(i).getType())) {
+ unionIndex = i;
+ }
+ }
+ if (unionIndex == -1) {
+ throw new IllegalArgumentException("Object type " +
avroType.getName() + " not found in union '" + fieldSchema.getName() + "'");
+ }
+
+ // Need nested vector offsets
+ MutableInt unionVectorOffset = new MutableInt(0);
+ putToRowBatch(union.fields[unionIndex], unionVectorOffset,
rowNumber, fieldSchema.getTypes().get(unionIndex), o);
+ } else {
+ // Find and use the non-null type from the union
+ List<Schema> types = fieldSchema.getTypes();
+ Schema effectiveType = null;
+ for (Schema type : types) {
+ if (!Schema.Type.NULL.equals(type.getType())) {
+ effectiveType = type;
+ break;
+ }
+ }
+ putToRowBatch(col, vectorOffset, rowNumber, effectiveType,
o);
+ }
+
+ } else if (Schema.Type.ARRAY.equals(fieldType)) {
+ Schema arrayType = fieldSchema.getElementType();
+ ListColumnVector array = ((ListColumnVector) col);
+ if (o instanceof int[]) {
--- End diff --
The logic for `int[]` and `long[]` appear to be the same and we are using
the LongColumnVector in both cases. Does it make sense to collapse these two
clauses into just a single conditional: `if (o instanceof int[] || o instanceof
long[]) {`
> Add support for ORC format
> --------------------------
>
> Key: NIFI-1663
> URL: https://issues.apache.org/jira/browse/NIFI-1663
> Project: Apache NiFi
> Issue Type: New Feature
> Reporter: Matt Burgess
> Assignee: Matt Burgess
> Fix For: 1.0.0
>
>
> From the Hive/ORC wiki
> (https://cwiki.apache.org/confluence/display/Hive/LanguageManual+ORC):
> The Optimized Row Columnar (ORC) file format provides a highly efficient way
> to store Hive data ... Using ORC files improves performance when Hive is
> reading, writing, and processing data.
> As users are interested in NiFi integrations with Hive (NIFI-981, NIFI-1193,
> etc.), NiFi should be able to support ORC file format to enable users to
> efficiently store flow files for use by Hive.
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