[
https://issues.apache.org/jira/browse/TAJO-774?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14043011#comment-14043011
]
ASF GitHub Bot commented on TAJO-774:
-------------------------------------
Github user hyunsik commented on a diff in the pull request:
https://github.com/apache/tajo/pull/13#discussion_r14168041
--- Diff:
tajo-core/src/main/java/org/apache/tajo/engine/planner/physical/WindowAggExec.java
---
@@ -0,0 +1,336 @@
+/**
+ * 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.tajo.engine.planner.physical;
+
+import com.google.common.collect.Lists;
+import org.apache.tajo.catalog.Column;
+import org.apache.tajo.catalog.Schema;
+import org.apache.tajo.catalog.SortSpec;
+import org.apache.tajo.datum.Datum;
+import org.apache.tajo.engine.eval.WindowFunctionEval;
+import org.apache.tajo.engine.function.FunctionContext;
+import org.apache.tajo.engine.planner.logical.WindowAggNode;
+import org.apache.tajo.engine.planner.logical.WindowSpec;
+import org.apache.tajo.storage.Tuple;
+import org.apache.tajo.storage.TupleComparator;
+import org.apache.tajo.storage.VTuple;
+import org.apache.tajo.worker.TaskAttemptContext;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Iterator;
+import java.util.List;
+
+/**
+ * The sort-based window aggregation operator
+ */
+public class WindowAggExec extends UnaryPhysicalExec {
+ // plan information
+ protected final int outputColumnNum;
+ protected final int nonFunctionColumnNum;
+ protected final int nonFunctionColumns[];
+
+ protected final int functionNum;
+ protected final WindowFunctionEval functions[];
+
+ protected Schema schemaForOrderBy;
+ protected int sortKeyColumns[];
+ protected final boolean hasPartitionKeys;
+ protected final int partitionKeyNum;
+ protected final int partitionKeyIds[];
+
+ // for evaluation
+ protected FunctionContext contexts [];
+ protected Tuple lastKey = null;
+ protected boolean noMoreTuples = false;
+ private boolean [] orderedFuncFlags;
+ private boolean [] aggFuncFlags;
+ private boolean [] windowFuncFlags;
+ private boolean [] endUnboundedFollowingFlags;
+ private boolean [] endCurrentRowFlags;
+
+ private boolean endCurrentRow = false;
+
+ // operator state
+ enum WindowState {
+ NEW_WINDOW,
+ ACCUMULATING_WINDOW,
+ EVALUATION,
+ RETRIEVING_FROM_WINDOW,
+ END_OF_TUPLE
+ }
+
+ // Transient state
+ boolean firstTime = true;
+ List<Tuple> evaluatedTuples = null;
+ List<Tuple> accumulatedInTuples = null;
+ List<Tuple> nextAccumulatedProjected = null;
+ List<Tuple> nextAccumulatedInTuples = null;
+ WindowState state = WindowState.NEW_WINDOW;
+ Iterator<Tuple> tupleInFrameIterator = null;
+
+ public WindowAggExec(TaskAttemptContext context, WindowAggNode plan,
PhysicalExec child) throws IOException {
+ super(context, plan.getInSchema(), plan.getOutSchema(), child);
+
+ if (plan.hasPartitionKeys()) {
+ final Column[] keyColumns = plan.getPartitionKeys();
+ partitionKeyNum = keyColumns.length;
+ partitionKeyIds = new int[partitionKeyNum];
+ Column col;
+ for (int idx = 0; idx < plan.getPartitionKeys().length; idx++) {
+ col = keyColumns[idx];
+ partitionKeyIds[idx] =
inSchema.getColumnId(col.getQualifiedName());
+ }
+ hasPartitionKeys = true;
+ } else {
+ partitionKeyNum = 0;
+ partitionKeyIds = null;
+ hasPartitionKeys = false;
+ }
+
+ if (plan.hasAggFunctions()) {
+ functions = plan.getWindowFunctions();
+ functionNum = functions.length;
+
+ orderedFuncFlags = new boolean[functions.length];
+ windowFuncFlags = new boolean[functions.length];
+ aggFuncFlags = new boolean[functions.length];
+
+ endUnboundedFollowingFlags = new boolean[functions.length];
+ endCurrentRowFlags = new boolean[functions.length];
+
+ List<Column> additionalSortKeyColumns = Lists.newArrayList();
+ Schema rewrittenSchema = new Schema(outSchema);
+ for (int i = 0; i < functions.length; i++) {
+ WindowSpec.WindowEndBound endBound =
functions[i].getWindowFrame().getEndBound();
+ switch (endBound.getBoundType()) {
+ case CURRENT_ROW:
+ endCurrentRowFlags[i] = true; break;
+ case UNBOUNDED_FOLLOWING:
+ endUnboundedFollowingFlags[i] = true; break;
+ default:
+ }
+
+ switch (functions[i].getFuncDesc().getFuncType()) {
+ case AGGREGATION:
+ case DISTINCT_AGGREGATION:
+ aggFuncFlags[i] = true; break;
+ case WINDOW:
+ windowFuncFlags[i] = true; break;
+ default:
+ }
+
+ if (functions[i].hasSortSpecs()) {
+ orderedFuncFlags[i] = true;
+
+ for (SortSpec sortSpec : functions[i].getSortSpecs()) {
+ if (!rewrittenSchema.contains(sortSpec.getSortKey())) {
+ additionalSortKeyColumns.add(sortSpec.getSortKey());
+ }
+ }
+ }
+ }
+
+ sortKeyColumns = new int[additionalSortKeyColumns.size()];
+ schemaForOrderBy = new Schema(outSchema);
+ for (int i = 0; i < additionalSortKeyColumns.size(); i++) {
+ sortKeyColumns[i] = i;
+ schemaForOrderBy.addColumn(additionalSortKeyColumns.get(i));
+ }
+ } else {
+ functions = new WindowFunctionEval[0];
+ functionNum = 0;
+ schemaForOrderBy = outSchema;
+ }
+
+
+ nonFunctionColumnNum = plan.getTargets().length - functionNum;
+ nonFunctionColumns = new int[nonFunctionColumnNum];
+ for (int idx = 0; idx < plan.getTargets().length - functionNum; idx++)
{
+ nonFunctionColumns[idx] =
inSchema.getColumnId(plan.getTargets()[idx].getCanonicalName());
+ }
+
+ outputColumnNum = nonFunctionColumnNum + functionNum;
+ }
+
+ private void transition(WindowState state) {
+ this.state = state;
+ }
+
+ @Override
+ public Tuple next() throws IOException {
+ Tuple currentKey = null;
+ Tuple readTuple = null;
+
+ while(!context.isStopped() && state != WindowState.END_OF_TUPLE) {
+
+ if (state == WindowState.NEW_WINDOW) {
+ initWindow();
+ transition(WindowState.ACCUMULATING_WINDOW);
+ }
+
+ if (state != WindowState.RETRIEVING_FROM_WINDOW) { // read an input
tuple and build a partition key
+ readTuple = child.next();
+
+ if (readTuple == null) { // the end of tuple
+ noMoreTuples = true;
+ transition(WindowState.EVALUATION);
+ }
+
+ if (readTuple != null && hasPartitionKeys) { // get a key tuple
+ currentKey = new VTuple(partitionKeyIds.length);
+ for (int i = 0; i < partitionKeyIds.length; i++) {
+ currentKey.put(i, readTuple.get(partitionKeyIds[i]));
+ }
+ }
+ }
+
+ if (state == WindowState.ACCUMULATING_WINDOW) {
+ accumulatingWindow(currentKey, readTuple);
+ }
+
+ if (state == WindowState.EVALUATION) {
+ evaluationWindowFrame();
+
+ tupleInFrameIterator = evaluatedTuples.iterator();
+ transition(WindowState.RETRIEVING_FROM_WINDOW);
+ }
+
+ if (state == WindowState.RETRIEVING_FROM_WINDOW) {
+ if (tupleInFrameIterator.hasNext()) {
+ return tupleInFrameIterator.next();
+ } else {
+ finalizeWindow();
+ }
+ }
+ }
+
+ return null;
+ }
+
+ private void initWindow() {
+ if (firstTime) {
+ accumulatedInTuples = Lists.newArrayList();
+
+ contexts = new FunctionContext[functionNum];
+ for(int evalIdx = 0; evalIdx < functionNum; evalIdx++) {
+ contexts[evalIdx] = functions[evalIdx].newContext();
+ }
+ firstTime = false;
+ }
+ }
+
+ private void accumulatingWindow(Tuple currentKey, Tuple inTuple) {
+ if (lastKey == null || lastKey.equals(currentKey)) {
+ accumulatedInTuples.add(new VTuple(inTuple));
+
+ } else {
+ preAccumulatingNextWindow(inTuple);
+ transition(WindowState.EVALUATION);
+ }
+
+ lastKey = currentKey;
+ }
+
+ private void preAccumulatingNextWindow(Tuple inTuple) {
+ Tuple projectedTuple = new VTuple(outSchema.size());
+ for(int idx = 0; idx < nonFunctionColumnNum; idx++) {
+ projectedTuple.put(idx, inTuple.get(nonFunctionColumns[idx]));
+ }
+ nextAccumulatedProjected = Lists.newArrayList();
+ nextAccumulatedProjected.add(projectedTuple);
+ nextAccumulatedInTuples = Lists.newArrayList();
+ nextAccumulatedInTuples.add(new VTuple(inTuple));
+ }
+
--- End diff --
I added more description on the source code. Here is the description.
if the current key is different from the previous key, the current key
belongs to the next window frame. preaccumulatingNextWindow() aggregates the
current key for next window frame.
> Implement logical plan part and physical executor for window function.
> ----------------------------------------------------------------------
>
> Key: TAJO-774
> URL: https://issues.apache.org/jira/browse/TAJO-774
> Project: Tajo
> Issue Type: Sub-task
> Components: planner/optimizer
> Reporter: Hyunsik Choi
> Assignee: Hyunsik Choi
> Fix For: 0.9.0
>
>
> See the title. The main objective of this issue is to implement the logical
> planning part for window function support.
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