/** * 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.hadoop.hive.ql.exec.vector.expressions; import org.apache.hadoop.hive.ql.exec.vector.DoubleColumnVector; import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; /** * Implement vectorized math function that takes a double (and optionally additional * constant argument(s)) and returns long. * May be used for functions like ROUND(d, N), Pow(a, p) etc. * * Do NOT use this for simple math functions like sin/cos/exp etc. that just take * a single argument. For those, modify the template ColumnUnaryFunc.txt * and expand the template to generate needed classes. */ public abstract class MathFuncDoubleToDouble extends VectorExpression { private static final long serialVersionUID = 1L; protected int colNum; private int outputColumn; // Subclasses must override this with a function that implements the desired logic. protected abstract double func(double d); public MathFuncDoubleToDouble(int colNum, int outputColumn) { this.colNum = colNum; this.outputColumn = outputColumn; } public MathFuncDoubleToDouble() { } @Override public void evaluate(VectorizedRowBatch batch) { if (childExpressions != null) { this.evaluateChildren(batch); } DoubleColumnVector inputColVector = (DoubleColumnVector) batch.cols[colNum]; DoubleColumnVector outputColVector = (DoubleColumnVector) batch.cols[outputColumn]; int[] sel = batch.selected; boolean[] inputIsNull = inputColVector.isNull; boolean[] outputIsNull = outputColVector.isNull; outputColVector.noNulls = inputColVector.noNulls; int n = batch.size; double[] vector = inputColVector.vector; double[] outputVector = outputColVector.vector; // return immediately if batch is empty if (n == 0) { return; } if (inputColVector.isRepeating) { outputVector[0] = func(vector[0]); // Even if there are no nulls, we always copy over entry 0. Simplifies code. outputIsNull[0] = inputIsNull[0]; outputColVector.isRepeating = true; } else if (inputColVector.noNulls) { if (batch.selectedInUse) { for(int j = 0; j != n; j++) { int i = sel[j]; outputVector[i] = func(vector[i]); } } else { for(int i = 0; i != n; i++) { outputVector[i] = func(vector[i]); } } outputColVector.isRepeating = false; } else /* there are nulls */ { if (batch.selectedInUse) { for(int j = 0; j != n; j++) { int i = sel[j]; outputVector[i] = func(vector[i]); outputIsNull[i] = inputIsNull[i]; } } else { for(int i = 0; i != n; i++) { outputVector[i] = func(vector[i]); } System.arraycopy(inputIsNull, 0, outputIsNull, 0, n); } outputColVector.isRepeating = false; } cleanup(outputColVector, sel, batch.selectedInUse, n); } // override this with a no-op if subclass doesn't need to treat NaN as null protected void cleanup(DoubleColumnVector outputColVector, int[] sel, boolean selectedInUse, int n) { MathExpr.NaNToNull(outputColVector, sel, selectedInUse, n); } @Override public int getOutputColumn() { return outputColumn; } public void setOutputColumn(int outputColumn) { this.outputColumn = outputColumn; } public int getColNum() { return colNum; } public void setColNum(int colNum) { this.colNum = colNum; } @Override public String getOutputType() { return "double"; } @Override public String vectorExpressionParameters() { return "col " + colNum; } }