@Override
  public void evaluate(VectorizedRowBatch batch) throws HiveException {

    if (childExpressions != null) {
      super.evaluateChildren(batch);
    }

    LongColumnVector inputColVector1 = (LongColumnVector) batch.cols[inputColumnNum[0]];
    LongColumnVector inputColVector2 = (LongColumnVector) batch.cols[inputColumnNum[1]];
    DoubleColumnVector outputColVector = (DoubleColumnVector) batch.cols[outputColumnNum];
    int[] sel = batch.selected;
    int n = batch.size;
    long[] vector1 = inputColVector1.vector;
    long[] vector2 = inputColVector2.vector;
    double[] outputVector = outputColVector.vector;

    // return immediately if batch is empty
    if (n == 0) {
      return;
    }

    /*
     * Propagate null values for a two-input operator and set isRepeating and noNulls appropriately.
     */
    NullUtil.propagateNullsColCol(
      inputColVector1, inputColVector2, outputColVector, sel, n, batch.selectedInUse);

    /* Disregard nulls for processing. In other words,
     * the arithmetic operation is performed even if one or
     * more inputs are null. This is to improve speed by avoiding
     * conditional checks in the inner loop.
     */
    boolean hasDivBy0 = false;
    if (inputColVector1.isRepeating && inputColVector2.isRepeating) {
      long denom = vector2[0];
      outputVector[0] = vector1[0] / (double) denom;
      hasDivBy0 = hasDivBy0 || (denom == 0);
    } else if (inputColVector1.isRepeating) {
      if (batch.selectedInUse) {
        for(int j = 0; j != n; j++) {
          int i = sel[j];
          long denom = vector2[i];
          outputVector[i] = vector1[0] / (double) denom;
          hasDivBy0 = hasDivBy0 || (denom == 0);
        }
      } else {
        for(int i = 0; i != n; i++) {
          long denom = vector2[i];
          outputVector[i] = vector1[0] / (double) denom;
          hasDivBy0 = hasDivBy0 || (denom == 0);
        }
      }
    } else if (inputColVector2.isRepeating) {
      if (vector2[0] == 0) {
        outputColVector.noNulls = false;
        outputColVector.isRepeating = true;
        outputColVector.isNull[0] = true;
      } else if (batch.selectedInUse) {
        for(int j = 0; j != n; j++) {
          int i = sel[j];
          outputVector[i] = vector1[i] / (double) vector2[0];
        }
      } else {
        for(int i = 0; i != n; i++) {
          outputVector[i] = vector1[i] / (double) vector2[0];
        }
      }
    } else {
      if (batch.selectedInUse) {
        for(int j = 0; j != n; j++) {
          int i = sel[j];
          long denom = vector2[i];
          outputVector[i] = vector1[i] / (double) denom;
          hasDivBy0 = hasDivBy0 || (denom == 0);
        }
      } else {
        for(int i = 0; i != n; i++) {
          long denom = vector2[i];
          outputVector[i] = vector1[i] / (double) denom;
          hasDivBy0 = hasDivBy0 || (denom == 0);
        }
      }
    }

    /* For the case when the output can have null values, follow
     * the convention that the data values must be 1 for long and
     * NaN for double. This is to prevent possible later zero-divide errors
     * in complex arithmetic expressions like col2 / (col1 - 1)
     * in the case when some col1 entries are null.
     */
    if (!hasDivBy0) {
      NullUtil.setNullDataEntriesDouble(outputColVector, batch.selectedInUse, sel, n);
    } else {
      NullUtil.setNullAndDivBy0DataEntriesDouble(
        outputColVector, batch.selectedInUse, sel, n, inputColVector2);
    }
  }
This operation is handled as a special case because Hive
long/long division returns double. This file is thus not generated
from a template like the other arithmetic operations are.
ql/src/java/org/apache/hadoop/hive/ql/exec/vector/expressions/LongColDivideLongColumn.java