siddharthteotia commented on code in PR #8993:
URL: https://github.com/apache/pinot/pull/8993#discussion_r910692036
##########
pinot-core/src/test/java/org/apache/pinot/queries/MultiValueRawQueriesTest.java:
##########
@@ -468,6 +907,80 @@ private void
validateSimpleAggregateQueryResults(ResultTable resultTable, DataSc
assertEquals(avgInt, avgIntRaw);
}
+ @Test
+ public void testAggregateWithFilterQueries() {
+ {
+ // Aggregation on int columns with filter
+ String query = "SELECT COUNTMV(mvIntCol), COUNTMV(mvRawIntCol),
SUMMV(mvIntCol), SUMMV(mvRawIntCol), "
+ + "MINMV(mvIntCol), MINMV(mvRawIntCol), MAXMV(mvIntCol),
MAXMV(mvRawIntCol), AVGMV(mvIntCol), "
+ + "AVGMV(mvRawIntCol) from testTable WHERE mvRawIntCol > 1000";
+ ResultTable resultTable = getBrokerResponse(query).getResultTable();
+
+ DataSchema dataSchema = new DataSchema(new String[]{
+ "countmv(mvIntCol)", "countmv(mvRawIntCol)", "summv(mvIntCol)",
"summv(mvRawIntCol)", "minmv(mvIntCol)",
+ "minmv(mvRawIntCol)", "maxmv(mvIntCol)", "maxmv(mvRawIntCol)",
"avgmv(mvIntCol)", "avgmv(mvRawIntCol)"
+ }, new DataSchema.ColumnDataType[]{
+ DataSchema.ColumnDataType.LONG, DataSchema.ColumnDataType.LONG,
DataSchema.ColumnDataType.DOUBLE,
+ DataSchema.ColumnDataType.DOUBLE, DataSchema.ColumnDataType.DOUBLE,
DataSchema.ColumnDataType.DOUBLE,
+ DataSchema.ColumnDataType.DOUBLE, DataSchema.ColumnDataType.DOUBLE,
DataSchema.ColumnDataType.DOUBLE,
+ DataSchema.ColumnDataType.DOUBLE
+ });
+ validateAggregateWithFilterQueryResults(resultTable, dataSchema);
+ }
+ {
+ // Aggregation on double columns with filter
+ String query = "SELECT COUNTMV(mvDoubleCol), COUNTMV(mvRawDoubleCol),
SUMMV(mvDoubleCol), SUMMV(mvRawDoubleCol), "
+ + "MINMV(mvDoubleCol), MINMV(mvRawDoubleCol), MAXMV(mvDoubleCol),
MAXMV(mvRawDoubleCol), AVGMV(mvDoubleCol), "
+ + "AVGMV(mvRawDoubleCol) from testTable WHERE mvRawDoubleCol >
1000.0";
+ ResultTable resultTable = getBrokerResponse(query).getResultTable();
+
+ DataSchema dataSchema = new DataSchema(new String[]{
+ "countmv(mvDoubleCol)", "countmv(mvRawDoubleCol)",
"summv(mvDoubleCol)", "summv(mvRawDoubleCol)",
+ "minmv(mvDoubleCol)", "minmv(mvRawDoubleCol)", "maxmv(mvDoubleCol)",
"maxmv(mvRawDoubleCol)",
+ "avgmv(mvDoubleCol)", "avgmv(mvRawDoubleCol)"
+ }, new DataSchema.ColumnDataType[]{
+ DataSchema.ColumnDataType.LONG, DataSchema.ColumnDataType.LONG,
DataSchema.ColumnDataType.DOUBLE,
+ DataSchema.ColumnDataType.DOUBLE, DataSchema.ColumnDataType.DOUBLE,
DataSchema.ColumnDataType.DOUBLE,
+ DataSchema.ColumnDataType.DOUBLE, DataSchema.ColumnDataType.DOUBLE,
DataSchema.ColumnDataType.DOUBLE,
+ DataSchema.ColumnDataType.DOUBLE
+ });
+ validateAggregateWithFilterQueryResults(resultTable, dataSchema);
+ }
+ }
+
+ private void validateAggregateWithFilterQueryResults(ResultTable
resultTable, DataSchema expectedDataSchema) {
+ assertNotNull(resultTable);
+ assertEquals(resultTable.getDataSchema(), expectedDataSchema);
+ List<Object[]> recordRows = resultTable.getRows();
+ assertEquals(recordRows.size(), 1);
+
+ Object[] values = recordRows.get(0);
+ long count = (long) values[0];
+ long countRaw = (long) values[1];
+ assertEquals(count, 80);
+ assertEquals(count, countRaw);
+
+ double sum = (double) values[2];
+ double sumRaw = (double) values[3];
+ assertEquals(sum, 84360.0);
+ assertEquals(sum, sumRaw);
+
+ double min = (double) values[4];
+ double minRaw = (double) values[5];
+ assertEquals(min, 1000.0);
+ assertEquals(min, minRaw);
+
+ double max = (double) values[6];
+ double maxRaw = (double) values[7];
+ assertEquals(max, 1109.0);
+ assertEquals(max, maxRaw);
+
+ double avg = (double) values[8];
+ double avgRaw = (double) values[9];
+ assertEquals(avg, 1054.5);
+ assertEquals(avg, avgRaw);
+ }
+
@Test
public void testAggregateWithGroupByQueries() {
Review Comment:
Have we covered following scenarios ?
- single GROUP BY MV column (raw)
- 2 or more GROUP BY MV columns (all raw)
- 2 or more GROUP BY MV columns (mix of raw and dict)
- 2 or more GROUP BY MV columns (all raw) along 1 or more GROUP BY SV column
(dict)
- 2 or more GROUP BY MV columns (mix of raw and dict) along 1 or more GROUP
BY SV column (raw or dict)
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