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Writing Custom Aggregation Function

Pinot has many inbuilt Aggregation Functions such as MIN, MAX, SUM, AVG etc. See PQL page for the list of aggregation functions.
Adding a new AggregationFunction requires two things
  • Implement AggregationFunction interface and make it available as part of the classpath
  • Register the function in AggregationFunctionFactory. As of today, this requires code change in Pinot but we plan to add the ability to plugin Functions without having to change Pinot code.
To get an overall idea, see MAX Aggregation Function implementation. All other implementations can be found here.
Lets look at the key methods to implements in AggregationFunction
interface AggregationFunction {
AggregationResultHolder createAggregationResultHolder();
GroupByResultHolder createGroupByResultHolder(int initialCapacity, int maxCapacity);
void aggregate(int length, AggregationResultHolder aggregationResultHolder, Map<String, BlockValSet> blockValSetMap);
void aggregateGroupBySV(int length, int[] groupKeyArray, GroupByResultHolder groupByResultHolder,
Map<String, BlockValSet> blockValSets);
void aggregateGroupByMV(int length, int[][] groupKeysArray, GroupByResultHolder groupByResultHolder,
Map<String, BlockValSet> blockValSets);
IntermediateResult extractAggregationResult(AggregationResultHolder aggregationResultHolder);
IntermediateResult extractGroupByResult(GroupByResultHolder groupByResultHolder, int groupKey);
IntermediateResult merge(IntermediateResult intermediateResult1, IntermediateResult intermediateResult2);
FinalResult extractFinalResult(IntermediateResult intermediateResult);
}
Before getting into the implementation, it's important to understand how Aggregation works in Pinot.
This is advanced topic and assumes you know Pinot concepts. All the data in Pinot is stored in segments across multiple nodes. The query plan at a high level comprises of 3 phases
1. Map phase
This phase works on the individual segments in Pinot.
  • Initialization: Depending on the query type the following methods are invoked to setup the result holder. While having different methods and return types adds complexity, it helps in performance.
  • Callback: For every record that matches the filter condition in the query,
    one of the following methods are invoked depending on the queryType(aggregation vs group by) and columnType(single-value vs multi-value). Note that we invoke this method for a batch of records instead of every row for performance reasons and allows JVM to vectorize some of parts of the execution if possible.
    • AGGREGATION: aggregate(int length, AggregationResultHolder aggregationResultHolder, Map<String,BlockValSet> blockValSetMap)
      • length: This represent length of the block. Typically < 10k
      • aggregationResultHolder: this is the object returned fromcreateAggregationResultHolder
      • blockValSetMap: Map of blockValSets depending on the arguments to the AggFunction
    • Group By Single Value: aggregateGroupBySV(int length, int[] groupKeyArray, GroupByResultHolder groupByResultHolder, Map blockValSets)
      • length: This represent length of the block. Typically < 10k
      • groupKeyArray: Pinot internally maintains a value to int mapping and this groupKeyArray maps to the internal mapping. These values together form a unique key.
      • groupByResultHolder: This is the object returned fromcreateGroupByResultHolder
      • blockValSetMap: Map of blockValSets depending on the arguments to the AggFunction
    • Group By Multi Value: aggregateGroupBySV(int length, int[] groupKeyArray, GroupByResultHolder groupByResultHolder, Map blockValSets)
      • length: This represent length of the block. Typically < 10k
      • groupKeyArray: Pinot internally maintains a value to int mapping and this groupKeyArray maps to the internal mapping. These values together form a unique key.
      • groupByResultHolder: This is the object returned fromcreateGroupByResultHolder
      • blockValSetMap: Map of blockValSets depending on the arguments to the AggFunction
2. Combine phase
In this phase, the results from all segments within a single pinot server are combined into IntermediateResult. The type of IntermediateResult is based on the Generic Type defined in the AggregationFunction implementation.
public interface AggregationFunction<IntermediateResult, FinalResult extends Comparable> {
IntermediateResult merge(IntermediateResult intermediateResult1, IntermediateResult intermediateResult2);
}
3. Reduce phase
There are two steps in the Reduce Phase
  • Merge all the IntermediateResult's from various servers using the merge function
  • Extract the final results by invoking the extractFinalResult method. In most cases, FinalResult is same type as IntermediateResult. AverageAggregationFunction is an example where IntermediateResult (AvgPair) is different from FinalResult(Double)
FinalResult extractFinalResult(IntermediateResult intermediateResult);