Proposal for hash based aggregation in map

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Introduction

Pig does (sort based) partial aggregation in map side through the use of combiner. MR serializes the output of map to a buffer, sorts it on the keys, deserializes and passes the values grouped on the keys to combiner phase. The same work of combiner can be done in the map phase itself by using a hash-map on the keys. This hash based (partial) aggregation can be done with or without a combiner phase.

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• Save on cost of serializing and de-serializing
• Save on cost of lock calls on the combiner input buffer. (I have found this to be a significant cost for a query that was doing multiple group-by's in a single MR job. -Thejas)
• The For some types of queries it is useful to turn off combiner in reduce side. This is not possible with hadoop combiner. By doing this, the problem of running out of memory in reduce side, for queries like COUNT(distinct col) can be avoided. The OOM issue happens because very large records get created after the combiner run on merged reduce input. In case of combiner, you have no way of telling MR not to combine records in reduce side. The workaround is to disable combiner completely, and the opportunity to reduce map output size is lost., which does not fit into MR framework's buffers. Usually the size after partial aggregation on map side does not cause records to be too large to cause a problem.
• Pig does not use hadoop combiner when there is a non algebraic function on the grouped bag column or if that column is projectedWhen the foreach after group-by has both algebraic and non-algebraic functions, or if a bag is being projected, the combiner is not used. This is because the data size reduction in typical those cases are usually not significant enough to justify the additional (de)serialization costs. But hash based aggregation can be used in such cases as wellbecause there is no (de)serialization overhead.
• It is possible to turn off the in-map combine automatically if there is not enough 'combination' that is taking place to justify the overhead of the in-map combiner. (Idea borrowed from Hive jira.)
• If input data is sorted, it is possible to do efficient map side (partial) aggregation with in-map combiner.

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It is not clear, if both MR-combiner and in-map combiner should be enabled in a pig MR job. If in-map combiner is used, the data reduction that would happen because of MR-combiner might not be sufficient to justify the costs. But MR-combiner might help in further reducing the data that gets written to disk, if multiple spill files get merged or if multiple waves of sort-merge happen on reduce side.

Changes

Design Option 1 - Simulate combiner in map task

In this option, the work done by MR to group values by key before invoking the combiner plan is simulated within the map using a hash-map. The hash-map will group rows on the group-by keys. When the hash-map is big enough, run the accumulated groups through the combiner plan. The results of the in-map combine plan execution get written as the map output. This option is easier to implement. But it is not going to be efficient in its memory footprint as input tuples will be kept around until the configured memory limit forces it to combine them. This will result in a smaller number of key-values that will be in memory at a time, and result in fewer values being aggregated, and a larger map output size.

query plan

MapReduceOper class that represents MR job in pig MR plan will now have a new member inMapCombinePlan, which is a PhysicalPlan. In the initial implementation, combiner physical plan (the member called combinePlan) can be cloned here.

But for supporting in-map combine for cases where combiner does not get used (eg. when there is a bag/non-algebraic udf), the MR plan optimizer rules need to change. But for such cases, the output type of map and combine plan would be different, that could be a problem.

plan execution

A new class that extends PigMapBase will have a collect call that accumulates the key-values into a hash-map. The hash-map will spill into the combine plan, when its estimated size exceeds a configurable threshold . This would be similar to the InternalCacheBag implementation.

Design Option 2

In this option, there will There will be a new physical operator, POHashAgg, that will do the hash based aggregation. This will be the last node before the LORearrange in the map plan of MR job for the group operation. Its input will be same as the input to LORearrange in plan that uses MR combiner - a foreach statement that computes the key and UDF$Initial.exec() .

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It might be useful to have a new udf interface that accepts a tuple at a time to compute a partial aggregate, so that new bags don't have to be created for each new tuple that needs to be aggregated. But the bag creation overhead and overhead of calling the udf multiple times could be reduced by calling the udf only after few values have been accumulated in the hash-map.

This design option will have a smaller memory footprint because input tuples can be aggregated sooner. It will also in smaller output size because more records can be held in the hash-map. But the work involved with this option is more because as new physical operator is needed and the MR plan optimization rules need to change to create internal plans for this new operator.

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Design 2 initial implementation

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Initial Implementation

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In the initial implementation, combiner will be supported only when all projections are either expressions on the group column or expressions on algebraic UDFs. This is because column pruning does not currently discard unused columns within a grouped-bag, and in such cases there will not be data size reduction happening because of the use of in-map combiner.

For the query -

l = load 'x'   as (a,b,c); 
g = group l by a;
f = foreach g generate  group, COUNT(l.b);

The existing plan -

Map Plan
g: Local Rearrange[tuple]{bytearray}(false) - scope-73
|   |
|   Project[bytearray][0] - scope-74
|
|---f: New For Each(false,false)[bag] - scope-61
    |   |
    |   Project[bytearray][0] - scope-62
    |   |
    |   POUserFunc(org.apache.pig.builtin.COUNT$Initial)[tuple] - scope-63
    |   |
    |   |---Project[bag][1] - scope-64
    |       |
    |       |---Project[bag][1] - scope-65
    |
    |---Pre Combiner Local Rearrange[tuple]{Unknown} - scope-75
        |
        |---l: New For Each(false,false,false)[bag] - scope-47

Will change to -

Map Plan
g: Local Rearrange[tuple]{bytearray}(false) - scope-73
|   |
|   Project[bytearray][0] - scope-74
|
|---f: HashAgg 
    |   |
    |   Project[bytearray][0] - scope-62
    |   |
    |   POUserFunc(org.apache.pig.builtin.COUNT$Intermediate)[tuple] - scope-63
    |   |
    |   |---Project[bag][1] - scope-64
    |
    |---f: New For Each(false,false)[bag] - scope-61
        |   |
        |   Project[bytearray][0] - scope-62
        |   |
        |   POUserFunc(org.apache.pig.builtin.COUNT$Initial)[tuple] - scope-63
        |   |
        |   |---Project[bag][1] - scope-64
        |       |
        |       |---Project[bag][1] - scope-65
        |
        |---Pre Combiner Local Rearrange[tuple]{Unknown} - scope-75
            |
            |---l: New For Each(false,false,false)[bag] - scope-47

The MR combiner will also be supported and by default in-map combiner will not be used. There will be a property that will need to be set to enable it. There will be another property that will control use of MR combiner along with in-map combiner. After sufficient testing is done, we can change the default execution mode and properties.

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2. Memory Management
Similar to the current strategy of InternalCachedBag, find the average size of the entries and estimate the size held by current number of entries. If the size exceeds the internal cached bag size threshold, it will write a portion of the hashmap entries to output.
In case of multi-query, there will be multiple such bags, and the memory limit will be shared equally between them.