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.
It will send fewer records to combiner and thereby -
Memory used by the in-map combiner will have to be managed carefully, to avoid any out of memory errors.
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.
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$Initial.exec() .
POHashAgg will have the plans used in combiner to generate intermediate values. For every new input record, POHashAgg will store the key and input values in a hashmap.
When there are two, or a small number of values for a group key (set), it will compute the new partial aggregate value and store back in the hash-map as intermediate result. The memory management of the hash-map will be similar to that of InternalCacheBag - it will estimate its memory footprint. It will flush some % (5%?) of entries and write them to output when it exceeds configurable memory limit. The least recently used keys can be chosen to be flushed (keeping track of few most recently used might be enough). These flushed entries will be written as map output.
It might be useful to have a new udf interface that accepts a tuple at a time to compute a partial aggregate, so that new bags don't have to be created for each new tuple that needs to be aggregated. But the bag creation overhead and overhead of calling the udf multiple times could be reduced by calling the udf only after few values have been accumulated in the hash-map.
In the initial implementation, combiner will be supported only when all projections are either expressions on the group column or expressions on algebraic UDFs. This is because column pruning does not currently discard unused columns within a grouped-bag, and in such cases there will not be data size reduction happening because of the use of in-map combiner.
For the query -
l = load 'x' as (a,b,c); g = group l by a; f = foreach g generate group, COUNT(l.b); |
The existing plan -
Map Plan g: Local Rearrange[tuple]{bytearray}(false) - scope-73 | | | Project[bytearray][0] - scope-74 | |---f: New For Each(false,false)[bag] - scope-61 | | | Project[bytearray][0] - scope-62 | | | POUserFunc(org.apache.pig.builtin.COUNT$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.
Query Plan
The CombinerOptimizer will generate the in-map plan as well. Except for the introduction of a new operator, the plan will remain the same.
Plan execution
1. Evaluating aggregate results
For every new input record, POHashAgg will do the following -
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.