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Daniel Dai commented on PIG-1295: --------------------------------- I run your main problem. It also counts the tuple bytes generation time, which dominate the cpu profile. I run it in another way, generate the bytes first and don't include in the timing, and then compare the bytes using different comparator, Here is my code snippet: {code} byte[][] toCompare1 = new byte[TIMES][]; byte[][] toCompare2 = new byte[TIMES][]; NullableTuple t; for (int i=0;i<TIMES;i++) { t = new NullableTuple(test.getRandomTuple(rand)); t.write(test.dos1); toCompare1[i] = test.baos1.toByteArray(); } for (int i=0;i<TIMES;i++) { t = new NullableTuple(test.getRandomTuple(rand)); t.write(test.dos2); toCompare2[i] = test.baos2.toByteArray(); } before = System.currentTimeMillis(); for (int loop = 0; loop < 10000; loop++) { for (int i = 0; i < TIMES; i++) { test.comparator.compare(toCompare1[i], 0, toCompare1[i].length, toCompare2[i], 0, toCompare2[i].length); } } after = System.currentTimeMillis(); before = System.currentTimeMillis(); for (int loop = 0; loop < 10000; loop++) { for (int i = 0; i < TIMES; i++) { test.oldComparator.compare(toCompare1[i], 0, toCompare1[i].length, toCompare2[i], 0, toCompare2[i].length); } } after = System.currentTimeMillis(); {code} In this comparison, I see 6.5 times speedup. Also notice the code style for Apache is always use space instead of tab, make sure you take care of this. > Binary comparator for secondary sort > ------------------------------------ > > Key: PIG-1295 > URL: https://issues.apache.org/jira/browse/PIG-1295 > Project: Pig > Issue Type: Improvement > Components: impl > Affects Versions: 0.7.0 > Reporter: Daniel Dai > Assignee: Daniel Dai > Attachments: PIG-1295_0.1.patch, PIG-1295_0.2.patch, > PIG-1295_0.3.patch > > > When hadoop framework doing the sorting, it will try to use binary version of > comparator if available. The benefit of binary comparator is we do not need > to instantiate the object before we compare. We see a ~30% speedup after we > switch to binary comparator. Currently, Pig use binary comparator in > following case: > 1. When semantics of order doesn't matter. For example, in distinct, we need > to do a sort in order to filter out duplicate values; however, we do not care > how comparator sort keys. Groupby also share this character. In this case, we > rely on hadoop's default binary comparator > 2. Semantics of order matter, but the key is of simple type. In this case, we > have implementation for simple types, such as integer, long, float, > chararray, databytearray, string > However, if the key is a tuple and the sort semantics matters, we do not have > a binary comparator implementation. This especially matters when we switch to > use secondary sort. In secondary sort, we convert the inner sort of nested > foreach into the secondary key and rely on hadoop to sorting on both main key > and secondary key. The sorting key will become a two items tuple. Since the > secondary key the sorting key of the nested foreach, so the sorting semantics > matters. It turns out we do not have binary comparator once we use secondary > sort, and we see a significant slow down. > Binary comparator for tuple should be doable once we understand the binary > structure of the serialized tuple. We can focus on most common use cases > first, which is "group by" followed by a nested sort. In this case, we will > use secondary sort. Semantics of the first key does not matter but semantics > of secondary key matters. We need to identify the boundary of main key and > secondary key in the binary tuple buffer without instantiate tuple itself. > Then if the first key equals, we use a binary comparator to compare secondary > key. Secondary key can also be a complex data type, but for the first step, > we focus on simple secondary key, which is the most common use case. > We mark this issue to be a candidate project for "Google summer of code 2010" > program. -- This message is automatically generated by JIRA. - You can reply to this email to add a comment to the issue online.