Thanks Gábor, now I also see it on the internal GSoC interface. I have indicated that I wish to mentor your project, I think you can hit finalize on your project there.
On Mon, Mar 14, 2016 at 11:16 AM, Gábor Horváth <xazax....@gmail.com> wrote: > Hi, > > I have updated this draft to include preliminary benchmarks, mentioned the > interaction of annotations with savepoints, extended it with a timeline, > and some notes about scala case classes. > > Regards, > Gábor > > On 9 March 2016 at 16:12, Gábor Horváth <xazax....@gmail.com> wrote: > > > Hi! > > > > As far as I can see the formatting was not correct in my previous mail. A > > better formatted version is available here: > > > https://docs.google.com/document/d/1VC8lCeErx9kI5lCMPiUn625PO0rxR-iKlVqtt3hkVnk > > Sorry for that. > > > > Regards, > > Gábor > > > > On 9 March 2016 at 15:51, Gábor Horváth <xazax....@gmail.com> wrote: > > > >> Hi,I did not want to send this proposal out before the I have some > >> initial benchmarks, but this issue was mentioned on the mailing list ( > >> > http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/Tuple-performance-and-the-curious-JIT-compiler-td10666.html > ), > >> and I wanted to make this information available to be able to > incorporate > >> this into that discussion. I have written this draft with the help of > Gábor > >> Gévay and Márton Balassi and I am open to every suggestion. > >> > >> > >> The proposal draft: > >> Code Generation in Serializers and Comparators of Apache Flink > >> > >> I am doing my last semester of my MSc studies and I’m a former GSoC > >> student in the LLVM project. I plan to improve the serialization code in > >> Flink during this summer. The current implementation of the serializers > can > >> be a performance bottleneck in some scenarios. These performance > problems > >> were also reported on the mailing list recently [1]. I plan to implement > >> code generation into the serializers to improve the performance (as > Stephan > >> Ewen also suggested.) > >> > >> TODO: I plan to include some preliminary benchmarks in this section. > >> Performance problems with the current serializers > >> > >> 1. > >> > >> PojoSerializer uses reflection for accessing the fields, which is > >> slow (eg. [2]) > >> > >> > >> - > >> > >> This is also a serious problem for the comparators > >> > >> > >> 1. > >> > >> When deserializing fields of primitive types (eg. int), the reusing > >> overload of the corresponding field serializers cannot really do any > reuse, > >> because boxed primitive types are immutable in Java. This results in > lots > >> of object creations. [3][7] > >> 2. > >> > >> The loop to call the field serializers makes virtual function calls, > >> that cannot be speculatively devirtualized by the JVM or predicted > by the > >> CPU, because different serializer subclasses are invoked for the > different > >> fields. (And the loop cannot be unrolled, because the number of > iterations > >> is not a compile time constant.) See also the following discussion > on the > >> mailing list [1]. > >> 3. > >> > >> A POJO field can have the value null, so the serializer inserts 1 > >> byte null tags, which wastes space. (Also, the type extractor logic > does > >> not distinguish between primitive types and their boxed versions, so > even > >> an int field has a null tag.) > >> 4. > >> > >> Subclass tags also add a byte at the beginning of every POJO > >> 5. > >> > >> getLength() does not know the size in most cases [4] > >> Knowing the size of a type when serialized has numerous performance > >> benefits throughout Flink: > >> 1. > >> > >> Sorters can do in-place, when the type is small [5] > >> 2. > >> > >> Chaining hash tables do not need resizes, because they know how > >> many buckets to allocate upfront [6] > >> 3. > >> > >> Different hash table architectures could be used, eg. open > >> addressing with linear probing instead of some chaining > >> 4. > >> > >> It is possible to deserialize, modify, and then serialize back a > >> record to its original place, because it cannot happen that the > modified > >> version does not fit in the place allocated there for the old > version (see > >> CompactingHashTable and ReduceHashTable for concrete instances of > this > >> problem) > >> > >> > >> Note, that 2. and 3. are problems with not just the PojoSerializer, but > >> also with the TupleSerializer. > >> Solution approaches > >> > >> 1. > >> > >> Run time code generation for every POJO > >> > >> > >> - > >> > >> 1. and 3 . would be automatically solved, if the serializers for > >> POJOs would be generated on-the-fly (by, for example, Javassist) > >> - > >> > >> 2. also needs code generation, and also some extra effort in the > >> type extractor to distinguish between primitive types and their > boxed > >> versions > >> - > >> > >> could be used for PojoComparator as well (which could greatly > >> increase the performance of sorting) > >> > >> > >> 1. > >> > >> Annotations on POJOs (by the users) > >> > >> > >> - > >> > >> Concretely: > >> - > >> > >> annotate fields that will never be nulls -> no null tag needed > >> before every field! > >> - > >> > >> make a POJO final -> no subclass tag needed > >> - > >> > >> annotating a POJO that it will not be null -> no top level null > >> tag needed > >> - > >> > >> These would also help with the getLength problem (6.), because the > >> length is often not known because currently anything can be null > or a > >> subclass can appear anywhere > >> - > >> > >> These annotations could be done without code generation, but then > >> they would add some overhead when there are no annotations > present, so this > >> would work better together with the code generation > >> - > >> > >> Tuples would become a special case of POJOs, where nothing can be > >> null, and no subclass can appear, so maybe we could eliminate the > >> TupleSerializer > >> - > >> > >> We could annotate some internal types in Flink libraries (Gelly > >> (Vertex, Edge), FlinkML) > >> > >> > >> TODO: what is the situation with Scala case classes? Run time code > >> generation is probably easier in Scala? (with quasiquotes) > >> > >> About me > >> > >> I am in the last year of my Computer Science MSc studies at Eotvos > Lorand > >> University in Budapest, and planning to start a PhD in the autumn. I > have > >> been working for almost three years at Ericsson on static analysis tools > >> for C++. In 2014 I participated in GSoC, working on the LLVM project, > and I > >> am a frequent contributor ever since. The next summer I was interning at > >> Apple. > >> > >> I learned about the Flink project not too long ago and I like it so far. > >> The last few weeks I was working on some tickets to familiarize myself > with > >> the codebase: > >> > >> https://issues.apache.org/jira/browse/FLINK-3422 > >> > >> https://issues.apache.org/jira/browse/FLINK-3322 > >> > >> https://issues.apache.org/jira/browse/FLINK-3457 > >> > >> My CV is available here: http://xazax.web.elte.hu/files/resume.pdf > >> References > >> > >> [1] > >> > http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/Tuple-performance-and-the-curious-JIT-compiler-td10666.html > >> > >> [2] > >> > https://github.com/apache/flink/blob/master/flink-java/src/main/java/org/apache/flink/api/java/typeutils/runtime/PojoSerializer.java#L369 > >> > >> [3] > >> > https://github.com/apache/flink/blob/master/flink-core/src/main/java/org/apache/flink/api/common/typeutils/base/IntSerializer.java#L73 > >> > >> [4] > >> > https://github.com/apache/flink/blob/master/flink-core/src/main/java/org/apache/flink/api/common/typeutils/TypeSerializer.java#L98 > >> > >> [5] > >> > https://github.com/apache/flink/blob/master/flink-runtime/src/main/java/org/apache/flink/runtime/operators/sort/FixedLengthRecordSorter.java > >> > >> [6] > >> > https://github.com/apache/flink/blob/master/flink-runtime/src/main/java/org/apache/flink/runtime/operators/hash/CompactingHashTable.java#L861 > >> [7] https://issues.apache.org/jira/browse/FLINK-3277 > >> > >> > >> Best Regards, > >> > >> Gábor > >> > > > > >
