Fabian Hueske – Juggling with Bits and Bytes
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How Apache Flink operates on binary data Flink Forward 2015
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Fabian Hueske – Juggling with Bits and Bytes
- 1. Juggling with Bits and Bytes How Apache Flink operates on binary data Fabian Hueske :ueske@apache.org @:ueske 1
- 2. Big Data frameworks on JVMs • Many (open source) Big Data frameworks run on JVMs – Hadoop, Drill, Spark, Hive, Pig, and ... – Flink as well • Common challenge: How to organize data in-‐memory? – In-‐memory processing (sorOng, joining, aggregaOng) – In-‐memory caching of intermediate results • Memory management of a system influences – Reliability – Resource efficiency, performance & performance predictability – Ease of configuraOon 2
- 3. The straight-‐forward approach Store and process data as objects on the heap • Put objects in an array and sort it A few notable drawbacks • PredicOng memory consumpOon is hard – If you fail, an OutOfMemoryError will kill you! • High garbage collecOon overhead – Easily 50% of Ome spend on GC • Objects have considerable space overhead – At least 8 bytes for each (nested) object! (Depends on arch) 3
- 5. Flink adopts DBMS technology • Allocates fixed number of memory segments upfront • Data objects are serialized into memory segments • DBMS-‐style algorithms work on binary representaOon 5
- 6. Why is that good? • Memory-‐safe execuOon – Used and available memory segments are easy to count – No parameter tuning for reliable operaOons! • Efficient out-‐of-‐core algorithms – Memory segments can be efficiently wrifen to disk • Reduced GC pressure – Memory segments are off-‐heap or never deallocated – Data objects are short-‐lived or reused • Space-‐efficient data representaOon • Efficient operaOons on binary data 6
- 7. What does it cost? • Significant implementaOon investment – Using java.uOl.HashMap vs. – ImplemenOng a spillable hash table backed by byte arrays and custom serializaOon stack • Other systems use similar techniques – Apache Drill, Apache AsterixDB (incubaOng) • Apache Spark evolves into a similar direcOon 7
- 9. Memory segments • Unit of memory distribuOon in Flink – Fixed number allocated when worker starts • Backed by a regular byte array (default 32KB) • On-‐heap or off-‐heap allocaOon • R/W access through Java’s efficient unsafe methods • MulOple memory segments can be logically concatenated to a larger chunk of memory 9
- 10. On-‐heap memory allocaOon 10
- 11. Off-‐heap memory allocaOon 11
- 12. On-‐heap vs. Off-‐heap • No significant performance difference in micro-‐benchmarks • Garbage CollecOon – Smaller heap -‐> faster GC • Faster start-‐up Ome – A mulO-‐GB JVM heap takes Ome to allocate 12
- 14. Custom de/serializaOon stack • Many alternaOves for Java object serializaOon – Dynamic: Kryo – Schema-‐dependent: Apache Avro, Apache Thrip, Protobufs • But Flink has its own serializaOon stack – OperaOng on serialized data requires knowledge of layout – Control over layout can improve efficiency of operaOons – Data types are known before execuOon 14
- 15. Rich & extensible type system • SerializaOon framework requires knowledge of types • Flink analyzes return types of funcOons – Java: ReflecOon based type analyzer – Scala: Compiler informaOon + CodeGen via Macros • Rich type system – Atomics: PrimiOves, Writables, Generic types, … – Composites: Tuples, Pojos, CaseClasses – Extensible by custom types 15
- 16. Serializing a Tuple3<Integer, Double, Person> 16
- 17. OPERATING ON BINARY DATA 17
- 18. Data processing algorithms • Flink’s algorithms are based on RDBMS technology – External Merge Sort, Hybrid Hash Join, Sort Merge Join, … • Algorithms receive a budget of memory segments – AutomaOc decision about budget size – No fine-‐tuning of operator memory! • Operate in-‐memory as long as data fits into budget – And gracefully spill to disk if data exceeds memory 18
- 19. In-‐memory sort – Fill the sort buffer 19
- 20. In-‐memory sort – Sort the buffer 20
- 21. In-‐memory sort – Read sorted buffer 21
- 22. SHOW ME NUMBERS! 22
- 23. Sort benchmark • Task: Sort 10 million Tuple2<Integer, String> records – String length 12 chars • Tuple has 16 Bytes of raw data • ~152 MB raw data – Integers uniformly, Strings long-‐tail distributed – Sort on Integer field and on String field • Generated input provided as mutable object iterator • Use JVM with 900 MB heap size – Minimum size to reliable run the benchmark 23
- 24. SorOng methods 1. Objects-‐on-‐Heap: – Put cloned data objects in ArrayList and use Java’s CollecOon sort. – ArrayList is iniOalized with right size. 2. Flink-‐serialized (on-‐heap): – Using Flink’s custom serializers. – Integer with full binary sorOng key, String with 8 byte prefix key. 3. Kryo-‐serialized (on-‐heap): – Serialize fields with Kryo. – No binary sorOng keys, objects are deserialized for comparison. • All implementaOons use a single thread • Average execuOon Ome of 10 runs reported • GC triggered between runs (does not go into reported Ome) 24
- 25. ExecuOon Ome 25
- 26. Garbage collecOon and heap usage 26 Objects-‐on-‐heap Flink-‐serialized
- 27. Memory usage 27 • Breakdown: Flink serialized -‐ Sort Integer – 4 bytes Integer – 12 bytes String – 4 bytes String length – 4 bytes pointer – 4 bytes Integer sorOng key – 28 bytes * 10M records = 267 MB Object-‐on-‐heap Flink-‐serialized Kryo-‐serialized Sort Integer Approx. 700 MB 277 MB 266 MB Sort String Approx. 700 MB 315 MB 266 MB
- 28. Going out-‐of-‐core 28 • Single thread HashJoin with 4GB memory budget • Build side varies, Probe side 64GB
- 29. WHAT’S NEXT? 29
- 30. We’re not done yet! • SerializaOon layouts tailored towards operaOons – More efficient operaOons on binary data • Table API provides full semanOcs for execuOon – Use code generaOon to operate fully on binary data • … 30
- 31. Summary • AcOve memory management avoids OOMErrors • Highly efficient data serializaOon stack – Facilitates operaOons on binary data – Makes more data fit into memory • DBMS-‐style operators operate on binary data – High performance in-‐memory processing – Graceful destaging to disk if necessary • Read Flink’s blog: – hfp://flink.apache.org/news/2015/05/11/Juggling-‐with-‐Bits-‐and-‐Bytes.html – hfp://flink.apache.org/news/2015/03/13/peeking-‐into-‐Apache-‐Flinks-‐Engine-‐Room.html – hfp://flink.apache.org/news/2015/09/16/off-‐heap-‐memory.html 31
- 32. 32 hfp://flink.apache.org @ApacheFlink Apache Flink