Instant Pickles: Generating Object-Oriented Pickler Combinators for Fast and Extensible Serialization

49a4815846825cd1334fa080c6e71c5d?s=47 Heather Miller
October 30, 2013

Instant Pickles: Generating Object-Oriented Pickler Combinators for Fast and Extensible Serialization

Presented at OOPSLA'13, Indianapolis, IN, USA
Full publication: http://infoscience.epfl.ch/record/187787/files/oopsla-pickling_1.pdf

Serialization or pickling, i.e., persisting runtime objects by converting them into a binary or text representation is ubiquitous in distributed programming. Pickler combinators are a popular approach from functional programming designed to alleviate some of the tedium of writing pickling code by hand, but they don't translate well to object-oriented programming due to qualities like open class hierarchies and subtyping polymorphism. Furthermore, both functional pickler combinators and Java-based serialization frameworks tend to be tied to a specific pickle format, leaving programmers no choice of how their data is persisted. In this paper, we present object-oriented pickler combinators and a framework for generating them at compile-time, designed to be the default serialization mechanism of the Scala programming language. Our framework is extensible; (1) using Scala's implicit parameters, users can add their own easily-swappable pickle format, (2) using the type class pattern, users can provide their own custom picklers to override the default behavior of the Scala pickling framework. In addition to extensibility and need for little to no boilerplate, the static generation of our OO picklers achieves a factor 6 speedup over Java Serialization, and performs on par or up to 3 times faster than popular "fast" Java serialization frameworks like Kryo.

49a4815846825cd1334fa080c6e71c5d?s=128

Heather Miller

October 30, 2013
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  1. Pickles Heather Miller heather.miller@epfl.ch : instant Generating Object-Oriented Pickler Combinators

    for Fast and Extensible Serialization Philipp Haller Eugene Burmako Martin Odersky OOPSLA’13, Indianapolis, IN, USA October 30th, 2013
  2. M v

  3. M v More and more traditional applications are moving to

    the cloud or to mobile devices. many applications are becoming distributed
  4. M v More and more traditional applications are moving to

    the cloud or to mobile devices. many applications are becoming distributed These applications run on a variety of distributed systems
  5. this kind of distributed system a cluster of commodity machines

    churning through a massive data-parallel job
  6. insert social network of your choice here This kind of

    distributed system ! a smartphone interacting with a service running in the cloud
  7. communication is central

  8. communication is central Serialization is central to communication

  9. communication is central interoperability between different services increasing in importance.

    Serialization is central to communication
  10. communication is central interoperability between different services increasing in importance.

    many message formats required often within the same application Serialization is central to communication
  11. communication is central Efficient communication required. interoperability between different services

    increasing in importance. many message formats required often within the same application Serialization is central to communication
  12. solved problem? B ,isn’t serialization a

  13. https://github.com/scala/pickling Java and others all have built-in support for serialization.

  14. https://github.com/scala/pickling Java and others all have built-in support for serialization.

    why do we care?
  15. https://github.com/scala/pickling S w! Java and others all have built-in support

    for serialization. why do we care?
  16. https://github.com/scala/pickling S w! The overhead of Java serialization nearly always

    dominates other communication costs. “ ” Object Serialization for Marshalling Data in a Java Interface to MPI Carpenter et al. Serialization slows communication by two orders of magnitude “ ” Java and others all have built-in support for serialization. why do we care?
  17. https://github.com/scala/pickling S w! Java and others all have built-in support

    for serialization. why do we care? Java Serialization is still slow. Industry knows this. jvm- serialization- group
  18. https://github.com/scala/pickling S w! Java and others all have built-in support

    for serialization. why do we care?
  19. C ! https://github.com/scala/pickling S w! Java and others all have

    built-in support for serialization. why do we care?
  20. /p 1 new approach to serialization Compile-time pickler generation. https://github.com/scala/pickling

    Enter:
  21. /p 1 new approach to serialization Compile-time pickler generation. https://github.com/scala/pickling

    FAST Enter:
  22. /p 1 new approach to serialization Compile-time pickler generation. https://github.com/scala/pickling

    FAST open, extensible 2 Serialize to any format. Enter:
  23. /p 1 new approach to serialization Compile-time pickler generation. json

    binary xml protobuf or, invent your own format! https://github.com/scala/pickling FAST open, extensible 2 Serialize to any format. Enter:
  24. /p 1 new approach to serialization Compile-time pickler generation. Customize

    how objects are pickled. https://github.com/scala/pickling FAST open, extensible 2 Serialize to any format. Enter:
  25. new scala module 3 (Incubating.) Increasing adoption, community libraries, external

    contributors. /p 1 new approach to serialization Compile-time pickler generation. Customize how objects are pickled. https://github.com/scala/pickling FAST open, extensible 2 Serialize to any format. Enter:
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  32. Motivation Usage Performance Pickler Generation/Composition Subtyping Summary/Contributions O ✔ Pickler

    Basics Extensibility
  33. https://github.com/scala/pickling W ? pickling:

  34. https://github.com/scala/pickling W ? import scala.pickling._ pickling:

  35. https://github.com/scala/pickling W ? import scala.pickling._ import json._ pickling:

  36. https://github.com/scala/pickling W ? import scala.pickling._ import json._ case class Person(name:

    String, age: Int) val p = Person("John Oliver", 36) pickling:
  37. https://github.com/scala/pickling W ? import scala.pickling._ import json._ case class Person(name:

    String, age: Int) val p = Person("John Oliver", 36) scala> p.pickle res1: scala.pickling.json.JSONPickle = JSONPickle({ "tpe": "Person", "name": "John Oliver", "age": 36 }) pickling:
  38. https://github.com/scala/pickling W ? import scala.pickling._ import json._ case class Person(name:

    String, age: Int) val p = Person("John Oliver", 36) unpickling:
  39. https://github.com/scala/pickling W ? val pckl = p.pickle import scala.pickling._ import

    json._ case class Person(name: String, age: Int) val p = Person("John Oliver", 36) unpickling:
  40. https://github.com/scala/pickling W ? val pckl = p.pickle scala>  pckl.unpickle[Person] res0:

     Person  =  Person(John  Oliver,36) import scala.pickling._ import json._ case class Person(name: String, age: Int) val p = Person("John Oliver", 36) unpickling:
  41. Motivation Usage Performance Pickler Generation/Composition Subtyping Summary/Contributions O ✔ ✔

    Pickler Basics Extensibility
  42. collections: Time B r size [#elements] scala.collection.immutable.Vector ... scala-pickling Kryo

    2 pickler-comb unsafe-pickler-comb
  43. B r collections: free Memory (more is better) size [#elements]

    scala.collection.immutable.Vector ...
  44. B r collections: size size [#elements] scala.collection.immutable.Vector ... scala-pickling Kryo

  45. B r evactor: time Java runs out of memory [#events]

    String Int String case  class  DataEvent  (            val  id:  String,      val  timestamp:  Int,      val  message:  String)   String Int String String Int String String Int String String Int String String Int String
  46. B r evactor: time (no java, more events) [#events] String

    Int String case  class  DataEvent  (            val  id:  String,      val  timestamp:  Int,      val  message:  String)   String Int String String Int String String Int String String Int String String Int String
  47. 6000 8000 10000 12000 14000 0 5 10 15 20

    25 30 35 40 Number of Wikipedia Nodes Time [ms] Wikipedia Cyclic Object Graph, Pickle Only 6 0 5 10 15 20 25 30 35 40 Time [ms] W Java Kryo v2 Scala Pickling (a) B r wikipedia: time (pickle only) cyclic object graph final  class  Vertex(    val  label:  String,      var  neighbors:  List[Vertex])
  48. Motivation Usage Performance Pickler Generation/Composition Subtyping Summary/Contributions O ✔ ✔

    ✔ Pickler Basics Extensibility
  49. https://github.com/scala/pickling P r Picklers are first-class objects. A pickler for

    some type T is an instance of a concrete subclass of the following trait: trait Pickler[T] { def pickle(obj: T, builder: PickleBuilder): Unit } basics
  50. https://github.com/scala/pickling P r Picklers are first-class objects. A pickler for

    some type T is an instance of a concrete subclass of the following trait: trait Pickler[T] { def pickle(obj: T, builder: PickleBuilder): Unit } pickle is a macro, therefore p.pickle is expanded as follows: val  pickler:  Pickler[Person]  =  ??? val  builder  =  pickleFormat.createBuilder() pickler.pickle(p,  builder) builder.result() basics
  51. https://github.com/scala/pickling P r Picklers are first-class objects. A pickler for

    some type T is an instance of a concrete subclass of the following trait: trait Pickler[T] { def pickle(obj: T, builder: PickleBuilder): Unit } pickle is a macro, therefore p.pickle is expanded as follows: val  pickler:  Pickler[Person]  =  ??? val  builder  =  pickleFormat.createBuilder() pickler.pickle(p,  builder) builder.result() inserted by the framework basics
  52. https://github.com/scala/pickling P r Picklers are first-class objects. A pickler for

    some type T is an instance of a concrete subclass of the following trait: trait Pickler[T] { def pickle(obj: T, builder: PickleBuilder): Unit } pickle is a macro, therefore p.pickle is expanded as follows: val  pickler:  Pickler[Person]  =  ??? val  builder  =  pickleFormat.createBuilder() pickler.pickle(p,  builder) builder.result() imported pickle format basics
  53. Motivation Usage Performance Pickler Generation/Composition Subtyping Summary/Contributions O ✔ ✔

    ✔ Pickler Basics ✔ Extensibility
  54. https://github.com/scala/pickling W b subtyping? Example: class Position(val title: String, val

    person: Person) What if we want to pickle Position, and Person has subclasses? dynamically, the person field could point to an instance of a subclass of Person of course... A modularly re-usable pickler for type Person must be able to pickle all possible subtypes of Person
  55. https://github.com/scala/pickling S b : Not all picklers are required to

    handle all possible subtypes. Dynamic PIcklers exist to handle subtypes. trait  DynamicPickler[T]  {    def  pickle(obj:  T,  builder:  PickleBuilder):  Unit } Contract: pickles any object of type T (including subtypes of T) the solution
  56. https://github.com/scala/pickling S b :how it works Within pickle, select Pickler[S]

    based on the dynamic type of the picklee: class  Person class  Firefighter  extends  Person   picklee  match  {    case  OfClass[Person]  =>  personPickler    case  OfClass[Firefighter]  =>  ffPickler    ...    case  p:  Pickleable  =>  p.pickler    case  _  =>  RuntimePickler.for(picklee.getClass)   }
  57. Motivation Usage Performance Pickler Generation/Composition Subtyping Summary/Contributions O ✔ ✔

    ✔ Pickler Basics ✔ Extensibility ✔
  58. GeneratingP r case class Person(name: String, age: Int) Let’s generate

    a pickler for Job case class Job(p: Person, title: String) Scala/pickling recursively generates and composes type-class instances that represent picklers at compile-time. essence Rather than diving into type- classes, let’s just develop some intuition...
  59. GeneratingP r case class Person(name: String, age: Int) case class

    Job(p: Person, title: String) graphical Intuition Let’s generate a pickler for Job
  60. GeneratingP r case class Person(name: String, age: Int) case class

    Job(p: Person, title: String) graphical Intuition Pickler[Job] stringPickler intPickler type sought pickler Let’s generate a pickler for Job
  61. GeneratingP r case class Person(name: String, age: Int) case class

    Job(p: Person, title: String) graphical Intuition Pickler[Job] Pickler[Person] Pickler[String] stringPickler intPickler type sought pickler Let’s generate a pickler for Job
  62. GeneratingP r case class Person(name: String, age: Int) case class

    Job(p: Person, title: String) graphical Intuition Pickler[Job] Pickler[String] Pickler[Int] Pickler[Person] Pickler[String] stringPickler intPickler type sought pickler Let’s generate a pickler for Job
  63. GeneratingP r graphical Intuition Pickler[Job] Pickler[String] Pickler[Int] Pickler[Person] Pickler[String] Pickler[String]

    intPickler type sought pickler stringPickler case class Person(name: String, age: Int) case class Job(p: Person, title: String) Let’s generate a pickler for Job
  64. GeneratingP r graphical Intuition Pickler[Job] Pickler[Person] Pickler[String] intPickler type sought

    pickler stringPickler case class Person(name: String, age: Int) case class Job(p: Person, title: String) Let’s generate a pickler for Job
  65. GeneratingP r graphical Intuition Pickler[Job] Pickler[Person] type sought pickler stringPickler

    stringPickler intPickler case class Person(name: String, age: Int) case class Job(p: Person, title: String) Let’s generate a pickler for Job
  66. GeneratingP r graphical Intuition Pickler[Job] type sought pickler stringPickler personPickler

    stringPickler intPickler case class Person(name: String, age: Int) case class Job(p: Person, title: String) Let’s generate a pickler for Job
  67. GeneratingP r graphical Intuition Pickler[Job] type sought pickler personPickler stringPickler

    intPickler stringPickler case class Person(name: String, age: Int) case class Job(p: Person, title: String)
  68. GeneratingP r graphical Intuition type sought pickler jobPickler personPickler stringPickler

    intPickler stringPickler case class Person(name: String, age: Int) case class Job(p: Person, title: String)
  69. Motivation Usage Performance Pickler Generation/Composition Subtyping Summary/Contributions O ✔ ✔

    ✔ Pickler Basics ✔ Extensibility ✔ ✔
  70. 1. Picklers are implicit values that are looked up by

    type (at compile time) 2. Custom picklers: implicit picklers with higher precedence than generated picklers customP r jobPickler myPersonPickler stringPickler intPickler stringPickler generated pickler custom user pickler
  71. customP F r class  JSONPickleFormat  extends  PickleFormat  {  ...  }

    package  object  json  {    implicit  val  pickleFormat  =  new  JSONPickleFormat } same principle as custom picklers
  72. Motivation Usage Performance Pickler Generation/Composition Subtyping Summary/Contributions O ✔ ✔

    ✔ Pickler Basics ✔ Extensibility ✔ ✔ ✔
  73. an extension of pickler combinators that supports subtyping polymorphism; Contributions

    of the Scala/Pickling project: a framework that enables retrofitting existing types with pickling support and that supports pluggable pickle formats; S r a composable mechanism for static pickler generation; an experimental evaluation that shows that statically-generated pickler combinators can outperform state-of-the-art serialization frameworks and reduce memory usage.
  74. qUESTIONS ? heather.miller@epfl.ch @heathercmiller C