Spark

Spark and Resilient Distributed Datasets
Amir H. Payberah
amir@sics.se
Amirkabir University of Technology
(Tehran Polytechnic)
Amir H. Payberah (Tehran Polytechnic) Spark 1393/8/17 1 / 49

Motivation
MapReduce greatly simpliﬁed big data analysis on large, unreliable
clusters.
But as soon as it got popular, users wanted more:
• Iterative jobs, e.g., machine learning algorithms
• Interactive analytics

Motivation
Both iterative and interactive queries need one thing that MapRe-
duce lacks:

Motivation
duce lacks:
Eﬃcient primitives for data sharing.

Motivation
duce lacks:
Eﬃcient primitives for data sharing.
In MapReduce, the only way to share data across jobs is stable
storage, which is slow.
Replication also makes the system slow, but it is necessary for fault
tolerance.

Proposed Solution
In-Memory Data Processing and Sharing.

Example

Challenge
How to design a distributed memory abstraction
that is both fault tolerant and eﬃcient?

Challenge
How to design a distributed memory abstraction
that is both fault tolerant and eﬃcient?
Solution
Resilient Distributed Datasets (RDD)

Resilient Distributed Datasets (RDD) (1/2)
A distributed memory abstraction.

A distributed memory abstraction.
Immutable collections of objects spread across a cluster.

An RDD is divided into a number of partitions, which are atomic
pieces of information.
Partitions of an RDD can be stored on diﬀerent nodes of a cluster.

Programming Model

Spark Programming Model (1/2)
Spark programming model is based on parallelizable operators.
Parallelizable operators are higher-order functions that execute user-
deﬁned functions in parallel.

Spark Programming Model (2/2)
A data ﬂow is composed of any number of data sources, operators,
and data sinks by connecting their inputs and outputs.
Job description based on directed acyclic graphs (DAG).

Higher-Order Functions (1/3)
Higher-order functions: RDDs operators.
There are two types of RDD operators: transformations and actions.

Transformations: lazy operators that create new RDDs.
Actions: lunch a computation and return a value to the program or
write data to the external storage.

RDD Transformations - Map
All pairs are independently processed.

RDD Transformations - Map
All pairs are independently processed.
// passing each element through a function.
val nums = sc.parallelize(Array(1, 2, 3))
val squares = nums.map(x => x * x) // {1, 4, 9}
// selecting those elements that func returns true.
val even = squares.filter(x => x % 2 == 0) // {4}
// mapping each element to zero or more others.
nums.flatMap(x => Range(0, x, 1)) // {0, 0, 1, 0, 1, 2}

RDD Transformations - Reduce
Pairs with identical key are grouped.
Groups are independently processed.

RDD Transformations - Reduce
Pairs with identical key are grouped.
Groups are independently processed.
val pets = sc.parallelize(Seq(("cat", 1), ("dog", 1), ("cat", 2)))
pets.reduceByKey((x, y) => x + y)
// {(cat, 3), (dog, 1)}
pets.groupByKey()
// {(cat, (1, 2)), (dog, (1))}

RDD Transformations - Join
Performs an equi-join on the key.
Join candidates are independently pro-
cessed.

RDD Transformations - Join
Performs an equi-join on the key.
Join candidates are independently pro-
cessed.
val visits = sc.parallelize(Seq(("index.html", "1.2.3.4"),
("about.html", "3.4.5.6"),
("index.html", "1.3.3.1")))
val pageNames = sc.parallelize(Seq(("index.html", "Home"),
("about.html", "About")))
visits.join(pageNames)
// ("index.html", ("1.2.3.4", "Home"))
// ("index.html", ("1.3.3.1", "Home"))
// ("about.html", ("3.4.5.6", "About"))

RDD Transformations - CoGroup
Groups each input on key.
Groups with identical keys are processed
together.

RDD Transformations - CoGroup
Groups each input on key.
Groups with identical keys are processed
together.
val visits = sc.parallelize(Seq(("index.html", "1.2.3.4"),
("about.html", "3.4.5.6"),
("index.html", "1.3.3.1")))
val pageNames = sc.parallelize(Seq(("index.html", "Home"),
("about.html", "About")))
visits.cogroup(pageNames)
// ("index.html", (("1.2.3.4", "1.3.3.1"), ("Home")))
// ("about.html", (("3.4.5.6"), ("About")))

RDD Transformations - Union and Sample
Union: merges two RDDs and returns a single RDD using bag se-
mantics, i.e., duplicates are not removed.
Sample: similar to mapping, except that the RDD stores a random
number generator seed for each partition to deterministically sample
parent records.

Basic RDD Actions (1/2)
Return all the elements of the RDD as an array.
nums.collect() // Array(1, 2, 3)

Return an array with the ﬁrst n elements of the RDD.
nums.take(2) // Array(1, 2)

Return an array with the ﬁrst n elements of the RDD.
nums.take(2) // Array(1, 2)
Return the number of elements in the RDD.
nums.count() // 3

Aggregate the elements of the RDD using the given function.
nums.reduce((x, y) => x + y)
or
nums.reduce(_ + _) // 6

Aggregate the elements of the RDD using the given function.
nums.reduce((x, y) => x + y)
or
nums.reduce(_ + _) // 6
Write the elements of the RDD as a text ﬁle.
nums.saveAsTextFile("hdfs://file.txt")

SparkContext
Main entry point to Spark functionality.
Available in shell as variable sc.
In standalone programs, you should make your own.
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
val sc = new SparkContext(master, appName, [sparkHome], [jars])

SparkContext
Main entry point to Spark functionality.
Available in shell as variable sc.
In standalone programs, you should make your own.
val sc = new SparkContext(master, appName, [sparkHome], [jars])
local
local[k]
spark://host:port
mesos://host:port

Creating RDDs
Turn a collection into an RDD.
val a = sc.parallelize(Array(1, 2, 3))

Creating RDDs
Turn a collection into an RDD.
val a = sc.parallelize(Array(1, 2, 3))
Load text ﬁle from local FS, HDFS, or S3.
val a = sc.textFile("file.txt")
val b = sc.textFile("directory/*.txt")
val c = sc.textFile("hdfs://namenode:9000/path/file")

Example (1/2)
Count the lines containing SICS.
val file = sc.textFile("hdfs://...")
val sics = file.filter(_.contains("SICS"))
val cachedSics = sics.cache()
val ones = cachedSics.map(_ => 1)
val count = ones.reduce(_+_)

Example (1/2)
Transformation
Transformation
Action

Example (2/2)
val count = file.filter(_.contains("SICS")).count()

Example (2/2)
Transformation
Action

Example - Standalone Application (1/2)
object WordCount {
def main(args: Array[String]) {
val sc = new SparkContext("local", "SICS", "127.0.0.1",
List("target/scala-2.10/sics-count_2.10-1.0.jar"))
val file = sc.textFile("...").cache()
}
}

Example - Standalone Application (2/2)
sics.sbt:
name := "SICS Count"
version := "1.0"
scalaVersion := "2.10.3"
libraryDependencies += "org.apache.spark" %% "spark-core" % "0.9.0-incubating"
resolvers += "Akka Repository" at "http://repo.akka.io/releases/"

Shared Variables (1/2)
When Spark runs a function in parallel as a set of tasks on diﬀerent
nodes, it ships a copy of each variable used in the function to each
task.
Sometimes, a variable needs to be shared across tasks, or between
tasks and the driver program.

Shared Variables (2/2)
No updates to the variables are propagated back to the driver pro-
gram.
General read-write shared variables across tasks is ineﬃcient.
• For example, to give every node a copy of a large input dataset.
Two types of shared variables: broadcast variables and accumula-
tors.

Shared Variables: Broadcast Variables
A read-only variable cached on each machine rather than shipping
a copy of it with tasks.
The broadcast values are not shipped to the nodes more than once.
scala> val broadcastVar = sc.broadcast(Array(1, 2, 3))
broadcastVar: spark.Broadcast[Array[Int]] = spark.Broadcast(b5c40191-...)
scala> broadcastVar.value
res0: Array[Int] = Array(1, 2, 3)

Shared Variables: Accumulators
They are only added.
They can be used to implement counters or sums.
Tasks running on the cluster can then add to it using the += oper-
ator.
scala> val accum = sc.accumulator(0)
accum: spark.Accumulator[Int] = 0
scala> sc.parallelize(Array(1, 2, 3, 4)).foreach(x => accum += x)
...
scala> accum.value
res2: Int = 10

Execution Engine
(SPARK)

Spark
Spark provides a programming interface in Scala.
Each RDD is represented as an object in Spark.

Spark Programming Interface
A Spark application consists of a driver program that runs the user’s
main function and executes various parallel operations on a cluster.

Lineage
Lineage: transformations used to build
an RDD.
RDDs are stored as a chain of objects
capturing the lineage of each RDD.

RDD Dependencies (1/3)
Two types of dependencies between RDDs: Narrow and Wide.

RDD Dependencies: Narrow (2/3)
Narrow: each partition of a parent RDD is used by at most one
partition of the child RDD.
Narrow dependencies allow pipelined execution on one cluster node:
a map followed by a ﬁlter.

RDD Dependencies: Wide (3/3)
Wide: each partition of a parent RDD is used by multiple partitions
of the child RDDs.

Job Scheduling (1/2)
When a user runs an action on an RDD:
the scheduler builds a DAG of stages
from the RDD lineage graph.
A stage contains as many pipelined
transformations with narrow dependen-
cies.
The boundary of a stage:
• Shuﬄes for wide dependencies.
• Already computed partitions.

Job Scheduling (2/2)
The scheduler launches tasks to compute
missing partitions from each stage until
it computes the target RDD.
Tasks are assigned to machines based on
data locality.
• If a task needs a partition, which is
available in the memory of a node, the
task is sent to that node.

RDD Fault Tolerance (1/3)
RDDs maintain lineage information that can be used to reconstruct
lost partitions.
Logging lineage rather than the actual data.
No replication.
Recompute only the lost partitions of an RDD.

The intermediate records of wide dependencies are materialized on
the nodes holding the parent partitions: to simplify fault recovery.
If a task fails, it will be re-ran on another node, as long as its stages
parents are available.
If some stages become unavailable, the tasks are submitted to com-
pute the missing partitions in parallel.

Recovery may be time-consuming for RDDs with long lineage chains
and wide dependencies.
It can be helpful to checkpoint some RDDs to stable storage.
Decision about which data to checkpoint is left to users.

Memory Management (1/2)
If there is not enough space in memory for a new computed RDD
partition: a partition from the least recently used RDD is evicted.
Spark provides three options for storage of persistent RDDs:
1 In memory storage as deserialized Java objects.
2 In memory storage as serialized Java objects.
3 On disk storage.

Memory Management (2/2)
When an RDD is persisted, each node stores any partitions of the
RDD that it computes in memory.
This allows future actions to be much faster.
Persisting an RDD using persist() or cache() methods.
Diﬀerent storage levels:
MEMORY ONLY
MEMORY AND DISK
MEMORY ONLY SER
MEMORY AND DISK SER
MEMORY ONLY 2, MEMORY AND DISK 2, etc.

RDD Applications
Applications suitable for RDDs
• Batch applications that apply the same operation to all elements of
a dataset.
Applications not suitable for RDDs
• Applications that make asynchronous ﬁne-grained updates to shared
state, e.g., storage system for a web application.

Summary
RDD: a distributed memory abstraction that is both fault tolerant
and eﬃcient
Two types of operations: Transformations and Actions.
RDD fault tolerance: Lineage

Questions?

Spark

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