Resilient Distributed Dataset (RDD)¶
Resilient Distributed Dataset (aka RDD) is the primary data abstraction in Apache Spark and the core of Spark (that I often refer to as "Spark Core").
.The origins of RDD
The original paper that gave birth to the concept of RDD is https://cs.stanford.edu/~matei/papers/2012/nsdi_spark.pdf[Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing] by Matei Zaharia, et al.
An RDD is a description of a fault-tolerant and resilient computation over a distributed collection of records (spread over <
NOTE: One could compare RDDs to collections in Scala, i.e. a RDD is computed on many JVMs while a Scala collection lives on a single JVM.
Using RDD Spark hides data partitioning and so distribution that in turn allowed them to design parallel computational framework with a higher-level programming interface (API) for four mainstream programming languages.
The features of RDDs (decomposing the name):
- Resilient, i.e. fault-tolerant with the help of <
> and so able to recompute missing or damaged partitions due to node failures.
- Distributed with data residing on multiple nodes in a spark-cluster.md[cluster].
- Dataset is a collection of spark-rdd-partitions.md[partitioned data] with primitive values or values of values, e.g. tuples or other objects (that represent records of the data you work with).
A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. Represents an immutable, partitioned collection of elements that can be operated on in parallel.
From the original paper about RDD - https://cs.stanford.edu/~matei/papers/2012/nsdi_spark.pdf[Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing]:
Resilient Distributed Datasets (RDDs) are a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner.
Beside the above traits (that are directly embedded in the name of the data abstraction - RDD) it has the following additional traits:
- In-Memory, i.e. data inside RDD is stored in memory as much (size) and long (time) as possible.
- Immutable or Read-Only, i.e. it does not change once created and can only be transformed using transformations to new RDDs.
- Lazy evaluated, i.e. the data inside RDD is not available or transformed until an action is executed that triggers the execution.
- Cacheable, i.e. you can hold all the data in a persistent "storage" like memory (default and the most preferred) or disk (the least preferred due to access speed).
- Parallel, i.e. process data in parallel.
- Typed -- RDD records have types, e.g.
- Partitioned -- records are partitioned (split into logical partitions) and distributed across nodes in a cluster.
- Location-Stickiness --
RDDcan define <
> to compute partitions (as close to the records as possible).
NOTE: Preferred location (aka locality preferences or placement preferences or locality info) is information about the locations of RDD records (that Spark's scheduler:DAGScheduler.md#preferred-locations[DAGScheduler] uses to place computing partitions on to have the tasks as close to the data as possible).
Computing partitions in a RDD is a distributed process by design and to achieve even data distribution as well as leverage spark-data-locality.md[data locality] (in distributed systems like HDFS or Cassandra in which data is partitioned by default), they are partitioned to a fixed number of spark-rdd-partitions.md[partitions] - logical chunks (parts) of data. The logical division is for processing only and internally it is not divided whatsoever. Each partition comprises of records.
spark-rdd-partitions.md[Partitions are the units of parallelism]. You can control the number of partitions of a RDD using spark-rdd-partitions.md#repartition[repartition] or spark-rdd-partitions.md#coalesce[coalesce] transformations. Spark tries to be as close to data as possible without wasting time to send data across network by means of spark-rdd-shuffle.md[RDD shuffling], and creates as many partitions as required to follow the storage layout and thus optimize data access. It leads to a one-to-one mapping between (physical) data in distributed data storage, e.g. HDFS or Cassandra, and partitions.
RDDs support two kinds of operations:
> - lazy operations that return another RDD.
> - operations that trigger computation and return values.
The motivation to create RDD were (https://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf[after the authors]) two types of applications that current computing frameworks handle inefficiently:
- iterative algorithms in machine learning and graph computations.
- interactive data mining tools as ad-hoc queries on the same dataset.
The goal is to reuse intermediate in-memory results across multiple data-intensive workloads with no need for copying large amounts of data over the network.
Technically, RDDs follow the <
[[dependencies]] Parent RDDs (aka rdd:RDD.md#dependencies[RDD dependencies])
An array of spark-rdd-partitions.md[partitions] that a dataset is divided to.
A rdd:RDD.md#compute[compute] function to do a computation on partitions.
An optional rdd:Partitioner.md[Partitioner] that defines how keys are hashed, and the pairs partitioned (for key-value RDDs)
> (aka locality info), i.e. hosts for a partition where the records live or are the closest to read from.
This RDD abstraction supports an expressive set of operations without having to modify scheduler for each one.
[[context]] An RDD is a named (by
name) and uniquely identified (by
id) entity in a ROOT:SparkContext.md (available as
RDDs live in one and only one ROOT:SparkContext.md that creates a logical boundary.
NOTE: RDDs cannot be shared between
SparkContexts (see ROOT:SparkContext.md#sparkcontext-and-rdd[SparkContext and RDDs]).
An RDD can optionally have a friendly name accessible using
name that can be changed using
scala> val ns = sc.parallelize(0 to 10) ns: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD at parallelize at <console>:24 scala> ns.id res0: Int = 2 scala> ns.name res1: String = null scala> ns.name = "Friendly name" ns.name: String = Friendly name scala> ns.name res2: String = Friendly name scala> ns.toDebugString res3: String = (8) Friendly name ParallelCollectionRDD at parallelize at <console>:24 
RDDs are a container of instructions on how to materialize big (arrays of) distributed data, and how to split it into partitions so Spark (using executor:Executor.md[executors]) can hold some of them.
In general data distribution can help executing processing in parallel so a task processes a chunk of data that it could eventually keep in memory.
Spark does jobs in parallel, and RDDs are split into partitions to be processed and written in parallel. Inside a partition, data is processed sequentially.
Saving partitions results in part-files instead of one single file (unless there is a single partition).
== [[transformations]] Transformations
A transformation is a lazy operation on a RDD that returns another RDD, e.g.
Find out more in rdd:spark-rdd-transformations.md[Transformations].
== [[actions]] Actions
An action is an operation that triggers execution of <
TIP: Go in-depth in the section spark-rdd-actions.md[Actions].
== [[creating-rdds]] Creating RDDs
One way to create a RDD is with
SparkContext.parallelize method. It accepts a collection of elements as shown below (
sc is a SparkContext instance):
scala> val rdd = sc.parallelize(1 to 1000) rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD at parallelize at <console>:25
You may also want to randomize the sample data:
scala> val data = Seq.fill(10)(util.Random.nextInt) data: Seq[Int] = List(-964985204, 1662791, -1820544313, -383666422, -111039198, 310967683, 1114081267, 1244509086, 1797452433, 124035586) scala> val rdd = sc.parallelize(data) rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD at parallelize at <console>:29
Given the reason to use Spark to process more data than your own laptop could handle,
SparkContext.parallelize is mainly used to learn Spark in the Spark shell.
SparkContext.parallelize requires all the data to be available on a single machine - the Spark driver - that eventually hits the limits of your laptop.
CAUTION: FIXME What's the use case for
scala> sc.makeRDD(0 to 1000) res0: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD at makeRDD at <console>:25
One of the easiest ways to create an RDD is to use
SparkContext.textFile to read files.
You can use the local
README.md file (and then
flatMap over the lines inside to have an RDD of words):
scala> val words = sc.textFile("README.md").flatMap(_.split("\\W+")).cache words: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD at flatMap at <console>:24
NOTE: You spark-rdd-caching.md[cache] it so the computation is not performed every time you work with
== [[creating-rdds-from-input]] Creating RDDs from Input
Refer to spark-io.md[Using Input and Output (I/O)] to learn about the IO API to create RDDs.
RDD transformations by definition transform an RDD into another RDD and hence are the way to create new ones.
Refer to <
== RDDs in Web UI
It is quite informative to look at RDDs in the Web UI that is at http://localhost:4040 for spark-shell.md[Spark shell].
Execute the following Spark application (type all the lines in
val ints = sc.parallelize(1 to 100) // <1> ints.setName("Hundred ints") // <2> ints.cache // <3> ints.count // <4>
<1> Creates an RDD with hundred of numbers (with as many partitions as possible) <2> Sets the name of the RDD <3> Caches the RDD for performance reasons that also makes it visible in Storage tab in the web UI <4> Executes action (and materializes the RDD)
With the above executed, you should see the following in the Web UI:
.RDD with custom name image::spark-ui-rdd-name.png[align="center"]
Click the name of the RDD (under RDD Name) and you will get the details of how the RDD is cached.
.RDD Storage Info image::spark-ui-storage-hundred-ints.png[align="center"]
Execute the following Spark job and you will see how the number of partitions decreases.
.Number of tasks after