[Spark_2] Spark Intro_Architecture and Components

Spark Intro

• reference: Apache Spark: core concepts, architecture and internals

  Keywords

• RDD
• Operation
  • Create RDD
  • Action: Count, Collect, Take
  • Transformation: Map, Filter
• Partition, Dependency

1. RDD (Resilient Distributed Datasets)

• Important aspect: Distributed/ Immutable/ Resilient
  • Distributed: Each RDD is split into multiple pieces(partitions) -> partitions are divided across the clusters
  • Immutable(read-only): cannot be changed after created -> remove all the potential problems due to updates from multiple threads
  • Resilient: in case of any node in the cluster goes down, can recover the parts of the RDDs(cuz can be recreated at any time & ‘Lineage’) -> make sure fault-tolerance
• Basic concept
  • can contain any types of obj, such as Python, Java, or Scala objects, including user-defined classes
  • is a capsulation around a very large dataset
  • will automatically distribute the data contained in RDDs, across your cluster and parallelize the operaton your perfom on them
  • paralleize applications(compute-intensive app, app requiring input from data streams, and etc) across clusters
• RDD workflow
  • Create initial RDDs from external data
  • Apply Transformation
  • Launch Actions

How to create a RDD

• 1) (Not practical to large datasets)Take an existing collection and pass it to SparkContext’s parallelize method.
• 2) Load RDDs from external strage by calling textFile method on SparkContext.
  • External storage: AWS S3, HDFS and etc. (JDBC, Cassandra, Elasticsearch)

2. Spark Architecture (link)

• Driver Program(Spark Application) eg. shell
  • Drive application
  • Consists of SparkContext and user code
• SparkContext
  • Take the job, break the job in tasks and distribute them to the worker nodes
  • Represent connection to spark Cluster Managers which allocate resourecs across app
  • Driver Program access Spark app through SparckContext obj
• Cluster Manager
  • not need to be on the same machines with Driver Program
  • Manage job within the cluster
  • Allocate resource
• Executor
  • Excute the tasks on the partitioned RDDs
  • Launch at the beginning of the Spark app
  • Return back result to SparkContext
  • Interact with storage system
• DAG(Direct Acyclic Graph): User code containing RDD transformations forms DAG
  • split into stages of task by DAGScheduler

3. Spark Components (link)

Main Components

• Spark Driver
• Executors
• Cluster Manger

Other Components

: Responsible for translation of user code into actual jobs executed on cluster

• SparkContext
• DAGScheduler
• TaskScheduler
• SchedulerBackend
• BlockManager

4. Operation

• 1) Action: compute(count, collect) a result based on an RDD and return result to Driver Program or write it to storage
• 2) Transformation: Apply some functions to the data in RDD to create a new RDD
  • filter: return a new RDD with a subset of the data in the original RDD(selecting elems from those passed to func())
    • used to remove invalid row to clean up
  • map: pass each elem through the function and yield the new value of each elem in the resulting RDD
• 3) Transformation vs Actions: due to Lazy Execution, all the RDDs won’t be computed until they are used in an ACTION()(eg. collect(), first())

5. Others

• Dependencies
  • Wide(shuffle): multiple child partitions may depend on one parent partition; require data from all parent partitions
  • Narrow: each partition of the parent RDD is used by at most one partition of the child RDD eg. Map(), Filter()

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Operation Code from Spark Doc

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Transformations

The following table lists some of the common transformations supported by Spark. Refer to the RDD API doc (Scala, Java, Python, R) and pair RDD functions doc (Scala, Java) for details.

Transformation	Meaning
map(func)	Return a new distributed dataset formed by passing each element of the source through a function func.
filter(func)	Return a new dataset formed by selecting those elements of the source on which func returns true.
flatMap(func)	Similar to map, but each input item can be mapped to 0 or more output items (so func should return a Seq rather than a single item).
mapPartitions(func)	Similar to map, but runs separately on each partition (block) of the RDD, so func must be of type Iterator<T> => Iterator<U> when running on an RDD of type T.
mapPartitionsWithIndex(func)	Similar to mapPartitions, but also provides func with an integer value representing the index of the partition, so func must be of type (Int, Iterator<T>) => Iterator<U> when running on an RDD of type T.
sample(withReplacement, fraction, seed)	Sample a fraction fraction of the data, with or without replacement, using a given random number generator seed.
union(otherDataset)	Return a new dataset that contains the union of the elements in the source dataset and the argument.
intersection(otherDataset)	Return a new RDD that contains the intersection of elements in the source dataset and the argument.
distinct([numPartitions]))	Return a new dataset that contains the distinct elements of the source dataset.
groupByKey([numPartitions])	When called on a dataset of (K, V) pairs, returns a dataset of (K, Iterable<V>) pairs. Note: If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using reduceByKey or aggregateByKey will yield much better performance. Note: By default, the level of parallelism in the output depends on the number of partitions of the parent RDD. You can pass an optional numPartitions argument to set a different number of tasks.
reduceByKey(func, [numPartitions])	When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function func, which must be of type (V,V) => V. Like in groupByKey, the number of reduce tasks is configurable through an optional second argument.
aggregateByKey(zeroValue)(seqOp, combOp, [numPartitions])	When called on a dataset of (K, V) pairs, returns a dataset of (K, U) pairs where the values for each key are aggregated using the given combine functions and a neutral "zero" value. Allows an aggregated value type that is different than the input value type, while avoiding unnecessary allocations. Like in groupByKey, the number of reduce tasks is configurable through an optional second argument.
sortByKey([ascending], [numPartitions])	When called on a dataset of (K, V) pairs where K implements Ordered, returns a dataset of (K, V) pairs sorted by keys in ascending or descending order, as specified in the boolean ascending argument.
join(otherDataset, [numPartitions])	When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (V, W)) pairs with all pairs of elements for each key. Outer joins are supported through leftOuterJoin, rightOuterJoin, and fullOuterJoin.
cogroup(otherDataset, [numPartitions])	When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (Iterable<V>, Iterable<W>)) tuples. This operation is also called groupWith.
cartesian(otherDataset)	When called on datasets of types T and U, returns a dataset of (T, U) pairs (all pairs of elements).
pipe(command, [envVars])	Pipe each partition of the RDD through a shell command, e.g. a Perl or bash script. RDD elements are written to the process's stdin and lines output to its stdout are returned as an RDD of strings.
coalesce(numPartitions)	Decrease the number of partitions in the RDD to numPartitions. Useful for running operations more efficiently after filtering down a large dataset.
repartition(numPartitions)	Reshuffle the data in the RDD randomly to create either more or fewer partitions and balance it across them. This always shuffles all data over the network.
repartitionAndSortWithinPartitions(partitioner)	Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys. This is more efficient than calling repartition and then sorting within each partition because it can push the sorting down into the shuffle machinery.

Actions

The following table lists some of the common actions supported by Spark. Refer to the RDD API doc (Scala, Java, Python, R)

and pair RDD functions doc (Scala, Java) for details.

Action	Meaning
reduce(func)	Aggregate the elements of the dataset using a function func (which takes two arguments and returns one). The function should be commutative and associative so that it can be computed correctly in parallel.
collect()	Return all the elements of the dataset as an array at the driver program. This is usually useful after a filter or other operation that returns a sufficiently small subset of the data.
count()	Return the number of elements in the dataset.
first()	Return the first element of the dataset (similar to take(1)).
take(n)	Return an array with the first n elements of the dataset.
takeSample(withReplacement, num, [seed])	Return an array with a random sample of num elements of the dataset, with or without replacement, optionally pre-specifying a random number generator seed.
takeOrdered(n, [ordering])	Return the first n elements of the RDD using either their natural order or a custom comparator.
saveAsTextFile(path)	Write the elements of the dataset as a text file (or set of text files) in a given directory in the local filesystem, HDFS or any other Hadoop-supported file system. Spark will call toString on each element to convert it to a line of text in the file.
saveAsSequenceFile(path) (Java and Scala)	Write the elements of the dataset as a Hadoop SequenceFile in a given path in the local filesystem, HDFS or any other Hadoop-supported file system. This is available on RDDs of key-value pairs that implement Hadoop's Writable interface. In Scala, it is also available on types that are implicitly convertible to Writable (Spark includes conversions for basic types like Int, Double, String, etc).
saveAsObjectFile(path) (Java and Scala)	Write the elements of the dataset in a simple format using Java serialization, which can then be loaded using SparkContext.objectFile().
countByKey()	Only available on RDDs of type (K, V). Returns a hashmap of (K, Int) pairs with the count of each key.
foreach(func)	Run a function func on each element of the dataset. This is usually done for side effects such as updating an Accumulator or interacting with external storage systems. Note: modifying variables other than Accumulators outside of the foreach() may result in undefined behavior. See Understanding closures for more details.