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MASTERING APACHE SPARK PDF

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mastering-apache-spark: Taking notes about the core of Apache Spark while exploring the lowest depths of The Internals of Apache Spark Download PDF . cittadelmonte.info - Ebook download as PDF File .pdf), Text File .txt) or read book online. Title Mastering Apache Spark ; Author(s) Jacek Laskowski; Publisher: GitHub Books (); Paperback: N/A; eBook PDF ( pages, MB); Language.


Mastering Apache Spark Pdf

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Contribute to vaquarkhan/vaquarkhan development by creating an account on GitHub. The book closely follows the material in the individual lessons of Minna no I. Any words Minna No Nihongo Shokyuu. Mastering. Apache Spark Highlights from Databricks Blogs, Spark Summit Talks, and Notebooks Apache Spark Machine Learning Model Persistence.

This preview shows page 1 out of pages. Unformatted text preview: Table of Contents Introduction 1. Task not serializable I offer courses, workshops, mentoring and software development services. If you like the Apache Spark notes you should seriously consider participating in my own, very hands-on Spark Workshops. Mastering Apache Spark 2 serves as the ultimate place of mine to collect all the nuts and bolts of using Apache Spark.

Task not serializable I offer courses, workshops, mentoring and software development services. If you like the Apache Spark notes you should seriously consider participating in my own, very hands-on Spark Workshops. Mastering Apache Spark 2 serves as the ultimate place of mine to collect all the nuts and bolts of using Apache Spark. The notes aim to help me designing and developing better products with Apache Spark.

It is also a viable proof of my understanding of Apache Spark. I do eventually want to reach the highest level of mastery in Apache Spark as do you! The collection of notes serves as the study material for my trainings, workshops, videos and courses about Apache Spark. Follow me on twitter jaceklaskowski to know it early.

You will also learn about the upcoming events about Apache Spark. Attribution follows. Figure 1. The Spark Platform You could also describe Spark as a distributed, data processing engine for batch and streaming modes featuring SQL queries, graph processing, and machine learning.

Spark aims at speed, ease of use, extensibility and interactive analytics. Spark is often called cluster computing engine or simply execution engine. Spark is a distributed platform for executing complex multi-stage applications, like machine learning algorithms, and interactive ad hoc queries. Spark provides an efficient abstraction for in-memory cluster computing called Resilient Distributed Dataset.

NET framework like C or F. Represents an immutable. Lazy evaluated. You can control the number of partitions of a RDD using repartition or coalesce operations. Each partition comprises of records. Figure 2. Resilient Distributed Datasets RDDs are a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner. RDDs Partitions are the units of parallelism. Spark tries to be as close to data as possible without wasting time to send data across network by means of RDD shuffling.

This RDD abstraction supports an expressive set of operations without having to modify scheduler for each one. 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.

RDDs support two kinds of operations: Mastering Apache Spark as many partitions as required to follow the storage layout and thus optimize data access.

It lives in a SparkContext and as a SparkContext creates a logical boundary. The motivation to create RDD were after the authors two types of applications that current computing frameworks handle inefficiently: It leads to a one-to-one mapping between physical data in distributed data storage. Each RDD is characterized by five main properties: An array of partitions that a dataset is divided to A function to do a computation for a partition List of parent RDDs An optional partitioner that defines how keys are hashed.

HDFS or Cassandra. MapPartitionsRDD - a result of calling operations like map. Spark does jobs in parallel. In general.

Inside a partition. Saving partitions results in part-files instead of one single file unless there is a single partition. Actions An action is an operation that triggers execution of RDD transformations and returns a value to a Spark driver - the user program.

Go in-depth in the section Transformations in Operations - Transformations and Actions. DoubleRDD implicit conversion as org. It accepts a collection of elements as shown below sc is a SparkContext instance: SequenceFileRDD implicit conversion as org. Int ]. Go in-depth in the section Actions in Operations - Transformations and Actions. RDD[ Int. PairRDD implicit conversion as org. Mastering Apache Spark Refer to Transformations section to learn more.

Creates an RDD with hundreds of numbers with as many partitions as possible 2. Sets the name of the RDD 3. Figure 3. Execute the following Spark application type all the lines in spark-shell: Executes action and materializes the RDD With the above executed.

It has to be implemented by any type of RDD in Spark and is called unless RDD is checkpointed and the result can be read from a checkpoint. Iterator[T] method that computes a given split partition to produce a collection of values. Preferred Locations A preferred location aka locality preferences or placement preferences is a block location for an HDFS file where to compute each partition on. It is built as a result of applying transformations to the RDD. Figure 6. Seq[String] specifies placement preferences for a partition in an RDD.

Starting job: RDD's recursive dependencies: RDD[ String.. Transformations are lazy and are not executed immediately. Operators - Transformations and Actions Transformations and Actions. Note There are a couple of transformations that do trigger jobs. Spark groups narrow transformations as a stage. Only a limited subset of partitions used to calculate the result. The data required to compute the records in a single partition may reside in many partitions of the parent RDD.

Mastering Apache Spark Figure 1. From SparkContext by transformations to the result You can chain transformations to create pipelines of lazy computations. RDD[ String. Job 18 finished: You can think of actions as a valve and until no action is fired.

Only actions can materialize the entire processing pipeline with real data. Mastering Apache Spark All of the tuples with the same key must end up in the same partition.

Actions in org. To satisfy these operations. They trigger execution of RDD transformations to return values. Spark must execute RDD shuffle.

Actions Actions are operations that return values. Note Actions are synchronous. Simply put. Cleaning can throw a SparkException if the computation cannot be cleaned. The following asynchronous methods are available: Note Spark uses ClosureCleaner to clean closures.

Before calling an action. The methods return a FutureAction. Tip You should cache an RDD you work with when you want to execute two or more actions on it for better performance. See https: How does the mapping between partitions and tasks correspond to data locality if any? Spark manages data using partitions that helps parallelize distributed data processing with minimal network traffic for sending data between executors.

Tuning Spark the official documentation of Spark Partitions By default. Since Spark usually accesses distributed partitioned data. How does the number of partitions map to the number of tasks? How to verify it? Spark tries to read data into an RDD from the nodes that are close to it. There is a one-to-one correspondence between how data is laid out in data storage like HDFS or Cassandra it is partitioned for the same reasons. Start spark-shell and see it yourself!

You use def getPartitions: When a stage executes. RDDs get partitioned automatically without programmer intervention. Total tasks in UI shows 2 partitions You can always ask for the number of partitions using partitions method of a RDD: You can get this computed value by calling sc. Partitions So if you have a cluster with 50 cores.

Increasing partitions count will make each partition to have less data or not at all! Spark can only run 1 concurrent task for every partition of an RDD.

In the first RDD transformation. As far as choosing a "good" number of partitions. The maximum size of a partition is ultimately limited by the available memory of an executor. When using textFile with compressed files file. Repartitioning may cause shuffle to occur in some situations. In this case. Some operations. And it usually happens during action stage.

Spark disables splitting that makes for an RDD with only 1 partition as reads against gzipped files cannot be parallelized. Repartitioning def repartition numPartitions: Int implicit ord: Mastering Apache Spark Partitions get redistributed among nodes whenever shuffle occurs. It uses coalesce and shuffle to redistribute data. It will only work as described for uncompressed files.

Starting task 1. Starting task 3. Starting task 4.

In such cases. Starting task 0. Please note that Spark disables splitting for compressed files and creates RDDs with only 1 partition. Adding task set 7. Adding task set 8. Starting task 2. It may often not be important to have a given number of partitions upfront at RDD creation time upon loading data from data sources. It can trigger RDD shuffling depending on the second shuffle boolean input parameter defaults to false. Mastering Apache Spark coalesce numPartitions: In the following sample.

Note the number of partitions that remains the same as the number of partitions in the source RDD rdd. RDD[T] The coalesce transformation is used to change the number of partitions.

Think of situations where kind has low cardinality or highly skewed distribution and using the technique for partitioning might be not an optimal solution. A scheduler can optimize future operations based on this. Option[Partitioner] specifies how the RDD is partitioned. You could do as follows: StorageLevel operation.

mastering-apache-spark.pdf

RDDs can be unpersisted. Does RDD use disk? How much of RDD is in memory? Does RDD use off-heap memory? Should an RDD be serialized while persisting? How many replicas default: Caching and Persistence NONE as the storage level. W pairs with all pairs of elements for each key. Tip Avoid shuffling at all cost. Shuffling V and K. Shuffling is a process of repartitioning redistributing data across partitions and may cause moving it across JVMs or even network when it is redistributed among executors.

Leverage partial aggregation to reduce data transfer. Example - join PairRDD offers join transformation that quoting the official documentation: When called on datasets of type K. It is still better than this page. Think about ways to leverage existing partitions. Here is how the job of executing joined. It means shuffling has indeed happened. IndexedSeq[ Int. It appears before the join operation so shuffle is expected. Executing joined.

Mastering Apache Spark join operation is one of the cogroup operations that uses defaultPartitioner. There are two types of checkpointing: This function has to be called before any job has been executed on this RDD.

The reason is that the driver may attempt to reconstruct the checkpointed RDD from its own local file system. Done checkpointing RDD 5 to file: The RDD will be saved to a file inside the checkpoint directory and all references to its parent RDDs will be removed.

Before checkpointing is used. String method.

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Note It is strongly recommended that a checkpointed RDD is persisted in memory. The directory must be a HDFS path if running on a cluster. When an action is called on a checkpointed RDD. String to set the checkpoint directory - the directory where RDDs are checkpointed. Reliable Checkpointing You call SparkContext. This RDD. Local checkpointing trades fault-tolerance for performance. This is useful for RDDs with long lineages that need to be truncated periodically. The checkpoint directory set through SparkContext.

RDD[T] method. RangeDependency 6f2ab3f You can use RDD. It also uses ShuffleManager to register itself using ShuffleManager. Mastering Apache Spark There are the following more specialized Dependency extensions: The places where ShuffleDependency is used: It uses partitioner to partition the shuffle output. Narrow dependencies allow for pipelined execution.

NarrowDependency NarrowDependency is an abstract extension of Dependency with narrow limited number of partitions of the parent RDD that are required to compute a partition of the child RDD. Dependencies NarrowDependency extends the base with the additional method: RangeDependency RangeDependency is a narrow dependency that represents a one-to-one dependency between ranges of partitions in the parent and child RDDs.

RangeDependency ad7. It uses ParallelCollectionPartition. The data collection is split on to numSlices slices. If it is true. MapPartitionsRDD is the result of the following transformations: Use RDD. For each key k in parent RDDs.

Mastering Apache Spark

You can register callbacks on TaskContext. Input split: The following properties are set upon partition execution: When an HadoopRDD is computed. When true. The javadoc of org. What is JobConf? Caution What are the InputSplits: FileSplit and CombineFileSplit? It creates a key and a value. Path method to ensure input-files are not split-up and are processed as a whole by Mappers.

Tip You may find the sources of org. They do nothing when checkpoint is called. What are they? What does TaskContext do? See InputFormat. As a hint it does not mean the number of partitions will be exactly the number given. For SparkContext. This provides a generic implementation of getSplits JobConf. FileInputFormat says: FileInputFormat is the base class for all file-based InputFormats. It can however be changed using ShuffledRDD. Such transformations ultimately call coalesce transformation with shuffle input parameter true default: Two stages in a job due to shuffling It can be the result of RDD transformations using Scala implicits: It is a shuffle step the result RDD for transformations that trigger shuffle at execution.

The function is a generic base function for combineByKey -based functions. Note This document uses spark-shell only. It is a very convenient tool to explore the many things available in Spark and one of the many reasons why Spark is so helpful even for very simple tasks see Why Spark. When you execute spark-shell it executes Spark submit as follows: Using Spark shell You start Spark shell using spark-shell script available in bin directory.

There are variants of Spark for different languages: Refer to Command of Spark Scripts. Spark Shell SparkSubmit --class org. SQL context available as sqlContext. SparkContext 2ac0cb64 Besides. Java 1. Master must start with yarn. Refer to Spark SQL. HiveContext 60aef To close Spark shell. Together with the source code it may be a viable tool to reach mastery. Main primaryResource spark-shell name Spark shell childArgs [] jars null packages null packagesExclusions null repositories null verbose true Spark properties used.

It offers pages tabs with the following information: You can view the web UI after the fact after setting spark. It tracks information to be displayed in the UI. If multiple SparkContexts attempt to run on the same host it is not possible to have two or more Spark contexts on a single JVM. Settings spark.

Deploy Modes Using --deploy-mode command-line option you can specify two different deploy modes: You can find spark-submit script in bin directory of the Spark distribution. Mastering Apache Spark spark-submit script You use spark-submit script to launch a Spark application.

Will search the local maven repo. The format for the coordinates should be groupId: Command-line Options Execute. If not specified. Spark standalone or Mesos with cluster deploy mode only: Spark standalone and Mesos only: Print the version of current Spark Spark standalone with cluster deploy mode only: This keytab will be copied to the node running the Application Master via the Secure Distributed Cache.

Note that jars added with --jars are automatically included in the classpath. Spark standalone and YARN only: Mastering Apache Spark --class --conf or -c --deploy-mode --driver-class-path --driver-cores for Standalone cluster mode only --driver-java-options --driver-library-path --driver-memory --executor-memory --files --jars --kill for Standalone cluster mode only --master --name --packages --exclude-packages --properties-file --proxy-user --py-files --repositories --status for Standalone cluster mode only --total-executor-cores List of switches.

Low-level details of spark-submit spark-submit SparkSubmit and the other command line arguments given to spark-submit at the very beginning. As the last step in the process. The source code of the script lies in https: At startup. And then spark-class searches for so-called the Spark assembly jar sparkassembly.

When you execute the spark-submit script you basically launch org. Main class is executed with org. SparkSubmit class via another spark-class script passing on the command line arguments.

The Main class programmatically computes the final command to be executed. Consult Environment Variables in the official documentation. Main org. It builds the command line for a Spark class using the environment variables: Note Ultimately. When started. Main is the command-line launcher used in Spark scripts. There is a driver that talks to a single coordinator called master that manages workers in which executors run.

Spark architecture The driver and the executors run in their own Java processes. Spark Architecture You can run them all on the same horizontal cluster or separate machines vertical cluster or in a mixed machine configuration. Spark architecture in detail Physical machines are called hosts or nodes.

A driver is where the task scheduler lives and spawns tasks across workers. Driver It hosts Web UI for the environment.

Driver requires the additional services beside the common ones like ShuffleManager. A driver coordinates workers and overall execution of tasks. Driver with the services It splits a Spark application into tasks and schedules them to run on executors. It is your Spark application that launches the main method in which the instance of SparkContext is created.

Perhaps it should be in the notes about RpcEnv? Mastering Apache Spark Master A master is a running Spark instance that connects to a cluster manager for resources.

The master acquires cluster nodes to run executors. Create RDD graph. When the driver quits. Shortly speaking. Stages are created by breaking the RDD graph at shuffle boundaries. A worker receives serialized tasks that it runs in a thread pool. A new process is not started for each step.

They are the compute nodes in Spark. It hosts a local Block Manager that serves blocks to other workers in a Spark cluster. A new process is started on each worker when the SparkContext is constructed.

Based on the plan. Create stage graph. Workers communicate among themselves using their Block Manager instances. In the WordCount example. The executors connect back to your driver program.

The executor deserializes the command this is possible because it has loaded your jar. Now the driver can send them commands. This is a separate process JVM. Mastering Apache Spark Workers Workers aka slaves are running Spark instances where executors live to execute tasks.

The number of tasks to be generated depends on how your files are distributed. Suppose that you have 3 three different files in three different nodes. Spark will generate tasks from stages. In the end. The stage creation rule is based on the idea of pipelining as many narrow transformations as possible. Mastering Apache Spark Based on this graph. Workers The first stage will create a series of ShuffleMapTask and the last stage will create ResultTasks because in the last stage.

RDD operations with "narrow" dependencies. A task belongs to a stage.

Mastering Apache Spark 2.0

The number of tasks being generated in each stage will be equal to the number of partitions. Each executor can run multiple tasks over its lifetime. Executors send active task metrics to a driver. They typically i. When executors are started they register themselves with the driver and communicate directly to execute tasks.

Executor logger to see what happens inside. Executors are described by their id. Note Executors are managed solely by executor backends. They also inform executor backends about task status updates task results including.

It is recommended to have as many executors as data nodes and as many cores as you can get from the cluster. Executor offers are described by executor id and the host on which an executor runs see Resource Offers in this document.

When an executor is started you should see the following INFO messages in the logs: INFO Executor: Mastering Apache Spark Executors Executors are distributed agents responsible for executing tasks. Executors use a thread pool for sending metrics and launching tasks by means of TaskRunner.

Tip log4j. The newly-created TaskRunner is registered in the internal runningTasks map. Consult Launching Tasks section.

Launching Tasks Executor. Executors maintain a mapping between task ids and running tasks as instances of TaskRunner in runningTasks internal map. Executors It requires a ExecutorBackend to send status updates to.

Executors use HeartbeatReceiver endpoint to report task metrics The structure sent is an array of Long. Information about it is sent to the driver using ExecutorBackend as TaskState. Why is blockManagerId sent? Sending task status updates to ExecutorBackend is a mechanism to inform the executor backend about task being started TaskState.

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Learn more Add to cart. Mastering Apache Spark 2. Paperback pages. Book Description Apache Spark is an in-memory cluster based parallel processing system that provides a wide range of functionality like graph processing, machine learning, stream processing and SQL.

Table of Contents Chapter 1: Apache Spark. Chapter 2: Apache Spark MLlib.

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