Getting started with apache spark pdf

Apache Spark is an open source big data processing framework built around speed, ease of use, and sophisticated analytics. It should also mention any large subjects within apache-spark, and link out to the related topics.

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Since the Documentation for apache-spark is new, you may need to create initial versions of those related topics. Compute the sum of a list and the length of that list. Return the result in a pair of sum, length. The first partition has the sublist [1, 2]. This applies the seqOp to each element of that list, which produces a local result - A pair of sum, length that will reflect the result locally, only in that first partition.

The local result is 1, 1which means the sum is 1 and the length 1 for the 1st partition after processing only the first element.

The local result is now 3, 2which will be the final result from the 1st partition, since they are no other elements in the sublist of the 1st partition. Doing the same for 2nd partition returns 7, 2. Spark uses lazy evaluation ; that means it will not do any work, unless it really has to. That approach allows us to avoid unnecessary memory usage, thus making us able to work with big data. A transformation is lazy evaluated and the actual work happens, when an action occurs.

So, in [1] we told Spark to read a file into an RDD, named lines. Spark heard us and told us: "Yes I will do it", but in fact it didn't yet read the file. In [2], we are filtering the lines of the file, assuming that its contents contain lines with errors that are marked with an error in their start.

So we tell Spark to create a new RDD, called errorswhich will have the elements of the RDD linesthat had the word error at their start. Now in [3]we ask Spark to count the errorsi. As a result, when [3] is reached, [1] and [2] will actually being performed, i.

For example if your data in the file do not support the startsWith I used, then [2] is going to be properly accepted by Spark and it won't raise any error, but when [3] is submitted, and Spark actually evaluates both [1] and [2]then and only then it will understand that something is not correct with [2] and produce a descriptive error.

As a result, an error may be triggered when [3] is executed, but that doesn't mean that the error must lie in the statement of [3]! Note, neither lines nor errors will be stored in memory after [3].

They will continue to exist only as a set of processing instructions. If there will be multiple actions performed on either of these RDDs, spark will read and filter the data multiple times.Apache Spark is an open-source cluster computing framework for real-time processing. Spark has clearly evolved as the market leader for Big Data processing. Today, Spark is being adopted by major players like Amazon, eBay, and Yahoo! Many organizations run Spark on clusters with thousands of nodes and t here is a huge opportunity in your career to become a Spark certified professional.

When it comes to Real Time Data Analytics, Spark stands as the go-to tool across all other solutions. The following are the topics covered in this Spark Tutorial blog:. Before we begin, let us have a look at the amount of data generated every minute by social media leaders. Figure: Amount of data generated every minute. From fraud detection in banking to live surveillance systems in government, automated machines in healthcare to live prediction systems in the stock market, everything around us revolves around processing big data in near real time.

Let us look at some of these use cases of Real Time Analytics:. Hadoop is based on the concept of batch processing where the processing happens of blocks of data that have already been stored over a period of time.

At the time, Hadoop broke all the expectations with the revolutionary MapReduce framework in This went on untiltill Spark overtook Hadoop.

The following figure gives a detailed explanation of the differences between processing in Spark and Hadoop. Here, we can draw out one of the key differentiators between Hadoop and Spark.

Hadoop is based on batch processing of big data. Whereas in Spark, processing can take place in real-time.

Quick Start

This real-time processing power in Spark helps us to solve the use cases of Real Time Analytics we saw in the previous section. Alongside this, Spark is also able to do batch processing times faster than that of Hadoop MapReduce Processing framework in Apache Hadoop. Therefore, Apache Spark is the go-to tool for big data processing in the industry. It has a thriving open-source community and is the most active Apache project at the moment.

Spark provides an interface for programming entire clusters with implicit data parallelism and fault-tolerance.

getting started with apache spark pdf

It was built on top of Hadoop MapReduce and it extends the MapReduce model to efficiently use more types of computations. Figure: Spark Tutorial — Spark Features. Spark code can be written in any of these four languages. It provides a shell in Scala and Python. The Scala shell can be accessed through. Spark runs up to times faster than Hadoop MapReduce for large-scale data processing. Spark is able to achieve this speed through controlled partitioning.This site uses cookies and by using the site you are consenting to this.

To learn more about cookies, click here to read our privacy statement. One of the talks described the evolution of big data processing frameworks. Recently, I also got the opportunity to work on Spark as part of client work for building and running machine learning models on sensor data. I will briefly go over Apache Spark, an open source cluster computing engine which has become really popular in the big data world over the past few years, and how to get started on it with a simple example.

It supports batch, iterative and near real-time stream processing. Spark is comparable to other big data and MapReduce technologies like Hadoop. The key components are:. RDD is a partitioned data set which represents computations.

Spark also allows to cache and checkpoint the lineage. Spark also breaks up the processing of RDD operations into multiple smaller tasks, to execute a job on large data sets in parallel. Each task is executed by an executor. The closure is those variables and methods which must be visible for the executor to perform its computations on the RDD. This closure is serialized and sent to each executor. Before we jump into the example, the first step is to make sure that Spark is downloaded and running locally.

I have installed spark Consider a use case wherein you want to group all words in a data set by the first letter and count their occurrences. A simple Spark program is going to look as follows where we read lines from a file, split words, group them by first letter and later count them. Note that the SparkContext is the entry point to any Spark program. It contains SparkConf that holds Spark related configuration parameters. The local[2] on line-5 tells Spark to run the program on 2 cores.

When you run the same job in a standalone cluster the configuration parameters can also be passed in as additional command line arguments, which I will show you in a bit.

The transformation operations flatMap on line splits the words in the source file, map on line creates pairs of words with key as the first letter of the word.Send us feedback. This tutorial module helps you to get started quickly with using Apache Spark.

We discuss key concepts briefly, so you can get right down to writing your first Apache Spark application. In the other tutorial modules in this guide, you will have the opportunity to go deeper into the article of your choice.

Apache Spark Full Course - Apache Spark Tutorial For Beginners - Learn Spark In 7 Hours -Simplilearn

We also provide sample notebooks that you can import to access and run all of the code examples included in the module. Complete Get started as a Databricks user. In many scenarios, especially with the performance optimizations embedded in DataFrames and Datasets, it will not be necessary to work with RDDs. But it is important to understand the RDD abstraction because:. When you develop Spark applications, you typically use DataFrames and Datasets.

To write your first Apache Spark application, you add code to the cells of a Databricks notebook. This example uses Python. This first command lists the contents of a folder in the Databricks File System :.

To count the lines of the text file, apply the count action to the DataFrame:. One thing you may notice is that the second command, reading the text file, does not generate any output while the third command, performing the countdoes. The reason for this is that the first command is a transformation while the second one is an action. Transformations are lazy and run only when an action is run. This allows Spark to optimize for performance for example, run a filter prior to a joininstead of running commands serially.

For a complete list of transformations and actions, refer to the Apache Spark Programming Guide: Transformations and Actions. Databricks includes a variety of datasets within the Workspace that you can use to learn Spark or test out algorithms. How to import a notebook Get notebook link. Updated Apr 16, Send us feedback.This use case will bring together the core concepts of Spark and use a large dataset to build a simple real-time dashboard that provides insight into customer behaviors.

Spark is an enabling technology in a wide variety of use cases across many industries. Spark is a great candidate anytime results are needed fast and much of the computations can be done in memory. The language used here will be Python, because it does a nice job of reducing the amount of boilerplate code required to illustrate these examples. Music streaming is a rather pervasive technology which generates massive quantities of data. This type of service is much like people would use every day on a desktop or mobile device, whether as a subscriber or a free listener perhaps even similar to a Pandora.

This will be the foundation of the use case to be explored. Data from such a streaming service will be analyzed. The basic layout consists of customers whom are logging into this service and listening to music tracks, and they have a variety of parameters:.

Python, PySpark and MLlib will be used to compute some basic statistics for a dashboard, enabling a high-level view of customer behaviors as well as a constantly updated view of the latest information. This service has users whom are continuously connecting to the service and listening to tracks.

Customers listening to music from this streaming service generate events, and over time they represent the highest level of detail about customers' behaviors. The data will be loaded directly from a CSV file. There are a couple of steps to perform before it can be analyzed.

getting started with apache spark pdf

The data will need to be transformed and loaded into a PairRDD. This is because the data consists of arrays of key, value tuples. The customer events-individual tracks dataset tracks.

Getting started with Apache Spark

This size is approximately 1M lines and contains simulated listener events over several months. Because this represents things that are happening at a very low level, this data has the potential to grow very large. The event, customer and track IDs show that a customer listened to a specific track.

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The other fields show associated information, like whether the customer was listening on a mobile device, and a geolocation. This will serve as the input into the first Spark job.Spark is a top-level project of the Apache Software Foundation, designed to be used with a range of programming languages and on a variety of architectures.

Spark's speed, simplicity, and broad support for existing development environments and storage systems make it increasingly popular with a wide range of developers, and relatively accessible to those learning to work with it for the first time. The project supporting Spark's ongoing development is one of Apache's largest and most vibrant, with over contributors from more than organizations responsible for code in the current software release.

Comprehensive support for the development languages with which developers are already familiar is important so that Spark can be learned relatively easily, and incorporated into existing applications as straightforwardly as possible.

Programming languages supported by Spark include:. Languages like Python are often regarded as poorly performing languages, especially in relation to alternatives such as Java. Although this concern is justified in some development environments, it is less significant in the distributed cluster model in which Spark will typically be deployed.

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Any slight loss of performance introduced by the use of Python can be compensated for elsewhere in the design and operation of the cluster. Familiarity with your chosen language is likely to be far more important than the raw speed of code prepared in that language. Extensive examples and tutorials exist for Spark in a number of places, including the Apache Spark project website itself. These tutorials normally include code snippets in Java, Python and Scala. The Structured Query Language, SQL, is widely used in relational databases, and simple SQL queries are normally well-understood by developers, data scientists and others who are familiar with asking questions of any data storage system.

The Apache Spark module--Spark SQL--offers native support for SQL and simplifies the process of querying data stored in Spark's own Resilient Distributed Dataset model, alongside data from external sources such as relational databases and data warehouses.

Support for the data science package, R, is more recent. The SparkR package first appeared in release 1. As noted in the previous chapter, Spark is easy to download and install on a laptop or virtual machine. Spark was built to be able to run in a couple different ways: standalone, or part of a cluster. But for production workloads that are operating at scale, a single laptop or virtual machine is not likely to be sufficient.

In these circumstances, Spark will normally run on an existing big data cluster. These clusters are often also used for Hadoop jobs, and Hadoop's YARN resource manager will generally be used to manage that Hadoop cluster including Spark.

For those who prefer alternative resource managers, Spark can also run just as easily on clusters controlled by Apache Mesos. Running Spark on Mesosfrom the Apache Spark project, provides more configuration details.

Although often linked with the Hadoop Distributed File System HDFSSpark can integrate with a range of commercial or open source third-party data storage systems, including:. Developers are most likely to choose the data storage system they are already using elsewhere in their workflow. The Spark project stack currently is comprised of Spark Core and four libraries that are optimized to address the requirements of four different use cases.

Individual applications will typically require Spark Core and at least one of these libraries. Spark's flexibility and power become most apparent in applications that require the combination of two or more of these libraries on top of Spark Core. The Resilient Distributed Dataset is a concept at the heart of Spark. It is designed to support in-memory data storage, distributed across a cluster in a manner that is demonstrably both fault-tolerant and efficient. Fault-tolerance is achieved, in part, by tracking the lineage of transformations applied to coarse-grained sets of data.

Efficiency is achieved through parallelization of processing across multiple nodes in the cluster, and minimization of data replication between those nodes. Once data is loaded into an RDD, two basic types of operation can be carried out:.Although now considered a key element of Spark, streaming capabilities were only introduced to the project with its 0. Rather than being integral to the design of Spark, stream processing is a capability that has been added alongside Spark Core and its original design goal of rapid in-memory data processing.

Other stream processing solutions exist, including projects like Apache Storm and Apache Flink.

getting started with apache spark pdf

In each of these, stream processing is a key design goal, offering some advantages to developers whose sole requirement is the processing of data streams. These solutions, for example, typically process the data stream event-by-event, while Spark adopts a system of chopping the stream into chunks or micro-batches to maintain compatibility and interoperability with Spark Core and Spark's other modules. Spark's real and sustained advantage over these alternatives is this tight integration between its stream and batch processing capabilities.

Running in a production environment, Spark Streaming will normally rely upon capabilities from external projects like ZooKeeper and HDFS to deliver resilient scalability. In real-world application scenarios, where observation of historical trends often augments stream-based analysis of current events, this capability is of great value in streamlining the development process. For workloads in which streamed data must be combined with data from other sources, Spark remains a strong and credible option.

A streaming framework is only as good as its data sources. A strong messaging platform is the best way to ensure solid performance for any streaming system.

Spark Streaming supports the ingest of data from a wide range of data sources, including live streams from Apache Kafka, Apache Flume, Amazon Kinesis, Twitter, or sensors and other devices connected via TCP sockets. Data is processed by Spark Streaming, using a range of algorithms and high-level data processing functions like mapreducejoin and window. Processed data can then be passed to a range of external file systems, or used to populate live dashboards. Logically, Spark Streaming represents a continuous stream of input data as a discretized stream, or DStream.

Each of these RDDs is a snapshot of all data ingested during a specified time period, which allows Spark's existing batch processing capabilities to operate on the data. The data processing capabilities in Spark Core and Spark's other modules are applied to each of the RDDs in a DStream in exactly the same manner as they would be applied to any other RDD: Spark modules other than Spark Streaming have no awareness that they are processing a data stream, and no need to know.

A basic RDD operation, flatMapcan be used to extract individual words from lines of text in an input source. When that input source is a data stream, flatMap simply works as it normally would, as shown below. Activities within a Spark cluster are orchestrated by a driver program using the SparkContext. In the case of stream-based applications, the StreamingContext is used. This exploits the cluster management capabilities of an external tool like Mesos or Hadoop's YARN to allocate resources to the Executor processes that actually work with data.

In a distributed and generally fault-tolerant cluster architecture, the driver is a potential point of failure, and a heavy load on cluster resources. Particularly in the case of stream-based applications, there is an expectation and requirement that the cluster will be available and performing at all times. Potential failures in the Spark driver must therefore be mitigated, wherever possible. Spark Streaming introduced the practice of checkpointing to ensure that data and metadata associated with RDDs containing parts of a stream are routinely replicated to some form of fault-tolerant storage.

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This makes it feasible to recover data and restart processing in the event of a driver failure. Spark Streaming itself supports commonly understood semantics for the processing of items in a data stream. These semantics ensure that the system is delivering dependable results, even in the event of individual node failures. Items in the stream are understood to be processed in one of the following ways:.

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