Spark examples github

In this Apache Spark Tutorial, you will learn Spark with Scala examples and every example explain here is available at Spark-examples Github project for reference. All Spark examples provided in this Spark Tutorials are basic, simple, easy to practice for beginners who are enthusiastic to learn Spark and were tested in our development environment.

In this section of the tutorial, you will learn different concepts of the Spark Core library with examples. RDDs are fault-tolerant, immutable distributed collections of objects, which means once you create an RDD you cannot change it.

Each dataset in RDD is divided into logical partitions, which can be computed on different nodes of the cluster. This is a work in progress section where you will see more articles coming. If your application is critical on performance try to avoid using custom UDF at all costs as these are not guarantee on performance. This section of the tutorial describes reading and writing data using the Spark Data Sources with scala examples.

Skip to content. Apache Spark Core In this section of the tutorial, you will learn different concepts of the Spark Core library with examples.

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Close Menu.Lightning-fast unified analytics engine. These examples give a quick overview of the Spark API. Spark is built on the concept of distributed datasetswhich contain arbitrary Java or Python objects. You create a dataset from external data, then apply parallel operations to it.

In the RDD API, there are two types of operations: transformationswhich define a new dataset based on previous ones, and actionswhich kick off a job to execute on a cluster. These high level APIs provide a concise way to conduct certain data operations. In this example, we use a few transformations to build a dataset of String, Int pairs called counts and then save it to a file. Spark can also be used for compute-intensive tasks.

We pick random points in the unit square 0, 0 to 1,1 and see how many fall in the unit circle. In Spark, a DataFrame is a distributed collection of data organized into named columns.

spark examples github

In this example, we read a table stored in a database and calculate the number of people for every age. A simple MySQL table "people" is used in the example and this table has two columns, "name" and "age".

These algorithms cover tasks such as feature extraction, classification, regression, clustering, recommendation, and more. MLlib also provides tools such as ML Pipelines for building workflows, CrossValidator for tuning parameters, and model persistence for saving and loading models.

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In this example, we take a dataset of labels and feature vectors. We learn to predict the labels from feature vectors using the Logistic Regression algorithm.

spark examples github

Toggle navigation. Latest News Spark 2. Every record of this DataFrame contains the label and features represented by a vector. Here, we limit the number of iterations to DoubleTypefalseMetadata.In this section we will setup a mock instance of Bullet to play around with.

Additional Examples

We will use Bullet Spark to run the backend of Bullet on the Spark framework. And we will use the Bullet Kafka PubSub. You can then continue this guide from here. If you want to manually run all the commands or if the script died while doing something above might want to perform the teardown firstyou can continue below. For this instance of Bullet we will use the Kafka PubSub implementation found in bullet-spark. So we will first download and run Kafka, and setup a couple Kafka topics. The Bullet Kafka PubSub uses two topics.

Apache Spark SQL - running a sample program

One to send messages from the Web Service to the Backend, and one to send messages from the Backend to the Web Service. So we will create a Kafka topic called "bullet. We will run the bullet-spark backend using Spark 2. The Backend will usually be up and running usually within seconds.

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To test it you can now run a Bullet query by hitting the Web Service directly:. This query will return a result JSON containing a "records" field containing a single record, and a "meta" field with some meta information.

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This data is randomly generated by the custom Spark Streaming Receiver that generates toy data to demo Bullet. In practice, your producer would read from an actual data source such as Kafka etc.

See UI usage for some example queries and interactions using this UI. You see what the Schema means by visiting the Schema section. Since the UI is a client-side app, the machine that your browser is running on will fetch the UI and attempt to use these settings to talk to the Web Service.

Since they point to localhost by default, your browser will attempt to connect there and fail. An easy fix is to change localhost in your env-settings. This will be the same as the UI host you use in the browser. Check out and follow along with the UI Usage page as it shows you some queries you can run using this UI.

When you are done trying out Bullet, you can stop the processes and cleanup using the instructions below. If you were using the Install Script or if you don't want to manually bring down everything, you can run:. If you were performing the steps yourself, you can also manually cleanup all the components and all the downloads using:. This section will go over the various custom pieces this example plugged into Bullet, so you can better understand what we did.

This Receiver and DataProducer are implemented in this example project and was already built for you when you downloaded the examples. It does not read from any data source and just produces random, structured data.

It also produces only up to a maximum number of records in a given period. Both this maximum and the length of a period are configured in the Receiver at most every 1 second. We launched the bullet-spark jar an uber or "fat" jar containing Bullet Spark and all its dependencies. We added our Pubsub see below implementation and our jar containing our custom Receiver to the Spark job's additional jars.

In practice, you would scale and run your components with CPU and memory configurations to accommodate for your data volume and querying needs. Let's look at the custom Receiver code that generates the data.

This method above emits the data. This method is wrapped in a thread that is called by the Spark framework. This function only emits at most the given maximum tuples per period. This generateRecord method generates some fields randomly and inserts them into a BulletRecord simple. Note that the BulletRecord is typed and all data must be inserted with the proper types.It enables running Spark jobs, as well as the Spark shell, on Hadoop MapReduce clusters without having to install Spark or Scala, or have administrative rights.

After downloading SIMR, it can be tried out by typing. Type :help for more information. Created spark context. Spark context available as sc.

Use Apache Spark with Amazon SageMaker

While this suffices for batch and interactive jobs, we recommend installing Spark for production use. If it is not provided, you will have to build it yourself. We've crafted some handsome templates for you to use. Go ahead and continue to layouts to browse through them. You can easily go back to edit your page before publishing.

After publishing your page, you can revisit the page generator and switch to another theme. Your Page content will be preserved if it remained markdown format. SIMR automatically includes Scala 2. They are already in the above jars and are thus not required. Java v1. Ensure the hadoop executable is in the PATH. Note that this jar file should contain all the third party dependencies that your job has this can be achieved with the Maven assembly plugin or sbt-assembly.

By default, SIMR sets the value to the number of nodes in the cluster. This value must be at least 2, otherwise no executors will be present and the task will never complete. Assuming spark-examples.Data is produced every second, it comes from millions of sources and is constantly growing.

Have you ever thought how much data you personally are generating every day? It also means storing logs and detailed information about every single micro step of the process, to be able to recover things if they go wrong. It also means analyzing peripheral information about it to determine if the transaction is fraudulent or not.

We log tons of data. We cache things for faster access. We replicate data and setup backups. We save data for future analysis. It can be regular system metrics that capture state of web servers and their load, performance, etc.

Or data that instrumented applications send out. Heart rate data, or blood sugar level data. Airplane location and speed data — to build trajectories and avoid collisions. Real-time camera monitors that observe spaces or objects to determine anomalies in process behavior. If we think about it — some of the data is collected to be stored and analyzed later.

And some of the data is extremely time sensitive. All of these real-life criteria translate to technical requirements for building a data processing system: Where and how the data is generated What is the frequency of changes and updates in the data How fast we need to react to the change.


When we, as engineers, start thinking of building distributed systems that involve a lot of data coming in and out, we have to think about the flexibility and architecture of how these streams of data are produced and consumed. On earlier stages, we might have just a few components, like a web application that produces data about user actions, then we have a database system where all this data is supposed to be stored. At this point we usually have a 1 to 1 mapping between data producer our web app and consumer database in this case.

However, when our application grows - infrastructure grows, you start introducing new software components, for example, cache, or an analytics system for improving users flow, which also requires that web application to send data to all those new systems.

What if we introduce a mobile app in addition, now we have two main sources of data with even more data to keep track of. Eventually we grow and end up with many independent data producers, many independent data consumers, and many different sorts of data flowing between them. Especially when same data should be available for some consumers after being read by other consumers. How to prepare for the need to scale based on changes in rates of events coming in?

Apache Kafka is an open-source streaming system. Kafka is used for building real-time streaming data pipelines that reliably get data between many independent systems or applications. It allows: Publishing and subscribing to streams of records Storing streams of records in a fault-tolerant, durable way It provides a unified, high-throughput, low-latency, horizontally scalable platform that is used in production in thousands of companies.Documentation here is always for the latest version of Spark.

Docs for spark-kotlin will arrive here ASAP. You can follow the progress of spark-kotlin on GitHub. Not familiar with Maven?

Click here for more detailed instructions. To see more console output from Spark debug info, etcyou have to add a logger to your project.

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The server is automatically started when you do something that requires the server to be started i. You can also manually start the server by calling init. The main building block of a Spark application is a set of routes. A route is made up of three simple pieces:. Routes are matched in the order they are defined.

spark examples github

The first route that matches the request is invoked. Always statically import Spark methods to ensure good readability:. Route patterns can include named parameters, accessible via the params method on the request object:.

Route patterns can also include splat or wildcard parameters. These parameters can be accessed by using the splat method on the request object:. If you have a lot of routes, it can be helpful to separate them into groups.

This can be done by calling the path method, which takes a String prefix and gives you a scope to declare routes and filters or nested paths in:. Query maps allows you to group parameters to a map by their prefix. This allows you to group two parameters like user[name] and user[age] to a user map. Every request has access to the session created on the server side, provided with the following methods:.

To immediately stop a request within a filter or route use halt :. To stop execution, use halt :. After-after-filters are evaluated after after-filters. Filters optionally take a pattern, causing them to be evaluated only if the request path matches that pattern:.

You can assign a folder in the classpath serving static files with the staticFiles.BigDL is a distributed deep learning library for Apache Spark; with BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters. Rich deep learning support. Modeled after TorchBigDL provides comprehensive support for deep learning, including numeric computing via Tensor and high level neural networks ; in addition, users can load pre-trained Caffe or Torch or Keras models into Spark programs using BigDL.

Extremely high performance. Consequently, it is orders of magnitude faster than out-of-box open source CaffeTorch or TensorFlow on a single-node Xeon i. Efficiently scale-out. BigDL can efficiently scale out to perform data analytics at "Big Data scale", by leveraging Apache Spark a lightning fast distributed data processing frameworkas well as efficient implementations of synchronous SGD and all-reduce communications on Spark.

You can post bug reports and feature requests at the Issue Page.

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You may refer to Analytics Zoo for high level pipeline APIs, built-in deep learning models, reference use cases, etc. Why BigDL?