Introduction. Mastering Apache Spark
This tutorial provides a quick introduction to using Spark. DataFrames can be created from sources such as CSVs, JSON, tables in Hive, external databases, or existing RDDs. On top of Spark Core, users can run Spark SQL, Spark Streaming, MLlib, and GraphX. PySpark is a tool for submitting Python jobs to the Spark cluster.
Note: this tutorial uses Spark v.1.6 with hadoop. Create a folder where you plan to run the application and put the executable JAR file into this folder. Usual you would use distributed computing tools like Hadoop and Apache Spark for that computation in a cluster with many machines.
Apache Spark is a powerful, fast open source framework for big data processing. On remote worker machines, PythonRDD objects launch Python subprocesses and communicate with them using pipes, sending the user's code and the data to be processed. The SparkContext represents the connection to a Spark cluster and can be used to create RDD's and DataFrames.
Once created, the distributed dataset (distData) can be operated on in parallel. Spark provides the shell in two programming languages : Scala and Python. It's easy to get started running Spark locally without a cluster, and then upgrade to a distributed deployment as needs increase.
Name your package something like com.spark.example and click Ok. Once the package is created, right click on the package and select New → Other. The WordCount application's main method accepts the source text file name from the command line and then invokes the workCountJava8() method.
More specifically, the performance improvements are due to two things, which you'll often come across when you're reading up DataFrames: custom memory management (project Tungsten), which will make sure that your Spark jobs much faster given CPU constraints, and optimized execution plans (Catalyst optimizer), of which the logical plan of the DataFrame is a part.
The reason people are so interested in Apache Spark is it puts the power of Hadoop in the hands of developers. In the DataFrame SQL query, we showed how to issue SQL like query We can re-write the dataframe like query to find all tags which start with the letter s using Spark SQL as shown below.
We'll be using Apache Spark 2.2.0 here, but the code in this tutorial should also work on Spark 2.1.0 and above. Spark Lazy Evaluation means the data inside RDDs are not evaluated on the go. Basically, only after an action triggers all the changes or the computation is performed.
It is easier to setup an Apache Spark cluster than an Hadoop Cluster. This article discusses Apache Spark terminology, ecosystem components , RDD, Apache Spark Tutorial and the evolution of Apache Spark. Spark applications may run as independent sets of parallel processes distributed across numerous nodes of computers.
Basically, to handle the failure of any worker node in the cluster, Spark RDDs are designed. Instead, they just remember” the operation to be performed and the dataset (e.g., file) to which the operation is to be performed. Finally, select Save As to create a virtual dataset.
While the Spark contains multiple closely integrated components, at its core, Spark is a computational engine that is responsible for scheduling, distributing, and monitoring applications consisting of many computational tasks on a computing cluster.