Tips and Tricks for Apache Spark

Big data refers to sizeable live data that is generated every minute on the internet through various resources with a rapid speed. It can be text, images, videos, social media, medical domains. This leads to the requirement of data processing software come into existence. One among the leading and on-demand frameworks fulfilling the task according to the increasing data is none other than the Apache Spark.

Apache Spark is most actively developed open source project in big data and probably the most widely used as well. Spark is a general purpose Execution Engine, which can perform its cluster management on top of Big Data very quickly and efficiently. It is rapidly increasing its features and capabilities like libraries to perform different types of Analytics.

About Spark at that highest level is the only open source framework that combines Data and AI. So, using Spark, you can do large scale data transformation and analysis and can immediately implement a state of the art machine learning and AI algorithms on it. Most importantly in building AI paradox, AI is the best only the day you applied it in paradox and is powered by data. There, if we come with an integrated open-source framework that can manage both designs as well as time parallelly, you can quickly build the highest quality application. This is the primary reason why we choose "Apache Spark" - the leading open-source framework to process Big data.

Tips and Tricks for Apache Spark

Apache Spark's Abstraction

Here are some tools to process big data the Apache Spark solution. The user can be either developer, architect, technical person etc.

RDD(Resilient Distributed Dataset):- The distributed data processing and its transformation all are managed by RDD, and that's why apache does most of the handling in terms of the change and maintaining the data lenient. It is immutable.

• DAG(Direct Acyclic Graph):- When you run an application within Apache Spark, it constructs a graph, comprising of nodes and edges and creates a sequence of computation to process data.
• Spark context:- Sparkcontext is the entrance of the Spark functionality and who performs this function is named " Spark Driver', who generates Spark context. It enables the Spark application to access the spark cluster with the help of the Resource Manager.
• Transformation:- While performing certain activities like creating filter, mapping operations leads to a generation of a new RDD, which is collectively called a Transformation.

Actions:-Anything within Spark is done through a process of lazy loading, which means, whenever a dag is created it does not don't perform any computation/execution of the underlying data, till it is required. This is why it is called a lazy loads process, but comes with numerous benefits in the form of resilience. If somebody wants to collect or extract the data and get its count is called the action.

Advantage of Apache Spark:-

• Process Data in Real-time
• Handle input from multiple sources
• Faster Processing
• Inbuilt machine learning libraries.
• Fast in-memory computation
• Helps for streaming data, interactive and declarative queries etc.

So after discussing the Spark features, I am interested in sharing some tips and tricks for better performance. Let's begin:-

Try to Avoid using custom UDFs (User Defined Function): -The feature of Spark SQL is used to define new functions to work with columns. That increase the vocabulary of Spark SQL DSL for transforming datasets.

The primary reason to avoid using UDFs is behind the Screen, the Enzymes that cannot process and Optimize UDFs are treated as a black box, which results in losing many optimisations like -predicate, pushdown etc. Therefore, avoiding UDFs is suggestible.

2 . Always try to see how Software goes about transmitting:- Call the explain() method when you use Dataframe or Dataset object.

For e.g,- dataset.explain(true) - "output of this function is a good way to notice wrong executions.

3 . Resolving local hostname:- When networking issues occur Sparks can't resolve your IP address or local hostname, use this for custom hostname- SPARK_LOCAL_HOSTNAME and for custom IP SPARK_LOCAL_IP

The WARN messages may appear in the log when Spark finished the resolving process:

Spark version in Spark shell:- In Spark shell write or you may code like org.apache.spark.SPARK_VERSION to know the version of Apache Spark.

1. Spark shines increases due to TUNGSTEN:- Tungsten is one of the significant factors improving the efficiency of Spark execution. Using Tungsten Spark operation, the user can directly work at the byte level of memory management, code generation and specific wire protocol.
2. Launch command of Spark Scripts:- Using this SPARK_PRINT_LAUNCH_COMMAND

Check whether the Spark launch command is displaying the standard error output code i.e. or not. Spark shell scripts use this command org .apache.spark.launcher.Main and the class internally checks SPARK_PRINT_LAUNCH_COMMAND to set any value which will print out the entire command line to launch it.

Conclusion

Apache Spark is the popular and most Advanced product of the Apache community, which provide the chance to work with streaming data. It is supported programming language and increasing momentum in terms of the product community using R and Apache Spark together.

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Priyanka vatsa is a content contributor at Mindmajix.com.She is a writer by day and a reader at night. She loathes to discuss herself as the third person but can be persuaded to do so from time to time. She has been a technology writer for the last 2 years.