Since its development, Spark is being considered as top favorite by renowned organizations like: Tencent, Baidu, Yahoo, etc. These large organizations adopt Apache Spark because of several exceptional benefits. Spark is capable of interactive algorithms and interactive queries. Interactive algorithms and queries were its primary concern as they weren’t served enough by batch frameworks. This is why spark was recognized from the moment of its development. Its popularity rose rapidly because it was simple, fast, unified and broadly compatible. Spark is now on its peak of popularity and it is worth it.
Some of Spark’s special features are discussed in this chapter. These are the qualities those made development with spark pleasant.
Language flexibility is one of the most popular components of Apache Spark. This feature has uplifted the popularity of spark among numerous developers. Spark offers eminent support to numerous development languages and among them Python, Java and Scala are well admired. Language flexibility has elevated the development experience excellently.
These languages are different from one another but they maintain one crucial fact. All of them use lambda function and closures to express operations. To classify functions inline with core logic, developers use closures. This also preserves the application and offers pleasant code. Closures in java, python and scala with apache spark is demonstrated below:
Closures in Java with Apache Spark
Closures in Python with Apache Spark
Closures in Scala with Apache Spark
Spark has done everything in its grasp so that these languages can run on the spark engine. Over the time it become successful too. Now developers are allowed to run these languages over Spark engine. It has also lessen the burden of developer s and it also offers tidy look too. The experience is so smooth that developers love to use Spark.
Spark is much more than its kind. You can say that it is a combination of most essential and renowned functionalities. Suppose you are in MapReduce. You have to consider custom Mapper/Reducer jobs due to the fact that there are no built-in feature. This is where higher level API come in handy. You will need these APIs for MapReduce task. If you are working in Apache Spark you are lucky. There is a solution for this situation in spark.
Though these seem enough but this is not the end. Spark is truly enriched and there are more than eighty operators found in Apache Spark. If you use these operators you can easily maintain MapReduce type operations. Spark, on the other hand, offers access to frameworks like Apache Pig too. These frameworks also allows some top notch operators too. This combination can provide excellent atmosphere for elite development if you use functions, classes and control statements. This means you have everything placed in spark to complete your task but if you are still in need of something, you can simply taste other frameworks by accessing them. In short, if you are collaborating with Apache Spark you do not need to worry anymore. You have got everything you need.
Parallelizing the correct sequence of complex pipeline of MapReduce task solely depends on you. You will require a scheduler tool if you decide to construct sequences carefully. In Apache Spark, you will find a series of tasks that express a single program flow. This program is ideally evaluated to offer the system a complete view of the execution graph. This becomes simple for the system and allows the scheduler to map the dependencies over different stages of the application. This also parallelize the operators flow automatically and without user interference. You can also enable certain optimizations to the system and this will reduce the burden too.
Though this is a simple application, it is a six stages complex flow and the actual flow is hidden. Apache Spark is capable of understanding this flow and constructs the graph correctly using the correct parallelization. But if you were using other system, you had to do this complex task manually. If you are trying to complete this manually this will kill huge time and there is also chances of mistakes. This is one of the reasons that attracts developer towards Apache Spark. Sparks has figured out everything and offering the best possible services.
Spark has specialized shell for Python and Scala. This shell is simple and allows developer to access datasets. This allows developers to access and manipulate datasets easily and without writing an end-to-end application. In short, you are allowed to taste program even before it is written. All you need is open the shell and write a few codes. You are good to go. This is one of the tremendous functions of Apache Spark.
Apache Spark is dedicated to efficiency and programmability. It has every quality that can attract a developer. But mostly, developers are attracted by Apache Spark because of its superior performance. Actually Apache Spark is admired all over the world just for its performance.
During the development of any application, it is required to run that application several times. Developers require working on full or partial data sets. They need to flow the develop-test-debug cycle too. If you have vast data sets, these mandatory routine tasks can be tedious and they take hours to execute. But this experience has become enjoyable with Apache Spark. Spark’s performance is superior and thus it’s allows developer to complete their routine cycle in a few moments.
In short you are getting the exact output with less time and effort. This is why developers love spark as they can now work faster than ever. There are some facts with Spark that elevate its performances and they are as follows:
Spark possess advanced DAG execution system. It allows in memory computing and cyclic data flow. All these allow Spark to run programs tremendously fast. To be exact, speed is not a problem anymore. Programming is much faster and easier with Spark.
Spark has more than eighty operators. These operators are among the best and they can serve a developer efficiently. Using these operators you can conduct complex task easily and quickly. This allows to write application in Java, Python and Scala more efficiently. In short, Spark lessen the burden of developer in an advanced way.
Spark runs everywhere. You can run it on Standalone, Mesos, Cloud or even on Hadoop. Its compatibility is admirable and it has opened a new era for developers.
Spark powers MLlib, SQL and Data Frames for Spark Streaming and GraphX. It also powers several other libraries too. These libraries can be combined too in a single application.
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