{"id":18805,"date":"2018-06-22T04:02:06","date_gmt":"2018-06-22T04:02:06","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=18805"},"modified":"2021-05-12T11:09:12","modified_gmt":"2021-05-12T05:39:12","slug":"pyspark-pros-and-cons","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/pyspark-pros-and-cons\/","title":{"rendered":"PySpark Pros and Cons | Characteristics of PySpark"},"content":{"rendered":"

In this\u00a0PySpark Tutorial<\/strong>, we will see PySpark Pros and Cons. Moreover, we will also discuss characteristics of PySpark. Since we were already working on Spark with Scala, so a question arises that why we need Python<\/strong>. <\/span><\/p>\n

So, here in article “PySpark Pros and cons and its characteristics”, we are discussing some Pros\/cons of using Python over Scala. Also, we will see some characteristics of PySpark to understand it well.<\/span><\/p>\n

Advantages of PySpark<\/h2>\n

Python\u2019s Pros while using it over Scala<\/strong>:<\/span><\/p>\n

\"Pros<\/a>

Advantages of PySpark<\/p><\/div>\n

i. Simple to write<\/h3>\n

We can say it is very simple to write parallelized code, for simple problems.<\/span><\/p>\n

ii. Framework handles errors<\/h3>\n

While it comes to Synchronization points as well as errors, framework easily handles them.<\/span><\/p>\n

iii. Algorithms<\/h3>\n

Many of the useful algorithms are already implemented in Spark<\/strong>.<\/span><\/p>\n

iv. Libraries<\/h3>\n

In comparison to Scala, Python is far better in the available libraries. Since the huge number of libraries are available so most of the data science related parts from R<\/strong> are ported to Python. Well, this does not happen in the case with Scala.<\/span><\/p>\n

v. Good Local Tools<\/h3>\n

For Scala, there are no good visualization tools but there are some good local tools available in Python.<\/span><\/p>\n

vi. Learning Curve<\/h3>\n

As compared to Scala, the learning curve is less in Python.<\/span><\/p>\n

vii. Ease of use<\/h3>\n

Again as compared to Scala, Python is easy to use.<\/span><\/p>\n

Disadvantages of PySpark<\/h2>\n

Python\u2019s Cons while using it over Scala:<\/span><\/p>\n

\"Pros<\/a>

Disadvantages of PySpark<\/p><\/div>\n

i. Difficult to express<\/h3>\n

While it comes to express a problem in MapReduce<\/strong> fashion, sometimes it’s difficult.<\/span><\/p>\n

ii. Less Efficient<\/h3>\n

Pythons are less efficient as compared to other programming models. For example as MPI when we need a lot of communication.<\/span><\/p>\n

iii. slow<\/h3>\n

Basically, Python is slow as compared to Scala for Spark Jobs, Performance wise.\u00a0Approximately,\u00a0<\/span>10x slower. That means if we want to do heavy processing then Python will be slower than Scala.<\/span><\/p>\n

iv. Immature<\/h3>\n

In Spark 1.2, Python does support for Spark Streaming still it is not as mature as Scala as of now. So,\u00a0we must go to Scala, if\u00a0we need Streaming.<\/span><\/p>\n

v. Cannot use internal functioning of Spark<\/h3>\n

As the whole of Spark is written in Scala,\u00a0so we have to work with Scala if\u00a0we want to or have to change from internal functioning of Spark for our project, we cannot use Python for it. <\/span><\/p>\n

However, let\u2019s understand it with an example, see using Scala in Spark core we can create a new RDD, but not create\u00a0it using Python.<\/span><\/p>\n

Characteristics of PySpark<\/span><\/h2>\n

Below, we are discussing some characteristics of PySpark:<\/p>\n

\"PySpark<\/a>

PySpark characteristics<\/p><\/div>\n

i.\u00a0Nodes are abstracted<\/h3>\n

That means we cannot address an individual node.<\/span><\/p>\n

ii. Network is abstracted<\/h3>\n

Only implicit communication is possible here.<\/span><\/p>\n

iii. Based on Map-Reduce<\/h3>\n

Moreover, programmers offer a map and a reduce function.<\/span><\/p>\n

iv. API for Spark<\/span><\/h3>\n

PySpark is one of the API for Spark.<\/span>
\nSo, this was all about PySpark Pros and Cons. hope you like our explanation.<\/p>\n

Conclusion: PySpark Pros and Cons<\/span><\/h2>\n

Hence, we have seen\u00a0all the Pros and Cons of using Python over Scala. So, it definitely clears the concept of using PySpark, even after the existence of Scala. Moreover, we also discussed PySpark Characteristics. Still, if any doubt regarding\u00a0PySpark Pros and Cons, ask in the comment tab.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

In this\u00a0PySpark Tutorial, we will see PySpark Pros and Cons. Moreover, we will also discuss characteristics of PySpark. Since we were already working on Spark with Scala, so a question arises that why we...<\/p>\n","protected":false},"author":6,"featured_media":73852,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[44],"tags":[272,1782,2477,2905,3957,8263,10215,10216,10298,10316],"class_list":["post-18805","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-pyspark","tag-advanatges-of-pyspark","tag-benefits-of-pyspark","tag-characteristics-of-pyspark","tag-cons-of-pyspark","tag-disadvantages-of-pyspark","tag-limitations-of-pyspark","tag-pros-of-pyspark","tag-pros-of-python-over-scala","tag-pyspark-characteristics","tag-pyspark-pros-cons"],"yoast_head":"\nPySpark Pros and Cons | Characteristics of PySpark - DataFlair<\/title>\n<meta name=\"description\" content=\"PySpark Pros and Cons, characteristics of PySpark, advantages and disadvantages of using Python over Scala, PySpark benefit, PySpark limitation\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/pyspark-pros-and-cons\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PySpark Pros and Cons | Characteristics of PySpark - 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