Edureka Fan

Edureka Fan

1606982795

What is Apache Spark? | Apache Spark Python | Spark Training

This Edureka “What is Apache Spark?” video will help you to understand the Architecture of Spark in depth. It includes an example where we Understand what is Python and Apache Spark.

#big-data #apache-spark #developer #apache #spark

What is GEEK

Buddha Community

What is Apache Spark? | Apache Spark Python | Spark Training
Ray  Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Ananya Gupta

Ananya Gupta

1602847162

What Are The Key Differences between Python 2 vs Python 3

Many beginning Python users are wondering with which version of Python they should start. My answer to this question is usually something along the lines “just go with the version your favorite tutorial was written in, and check out the differences later on.”

But what if you are starting a new project and have the choice to pick? I would say there is currently no “right” or “wrong” as long as both Python 2.7.x and Python 3.x support the libraries that you are planning to use.

However, it is worthwhile to have a look at the major differences between those two most popular versions of Python to avoid common pitfalls when writing the code for either one of them, or if you are planning to port your project.The its good to join best python training program which help to improve your skills.

What is Python 2?
Python 2 made code development process easier than earlier versions. It implemented technical details of Python Enhancement Proposal (PEP). Python 2.7 (last version in 2.x ) is no longer under development and in 2020 will be discontinued.

What is Python 3?
On December 2008, Python released version 3.0. This version was mainly released to fix problems that exist in Python 2. The nature of these changes is such that Python 3 was incompatible with Python 2.

It is backward incompatible Some features of Python 3 have been backported to Python 2.x versions to make the migration process easy in Python 3.

KEY DIFFERENCE
Python 3 syntax is simpler and easily understandable whereas Python 2 syntax is comparatively difficult to understand.
Python 3 default storing of strings is Unicode whereas Python 2 stores need to define Unicode string value with “u.”
Python 3 value of variables never changes whereas in Python 2 value of the global variable will be changed while using it inside for-loop.
Python 3 exceptions should be enclosed in parenthesis while Python 2 exceptions should be enclosed in notations.
Python 3 rules of ordering comparisons are simplified whereas Python 2 rules of ordering comparison are complex.
Python 3 offers Range() function to perform iterations whereas, In Python 2, the xrange() is used for iterations.

Which Python Version to Use?
When it comes to Python version 2 vs. 3 today, Python 3 is the outright winner. That’s because Python 2 won’t be available after 2020. Mass Python 3 adoption is the clear direction of the future.

After considering declining support for Python 2 programming language and added benefits from upgrades to Python 3, it is always advisable for a new developer to select Python version 3. However, if a job demands Python 2 capabilities, that would be an only compelling reason to use this version.

#python online training #python online course #python training #python course #python training in noida #python training in delhi

Shardul Bhatt

Shardul Bhatt

1626775355

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Edureka Fan

Edureka Fan

1606982795

What is Apache Spark? | Apache Spark Python | Spark Training

This Edureka “What is Apache Spark?” video will help you to understand the Architecture of Spark in depth. It includes an example where we Understand what is Python and Apache Spark.

#big-data #apache-spark #developer #apache #spark

kiran sam

1619408437

Apache Spark Training Course Online - Learn Scala

R is perhaps the most popular computer dialects in information science, explicitly committed to measurable investigation with a number of augmentations, for example, RStudio addins and other R packages, for information processing and machine learning assignments. Moreover, it empowers information researchers to effortlessly imagine their informational collection.

By using SparkR in Apache SparkTM, R code can without much of a stretch be scaled. To interactively run occupations, you can without much of a stretch run the distributed calculation by running a R shell.

At the point when SparkR doesn’t require interaction with the R process, the performance is virtually indistinguishable from other language APIs like Scala, Java and Python. However, huge performance degradation happens when SparkR occupations interact with local R capacities or information types.

Databricks Runtime introduced vectorization in SparkR to improve the performance of information I/O among Spark and R. We are eager to declare that using the R APIs from Apache Arrow 0.15.1, the vectorization is presently accessible in the upcoming Apache Spark 3.0 with the significant performance improvements.

This blog entry outlines Spark and R interaction inside SparkR, the current local execution and the vectorized execution in SparkR with benchmark results.Native implementation

The calculation on SparkR DataFrame gets distributed across every one of the hubs accessible on the Spark cluster. There’s no correspondence with the R processes above in driver or executor sides on the off chance that it doesn’t have to collect information as R data.frame or to execute R native capacities. At the point when it requires R data.frame or the execution of R native capacity, they convey using attachments among JVM and R driver/executors.

It (de)serializes and transfers information row by row among JVM and R with an inefficient encoding format, which doesn’t consider the modern CPU plan, for example, CPU pipelining.

Vectorized implementation

In Apache Spark 3.0, another vectorized implementation is introduced in SparkR by leveraging Apache Arrow to trade information directly among JVM and R driver/executors with minimal (de)serialization cost

Instead of (de)serializing the information row by row using an inefficient format among JVM and R, the new implementation leverages Apache Arrow to permit pipelining and Single Instruction Multiple Data (SIMD) with a productive columnar format.

The new vectorized SparkR APIs are not empowered as a matter of course yet can be empowered by setting spark.sql.execution.arrow.sparkr.enabled to true in the upcoming Apache Spark 3.0. Note that vectorized dapplyCollect() and gapplyCollect() are not executed at this point. It is encouraged for users to utilize dapply() and gapply() instead.

Benchmark results

The benchmarks were performed with a basic informational collection of 500,000 records by executing similar code and comparing the all out passed times when the vectorization is empowered and impaired.

If there should be an occurrence of collect() and createDataFrame() with R DataFrame, it turned out to be approximately 17x and 42x faster when the vectorization was empowered. For dapply() and gapply(), it was 43x and 33x faster than when the vectorization is handicapped, respectively.

There was a performance improvement of up to 17x–43x when the streamlining was empowered by spark.sql.execution.arrow.sparkr.enabled to true. The larger the information was, the higher performance anticipated

End

The upcoming Apache Spark 3.0, supports the vectorized APIs, dapply(), gapply(), collect() and createDataFrame() with R DataFrame by leveraging Apache Arrow. Enabling vectorization in SparkR improved the performance up to 43x faster, and more lift is normal when the size of information is larger.

Concerning future work, there is an ongoing issue in Apache Arrow, ARROW-4512. The correspondence among JVM and R isn’t completely in a streaming manner currently. It needs to (de)serialize in clump since Arrow R API doesn’t support this out of the container. Also, dapplyCollect() and gapplyCollect() will be supported in Apache Spark 3.x releases. Users can work around through dapply() and collect(), and gapply() and collect() individually in the interim.

Try out these new abilities today on Databricks, through our DBR 7.0 Beta, which includes a preview of the upcoming Spark 3.0 release. Learn more about Spark 3.0 in our Spark Certification

Spark and R interaction

SparkR supports not just a rich arrangement of ML and SQL-like APIs yet additionally a bunch of APIs normally used to directly interact with R code — for instance, the consistent conversion of Spark DataFrame from/to R DataFrame, and the execution of R local capacities on Spark DataFrame in a distributed manner.

In many cases, the performance is virtually steady across other language APIs in Spark — for instance, when user code relies on Spark UDFs or potentially SQL APIs, the execution happens entirely inside the JVM with no performance punishment in I/O. See the cases beneath which take ~1 second similarly.

/Scala API

/~1 second

sql(“SELECT id FROM range(2000000000)”).filter(“id > 10”).count()

R API

~1 second

count(filter(sql(“SELECT * FROM range(2000000000)”), “id > 10”))

However, in situations where it requires to execute the R local capacity or convert it from/to R local sorts, the performance is immensely different as underneath.

/Scala API

val ds = (1L to 100000L).toDS

/~1 second

ds.mapPartitions(iter => iter.filter(_ < 50000)).count()

R API

df <-createDataFrame(lapply(seq(100000), work (e) list(value=e)))

~15 seconds - multiple times slower

count(dapply(

df, function(x) as.data.frame(x[x$value < 50000,]), schema(df)))

Albeit this basic case above filters the qualities lower than 50,000 for each partition, SparkR is 15x slower.

/Scala API

/~0.2 seconds

val df = sql(“SELECT * FROM range(1000000)”).collect()

R API

~8 seconds - multiple times slower

df <-collect(sql(“SELECT * FROM range(1000000)”))

The case above is far and away more terrible. It just collects a similar information into the driver side, yet it is 40x slower in SparkR.

This is on the grounds that the APIs that require the interaction with R local capacity or information types and its execution are not very productive. There are six APIs that have the striking performance punishment:

createDataFrame()

collect()

dapply()

dapplyCollect()

gapply()

gapplyCollect()

In short, createDataFrame() and collect() require to (de)serialize and convert the information from JVM from/to R driver side. For instance, String in Java becomes character in R. For dapply() and gapply(), the conversion among JVM and R executors is required in light of the fact that it needs to (de)serialize both R local capacity and the information. If there should arise an occurrence of dapplyCollect() and gapplyCollect(), it requires the overhead at both driver and executors among JVM and R

Native implementation

The calculation on SparkR DataFrame gets distributed across every one of the hubs accessible on the Spark cluster. There’s no correspondence with the R processes above in driver or executor sides on the off chance that it doesn’t have to collect information as R data.frame or to execute R native capacities. At the point when it requires R data.frame or the execution of R native capacity, they convey using attachments among JVM and R driver/executors.

It (de)serializes and transfers information row by row among JVM and R with an inefficient encoding format, which doesn’t consider the modern CPU plan, for example, CPU pipelining.

Vectorized implementation

In Apache Spark 3.0, another vectorized implementation is introduced in SparkR by leveraging Apache Arrow to trade information directly among JVM and R driver/executors with minimal (de)serialization cost

Instead of (de)serializing the information row by row using an inefficient format among JVM and R, the new implementation leverages Apache Arrow to permit pipelining and Single Instruction Multiple Data (SIMD) with a productive columnar format.

The new vectorized SparkR APIs are not empowered as a matter of course yet can be empowered by setting spark.sql.execution.arrow.sparkr.enabled to true in the upcoming Apache Spark 3.0. Note that vectorized dapplyCollect() and gapplyCollect() are not executed at this point. It is encouraged for users to utilize dapply() and gapply() instead.

Benchmark results

The benchmarks were performed with a basic informational collection of 500,000 records by executing similar code and comparing the all out passed times when the vectorization is empowered and impaired.

If there should be an occurrence of collect() and createDataFrame() with R DataFrame, it turned out to be approximately 17x and 42x faster when the vectorization was empowered. For dapply() and gapply(), it was 43x and 33x faster than when the vectorization is handicapped, respectively.

There was a performance improvement of up to 17x–43x when the streamlining was empowered by spark.sql.execution.arrow.sparkr.enabled to true. The larger the information was, the higher performance anticipated

End

The upcoming Apache Spark 3.0, supports the vectorized APIs, dapply(), gapply(), collect() and createDataFrame() with R DataFrame by leveraging Apache Arrow. Enabling vectorization in SparkR improved the performance up to 43x faster, and more lift is normal when the size of information is larger.

Concerning future work, there is an ongoing issue in Apache Arrow, ARROW-4512. The correspondence among JVM and R isn’t completely in a streaming manner currently. It needs to (de)serialize in clump since Arrow R API doesn’t support this out of the container. Also, dapplyCollect() and gapplyCollect() will be supported in Apache Spark 3.x releases. Users can work around through dapply() and collect(), and gapply() and collect() individually in the interim.

Try out these new abilities today on Databricks, through our DBR 7.0 Beta, which includes a preview of the upcoming Spark 3.0 release.

#apache spark #apache spark training #apache spark course