Have you ever heard about such technologies as HDFS, MapReduce, Spark? Always wanted to learn these new tools but missed concise starting material? Don’t miss this course either! In this 6-week course you will: - learn some basic technologies of the modern Big Data landscape, namely: HDFS, MapReduce and Spark; - be guided both through systems internals and their applications; - learn about distributed file systems, why they exist and what function they serve; - grasp the MapReduce framework, a workhorse for many modern Big Data applications; - apply the framework to process texts and solve sample business cases; - learn about Spark, the next-generation computational framework; - build a strong understanding of Spark basic concepts; - develop skills to apply these tools to creating solutions in finance, social networks, telecommunications and many other fields. Your learning experience will be as close to real life as possible with the chance to evaluate your practical assignments on a real cluster. No mocking, a friendly considerate atmosphere to make the process of your learning smooth and enjoyable. Get ready to work with real datasets alongside with real masters! Special thanks to: - Prof. Mikhail Roytberg, APT dept., MIPT, who was the initial reviewer of the project, the supervisor and mentor of half of the BigData team. He was the one, who helped to get this show on the road. - Oleg Sukhoroslov (PhD, Senior Researcher at IITP RAS), who has been teaching MapReduce, Hadoop and friends since 2008. Now he is leading the infrastructure team. - Oleg Ivchenko (PhD student APT dept., MIPT), Pavel Akhtyamov (MSc. student at APT dept., MIPT) and Vladimir Kuznetsov (Assistant at P.G. Demidov Yaroslavl State University), superbrains who have developed and now maintain the infrastructure used for practical assignments in this course. - Asya Roitberg, Eugene Baulin, Marina Sudarikova. These people never sleep to babysit this course day and night, to make your learning experience productive, smooth and exciting.
Transformations 1
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From the course by Yandex
Big Data Essentials: HDFS, MapReduce and Spark RDD
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From the lesson
Introduction to Apache Spark
Meet the Instructors
Ivan PuzyrevskiyTechnical Team Lead
Emeli Dral
Evgeniy Riabenko
Alexey A. DralHead of Big Data and Machine Learning
Algorithms and Programming Technologies dept. MIPT
Hello. In the previous video,
you have learned about RDDs.
You have seen that you can use data stored in a stable storage as an RDD.
That is the first way to create an RDD.
However, if you recall the definition of RDD,
it is said to be a read-only collection of records.
So the question is,
how can we work with the data?
That's where transformation come in.
They are the second way how you can create an RDD from the existing one.
For example, with transformations,
you can filter records and group them by a key.
More precisely, transformations allow you to create new RDDs from
the existing RDDs by specifying how to obtain new items from the existing items.
That implies that the transformed RDD depends on the source RDD to be useful.
To make this definition more accurate,
let's take a look at the examples.
A simplest transformation is the filter transformation.
It is biometrized by a predicate.
The result in DataSet only contains items that satisfy the predicate.
As you can see here in the example,
if you take the RDD with numbers for one to five,
and apply the filtered transformation that keeps only even numbers,
you will get the RDD that effectively contains numbers two and four.
Another transformation that should be familiar for you,
after the MapReduce lesson,
is the Map Transformation.
It is biometrized by a math function that transforms or
maps a single source item into a single transformed item.
For example, the map transformation with a function that doubles the input number,
converts the RDD with numbers from one to five to the RDD with numbers two,
four, six, eight and ten.
A generalization of filter and map is called flatMap.
Here, a function maps
a single source item into a possibly empty array of transformed items.
For example, to filter out the item,
you simply need to map it into the empty array.
The map function would be implemented as mapping to the singleton array.
Let's take a look at how you can implement the filtered RDD.
Recall, you need to implement three functions: partitions, iterator and dependencies.
Since the transformed RDD's mostly the same as source one,
you can reuse partitions of the source
RDD as there is no need to change the partitioning.
Now, every field or partition depends on the source partition.
You can establish this relation by providing a dependency object that
establishes one-to-one correspondence between the filtered and the source partitions.
Now, as you provided dependencies for every partition,
when creating an iterator over the filter partition,
Spark would inject an iterator of the source partition into the iterator function call.
So, your job now is to build an iterator over the filter partition.
This could be done by reusing the parent iterator.
When requested for the next value,
you can pull values from the parent iterator
until it returns you an item that satisfies the predicate.
And I think here is that you do not need to
know about implementation details or the parent RDD at all.
If you noticed, you have implemented this so-called lazy iterator.
That means that when you create a filtered RDD,
no immediate filtering occurs.
The filtering starts to happen only when you start to pull items from the iterator.
This is a property of laziness.
All the transformations in Spark are lazy.
They merely create a description of what needs to be
done and then wait until you start requesting the data,
and that makes sense.
If you are working with a cluster and you have just made a filter RDD,
it is unclear where to start executing the operation and where to deliver the result.
Obviously, you do not want all the data to go through your computer.
And as you will learn later,
there are other operations available in Spark
called "Actions" that trigger the actual computation,
and hence, involve the postponed transformations.
One important technical detail,
as the filtering is done lazily,
the predicate closure must be captured within the RDD.
That means that you have to be careful about the code you put in the predicate.
You have no control on when and where this code will be executed.
That means it is a bad idea to, say,
access the local files or to capture the local references in the closure.
One more thing, whenever you apply a transformation to the RDD,
you implicitly construct a dependency graph on the RDDs,
as shown in this slide.
This graph is used by the framework to schedule jobs as we will see later.
Moreover, you can imagine a partition level dependency graph where
vertices correspond to partitions and edges to inter-partition dependencies.
In the case of filtered transformation,
this is a slice graph with a few edges.
I think it is a good idea to make a short break here.
Please, take a quiz to check yourself,
and when ready, proceed to the next video. See you.
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