PageRank Optimization

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Do curso por Yandex

Big Data Analysis: Hive, Spark SQL, DataFrames and GraphFrames

47 ratings

Yandex

Big Data Analysis: Hive, Spark SQL, DataFrames and GraphFrames

47 ratings

Curso 2 de 5 em Specialization Big Data for Data Engineers

No doubt working with huge data volumes is hard, but to move a mountain, you have to deal with a lot of small stones. But why strain yourself? Using Mapreduce and Spark you tackle the issue partially, thus leaving some space for high-level tools. Stop struggling to make your big data workflow productive and efficient, make use of the tools we are offering you. This course will teach you how to: - Warehouse your data efficiently using Hive, Spark SQL and Spark DataFframes. - Work with large graphs, such as social graphs or networks. - Optimize your Spark applications for maximum performance. Precisely, you will master your knowledge in: - Writing and executing Hive & Spark SQL queries; - Reasoning how the queries are translated into actual execution primitives (be it MapReduce jobs or Spark transformations); - Organizing your data in Hive to optimize disk space usage and execution times; - Constructing Spark DataFrames and using them to write ad-hoc analytical jobs easily; - Processing large graphs with Spark GraphFrames; - Debugging, profiling and optimizing Spark application performance. Still in doubt? Check this out. Become a data ninja by taking this course! 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.

Na lição

Spark Internals and Optimization

Spark Execution Model5:34

Shuffle. Where to send data?5:19

Shuffle. How to send data?4:30

Optimizing Functions4:01

PageRank Optimization5:59

Conheça os instrutores

Natalia Pritykovskaya
Pavel Klemenkov
Chief Data Scientist
NVIDIA
Pavel Mezentsev
Senior Data Scientist
PulsePoint inc
Alexey A. Dral
Head of Big Data and Machine Learning
Algorithms and Programming Technologies dept. MIPT

All right. Let's now use everything you have learned in this lesson to optimize

our real world SPARC application and this will be their PageRank algorithm.

You already managed to figure out how PageRank works and

also created a solid implementation of this algorithm in SPARC.

This implementation already uses some smart performance tricks.

For example, you know that the links RDD is

static and will be used iteratively so you persisted it in memory.

Or you know that reducing the volume of the shuffle is great so you use

the reduceByKey transformation to sum up all the contributing PageRanks but,

as always, you can do better.

We will start with a simple example of a graph with only 5 nodes and 9 edges.

This makes things simple, fast, and manageable.

As usual, our test environment has 10 executors,

each having two cores and four gigabytes of memory.

The input file contains just 9 lines and we will do 30 iterations.

The test run has been finished in 24 seconds.

It has generated 63 stages but more than 2,000 tasks

and it looks a bit strange that just nine lines of the input produced 2,000 tasks.

It becomes pretty obvious why this happens when you

look at the deck which was generated by the job.

It has these repetitive redundant stages marked in

red rectangles and what you want to do is to get rid of these unnecessary shuffles.

The cause of these shuffles is the join of the links with the ranks.

Why are these join generates a shuffle?

Simple. Join just generates shuffles due to their nature.

But you know something about this specific problem.

You know that links and ranks has the same keys and they don't

change so it may be possible to use this knowledge and optimize things a bit.

You know that SPARC uses a partitioner to perform a shuffle.

In this implementation of the PageRank,

the links RDD has two partitions and a none partitioner but their ranks RDD,

though having two partitions as well,

does not have a partitioner.

This is not good because ranks who are created by

the transformation of the values, not the keys.

And that means that the links RDD and the ranks RDD should be co-partitioned.

While they are, in fact,

not, their partitioners are not equal.

To make things explicit,

we will use their partition byte method on the links RDD to introduce a partitioner.

We will use two partitions as the data set

is small but we will increase this number later.

Okay, you have specified their partitioner but nothing has really changed.

What should you do next?

To make your RDDs co-partitioned,

you should use the same partitioner for the ranks.

It can be achieved by preserving the partitioner in that map transformation.

As you see, both RDDs now have a known partitioner and they are equal.

Are we done? Not quite.

The ranks RDD is more defined in the full loop using the PageRanks of

the contributing links and this modification is done by

the reduceByKey transformation which also needs to do a shuffle.

But if you ensure that the reduceByKey generates

the same number of partitions in the rank RDD as before,

everything will be okay.

One final note, that damping factor is applied

via their mapValues transformation which preserve their partitioner.

If this was done with the map transformation,

you would have to explicitly preserve the partitioner.

Great. The final implementation is here.

Is this mostly the same as before?

With only three minor modifications.

Let's test it.

As you see, the execution time has halved as well as the number of

stages and the number of tasks has decreased 31 times.

Now SPARC UI tells you that there are no redundant stages and

everything that is happening is the same stage recomputing the ranks on each iteration.

Let's now test your modified solution on a realistic problem.

We'll use the Berkeley-Stanford web graph which contains about

700,000 nodes and more than 7 million edges.

The algorithm will iterate five times.

The first implementation, which did not use

all the partition and optimizations has been finished in 4.1

minutes and the optimized solution took 3.5

minutes to finish which gives an increase in performance by about 20%.

And that is pretty great because the algorithm used just 5 iterations,

which is too small for a web graph of this size.

So, summing up, the most important thing you should care

about in your SPARC applications is reducing shuffles.

You may achieve this if the partitioners of RDDs are

known and it is better to explicitly assign a partitioner.

If your transformations do not change the keys,

you should preserve the partitioner.

And if your job has to shuffle,

make sure that you decrease the volume of the shuffle by

using the appropriate transformation like reduceByKey.

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