- #Download spark sequencer how to#
- #Download spark sequencer full#
- #Download spark sequencer software#
When I first ran the code, by far the slowest thing in the list of jobs was the count() on line 78, taking 2 seconds. The jobs run on RDDs will just be a few milliseconds each because of the strategy of deferring the bulk of the work to the end (lazy evaluation), where something like collect() is called. Now there is a list of "jobs," the subtasks Spark parallelized and how long they took. Then navigate in your browser to You will see a list representing all the times you called spark-submit. But the main thing is to run this from the Spark root: The next question is: how fast was it really? The Spark history server can look at the application logs stored on the filesystem and construct a UI. Then you will just see the result at the end. If you want to turn off excessive stdout logging in the meantime, go to Spark's conf directory, copy to log4j.properties and set log4j.rootCategory to WARN. You might prefer to see this in the Web UI later. When you run spark-submit it will produce a lot of output about how it is parallelizing tasks. The local means that for test purposes we will just be running the program locally rather than on a real cluster, but trying to distribute the work to all 4 processors of the local machine. Spark-submit -class "MergeFasta" -master local target/scala-2.11/mergefasta_2.11-0.1.jar When you've cloned or downloaded that, call sbt package in the normal way and then issue the command:
#Download spark sequencer full#
The full code is available as an sbt project here.
#Download spark sequencer how to#
But how to run it? First compile it to a jar file. To see the final result we can just use println . Each sequence found is appended using the given length (7 in all cases here) to get a final sequence of ATTAGACCTGCCGGAATAC. To implement this, we may call lookup on the RDD repeatedly inside of a recursive function that builds the String. We extract the starting sequence (signalling an error if there wasn't one and only one), and then "follow" that through the matrix: from ATTAGACCTG to AGACCTGCCG to CTGCCGGAA to GCCGGAATAC where the trail ends. There are examples of using each one here. If you want to see all the available RDD transformations check the Spark API. Subtract is one of the many useful transformations available on an RDD, and does what you would expect. Which is the first sequence? It will be any sequence that does not appear as a value anywhere. We still need to get them in the right order though. Now we have the sequences that we can put together to make the final sequence. The skeleton of a Spark program looks like this: (However, I will continue to illustrate the algorithm using the toy data above.) We need Spark because the number of sequence fragments in a real-life scenario would be many millions. Let's try a better algorithm, one which divides the solution into a number of tasks, each of which can be parallelized. and so on, until all sequences have been handled or there are no more matches. Here are four sample sequence fragments:Ĭan you see the overlapping parts? If you put the sequences together, the result is be ATTAGACCTGCCGGAATAC.Ī naive solution would do it the same way you do it in your head: pick one sequence, look through all the others for a match, combine the match with what you already have, look though all the remaining ones for a match again. I will take a simplified task from the area of genomics.ĭNA may be sequenced by fragmenting a chromosome into many small subsequences, then reassembling them by matching overlapping parts to get the right order and forming a single correct sequence.In the FASTA format, each sequence is composed of characters: one of T/C/G/A. The API supports other languages too like Python and R, but let's stick with Scala, and illustrate how a sample problem can be solved by modelling the solution in this way and feeding the code to Spark.
Such a collection in the Spark system is called an RDD (Resilient Distributed Dataset). Specifically, your program should be a pipeline of transformations between collections of elements, especially collections of key-value pairs. This assumes that you follow certain conventions.
#Download spark sequencer software#
Still, if we want to spread the code over thousands of processors, there is extra work involved in distributing it and managing failure scenarios.įortunately things have got much easier in recent years with the emergence of helper software like Spark which will take your code and run it reliably in a cluster.
To do it efficiently we can write code in a functional style and use the opportunities for parallelism that FP allows us. Processing huge amounts of data takes time.
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