Distributed processing

Oct 13, 20130 likes1,682 views

This document discusses using distributed processing to increase computational power. It introduces the concepts of using a distributed file system like HDFS to store large files across multiple machines. MapReduce is presented as a way to process large amounts of data in parallel by splitting it into chunks, mapping functions to each chunk, and then recombining the results. Hadoop is introduced as an open-source MapReduce framework that uses HDFS for storage and allows processing of data across a cluster of machines. Examples are given of writing Map and Reduce functions and running jobs on Hadoop to demonstrate distributed processing of large datasets.

Increase computational power
with distributed processing
Neil Stein 03 Nov 2012

A Discussion Example……..
Getting the data, and ordering it as needed…..
Familiar with grep and sort?

—  “grep” extracts all the matching lines
—  “sort” sorts all the lines
grep “some_record_parameters” hl7_transfer.data-file | sort
[2012/02/25/ 9:15] records sent to healthcare-1
[2012/02/28/ 6:15] records sent to healthcare-2
[2012/03/12/ 10:30] records sent to healthcare-3

A Discussion Example……..
—  As the amount of data increases, process requires more and
more resources

—  What if hl7_transfor.data-file is 500GB or bigger?
—  What if there are hundreds or thousands of data files?
—  What if there are multiple types of data files?
grep “provider 1” hl7_transfor.data-file | sort

—  Ignoring the process for a moment, how do we write all the data to
disk in the first place?

Need to rethink the process

Distributed File-System – “the cloud”
—  Files can be stored across many machines
—  Files can be replicated across many machines
—  Files can be in a hyrbid-cloud model
—  Share the file-system transparently
—  You simply see the usual file structure
—  Opportunity to leverage private and public cloud environments

Map-Reduce – the cloud
—  A way of processing large amounts of data across many machines
—  Must be able to split-up the data in chunks for processing, (Map)
—  Recombined after processing (Reduce)
—  Requires a constant flow of data from one simple state to another
—  Allows for a simple way of breaking down a large task into smaller
manageable tasks

—  Increase the available computational power

What is Hadoop
—  A Map-Reduce framework
—  Designed to run applications on clusters of
local and remote systems

—  HDFS
—  The file system of Hadoop (Hadoop Distributed
File System)
—  Designed to access clusters of local and
remote systems

Map

Hadoop streams information on STDIN
Separate value with a newline (for Hadoop)

Reduce

Hadoop streams back to us on STDIN
Output the aggregated records

Sanity Checking
Command

Results
This should work with small data-sets

Push file to “the distributed file system”

Put file on the DFS

Check that the file is in the cloud

Running in “the distributed environment”

Call the Hadoop streaming command
Pass the appropriate parameters

Running in “the distributed environment”

Checking Status
—  Cluster Summary
—  Running Jobs
—  Completed Jobs
—  Failed Jobs
—  Job Statistics
—  Detailed Job Logs

Checking Distributed Cluster Health
—  List Data-Nodes
—  Dead Nodes
—  Node Heart-beat information
—  Failed Jobs
—  Job Statistics
—  Detailed Job Logs

Conclusion
—  A different paradigm for solving large-scale problems
—  Designed to solve specific problems that can be defined
in a focused map-reduce manner

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Distributed processing

1. Increase computational power with distributed processing Neil Stein 03 Nov 2012

3. A Discussion Example…….. Getting the data, and ordering it as needed….. Familiar with grep and sort? —  “grep” extracts all the matching lines —  “sort” sorts all the lines grep “some_record_parameters” hl7_transfer.data-file | sort [2012/02/25/ 9:15] records sent to healthcare-1 [2012/02/28/ 6:15] records sent to healthcare-2 [2012/03/12/ 10:30] records sent to healthcare-3

4. A Discussion Example…….. —  As the amount of data increases, process requires more and more resources —  What if hl7_transfor.data-file is 500GB or bigger? —  What if there are hundreds or thousands of data files? —  What if there are multiple types of data files? grep “provider 1” hl7_transfor.data-file | sort —  Ignoring the process for a moment, how do we write all the data to disk in the first place? Need to rethink the process

6. Distributed File-System – “the cloud” —  Files can be stored across many machines —  Files can be replicated across many machines —  Files can be in a hyrbid-cloud model —  Share the file-system transparently —  You simply see the usual file structure —  Opportunity to leverage private and public cloud environments

8. Map-Reduce – the cloud —  A way of processing large amounts of data across many machines —  Must be able to split-up the data in chunks for processing, (Map) —  Recombined after processing (Reduce) —  Requires a constant flow of data from one simple state to another —  Allows for a simple way of breaking down a large task into smaller manageable tasks —  Increase the available computational power

9. A look at Hadoop

10. What is Hadoop —  A Map-Reduce framework —  Designed to run applications on clusters of local and remote systems —  HDFS —  The file system of Hadoop (Hadoop Distributed File System) —  Designed to access clusters of local and remote systems

11. Putting the pieces together….

12. First, we need some code…… Map Reduce

13. Map Hadoop streams information on STDIN Separate value with a newline (for Hadoop)

14. Reduce Hadoop streams back to us on STDIN Output the aggregated records

15. Sanity Checking Command Results This should work with small data-sets

16. Push file to “the distributed file system” Put file on the DFS Check that the file is in the cloud

17. Running in “the distributed environment” Call the Hadoop streaming command Pass the appropriate parameters

18. Running in “the distributed environment”

19. Running in “the distributed environment”

20. Running in “the distributed environment”

21. Running in “the distributed environment”

22. Checking Status —  Cluster Summary —  Running Jobs —  Completed Jobs —  Failed Jobs —  Job Statistics —  Detailed Job Logs

23. Checking Distributed Cluster Health —  List Data-Nodes —  Dead Nodes —  Node Heart-beat information —  Failed Jobs —  Job Statistics —  Detailed Job Logs

24. Conclusion —  A different paradigm for solving large-scale problems —  Designed to solve specific problems that can be defined in a focused map-reduce manner

Distributed processing

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