This document discusses strategies for casual mass parallel data processing using Java. It describes using a simple master-slave topology with a task queue and scheduler to distribute work. The data plane should avoid sending data over RMI and instead use local file systems or in-memory databases. NanoCloud is introduced as a way to drastically simplify coding for computing clusters by allowing remote code execution over SSH.

1. Casual mass parallel data
processing in Java
Alexey Ragozin
Mar 2014

2. Building new bicycle …

3. Build Vs. Buy
Build
• No dedicated team to
support infrastructure
• Very specific tasks
• Exclusive use of
infrastructure
• Reasonable scale
Buy
• Product can bought as
service (internal or external)
• Large scale
• Multi tenancy
• You are going to use
advanced features
(e.g. map/reduce)

4. “Casual” computing
• Small computation farms (< 100 servers)
• Team owns both application and grid
• Java platform
• Reasonably short batches (< 24 hours)
• Reasonably small data sets (< 10 TiB)

5. Simple master slave topology
Master process
Task queue
Slave Slave Slave
Scheduler
AdvertiseTask
Report

6. Simple master slave topology
Control plane
 RMI
Queue / scheduler
 Simple in memory queue
 May be more complex than just task queue
Data plane
…

7. Data plane
Never, ever, try to send data over RMI 
File system
 Avoid network mounts!
In-memory key-value
 Client side sharding works best
Disk database (RDBMS or NoSQL)
 Consider prefetch of data
Direct socket streaming
…

8. Distributed objects revised
Pit falls of CORBA/RMI
• IDL – functional contract
• IDL – protocol
Separating concerns
• Functional contract – wrapper object
• Protocol – hidden remote interface

9. Distributed objects revised
Renewed distributed objects paradigm
Strong
• Polymorphism
• Encapsulation
 Network protocol, caching aspects etc
Weak
• Homogenous code base required
• Synchronous network communications

10. Brute force
 Build / package
 Deploy / SCP
 Restart slaves
 Start batch
 Change code, repeat
Deployment problem
Computation grid software
 Compile and run batch
Behind scene
 Your classes would be collected
 Associated with batch
 Deployed on participating slaves

11. Central scheduler topology
Batch controller
Slave Slave Slave
Pull task
Task
Report
Queue server
Task queue
Batch controller
Add tasks
Consume
reports

12. Or more elaborated

13. Flow organized tasks
• Input data available before
task starts
• e.g. Map/Reduce
Collaborative tasks
• Tasks communicate
intermediate results to each
other
• e.g. physic simulations
Flavors of parallel processing

14. Get back to data plane
Rules of thumb
• Insert / delete – never update
• Write locally (reducing risks)
• Read remotely (retry on error)
• Store input as is
 File system
 Document / column oriented NoSQL
• Input and temporary data is different
 Choose right store for each

15. Exploiting file system
Avoid network file systems
• File system concept is not designed to be distributed
• Good network file system cannot not exists
• Use simple remote file access protocols
• SCP (unencrypteddatatransferoptionsaddedbyCERNguys)
• HTTP (ifyoureallydonotwantSCP)
Cheap SAN could be build from open source

16. Algorithmic optimization
Parallel computing
• N times speed up will increase
your OPEX and CAPEX cost by N*lg(N)
Algorithmic optimization
• Up front costs only
• Orders of magnitude optimization opportunities
• Exciting coding
• Ecological way of computing 

17. Streaming algorithms
Finding N most frequent elements
• Min-Count
Estimating number of unique values
• HyperLogLog
Distribution histograms
https://github.com/addthis/stream-lib
https://github.com/rwl/ParallelColt

18. NanoCloud – drastically simplified
coding for computing clusters

19. @Test
public void hello_remote_world() {
Cloud cloud = CloudFactory.createSimpleSshCloud();
cloud.node("myserver.acme.com").exec(new Callable<Void>(){
@Override
public Void call() throws Exception {
String localhost = InetAddress.getLocalHost().toString();
System.out.println("Hi! I'm running on " + localhost);
return null;
}
});
}
As easy as …

20. All you need is …
NanoCloud requirements
 SSHd
 Java (1.6 and above) present
 Works though NAT and firewalls
 Works on Amazon EC2
 Works everywhere where SSH works

21. Master – slave communications
Master process Slave hostSSH
(Single TCP)
Slave
Slave
RMI
(TCP)
std err
std out
std in
diag
Slave
controller
Slave
controller
multiplexed slave streams Agent

22. Links
NanoCloud
• https://code.google.com/p/gridkit/wiki/NanoCloudTutorial
• Maven Central: org.gridkit.lab:telecontrol-ssh:0.7.23
• http://blog.ragozin.info/2013/01/remote-code-execution-in-java-made.html
ANT task
• https://github.com/gridkit/gridant

23. Thank you
Alexey Ragozin
alexey.ragozin@gmail.com
http://blog.ragozin.info
- my articles
http://code.google.com/p/gridkit
http://github.com/gridkit
- my open source code
http://aragozin.timepad.ru
- community events in Moscow