Difference between revisions of "Data"
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File:Pipeline.png|We can think of networks as pipelines for data. | File:Pipeline.png|We can think of networks as pipelines for data. | ||
File:Skater.png|Unless we fill the pipeline we won't achieve it's throughput capacity. | File:Skater.png|Unless we fill the pipeline we won't achieve it's throughput capacity. | ||
− | <gallery> | + | </gallery> |
=Data when Writing your own Code= | =Data when Writing your own Code= |
Revision as of 10:07, 25 June 2013
Data: How to surf, rather than drown!
Introduction
Data on Disk
BCp2$ wget https://www.kernel.org/pub/linux/kernel/v3.x/testing/linux-3.10-rc7.tar.xz BCp2$ time cp linux-3.10-rc7.tar.xz linux-3.10-rc7.tar.xz.copy real 0m3.530s user 0m0.000s sys 0m0.068s
BCp2$ tar --use-compress-program=xz -xf linux-3.10-rc7.tar.xz BCp2$ time cp
These timings were taken at ~10:45 on the 25 Jun 2013. Your mileage may vary!
Data over the Network
Filling the pipe.
Data when Writing your own Code
Memory Hierarchy
Files & File Formats
Data Analytics
Some common operations you may want to perform on your data:
- Cleaning
- Filtering
- Calculating summary statics (means, medians, variances)
- Creating plots & graphics
- Tests of statistical significance
- Sorting and searching
Selecting the right tools.
Databases
GUI. Accessing from a program or script. Enterprise Grade The data haven.
Numerical Packages
Such as R, MATLAB & Python.
Bespoke Applications
Rolling Your Own
Principles: Sort & binary search.
Tools: Languages, libraries and packages.
When Data gets Big
Quotas.
Local Disks.
Swapping.
Data the Google way - Map-Reduce.
Hadoop & Friends.
Summary
- Use large files whenever possible.
- Disks are poor at servicing a large number of seek requests.
- Check that you're making best use of a computer's memory hierarchy, i.e.:
- Think about locality of reference.
- Go to main memory as infrequently as possible.
- Go to disk as infrequently as possible as possible.
- Check that your are still using the right tools if your data grows.