Difference between revisions of "Backup strategy"

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State how often the data will be backed up and to which locations. How many copies are being made? Storing data on laptops, computer hard drives or external storage devices alone is very risky. The use of robust, managed storage provided by university IT teams is preferable. Similarly, it is normally better to use automatic backup services provided by IT Services than rely on manual processes.
 
 
Have a recovery strategy worked out
 
  
 
 
 
 
  
Examples
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="caret-color:#000000"><span style="color:#000000"><span style="font-family:-webkit-standard, serif">Intro</span></span></span></span></span>
  
A few years ago several hard drives in NCI's data centre failed, meaning the supercomputer and main disk storage had to be brought offline while repairs were made. Thankfully no data was lost due to the use of redundant components, however one more failed drive would have meant the loss of the /short filesystem. Model output generally goes to this filesystem, and because of its size NCI doesn't perform backups. This means that if you aren't performing backups yourself there is a possibility of losing data, so it's important to make plans to avoid this.
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="color:#000000"><span style="caret-color:#000000"><span style="font-family:-webkit-standard, serif">A large amount of a researcher's time is spent in the production and manipulation of data. No technology is perfect, and sometimes disasters can happen, from a fire destroying your computer, a hard drive failing or just deleting the wrong directory. No-one wants to lose all of their data; results can be costly or even impossible to reproduce. It's important to have some idea of how you could recover if something were to happen to your files. Consider also situations in which the data is not irretrievably lost but might not be available for a while, this could be an issue if you have a deadline to meet.</span></span></span></span></span>
  
Intro
+
<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="color:#000000"><span style="caret-color:#000000"><span style="font-family:-webkit-standard, serif">Have a recovery strategy worked out</span></span></span></span></span>
  
A large amount of a researcher's time is spent in the production and manipulation of data.&nbsp;No technology is perfect, and sometimes disasters can happen, from a fire destroying your computer, a hard drive failing or just deleting the wrong directory. No-one wants to lose all of their data, results can be costly or even impossible to reproduce. It's important to have some idea of how you could recover if something were to happen to your files.
+
<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="color:#000000"><span style="caret-color:#000000"><span style="font-family:-webkit-standard, serif">State how often the data will be backed up and to which locations. How many copies are being made? Storing data on laptops, computer hard drives or external storage devices alone is very risky. The use of robust, managed storage provided by university IT teams is preferable. Similarly, it is normally better to use automatic backup services provided by IT Services than rely on manual processes.</span></span></span></span></span>
  
Strategy
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="color:#000000"><span style="caret-color:#000000"><span style="font-family:-webkit-standard, serif">What is essential?</span></span></span></span></span>
  
What is essential?
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="color:#000000"><span style="caret-color:#000000"><span style="font-family:-webkit-standard, serif">The first thing to think about for backups is which part of your data is essential to have, especially when you have a lot of it. You may want to preserve output files from a very long simulation, but not care about the log files once you have verified your results. Or perhaps you have collected observations, and these cannot be reproduced. Particularly if they are observations of a rare phenomenon. The scripts to create all the plots used in your latest paper can probably be reproduced but with lots of time and effort.</span></span></span></span></span>
  
The first thing to think about for backups is which part of your data is essential to have, especially when you have a lot of it. You may want to preserve output files from a 10,000 hour simulation, but not care about the log files once you've verified your results. Or perhaps you have performed observations of an unusual weather system which couldn't be easily reproduced, or scripts to create all the plots used in your latest paper. Also think about who else might be making use of your files, are other people using your results as input to their own simulations?
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="caret-color:#000000"><span style="color:#000000"><span style="font-family:-webkit-standard, serif">Also think about who else might be making use of your files, are other people using your results in their own research projects?</span></span></span></span></span>
  
What are your vulnerable points?
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="color:#000000"><span style="caret-color:#000000"><span style="font-family:-webkit-standard, serif">What are your vulnerable points?</span></span></span></span></span>
  
Alternately, are you making use of files in other people's directories? Do not depend on other people keeping files in the same place.
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&nbsp;
  
How big is your data?
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="caret-color:#000000"><span style="color:#000000"><span style="font-family:-webkit-standard, serif">Is your laptop old or has been showing sign of potential issues?</span></span></span></span></span>
  
The next thing to consider is how big your data is. Your home directory can easily be backed up to a portable hard drive or to Dropbox, this is not really an option when you have terabytes of model output however. NCI provides a data archive called MDSS to archive large files, most institutions also have their own archives for important data created by their researchers.
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="caret-color:#000000"><span style="color:#000000"><span style="font-family:-webkit-standard, serif">Did you back up results on an external hard drive years ago? Remember technology can become obsolete and disk deteriorates with time</span></span></span></span></span>
  
How often do you update your data?
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="caret-color:#000000"><span style="color:#000000"><span style="font-family:-webkit-standard, serif">If you are you using files managed by others, make sure you know how they are managed. If they are likely to be removed and if they are backed up.</span></span></span></span></span>
  
You should also think about how often the data changes. Model output is unlikely to change, but programs are often improved with time. If you wanted to reproduce old results using a script you wrote some time ago, then you'd need to recover the state of the file at the time you first ran it. Revision control software like subversion and git is designed for this use case, there are a variety of hosting services available on the web that help you to manage software development.
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&nbsp;
 
 
Characteristic of differen backup options
 
  
Not all backup solutions provide the same protection. Creating a copy of a file in a separate directory on your computer provides a measure of protection against accidental deletion, however it could be lost if your hard drive fails. On the other end of the scale you could store copies of your data on archival tapes in different cities, so that it could still be recoverable in the case of natural disasters. Generally the latter is costly for a large data set, the amount of protection needed should be balanced against the value of your data.
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="caret-color:#000000"><span style="color:#000000"><span style="font-family:-webkit-standard, serif">How big is your data?</span></span></span></span></span>
  
Knowing all of this, what are the best options to use?
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="color:#000000"><span style="caret-color:#000000"><span style="font-family:-webkit-standard, serif">The next thing to consider is how big your data is. Your home directory can easily be backed up to a portable hard drive or to Dropbox, this is not really an option when you have terabytes of model output. However, supercomputer providers use a tape data archive, known as MDSS at NCI, to archive large files. Most institutions also have their own archives for important data created by their researchers.</span></span></span></span></span>
  
The first thing to do is to protect your workstation. Check what is already being backed up by your local IT support, they may have an existing backup strategy. For instance NCI backs up user home directories on Vayu. Get an external hard drive at least as big as is in your computer, then either set up a cron job to automatically rsync your internal and external hard drives on linux or enable time machine if you use a mac. This gets you a backup of your whole workstation that you can use to recover all your installed programs and local data if a hard drive fails. You can also use a service like dropbox to automatically back up your home directory, these services limit the space you can use, however they provide a remote backup and allow you to access files from anywhere.
+
<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="color:#000000"><span style="caret-color:#000000"><span style="font-family:-webkit-standard, serif">How often do you update your data?</span></span></span></span></span>
  
Ask the CMS team or your institution's data services about archiving services available to store large data sets. These services have their own data management plans in place to ensure the integrity of data, you may need to provide a data management plan describing the value of the data & how long it needs to be stored for.
+
<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="color:#000000"><span style="caret-color:#000000"><span style="font-family:-webkit-standard, serif">You should also think about how often the data changes. Codes change often and using a version control system is the only way to really keep track of all the versions. Model output or recorded observations are unlikely to change, you might need to back them up only once. Post processed output will change more frequently while you are still conducting your analysis, it might be worth to have some level of automation and back up your files every few days. University IT services might be your best option, depending on the file sizes. Version control is not a good option for data files as they are usually binary. However, you can use it to keep track of readme files describing your workflow and backup these instead.</span></span></span></span></span>
  
&nbsp;
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="color:#000000"><span style="caret-color:#000000"><span style="font-family:-webkit-standard, serif">Characteristic of different backup options</span></span></span></span></span>
  
[[Backup_checklist|Backup checklist]]
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="color:#000000"><span style="caret-color:#000000"><span style="font-family:-webkit-standard, serif">Not all backup solutions provide the same protection. Creating a copy of a file in a separate directory on your computer provides a measure of protection against accidental deletion, however it could be lost if your hard drive fails. On the other end of the scale you could store copies of your data on archival tapes in different cities, so that it could still be recoverable in the case of natural disasters. Generally, the latter is costly for a large data set, the amount of protection needed should be balanced against the value of your data.</span></span></span></span></span>
  
----
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="color:#000000"><span style="caret-color:#000000"><span style="font-family:-webkit-standard, serif">Knowing all of this, what are the best options to use?</span></span></span></span></span>
  
Checklist
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="color:#000000"><span style="caret-color:#000000"><span style="font-family:-webkit-standard, serif">The first thing to do is to protect your laptop/workstation. Check what is already being backed up by your local IT support, they may have an existing backup strategy. For instance NCI backups user home directories on their servers. Get an external hard drive at least as big as is in your computer, then either set up a cron job to automatically rsync your internal and external hard drives on linux or enable time machine if you use a mac. This gets you a backup of your whole workstation that you can use to recover all your installed programs and local data if a hard drive fails. You can also use a service like dropbox to automatically back up your home directory, these services limit the space you can use, however they provide a remote backup and allow you to access files from anywhere.</span></span></span></span></span>
  
*How essential is the data - does it have to be backed up?
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<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="color:#000000"><span style="caret-color:#000000"><span style="font-family:-webkit-standard, serif">If you are managing cloud servers via NCI cloud or Nectar, take regular snapshots of your instances. It is not unlikely to bump into issues when updating a VM, and it is really useful to be able to restart your instance from a working state.</span></span></span></span></span>
**Intermediate output, run logs
 
***Not useful once an experiment is finished 
 
**Processing scripts
 
***Useful to reproduce an experiment but could be redone 
 
**Model input
 
***Essential to reproduce an experiment 
 
**Ease of reproducing the data
 
***observations cannot be recreated
 
***some&nbsp;input data might be available elsehwere but difficult to obtain
 
***can be reproduced&nbsp;but slow and cpu consuming process
 
***easy to reproduce: backup&nbsp;code/workflow 
 
**Published results
 
***they should be backed up, check what is repository strategy
 
***data underlining publications or a PhD thesis has to be available in case of legal dispute for &nbsp;5 years from publication 
 
**Number of people accessing the data
 
***Just you, your group, people from around the world   
 
  
*How big is the data?
+
<span style="font-size:medium"><span style="font-family:" times="" new="" roman",="" serif"=""><span style="color:#000000"><span style="caret-color:#000000"><span style="font-family:-webkit-standard, serif">Ask the CMS team or your institution's data services about archiving services available to store large data sets. These services have their own data management plans in place to ensure the integrity of data, you may need to provide a data management plan describing the value of the data & how long it needs to be stored for.</span></span></span></span></span>
**Text files - Source code, scripts, configuration files, small data files
 
***Sizes < 100 MB
 
***Not suitable for archives unless bundled into a tar file 
 
**Data files - NetCDF &c
 
***1 GB to 100's of GB
 
***Archive systems like tape are specifically designed for this   
 
  
*How often is updated?
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[[Backup_checklist|Backup checklist]]
**Unchanging once produced - e.g. raw model output
 
***Tape archives 
 
**It is updated and reviewed occasionally but not too frequently: post-processed output
 
***external drives, cloud and IT services, faster to retrieve than tape 
 
**Continually changing - source code under development
 
***Use automated revision control system - subversion, git   
 
  
*How safe is the backup system?
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&nbsp;
**What happens if...
 
***You delete a file
 
***The local file system fails
 
****Back up on a different file system 
 
***Your storage provider goes bankrupt
 
****Multiple backup providers 
 
***A fire destroys a building
 
****Offsite/multiple backups 
 
***A flood/earthquake damages a city
 
****Offsite/multiple backups with a wide separation   
 
**Check regularly&nbsp;that you can actually recover the files? 
 
 
 
*Options
 
**/home at NCI/University workstation
 
***Limited space
 
***Backed up - Institutions will have their own strategies 
 
**Tape&nbsp;archives/ Data repositories
 
***Designed for archiving large and/or important data sets
 
***Will have their own backup strategies - e.g. NCI tape is duplicated to two separate buildings at ANU 
 
**USB hard drive
 
***Simple way to have a separate backup, cheap
 
***Can manage history manually or with something like time machine
 
***Not suitable for long-term storage - disks have limited lifetimes 
 
**Cloud storage, e.g. Dropbox, Github
 
***Can access from anywhere
 
***Offsite backup
 
***Can set up folders to automatically be backed up
 
***Is the service still going to be there in 5 years?   
 
  
[[Category:Data]]
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[[Category:Data induction]]

Revision as of 02:44, 13 July 2021

 

Template:Working on New page under construction

 

 

Intro

A large amount of a researcher's time is spent in the production and manipulation of data. No technology is perfect, and sometimes disasters can happen, from a fire destroying your computer, a hard drive failing or just deleting the wrong directory. No-one wants to lose all of their data; results can be costly or even impossible to reproduce. It's important to have some idea of how you could recover if something were to happen to your files. Consider also situations in which the data is not irretrievably lost but might not be available for a while, this could be an issue if you have a deadline to meet.

Have a recovery strategy worked out

State how often the data will be backed up and to which locations. How many copies are being made? Storing data on laptops, computer hard drives or external storage devices alone is very risky. The use of robust, managed storage provided by university IT teams is preferable. Similarly, it is normally better to use automatic backup services provided by IT Services than rely on manual processes.

What is essential?

The first thing to think about for backups is which part of your data is essential to have, especially when you have a lot of it. You may want to preserve output files from a very long simulation, but not care about the log files once you have verified your results. Or perhaps you have collected observations, and these cannot be reproduced. Particularly if they are observations of a rare phenomenon. The scripts to create all the plots used in your latest paper can probably be reproduced but with lots of time and effort.

Also think about who else might be making use of your files, are other people using your results in their own research projects?

What are your vulnerable points?

 

Is your laptop old or has been showing sign of potential issues?

Did you back up results on an external hard drive years ago? Remember technology can become obsolete and disk deteriorates with time

If you are you using files managed by others, make sure you know how they are managed. If they are likely to be removed and if they are backed up.

 

How big is your data?

The next thing to consider is how big your data is. Your home directory can easily be backed up to a portable hard drive or to Dropbox, this is not really an option when you have terabytes of model output. However, supercomputer providers use a tape data archive, known as MDSS at NCI, to archive large files. Most institutions also have their own archives for important data created by their researchers.

How often do you update your data?

You should also think about how often the data changes. Codes change often and using a version control system is the only way to really keep track of all the versions. Model output or recorded observations are unlikely to change, you might need to back them up only once. Post processed output will change more frequently while you are still conducting your analysis, it might be worth to have some level of automation and back up your files every few days. University IT services might be your best option, depending on the file sizes. Version control is not a good option for data files as they are usually binary. However, you can use it to keep track of readme files describing your workflow and backup these instead.

Characteristic of different backup options

Not all backup solutions provide the same protection. Creating a copy of a file in a separate directory on your computer provides a measure of protection against accidental deletion, however it could be lost if your hard drive fails. On the other end of the scale you could store copies of your data on archival tapes in different cities, so that it could still be recoverable in the case of natural disasters. Generally, the latter is costly for a large data set, the amount of protection needed should be balanced against the value of your data.

Knowing all of this, what are the best options to use?

The first thing to do is to protect your laptop/workstation. Check what is already being backed up by your local IT support, they may have an existing backup strategy. For instance NCI backups user home directories on their servers. Get an external hard drive at least as big as is in your computer, then either set up a cron job to automatically rsync your internal and external hard drives on linux or enable time machine if you use a mac. This gets you a backup of your whole workstation that you can use to recover all your installed programs and local data if a hard drive fails. You can also use a service like dropbox to automatically back up your home directory, these services limit the space you can use, however they provide a remote backup and allow you to access files from anywhere.

If you are managing cloud servers via NCI cloud or Nectar, take regular snapshots of your instances. It is not unlikely to bump into issues when updating a VM, and it is really useful to be able to restart your instance from a working state.

Ask the CMS team or your institution's data services about archiving services available to store large data sets. These services have their own data management plans in place to ensure the integrity of data, you may need to provide a data management plan describing the value of the data & how long it needs to be stored for.

Backup checklist