--- layout: default navsection: userguide title: "Storing and Retrieving data using Keep" ... This tutorial introduces you to the Arvados file storage system. *This tutorial assumes that you are "logged into an Arvados VM instance":{{site.baseurl}}/user/getting_started/ssh-access.html#login, and have a "working environment.":{{site.baseurl}}/user/getting_started/check-environment.html* The Arvados distributed file system is called *Keep*. Keep is a content-addressable file system. This means that files are managed using special unique identifiers derived from the _contents_ of the file, rather than human-assigned file names (specifically, the md5 hash). This has a number of advantages: * Files can be stored and replicated across a cluster of servers without requiring a central name server. * Systematic validation of data integrity by both server and client because the checksum is built into the identifier. * Minimizes data duplication (two files with the same contents will result in the same identifier, and will not be stored twice.) * Avoids data race conditions (an identifier always points to the same data.) h1. Putting Data into Keep We will start with downloading a freely available VCF file from the "Personal Genome Project (PGP)":http://www.personalgenomes.org subject "hu599905":https://my.personalgenomes.org/profile/hu599905 to a staging directory on the VM, and then add it to Keep. In the following tutorials, replace you with your user id. First, log into the Arvados VM instance and set up the staging area: notextile.
~$ mkdir /scratch/you
Next, download the file:
~$ cd /scratch/you
/scratch/you$ curl -o var-GS000016015-ASM.tsv.bz2 'https://warehouse.personalgenomes.org/warehouse/f815ec01d5d2f11cb12874ab2ed50daa+234+K@ant/var-GS000016015-ASM.tsv.bz2'
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  216M  100  216M    0     0  10.0M      0  0:00:21  0:00:21 --:--:-- 9361k
{% include 'notebox_begin' %} If you have your own data, for example @MyData.vcf@, you can use @scp@ or @rsync@ to copy from your local workstation to the shell VM (run this on your local workstation): notextile.
~$ scp MyData.vcf you@shell.arvados:/scratch/you/MyData.vcf
{% include 'notebox_end' %} Now use @arv keep put@ to add your VCF data to Keep, then delete the local copy of the file:
/scratch/you$ arv keep put var-GS000016015-ASM.tsv.bz2
c1bad4b39ca5a924e481008009d94e32+210
/scratch/you$ rm var-GS000016015-ASM.tsv.bz2
The output value @c1bad4b39ca5a924e481008009d94e32+210@ from @arv keep put@ is the Keep locator. This enables you to access the file you just uploaded, and is explained in the next section. h2(#dir). Putting a directory You can also use @arv keep put@ to add an entire directory:
/scratch/you$ mkdir tmp
/scratch/you$ echo "hello alice" > tmp/alice.txt
/scratch/you$ echo "hello bob" > tmp/bob.txt
/scratch/you$ echo "hello carol" > tmp/carol.txt
/scratch/you$ arv keep put tmp
0M / 0M 100.0% 
887cd41e9c613463eab2f0d885c6dd96+83
The locator @887cd41e9c613463eab2f0d885c6dd96+83@ represents a collection with multiple files. h1. Getting Data from Keep h2. Using Workbench You may access collections through the "Collections section of Arvados Workbench":https://{{ site.arvados_workbench_host }}/collections located at "https://{{ site.arvados_workbench_host }}/collections":https://{{ site.arvados_workbench_host }}/collections . You can also access individual collections and individual files within a collection. Some examples: * "https://{{ site.arvados_workbench_host }}/collections/c1bad4b39ca5a924e481008009d94e32+210":https://{{ site.arvados_workbench_host }}/collections/c1bad4b39ca5a924e481008009d94e32+210 * "https://{{ site.arvados_workbench_host }}/collections/887cd41e9c613463eab2f0d885c6dd96+83/alice.txt":https://{{ site.arvados_workbench_host }}/collections/887cd41e9c613463eab2f0d885c6dd96+83/alice.txt h2. Using arv-get You can view the contents of a collection using @arv keep ls@:
/scratch/you$ arv keep ls c1bad4b39ca5a924e481008009d94e32+210
var-GS000016015-ASM.tsv.bz2
/scratch/you$ arv keep ls 887cd41e9c613463eab2f0d885c6dd96+83
alice.txt
bob.txt
carol.txt
Use @-s@ to print file sizes rounded up to the nearest kilobyte:
/scratch/you$ arv keep ls -s c1bad4b39ca5a924e481008009d94e32+210
221887 var-GS000016015-ASM.tsv.bz2
Use @arv keep get@ to download the contents of a collection and place it in the directory specified in the second argument (in this example, @.@ for the current directory):
/scratch/you$ arv keep get c1bad4b39ca5a924e481008009d94e32+210/ .
You can also download indvidual files:
/scratch/you$ arv keep get 887cd41e9c613463eab2f0d885c6dd96+83/alice.txt .
With a local copy of the file, we can do some computation, for example computing the md5 hash of the complete file:
/scratch/you$ md5sum var-GS000016015-ASM.tsv.bz2
44b8ae3fde7a8a88d2f7ebd237625b4f  var-GS000016015-ASM.tsv.bz2
h2. Using arv-mount Use @arv-mount@ to take advantage of the "File System in User Space / FUSE":http://fuse.sourceforge.net/ feature of the Linux kernel to mount a Keep collection as if it were a regular directory tree.
/scratch/you$ mkdir mnt
/scratch/you$ arv-mount --collection c1bad4b39ca5a924e481008009d94e32+210 mnt &
/scratch/you$ cd mnt
/scratch/you/mnt$ ls
var-GS000016015-ASM.tsv.bz2
/scratch/you/mnt$ md5sum var-GS000016015-ASM.tsv.bz2
44b8ae3fde7a8a88d2f7ebd237625b4f  var-GS000016015-ASM.tsv.bz2
/scratch/you/mnt$ cd ..
/scratch/you$ fusermount -u mnt
You can also mount the entire Keep namespace in "magic directory" mode:
/scratch/you$ mkdir mnt
/scratch/you$ arv-mount mnt &
/scratch/you$ cd mnt/c1bad4b39ca5a924e481008009d94e32+210
/scratch/you/mnt/c1bad4b39ca5a924e481008009d94e32+210$ ls
var-GS000016015-ASM.tsv.bz2
/scratch/you/mnt/c1bad4b39ca5a924e481008009d94e32+210$ md5sum var-GS000016015-ASM.tsv.bz2
44b8ae3fde7a8a88d2f7ebd237625b4f  var-GS000016015-ASM.tsv.bz2
/scratch/you/mnt/c1bad4b39ca5a924e481008009d94e32+210$ cd ../..
/scratch/you$ fusermount -u mnt
Using @arv-mount@ has several significant benefits: * You can browse, open and read Keep entries as if they are regular files. * It is easy for existing tools to access files in Keep. * Data is downloaded on demand, it is not necessary to download an entire file or collection to start processing
You are now ready to proceed to the next tutorial, "running a crunch job.":tutorial-job1.html