# Files, files everything is just a file!

GRR’s original design abstracted the data storage to a simple key/value store originally based around Bigtable. For open source deployments various key value stores were used starting from MongoDB, to SQLite and finally MySQL. Although the original idea was to use a simple key/value implementation, due to locking requirements the data store implementation became very complex.

As Velociraptor introduced a major redesign of the underlying data store architecture, we are now able to relax our demands of the datastore and use a true key/value model (since we have no requirements for locking and synchronization). The default data store is now the FileBasedDataStore which stores all data in flat files.

Using flat files over a database has many advantages, including ease of deployment, and simplification of the data model. Having flat files allows one to use standard tools to visualize Velociraptor’s data structures (e.g. with less), archive old data (e.g. with tar/zip) and clean up old data (e.g. with find/rm). Velociraptor also includes an inspect command which allows users to decode the stored files and provides context as to what these files actually mean. This simplicity increases the transparency in the system and makes it more accessible for deployers, while increasing reliability, stability and speed.

In the following section we examine some of the files in the datastore and see how they relate to the features we discuss elsewhere in this document.

## File organization

The Velociraptor data store needs to provide only two types of operations: Read and Write complete files and list files in a directory. Using only these primitives we can implement the entire filestore. Most modern file systems provide very fast file creation, reading and deletion, as well as fast directory listing, even when containing millions of files. Modern file systems also provide advanced features like caching, journaling and rollbacks so it is not such a crazy idea to use the file systems themselves as a data store.

Let’s begin by listing the files in a typical Velociraptor file store using the find command. We then use the velociraptor inspect command to view the file’s content.

### Searching

Searching for clients is implemented by simply creating empty files in directories based on the search term. For example in order to retrieve all clients which have the user “mic”, we simply list the directory “client_index/user%3Amic”:

 $find ./client_index/ ./client_index/c.84216c7aab97557d ./client_index/c.84216c7aab97557d/C.84216c7aab97557d.db./client_index/user%3Amic ./client_index/user%3Amic/C.84216c7aab97557d.db ./client_index/user%3Amic/C.1b0cddfffbfe40f5.db./client_index/all ./client_index/all/C.84216c7aab97557d.db ./client_index/all/C.1b0cddfffbfe40f5.db  Modern file systems can hold many thousands of files in the same directory and list these very quickly. This feature is only really used in the GUI’s search box but can also be used to script or post process collected data. ### Client information Information about each client is kept in a directory based on the client’s ID: ./C.0fc63b45671af1a6/ping.db <- Last ping stats. ./C.0fc63b45671af1a6/key.db <- Client's public key ./C.0fc63b45671af1a6/flows ./C.0fc63b45671af1a6/flows/F.a8787c26.db <- Flows running on this client. ./C.0fc63b45671af1a6/flows/F.e05952ff.db ./C.0fc63b45671af1a6/tasks ./C.0fc63b45671af1a6/tasks/1533517805834284.db <- Client messages waiting to be collected. ./C.0fc63b45671af1a6/tasks/1533517805834283.db ./C.0fc63b45671af1a6/tasks/1533517206859989.db ./C.0fc63b45671af1a6/tasks/1533517206860477.db ./C.84216c7aab97557d.db <- Client information (from Interrogate).  Each piece of data is kept in its own file as an encoded protobuf. Files all have their names end with “.db”. Velociraptor has an inspect command which decodes the protobuf and displays it in a human friendly way. For example let us see what information we keep about each s last poll: $ velociraptor --config server.yaml inspect /tmp/velociraptor/C.2d406f47d80f5583/ping.db
{
"ipAddress": "127.0.0.1:33600","ping": "1533517053018582"
}


### The Flow’s results.

Velociraptor’s flows typically only produce VQL results. As described above, the VQL results are typically split into parts by the client (by default 10000 rows per part), and Velociraptor simply writes these in the flow’s directory:

./C.1b0cddfffbfe40f5/flows/F.a31255a1
./C.1b0cddfffbfe40f5/flows/F.a31255a1/results
./C.1b0cddfffbfe40f5/flows/F.a31255a1/results/0.db   <- VQL result part 1.
./C.1b0cddfffbfe40f5/flows/F.a31255a1.db             <- Flow information.


Velociraptor’s inspect command understands that VQL collections represent a table of results, and so it displays these in a more friendly way.

$velociraptor --config server.yaml inspect /tmp/velociraptor/C.1b0cddfffbfe40f5/flows/F.a31255a1/results/0.db +-------+----------------+---------+------+-----------------------------+----------------------------+ | ISDIR | FULLPATH | SIZE | MODE | MTIME | ATIME | +-------+----------------+---------+------+-----------------------------+----------------------------+ | false | /bin/bash | 1037528 | 493 | 2017-05-16T22:49:55+10:00 | 2018-01-22T12:47:25+10:00 | | false | /bin/busybox | 1964536 | 493 | 2015-08-19T22:07:39+10:00 | 2018-01-23T15:41:46+10:00 | +-------+----------------+---------+------+-----------------------------+----------------------------+ File Finder Response: SELECT IsDir , FullPath , Size , Mode , mtime , atime , ctime, upload(file=FullPath)as Upload FROM files  We can also see the original VQL query which was run to produce this output. The bottom line, though, is that the entire flow’s result is just a flat JSON encoded file. You can easily decode the data using any programming language and post process it in whatever way is appropriate (e.g. export the results to BigQuery or ElasticSearch). Velociraptor does not really do anything with the result other than just store it on disk. ### The Virtual File System As described above, Velociraptor’s VFS consists of VQL tables for each directory on the client, listing the entire directory content: ./C.1b0cddfffbfe40f5/vfs/usr/share/doc/gir1.2-freedesktop.db ./C.1b0cddfffbfe40f5/vfs/usr/share/doc/libdatrie1.db ./C.1b0cddfffbfe40f5/vfs/usr/share/doc/dh-strip-nondeterminism.db ./C.1b0cddfffbfe40f5/vfs/usr/share/doc/libcap2-bin.db ./C.1b0cddfffbfe40f5/vfs/usr/share/doc/libsoup2.4-1.db ./C.1b0cddfffbfe40f5/vfs/usr/share/doc/libgphoto2-port12.db ./C.1b0cddfffbfe40f5/vfs/usr/share/doc/libsodium18.db  Inspecting each of these shows it is just a simple VQL table. This particular VFS entry was produced from a recursive directory listing of /usr (of depth 5). $ velociraptor --config server.yaml inspect .../vfs/usr/share/doc/libcap2-bin.db
+-------+--------------------------------+---------------------+------+-----------+--------------------
| ISDIR |            FULLPATH            |        NAME         | SIZE |   MODE    |           MTIME
+-------+--------------------------------+---------------------+------+-----------+--------------------
| false | /usr/share/doc/libcap2-bin/REA | README.Debian       | 1149 |       420 | 2015-10-02T23:34:07
|       | DME.Debian                     |                     |      |           |
| false | /usr/share/doc/libcap2-bin/cha | changelog.Debian.gz |   30 | 134218239 | 2015-10-24T07:11:34
|       | ngelog.Debian.gz               |                     |      |           |
| false | /usr/share/doc/libcap2-bin/cop | copyright           | 4367 |       420 | 2015-10-02T23:34:07
|       | yright                         |                     |      |           |
+-------+--------------------------------+---------------------+------+-----------+--------------------
/usr: SELECT IsDir, FullPath as _FullPath, Name, Size, Mode, timestamp(epoch=Sys.Mtim.Sec) as mtime,
timestamp(epoch=Sys.Atim.Sec) as ys.Ctim.Sec) as ctime FROM glob(globs=path + '/**5')