Filebench - A Model Based File System Workload Generator
========================================================


INTRODUCTION
------------

Filebench is a file system and storage benchmark that can generate a large
variety of workloads. Unlike typical benchmarks it is extremely flexible and
allows to specify application's I/O behavior using its extensive Workload Model
Language (WML). Users can either describe desired workloads from scratch or use
(with or without modifications) workload personalities shipped with Filebench
(e.g., mail-, web-, file-, and database-server workloads). Filebench is equally
good for micro- and macro-benchmarking, quick to setup, and relatively easy to
use.


INSTALLATION
------------

Filebench compilation and installation is simple, and can be completed in two
steps. However, if you have downloaded a release tarball from Github
(https://github.com/filebench/filebench/releases), you can go ahead and skip to
Step 2.


Step 1: Generating autotool scripts

Makefile.in and configure files are not included in the repo, so they will have
to be generated. To do so, make sure you have libtoolize and automake tools
installed and run the following commands:

$ libtoolize
$ aclocal
$ autoheader
$ automake --add-missing
$ autoconf


Step 2: Compilation and installation

Before proceeding, make sure yacc and lex are available in your system. Then,
you can proceed with configuration, compilation, and installation as usual:

$ ./configure
$ make
$ sudo make install


QUICK-START GUIDE
-----------------

To quickly introduce a new user to Filebench we use two examples demonstrating
two different typical Filebench workflows: 1) describe a user-defined workload
in Workload Model Language (WML) and generate the workload; 2) generate one of
the predefined Filebench workloads. 
The description in this guide assumes Filebench 1.5-alpha1 or higher.


Example 1: User-defined workloads

First step is to create the description of the workload (so called workload
personality) in WML language. Typically, workload personalities are stored in
files with '.f' extension. In this example we describe a very simple workload
consisting of two processes with three threads each. Every thread continuously
picks a file among many, reads it, and then closes the file. Here is the
corresponding workload personality:

01  define fileset name="testF",entries=10000,filesize=16k,prealloc,path="/tmp"
02
03  define process name="readerP",instances=2 {
04    thread name="readerT",instances=3 {
05      flowop openfile name="openOP",filesetname="testF"
06      flowop readwholefile name="readOP",filesetname="testF"
07      flowop closefile name="closeOP"
08    }
09  }
10
11  run 60

Four main entities in Filebench are 'filesets', 'processes' consisting of
'threads', and 'flowops'. In the first line we define a fileset containing
10,000 files of 16KiB size each in /tmp directory. Filebench is instructed to
precreate all files in the fileset before executing the actual workload.

In the third and fourth lines we define two identical processes each consisting
of three identical threads. Every thread in Filebench repeats flowops (operations)
defined in it in a loop. Lines 05-07 describe the flowops that constitute the
threads: open a file in "testF" fileset, read the file  completely, and close
it. Finally, in the 11th line we indicate to run the workload for 60 seconds.

In more complex workloads one can define any number of filesets, multiple
different processes and threads, use a variety of flowops and attributes, and
more. Refer to the complete WML vocabulary for details at
https://github.com/filebench/filebench/wiki/Workload-model-language

Assuming that the personality is saved in 'readfiles.f' file, one can then
generate corresponding workload by running 'filebench -f readfiles.f' command.


Example 2: Pre-defined workloads

Filebench comes with several predefined micro- and macro-workloads (e.g.,
webserver, fileserver, mailserver) which are also described in WML, not
differently than the workload in Example 1 above. In the source code tree,
workloads are located in the workloads/ directory and are typically installed in
/usr/local/share/filebench/workloads/ during 'make install' (though this can
differ from one installation to another).

We do *not* recommend to directly use workload files from workloads/ or
/usr/local/share/filebench/workloads/ directories. The main reason is that these
workloads *are not properly sized* (e.g., in terms of the dataset sizes) to a
particular system. For instance, the initial dataset size of the webserver
workload is only slightly larger than 16MiB, which is typically not the size you
want to test the system containing multiple gigabytes of RAM with.

So, instead, copy webserver workload to any other directory:

$ cp /usr/local/share/filebench/workloads/webserver.f mywebserver.f

Then edit the copied file to increase the dataset size by setting the number of
files ('entries' attribute of a fileset) to an appropriate value. Finally, run
the workload:

$ filebench -f mywebserver.f

An extended discussion on how to scale Filebench workloads can be found at
https://github.com/filebench/filebench/wiki/Scaling-Filebench-workloads


SUPPORT
-------

To ask questions about Filebench, report bugs, or request new features, you can
use GitHub's issue tracking system. This is the central hub for both user support
and bug tracking, and can be found at https://github.com/filebench/filebench/issues