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SwiftStack Swift Benchmarking Suite

Project description

SwiftStack Benchmark Suite (ssbench) is a flexible and scalable benchmarking tool for the OpenStack Swift object storage system.

The ssbench-master run-scenario command will run benchmark “scenarios” against an OpenStack Swift cluster, utilizing one or more distributed ssbench-worker processes, saving statistics about the run to a file. The ssbench-master report-scenario command can then generate a report from the saved statstics. By default, ssbench-master run-scenario will generate a report to STDOUT immediately following a benchmark run in addition to saving the raw results to a file.

Coordination between the ssbench-master and one or more ssbench-worker processes is managed through a pair of PyZMQ sockets. This allows ssbench-master to distribute the benchmark run across many, many client servers while still coordinating the entire run (each worker can be given a job referencing an object created by a different worker).

Installation on Ubuntu

I apologize for this stupid dependency dance with Ubuntu (tested with 12.04 LTS Precise). With the –noop benchmark, gevent-zeromq is about 25% faster than pyzmq’s module, so I consider the annoying gevent-zeromq dependency worth it. The gevent-zeormq Cython build doesn’t work with Ubuntu 12.04’s Python’s distribute, and Cython has to be installed in a prior “pip” command to be recognized by gevent-zeromq’s

$ sudo apt-get install -y python-dev python-pip 'g++' libzmq-dev libevent-dev
$ sudo pip install --upgrade distribute
$ sudo pip install Cython gevent pyzmq==2.2.0
$ sudo pip install ssbench

Installation on CentOS 6.3

Installation on CentOS 6.3 using its stock Python 2.6:

$ sudo rpm -Uvh
$ sudo yum install -y gcc gcc-c++ python-setuptools python-devel libevent-devel python-pip zeromq3-devel
$ sudo pip-python install --upgrade argparse distribute Cython gevent pyzmq==2.2.0
$ sudo pip-python install gevent-zeromq
$ sudo pip-python install ssbench

Installation on OS X

On the Mac, I recommend installing Homebrew and using that to install Python 2.7 and libevent. I haven’t tested a fresh install in a while, but I had far less problems with Cython and gevent-zeormq on OS X, probably because the Homebrew Python was newer than Ubuntu 12.04’s?

Then you should be able to just pip install ssbench.

Gevent 1.0beta

I have not tested ssbench against gevent 1.0rc2, but according to an old gevent blog post, gevent v1.x will bundle libev and not require the installation of libevent or libev_. If you try ssbench with gevent 1.0rc2, please let me know if and how that works…


A “scenario” (sometimes called a “CRUD scenario”) is a utf8-encoded JSON file defining a benchmark run. Specifically, it defines:

  • A name for the scenario (an arbitrary string)
  • A sizes list of “object size” classes. Each object size class has a name, a size_min minimum object size, a size_max maximum object size (in bytes), and an optional crud_profile for just this size. If crud_profile is not given for a size, the top-level crud_profile will be used. The crud_profile here is just like the top-level one, an array of 4 numbers whose relative sizes determine the percent chance of a Create, Read, Update, or Delete operation. Objects created or updated within an object size class will have a size (in bytes) chosen at random uniformly between the minimum and maximum sizes.
  • An initial_files dictionary of initial file-counts per size class. Each size class can have zero or more objects uploaded prior to the benchmark run itself. The proportion of initial files also defines the probability distribution of object sizes during the benchmark run itself. So if a particular object size class is not included in initial_files or has a value of 0 in initial_files, then no objects in that size class will be used during the benchmark run. Each initial object’s name and container is deterministic and, as an optimization, if an object of the right name is in the right container, it will not be uploaded again; note that initial objects are not deleted after each benchmark run, so this can speed up subsequent runs quite a bit.
  • An operation_count of operations to perform during the benchmark run. An operation is either a CREATE, READ, UPDATE, or DELETE of an object. This value may be overridden for any given run with the -o COUNT flag to ssbench-master run-scenario.
  • A run_seconds number of seconds the benchmark scenario should run. This is mutually exclusive with operation_count, so only one of those two should be specified. Both values may be overridden with command-line arguments to ssbench-master.
  • A crud_profile which determines the distribution of each kind of operation. For instance, [3, 4, 2, 2] would mean 27% CREATE, 36% READ, 18% UPDATE, and 18% DELETE.
  • A user_count which determines the maxiumum client concurrency during the benchmark run. The user is responsible for ensuring there are enough workers running to support the scenario’s defined user_count. (Each ssbench-worker process uses gevent to achive very efficient concurrency for the benchmark client requests.) This value may be overridden for any given run with the -u COUNT flag to ssbench-master run-scenario.
  • A container_base which is a string used to construct the names of containers used by ssbench. It defaults to ssbench, resulting in container names like ssbench_000061.
  • A container_count which determines how many Swift containers are used for the benchmark run. This key is optional in the scenario file and defaults to 100. This value may be overridden for any given run with the -c COUNT flag to ssbench-master run-scenario.
  • A container_concurrency value which determines the level of client concurrency used by ssbench-master to create the benchmark containers. This value is optional and defaults to 10.

For each operation of the benchmark run, a size category is first chosen based on the relative counts for each size category in the initial_files dictionary. This probability for each size category appears under the “% Ops” column in the report. Then an operation type is chosen based on that size category’s CRUD profile (which can be individually specified or may be inherited from the “top level” CRUD profile).

If each size category has its own CRUD profile, then the overall CRUD profile of the benchmark run will be a weighted average between the values in the “% Ops” column and the CRUD profile of each size category. This weighted average CRUD profile is included in the report on the “CRUD weighted average” line.

ssbench comes with a few canned scenarios, but users are encouraged to experiment and define their own.

Here is an example JSON scenario file:

  "name": "Small test scenario",
  "sizes": [{
    "name": "tiny",
    "size_min": 4096,
    "size_max": 65536
  }, {
    "name": "small",
    "size_min": 100000,
    "size_max": 200000
  "initial_files": {
    "tiny": 100,
    "small": 10
  "operation_count": 500,
  "crud_profile": [3, 4, 2, 2],
  "user_count": 7

Beware: hand-editing JSON is error-prone. Watch out for trailing commas, in particular.


The ssbench-worker script’s usage message may be generated with:

$ ssbench-worker -h
usage: ssbench-worker [-h] [--zmq-host ZMQ_HOST]
                      [--zmq-work-port ZMQ_WORK_PORT]
                      [--zmq-results-port ZMQ_RESULTS_PORT] [-c CONCURRENCY]
                      [--retries RETRIES] [--batch-size COUNT] [-p COUNT] [-v]


The ssbench-master command requires one sub-command, which is currently either run-scenario to actually run a benchmark scenario, report-scenario to report on an existing scenario result data file, or kill-workers to tell connected ssbench-worker processes not started with --workers to kill themselves:

usage: ssbench-master [-h] [-v] [-q]


SwiftStack Benchmark (ssbench) version 0.2.20

positional arguments:
    kill-workers        Tell all workers to exit.
    run-scenario        Run CRUD scenario, saving statistics. You must supply
                        a valid set of v1.0 or v2.0 auth credentials. See
                        usage message for run-scenario for more details.
    report-scenario     Generate a report from saved scenario statistics.
                        Various types of reports may be generated, with the
                        default being a "textual summary".
    cleanup-containers  Recursively delete all ssbench containers and their

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         Enable more verbose output. (default: False)
  -q, --quiet           Suppress most output (including progress characters
                        during run). (default: False)

The run-scenario sub-command of ssbench-master actually runs a benchmark scenario:

$ ssbench-master run-scenario -h
usage: ssbench-master run-scenario [-h] -f SCENARIO_FILE
                                   [--zmq-bind-ip BIND_IP]
                                   [--zmq-work-port PORT]
                                   [--zmq-results_port PORT] [-V AUTH_VERSION]
                                   [-A AUTH_URL] [-U USER] [-K KEY]
                                   [--os-username <auth-user-name>]
                                   [--os-password <auth-password>]
                                   [--os-tenant-id <auth-tenant-id>]
                                   [--os-tenant-name <auth-tenant-name>]
                                   [--os-auth-url <auth-url>]
                                   [--os-auth-token <auth-token>]
                                   [--os-storage-url <storage-url>]
                                   [--os-region-name <region-name>]
                                   [--os-service-type <service-type>]
                                   [--os-endpoint-type <endpoint-type>]
                                   [--os-cacert <ca-certificate>] [--insecure]
                                   [-S STORAGE_URL] [-T TOKEN] [-c COUNT]
                                   [-u COUNT] [-o COUNT] [-r SECONDS]
                                   [-b BYTES] [--workers COUNT]
                                   [--batch-size COUNT] [--profile] [--noop]
                                   [-k] [--connect-timeout CONNECT_TIMEOUT]
                                   [--network-timeout NETWORK_TIMEOUT]
                                   [-s STATS_FILE] [-R] [--csv]
                                   [--pctile PERCENTILE]

The report-scenario sub-command of ssbench-master reports on a previously-run benchmark scenario:

$ ssbench-master report-scenario -h
usage: ssbench-master report-scenario [-h] -s STATS_FILE [-f REPORT_FILE]
                                      [--pctile PERCENTILE] [--csv]
                                      [-r RPS_HISTOGRAM] [--profile]

The kill-workers sub-command of ssbench-master kills all ssbench-worker processes which are pointed at the ssbench-master ZMQ sockets (this is useful for multi-server benchmark runs where the workers were not started with ssbench-master’s --workers option):

$ ssbench-master kill-workers -h
usage: ssbench-master kill-workers [-h] [--zmq-bind-ip BIND_IP]
                                   [--zmq-work-port PORT]
                                   [--zmq-results_port PORT]

The cleanup-containers sub-command of ssbench-master recursively deletes all ssbench-created containers and objects. It takes all the same authorization-related options as run-scenario:

$ ssbench-master cleanup-containers -h
usage: ssbench-master cleanup-containers [-h] [-b CONTAINER_BASE]
                                         [-c CONCURRENCY] [-V AUTH_VERSION]
                                         [-A AUTH_URL] [-U USER] [-K KEY]
                                         [--os-username <auth-user-name>]
                                         [--os-password <auth-password>]
                                         [--os-tenant-id <auth-tenant-id>]
                                         [--os-tenant-name <auth-tenant-name>]
                                         [--os-auth-url <auth-url>]
                                         [--os-auth-token <auth-token>]
                                         [--os-storage-url <storage-url>]
                                         [--os-region-name <region-name>]
                                         [--os-service-type <service-type>]
                                         [--os-endpoint-type <endpoint-type>]
                                         [--os-cacert <ca-certificate>]
                                         [--insecure] [-S STORAGE_URL]
                                         [-T TOKEN]


ssbench-master supports all the same authentication arguments, with similar semantics, as python-swiftclient’s command-line tool, swift.

For v1.0 authentication, you just need ST_AUTH, ST_USER, and ST_KEY defined in the environment or overridden/set on the command-line with -A, -U, and -K, respectively.

For v2.0 authentication (Keystone), it’s more complicated and you should refer to Keystone and/or python-swiftclient documentation for more help.

Regardless of which version of authentication is used, you may specify -S <storage_url> on the command-line to override the Storage URL returned from the authentication system.

Load Balancing

You can bypass your normal load-balancing scheme by telling ssbench-master to distribute load across a specified set of Storage URLs. This is done by specifiying one or more -S STORAGE_URL options to ssbench-master. Any storage URL returned from the auth server will be ignored and a randomly chosen command-line-specified storage URL will be used instead.

Note that each ssbench-worker process will create a fully-populated connection pool for each unique -S argument specified. Each connection pool will contain a number of sockets equal to the -c option (which defaults to 64). So a large number of unique -S arguments for ssbench-worker and a large -c value for ssbench-worker processes will not mix well.

Example Multi-Server Run

Start one or more ssbench-worker processes on each server (each ssbench-worker process defaults to a maximum gevent-based concurrency of 64, but the -c option can override that default). Use the --zmq-host command-line parameter to specify the host on which you will run ssbench-master.:

bench-host-01$ ssbench-worker -c 1000 --zmq-host bench-host-01 1 &
bench-host-01$ ssbench-worker -c 1000 --zmq-host bench-host-01 2 &

bench-host-02$ ssbench-worker -c 1000 --zmq-host bench-host-01 3 &
bench-host-02$ ssbench-worker -c 1000 --zmq-host bench-host-01 4 &

Finally, run one ssbench-master process which will manage and coordinate the multi-server benchmark run:

bench-host-01$ ssbench-master run-scenario -f scenarios/very_small.scenario -u 2000 -o 40000

The above example would involve a total client concurrency of 2000, spread evenly among the four workers on two hosts (bench-host-01 and bench-host-02). The four workers, as started in the above example, could support a maximum total client concurrency (-u option to ssbench-master) up to 4000.

Example Simple Single-Server Run

If you only need workers running on the local host, you can do so with a single command. Simply use the --workers COUNT option to ssbench-master:

$ ssbench-master run-scenario -f scenarios/very_small.scenario -u 4 -c 80 -o 613 --pctile 50 --workers 2
INFO:SwiftStack Benchmark (ssbench version 0.2.14)
INFO:Spawning local ssbench-worker (logging to /tmp/ssbench-worker-local-0.log) with ssbench-worker ... --concurrency 2 --batch-size 1 0
INFO:Spawning local ssbench-worker (logging to /tmp/ssbench-worker-local-1.log) with ssbench-worker ... --concurrency 2 --batch-size 1 1
INFO:Starting scenario run for "Small test scenario"
INFO:Ensuring 80 containers (ssbench_*) exist; concurrency=10...
INFO:Initializing cluster with stock data (up to 4 concurrent workers)
INFO:Starting benchmark run (up to 4 concurrent workers)
Benchmark Run:
  X    work job raised an exception
  .  <  1s first-byte-latency
  o  <  3s first-byte-latency
  O  < 10s first-byte-latency
  * >= 10s first-byte-latency
  _  <  1s last-byte-latency  (CREATE or UPDATE)
  |  <  3s last-byte-latency  (CREATE or UPDATE)
  ^  < 10s last-byte-latency  (CREATE or UPDATE)
  @ >= 10s last-byte-latency  (CREATE or UPDATE)
INFO:Deleting population objects from cluster
INFO:Calculating statistics...

Small test scenario  (generated with ssbench version 0.2.14)
Worker count:   2   Concurrency:   4  Ran 2013-06-07 17:23:16 UTC to 2013-06-07 17:23:22 UTC (5s)

% Ops    C   R   U   D       Size Range       Size Name
 91%   % 10  75  15   0        4 kB -   8 kB  tiny
  9%   % 10  75  15   0       20 kB -  40 kB  small
         10  75  15   0      CRUD weighted average

       Count:   613  (   0 error;    0 retries:  0.00%)  Average requests per second: 118.7
                            min       max      avg      std_dev  50%-ile                   Worst latency TX ID
       First-byte latency:  0.004 -   0.044    0.017  (  0.008)    0.016  (all obj sizes)  txe026893bbf09486c83fcdb629f6f25a3
       Last-byte  latency:  0.004 -   0.157    0.029  (  0.024)    0.019  (all obj sizes)  tx6f988120ec5044329f817-0051b21708
       First-byte latency:  0.004 -   0.044    0.016  (  0.007)    0.016  (    tiny objs)  tx1d35c8e273bf4bbeb6298-0051b21705
       Last-byte  latency:  0.004 -   0.157    0.028  (  0.024)    0.019  (    tiny objs)  tx6f988120ec5044329f817-0051b21708
       First-byte latency:  0.005 -   0.044    0.018  (  0.008)    0.016  (   small objs)  txe026893bbf09486c83fcdb629f6f25a3
       Last-byte  latency:  0.005 -   0.120    0.031  (  0.026)    0.021  (   small objs)  tx87bf30db5a70412b97a5c71ae60036c1

       Count:    64  (   0 error;    0 retries:  0.00%)  Average requests per second: 12.5
                            min       max      avg      std_dev  50%-ile                   Worst latency TX ID
       First-byte latency:  N/A   -   N/A      N/A    (  N/A  )    N/A    (all obj sizes)
       Last-byte  latency:  0.024 -   0.157    0.067  (  0.023)    0.060  (all obj sizes)  tx6f988120ec5044329f817-0051b21708
       First-byte latency:  N/A   -   N/A      N/A    (  N/A  )    N/A    (    tiny objs)
       Last-byte  latency:  0.024 -   0.157    0.064  (  0.022)    0.059  (    tiny objs)  tx6f988120ec5044329f817-0051b21708
       First-byte latency:  N/A   -   N/A      N/A    (  N/A  )    N/A    (   small objs)
       Last-byte  latency:  0.061 -   0.120    0.087  (  0.020)    0.089  (   small objs)  tx87bf30db5a70412b97a5c71ae60036c1

       Count:   459  (   0 error;    0 retries:  0.00%)  Average requests per second: 88.9
                            min       max      avg      std_dev  50%-ile                   Worst latency TX ID
       First-byte latency:  0.004 -   0.044    0.017  (  0.008)    0.016  (all obj sizes)  txe026893bbf09486c83fcdb629f6f25a3
       Last-byte  latency:  0.004 -   0.044    0.017  (  0.008)    0.016  (all obj sizes)  txe026893bbf09486c83fcdb629f6f25a3
       First-byte latency:  0.004 -   0.044    0.016  (  0.007)    0.016  (    tiny objs)  tx1d35c8e273bf4bbeb6298-0051b21705
       Last-byte  latency:  0.004 -   0.044    0.017  (  0.007)    0.016  (    tiny objs)  tx1d35c8e273bf4bbeb6298-0051b21705
       First-byte latency:  0.005 -   0.044    0.018  (  0.008)    0.016  (   small objs)  txe026893bbf09486c83fcdb629f6f25a3
       Last-byte  latency:  0.005 -   0.044    0.019  (  0.008)    0.017  (   small objs)  txe026893bbf09486c83fcdb629f6f25a3

       Count:    90  (   0 error;    0 retries:  0.00%)  Average requests per second: 18.1
                            min       max      avg      std_dev  50%-ile                   Worst latency TX ID
       First-byte latency:  N/A   -   N/A      N/A    (  N/A  )    N/A    (all obj sizes)
       Last-byte  latency:  0.021 -   0.143    0.062  (  0.021)    0.061  (all obj sizes)  tx9a502107a0c246e69a987d120a2b9919
       First-byte latency:  N/A   -   N/A      N/A    (  N/A  )    N/A    (    tiny objs)
       Last-byte  latency:  0.021 -   0.143    0.062  (  0.022)    0.061  (    tiny objs)  tx9a502107a0c246e69a987d120a2b9919
       First-byte latency:  N/A   -   N/A      N/A    (  N/A  )    N/A    (   small objs)
       Last-byte  latency:  0.036 -   0.085    0.065  (  0.015)    0.065  (   small objs)  tx732aae54c9484689b8fea-0051b21709

INFO:Scenario run results saved to /tmp/ssbench-results/Small_test_scenario.u4.o613.r-.2013-06-07.102314.stat.gz
INFO:You may generate a report with:
  .../ssbench-master report-scenario -s /tmp/ssbench-results/Small_test_scenario.u4.o613.r-.2013-06-07.102314.stat.gz

Benchmark Reports

The default, textual table report may be seen in the above example output. You can also specify --csv when running a scenario or generating a report later to generate a CSV report instead. This feature is still pretty new so expect the CSV report output to change over time.

Right now, the default report’s CSV version is two lines: a line of column header names and one line of actual data. Both lines are very long and the set of columns present in any given CSV report will depend on the scenario which was run. Some column names have the --pctile value in them and many columns have the object sizes in them, which are defined in the scenario file. You can think of the two CVS lines as a linear denormalization of the contents of the two-dimensional table output.

Scalability and Throughput

Assuming the Swift cluster being benchmarked is not the bottleneck, the scalability of ssbench may be increased by

  • Running up to one ssbench-worker process per CPU core on any number of benchmarking servers.
  • Increasing the default --batch-size parameter (defaults to 1) on both the ssbench-master and ssbench-worker command-lines. Note that if you are running everything on one server and using the --workers argument to ssbench-master, the --batch-size parameter passed to ssbench-master will be passed on to the automatically-started ssbench-worker processes.
  • For optimal scalability, the user-count (concurrency) should be greater than and also an even multiple of both the batch-size and number of ssbench-worker processes.

As a simple example, on my quad-core MacBook Pro, I get around 9,800 requests per second with --noop (see below) with this command-line (a --batch-size of 1):

$ ssbench-master run-scenario ... -u 24 -o 30000 --workers 3 --noop

But with a --batch-size of 8, I can get around 19,500 requests per second:

$ ssbench-master run-scenario ... -u 24 -o 30000 --workers 3 --noop --batch-size 8


When running ssbench-worker on a Mac, using HTTPS, I got a significant speed-up when setting OPENSSL_X509_TEA_DISABLE=1 in the environment of my ssbench-worker processes. I found this tip via a curl blog post after noticing a process named trustevaluationagent chewing up a lot of CPU during a benchmark run against a cluster using HTTPS.

The No-op Mode

To test the maximum throughput of the ssbench-master <==> ssbench-worker infrastructure, you can add --noop to a ssbench-master run-scenario command and the scenario will be “run” but the ssbench-worker processes will not actually talk to the Swift cluster.

In this manner, you may determine your maximum requests per second if talking to the Swift cluster were free.

The reported “Average requests per second:” value in the “TOTAL” section of the report should be higher than you expect to get out of the Swift cluster itself.

With an older version of ssbench which used a beanstalkd server to manage master/worker communication, my 2012 15” Retina Macbook Pro could get ~2,700 requests per second with --noop using a local beanstalkd, one ssbench-worker, and a user count (concurrency) of 4.

With ZeorMQ sockets (no beanstalkd involved), the same laptop can get between 7,000 and 8,000 requests per second with --noop.

Contributing to ssbench

First, please use the Github Issues for the project when submitting bug reports or feature requests.

Code submissions should be submitted as pull requests and all code should be PEP8 (v. 1.4.2) compliant. Current unit test line coverage is not 100%, but code contributions should not lower the code coverage (so please include new tests or update existing ones as part of your change). Running tests will probably require Python 2.7 and a few additional modules like flexmock and nose.

Regarding test tools, I started out using flexmock, but plan to mostly add new tests using the mock library since that’s been included in the stdlib and the Python community seems to be converging on it. So please use mock instead of flexmock for new tests.

If contributing code which implements a feature or fixes a bug, please ensure a Github Issue exists prior to submitting the pull request and reference the Issue number in your commit message.

When submitting your first pull request, please also update AUTHORS to include yourself, maintaining alphabetical ordering by last name.

If any of the file(s) you change do not yet have a copyright line with your name, please add one at the bottom of the others, above the license text (but never remove any existing copyright lines). Your copyright line should look something like:

# Copyright (c) 2013 FirstName LastName

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