PerfKit Benchmarker is an open effort to define a canonical set of benchmarks to measure and compare cloud offerings. It's designed to operate via vendor provided command line tools. The benchmark default settings are not tuned for any particular platform or instance type. These settings are recommended for consistency across services. Only in the rare case where there is a common practice like setting the buffer pool size of a database do we change any settings.
This README is designed to give you the information you need to get running with the benchmarker and the basics of working with the code. The wiki contains more detailed information:
- FAQ
- Tech Talks
- Governing rules
- Community meeting decks and notes
- Design documents
- You are always welcome to open an issue, or to join us on #PerfKitBenchmarker on freenode to discuss issues you're having, pull requests, or anything else related to PerfKitBenchmarker
PerfKit Benchmarker provides wrappers and workload definitions around popular benchmark tools. We made it very simple to use and automate everything we can. It instantiates VMs on the Cloud provider of your choice, automatically installs benchmarks, and runs the workloads without user interaction.
Due to the level of automation you will not see prompts for software installed as part of a benchmark run. Therefore you must accept the license of each of the benchmarks individually, and take responsibility for using them before you use the PerfKit Benchmarker.
Moving forward, you will need to run PKB with the explicit flag --accept-licenses.
In its current release these are the benchmarks that are executed:
aerospike: Apache v2 for the client and GNU AGPL v3.0 for the serverbonnie++: GPL v2cassandra_ycsb: Apache v2cassandra_stress: Apache v2cloudsuite3.0: CloudSuite 3.0 licensecluster_boot: MIT Licensecoremark: EEMBCcopy_throughput: Apache v2fio: GPL v2gpu_pcie_bandwidth: NVIDIA Software Licence Agreementhadoop_terasort: Apache v2hpcc: Original BSD licensehpcg: BSD 3-clauseiperf: UIUC Licensememtier_benchmark: GPL v2mesh_network: HP licensemongodb: Deprecated. GNU AGPL v3.0mongodb_ycsb: GNU AGPL v3.0multichase: Apache v2netperf: HP licenseoldisim: Apache v2object_storage_service: Apache v2pgbench: PostgreSQL Licenceping: No license needed.silo: MIT Licensescimark2: public domainspeccpu2006: SPEC CPU2006SHOC: BSD 3-clausesysbench_oltp: GPL v2TensorFlow: Apache v2tomcat: Apache v2unixbench: GPL v2wrk: Modified Apache v2ycsb(used bymongodb,hbase_ycsb, and others): Apache v2
Some of the benchmarks invoked require Java. You must also agree with the following license:
openjdk-7-jre: GPL v2 with the Classpath Exception
SPEC CPU2006 benchmark setup cannot be
automated. SPEC requires that users purchase a license and agree with their
terms and conditions. PerfKit Benchmarker users must manually download
cpu2006-1.2.iso from the SPEC website, save it under the
perfkitbenchmarker/data folder (e.g.
~/PerfKitBenchmarker/perfkitbenchmarker/data/cpu2006-1.2.iso), and also supply
a runspec cfg file (e.g.
~/PerfKitBenchmarker/perfkitbenchmarker/data/linux64-x64-gcc47.cfg).
Alternately, PerfKit Benchmarker can accept a tar file that can be generated
with the following steps:
- Extract the contents of
cpu2006-1.2.isointo a directory namedcpu2006 - Run
cpu2006/install.sh - Copy the cfg file into
cpu2006/config - Create a tar file containing the
cpu2006directory, and place it under theperfkitbenchmarker/datafolder (e.g.~/PerfKitBenchmarker/perfkitbenchmarker/data/cpu2006v1.2.tgz).
PerfKit Benchmarker will use the tar file if it is present. Otherwise, it will search for the iso and cfg files.
To quickly get started running PKB, follow one of our tutorials:
- Beginner tutorial for an in-depth but beginner friendly look at PKB's architectures, flags, and even data visualization, using GCP's Cloud Shell & netperf benchmarks.
- Docker tutorial to run PKB in just a few steps, using GCP & docker.
- Continue reading below for installation & setup on all Clouds + discussion of many topics like flags, configurations, preprovisioned data, & how to make contributions.
Before you can run the PerfKit Benchmarker, you need account(s) on the cloud provider(s) you want to benchmark (see providers). You also need the software dependencies, which are mostly command line tools and credentials to access your accounts without a password. The following steps should help you get up and running with PKB.
The recommended way to install and run PKB is in a virtualenv with the latest
version of Python 3 (at least Python 3.9). Most Linux distributions and recent
Mac OS X versions already have Python 3 installed at /usr/bin/python3.
If Python is not installed, you can likely install it using your distribution's package manager, or see the Python Download page.
python3 -m venv $HOME/my_virtualenv
source $HOME/my_virtualenv/bin/activateDownload the latest PerfKit Benchmarker release from GitHub. You can also clone the working version with:
$ cd $HOME
$ git clone https://github.com/GoogleCloudPlatform/PerfKitBenchmarker.gitInstall Python library dependencies:
$ pip3 install -r $HOME/PerfKitBenchmarker/requirements.txtYou may need to install additional dependencies depending on the cloud provider you are using. For example, for AWS:
$ cd $HOME/PerfKitBenchmarker/perfkitbenchmarker/providers/aws
$ pip3 install -r requirements.txtSome benchmarks may require data to be preprovisioned in a cloud. To preprovision data, you will need to obtain the data and then upload it to that cloud. See more information below about which benchmarks require preprovisioned data and how to upload it to different clouds.
PerfKit Benchmarker can run benchmarks both on Cloud Providers (GCP, AWS, Azure, DigitalOcean) as well as any "machine" you can SSH into.
$ ./pkb.py --project=<GCP project ID> --benchmarks=iperf --machine_type=f1-micro$ cd PerfKitBenchmarker
$ ./pkb.py --cloud=AWS --benchmarks=iperf --machine_type=t2.micro$ ./pkb.py --cloud=Azure --machine_type=Standard_A0 --benchmarks=iperf$ ./pkb.py --cloud=IBMCloud --machine_type=cx2-4x8 --benchmarks=iperf$ ./pkb.py --cloud=AliCloud --machine_type=ecs.s2.large --benchmarks=iperf$ ./pkb.py --cloud=DigitalOcean --machine_type=16gb --benchmarks=iperf$ ./pkb.py --cloud=OpenStack --machine_type=m1.medium \
--openstack_network=private --benchmarks=iperf$ ./pkb.py --cloud=Kubernetes --benchmarks=iperf --kubectl=/path/to/kubectl --kubeconfig=/path/to/kubeconfig --image=image-with-ssh-server --ceph_monitors=10.20.30.40:6789,10.20.30.41:6789$ ./pkb.py --cloud=Mesos --benchmarks=iperf --marathon_address=localhost:8080 --image=image-with-ssh-server./pkb.py --cloud=CloudStack --benchmarks=ping --cs_network_offering=DefaultNetworkOffering$ ./pkb.py --cloud=Rackspace --machine_type=general1-2 --benchmarks=iperf$ ./pkb.py --cloud=ProfitBricks --machine_type=Small --benchmarks=iperfInstall all dependencies as above and ensure that smbclient is installed on your system if you are running on a linux controller:
$ which smbclient
/usr/bin/smbclientNow you can run Windows benchmarks by running with --os_type=windows. Windows
has a different set of benchmarks than Linux does. They can be found under
perfkitbenchmarker/windows_benchmarks/.
The target VM OS is Windows Server 2012 R2.
Juju is a service orchestration tool that enables you
to quickly model, configure, deploy and manage entire cloud environments.
Supported benchmarks will deploy a Juju-modeled service automatically, with no
extra user configuration required, by specifying the --os_type=juju flag.
$ ./pkb.py --cloud=AWS --os_type=juju --benchmarks=cassandra_stressBenchmark/Package authors need to implement the JujuInstall() method inside
their package. This method deploys, configures, and relates the services to be
benchmarked. Please note that other software installation and configuration
should be bypassed when FLAGS.os_type == JUJU. See
perfkitbenchmarker/linux_packages/cassandra.py
for an example implementation.
Run with --benchmarks="standard_set" and every benchmark in the standard set
will run serially which can take a couple of hours. Additionally, if you don't
specify --cloud=..., all benchmarks will run on the Google Cloud Platform.
Named sets are are groupings of one or more benchmarks in the benchmarking
directory. This feature allows parallel innovation of what is important to
measure in the Cloud, and is defined by the set owner. For example the GoogleSet
is maintained by Google, whereas the StanfordSet is managed by Stanford. Once a
quarter a meeting is held to review all the sets to determine what benchmarks
should be promoted to the standard_set. The Standard Set is also reviewed to
see if anything should be removed. To run all benchmarks in a named set, specify
the set name in the benchmarks parameter (e.g., --benchmarks="standard_set").
Sets can be combined with individual benchmarks or other named sets.
The following are some common flags used when configuring PerfKit Benchmarker.
| Flag | Notes |
|---|---|
--helpmatch=pkb |
see all global flags |
--helpmatch=hpcc |
see all flags associated with the hpcc benchmark. You |
| : : can substitute any benchmark name to see the : | |
| : : associated flags. : | |
--benchmarks |
A comma separated list of benchmarks or benchmark |
: : sets to run such as --benchmarks=iperf,ping . To : |
|
| : : see the full list, run `./pkb.py : | |
| : : --helpmatch=benchmarks | grep perfkitbenchmarker` : |
--cloud |
Cloud where the benchmarks are run. See the table |
| : : below for choices. : | |
--machine_type |
Type of machine to provision if pre-provisioned |
| : : machines are not used. Most cloud providers accept : | |
| : : the names of pre-defined provider-specific machine : | |
| : : types (for example, GCP supports : | |
: : --machine_type=n1-standard-8 for a GCE : |
|
| : : n1-standard-8 VM). Some cloud providers support YAML : | |
| : : expressions that match the corresponding VM spec : | |
| : : machine_type property in the [YAML : | |
| : : configs](#configurations-and-configuration-overrides) : | |
| : : (for example, GCP supports `--machine_type="{cpus: : | |
| : : 1, memory: 4.5GiB}"` for a GCE custom VM with 1 vCPU : | |
| : : and 4.5GiB memory). Note that the value provided by : | |
| : : this flag will affect all provisioned machines; users : | |
| : : who wish to provision different machine types for : | |
| : : different roles within a single benchmark run should : | |
| : : use the [YAML : | |
| : : configs](#configurations-and-configuration-overrides) : | |
| : : for finer control. : | |
--zones |
This flag allows you to override the default zone. |
| : : See the table below. : | |
--data_disk_type |
Type of disk to use. Names are provider-specific, but |
| : : see table below. : |
The default cloud is 'GCP', override with the --cloud flag. Each cloud has a
default zone which you can override with the --zones flag, the flag supports
the same values that the corresponding Cloud CLIs take:
| Cloud name | Default zone | Notes |
|---|---|---|
| GCP | us-central1-a | |
| AWS | us-east-1a | |
| Azure | eastus2 | |
| IBMCloud | us-south-1 | |
| AliCloud | West US | |
| DigitalOcean | sfo1 | You must use a zone that supports the |
| : : : features 'metadata' (for cloud config) and : | ||
| : : : 'private_networking'. : | ||
| OpenStack | nova | |
| CloudStack | QC-1 | |
| Rackspace | IAD | OnMetal machine-types are available only in |
| : : : IAD zone : | ||
| Kubernetes | k8s | |
| ProfitBricks | AUTO | Additional zones: ZONE_1, ZONE_2, or ZONE_3 |
Example:
./pkb.py --cloud=GCP --zones=us-central1-a --benchmarks=iperf,pingThe disk type names vary by provider, but the following table summarizes some useful ones. (Many cloud providers have more disk types beyond these options.)
| Cloud name | Network-attached SSD | Network-attached HDD |
|---|---|---|
| GCP | pd-ssd | pd-standard |
| AWS | gp3 | st1 |
| Azure | Premium_LRS | Standard_LRS |
| Rackspace | cbs-ssd | cbs-sata |
Also note that --data_disk_type=local tells PKB not to allocate a separate
disk, but to use whatever comes with the VM. This is useful with AWS instance
types that come with local SSDs, or with the GCP --gce_num_local_ssds flag.
If an instance type comes with more than one disk, PKB uses whichever does not
hold the root partition. Specifically, on Azure, PKB always uses /dev/sdb as
its scratch device.
If the VM guests do not have direct Internet access in the cloud environment,
you can configure proxy settings through pkb.py flags.
To do that simple setup three flags (All urls are in notation ): The flag values
use the same <protocol>://<server>:<port> syntax as the corresponding
environment variables, for example --http_proxy=http://proxy.example.com:8080
.
| Flag | Notes |
|---|---|
--http_proxy |
Needed for package manager on Guest OS and for some |
| : : Perfkit packages : | |
--https_proxy |
Needed for package manager or Ubuntu guest and for from |
| : : github downloaded packages : | |
--ftp_proxy |
Needed for some Perfkit packages |
As mentioned above, some benchmarks require preprovisioned data. This section describes how to preprovision this data.
This benchmark demonstrates the use of preprovisioned data. Create the following file to upload using the command line:
echo "1234567890" > preprovisioned_data.txtTo upload, follow the instructions below with a filename of
preprovisioned_data.txt and a benchmark name of sample.
To preprovision data on Google Cloud, you will need to upload each file to Google Cloud Storage using gsutil. First, you will need to create a storage bucket that is accessible from VMs created in Google Cloud by PKB. Then copy each file to this bucket using the command
gsutil cp <filename> gs://<bucket>/<benchmark-name>/<filename>To run a benchmark on Google Cloud that uses the preprovisioned data, use the
flag --gcp_preprovisioned_data_bucket=<bucket>.
To preprovision data on AWS, you will need to upload each file to S3 using the AWS CLI. First, you will need to create a storage bucket that is accessible from VMs created in AWS by PKB. Then copy each file to this bucket using the command
aws s3 cp <filename> s3://<bucket>/<benchmark-name>/<filename>To run a benchmark on AWS that uses the preprovisioned data, use the flag
--aws_preprovisioned_data_bucket=<bucket>.
Each benchmark now has an independent configuration which is written in YAML. Users may override this default configuration by providing a configuration. This allows for much more complex setups than previously possible, including running benchmarks across clouds.
A benchmark configuration has a somewhat simple structure. It is essentially
just a series of nested dictionaries. At the top level, it contains VM groups.
VM groups are logical groups of homogenous machines. The VM groups hold both a
vm_spec and a disk_spec which contain the parameters needed to create
members of that group. Here is an example of an expanded configuration:
hbase_ycsb:
vm_groups:
loaders:
vm_count: 4
vm_spec:
GCP:
machine_type: n1-standard-1
image: ubuntu-16-04
zone: us-central1-c
AWS:
machine_type: m3.medium
image: ami-######
zone: us-east-1a
# Other clouds here...
# This specifies the cloud to use for the group. This allows for
# benchmark configurations that span clouds.
cloud: AWS
# No disk_spec here since these are loaders.
master:
vm_count: 1
cloud: GCP
vm_spec:
GCP:
machine_type:
cpus: 2
memory: 10.0GiB
image: ubuntu-16-04
zone: us-central1-c
# Other clouds here...
disk_count: 1
disk_spec:
GCP:
disk_size: 100
disk_type: standard
mount_point: /scratch
# Other clouds here...
workers:
vm_count: 4
cloud: GCP
vm_spec:
GCP:
machine_type: n1-standard-4
image: ubuntu-16-04
zone: us-central1-c
# Other clouds here...
disk_count: 1
disk_spec:
GCP:
disk_size: 500
disk_type: remote_ssd
mount_point: /scratch
# Other clouds here...For a complete list of keys for vm_specs and disk_specs see
virtual_machine.BaseVmSpec
and
disk.BaseDiskSpec
and their derived classes.
User configs are applied on top of the existing default config and can be
specified in two ways. The first is by supplying a config file via the
--benchmark_config_file flag. The second is by specifying a single setting to
override via the --config_override flag.
A user config file only needs to specify the settings which it is intended to
override. For example if the only thing you want to do is change the number of
VMs for the cluster_boot benchmark, this config is sufficient:
cluster_boot:
vm_groups:
default:
vm_count: 100You can achieve the same effect by specifying the --config_override flag. The
value of the flag should be a path within the YAML (with keys delimited by
periods), an equals sign, and finally the new value:
--config_override=cluster_boot.vm_groups.default.vm_count=100See the section below for how to use static (i.e. pre-provisioned) machines in your config.
It is possible to run PerfKit Benchmarker without running the Cloud provisioning steps. This is useful if you want to run on a local machine, or have a benchmark like iperf run from an external point to a Cloud VM.
In order to do this you need to make sure:
- The static (i.e. not provisioned by PerfKit Benchmarker) machine is ssh'able
- The user PerfKitBenchmarker will login as has 'sudo' access. (*** Note we hope to remove this restriction soon ***)
Next, you will want to create a YAML user config file describing how to connect to the machine as follows:
static_vms:
- &vm1 # Using the & character creates an anchor that we can
# reference later by using the same name and a * character.
ip_address: 170.200.60.23
user_name: voellm
ssh_private_key: /home/voellm/perfkitkeys/my_key_file.pem
zone: Siberia
disk_specs:
- mount_point: /data_dir- The
ip_addressis the address where you want benchmarks to run. ssh_private_keyis where to find the private ssh key.zonecan be anything you want. It is used when publishing results.disk_specsis used by all benchmarks which use disk (i.e.,fio,bonnie++, many others).
In the same file, configure any number of benchmarks (in this case just iperf), and reference the static VM as follows:
iperf:
vm_groups:
vm_1:
static_vms:
- *vm1I called my file iperf.yaml and used it to run iperf from Siberia to a GCP VM
in us-central1-f as follows:
$ ./pkb.py --benchmarks=iperf --machine_type=f1-micro --benchmark_config_file=iperf.yaml --zones=us-central1-f --ip_addresses=EXTERNALip_addresses=EXTERNALtells PerfKit Benchmarker not to use 10.X (ie Internal) machine addresses that all Cloud VMs have. Just use the external IP address.
If a benchmark requires two machines like iperf, you can have two machines in the same YAML file as shown below. This means you can indeed run between two machines and never provision any VMs in the Cloud.
static_vms:
- &vm1
ip_address: <ip1>
user_name: connormccoy
ssh_private_key: /home/connormccoy/.ssh/google_compute_engine
internal_ip: 10.240.223.37
install_packages: false
- &vm2
ip_address: <ip2>
user_name: connormccoy
ssh_private_key: /home/connormccoy/.ssh/google_compute_engine
internal_ip: 10.240.234.189
ssh_port: 2222
iperf:
vm_groups:
vm_1:
static_vms:
- *vm2
vm_2:
static_vms:
- *vm1You can now specify flags in configuration files by using the flags key at the
top level in a benchmark config. The expected value is a dictionary mapping flag
names to their new default values. The flags are only defaults; it's still
possible to override them via the command line. It's even possible to specify
conflicting values of the same flag in different benchmarks:
iperf:
flags:
machine_type: n1-standard-2
zone: us-central1-b
iperf_sending_thread_count: 2
netperf:
flags:
machine_type: n1-standard-8The new defaults will only apply to the benchmark in which they are specified.
PerfKit data can optionally be published to an Elasticsearch server. To enable
this, the elasticsearch Python package must be installed.
$ pip install elasticsearchNote: The elasticsearch Python library and Elasticsearch must have matching
major versions.
The following are flags used by the Elasticsearch publisher. At minimum, all
that is needed is the --es_uri flag.
| Flag | Notes |
|---|---|
--es_uri |
The Elasticsearch server address and port (e.g. |
| : : localhost:9200) : | |
--es_index |
The Elasticsearch index name to store documents (default: |
| : : perfkit) : | |
--es_type |
The Elasticsearch document type (default: result) |
Note: Amazon ElasticSearch service currently does not support transport on port
9200 therefore you must use endpoint with port 80 eg.
search-<ID>.es.amazonaws.com:80 and allow your IP address in the cluster.
No additional packages need to be installed in order to publish Perfkit data to an InfluxDB server.
InfluxDB Publisher takes in the flags for the Influx uri and the Influx DB name.
The publisher will default to the pre-set defaults, identified below, if no uri
or DB name is set. However, the user is required to at the very least call the
--influx_uri flag to publish data to Influx.
| Flag | Notes | Default |
|---|---|---|
--influx_uri |
The Influx DB address and port. | localhost:8086 |
| : : Expects the format hostname:port : : | ||
--influx_db_name |
The name of Influx DB database that | perfkit |
| : : you wish to publish to or create : : |
First start with the CONTRIBUTING.md file. It has the basics on how to work with PerfKitBenchmarker, and how to submit your pull requests.
In addition to the CONTRIBUTING.md file we have added a lot of comments into the code to make it easy to:
- Add new benchmarks (e.g.:
--benchmarks=<new benchmark>) - Add new package/os type support (e.g.:
--os_type=<new os type>) - Add new providers (e.g.:
--cloud=<new provider>) - etc.
Even with lots of comments we make to support more detailed documention. You will find the documentation we have on the wiki. Missing documentation you want? Start a page and/or open an issue to get it added.
If you wish to run unit or integration tests, ensure that you have tox >= 2.0.0 installed.
In addition to regular unit tests, which are run via
hooks/check-everything, PerfKit Benchmarker has
integration tests, which create actual cloud resources and take time and money
to run. For this reason, they will only run when the variable
PERFKIT_INTEGRATION is defined in the environment. The command
$ tox -e integrationwill run the integration tests. The integration tests depend on having installed and configured all of the relevant cloud provider SDKs, and will fail if you have not done so.
Many... please add new requests via GitHub issues.
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