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|
%global _empty_manifest_terminate_build 0
Name: python-benchmark-runner
Version: 1.0.465
Release: 1
Summary: Benchmark Runner Tool
License: Apache License 2.0
URL: https://github.com/redhat-performance/benchmark-runner
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/1c/91/6d6f758ed16308882d80f9521995c1c14acdc89abf2c3dfabecbc69082c5/benchmark-runner-1.0.465.tar.gz
BuildArch: noarch
Requires: python3-attrs
Requires: python3-azure
Requires: python3-boto3
Requires: python3-botocore
Requires: python3-cryptography
Requires: python3-elasticsearch
Requires: python3-elasticsearch-dsl
Requires: python3-jinja2
Requires: python3-myst-parser
Requires: python3-openshift-client
Requires: python3-prometheus-api-client
Requires: python3-pandas
Requires: python3-paramiko
Requires: python3-PyGitHub
Requires: python3-PyYAML
Requires: python3-sphinx
Requires: python3-sphinx-rtd-theme
Requires: python3-tenacity
Requires: python3-typeguard
Requires: python3-typing
%description
# Benchmark-Runner: Running benchmarks
[](https://github.com/redhat-performance/benchmark-runner/actions)
[](https://pypi.org/project/benchmark-runner/)
[](https://quay.io/repository/ebattat/benchmark-runner?tab=tags)
[](https://coveralls.io/github/redhat-performance/benchmark-runner?branch=main&kill_cache=1)
[](https://benchmark-runner.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.org/project/benchmark-runner)
[](https://github.com/redhat-performance/benchmark-runner/blob/main/LICENSE)
## What is it?
**benchmark-runner** is a containerized Python lightweight and flexible framework for running benchmark workloads
on Kubernetes/OpenShift runtype kinds Pod, kata and VM.
This framework support the following embedded workloads:
* [hammerdb](https://hammerdb.com/): running hammerdb workload on the following databases: MSSQL, Mariadb, Postgresql in Pod, Kata or VM with [Configuration](benchmark_runner/common/template_operations/templates/hammerdb)
* [stressng](https://wiki.ubuntu.com/Kernel/Reference/stress-ng): running stressng workload in Pod, Kata or VM [Configuration](benchmark_runner/common/template_operations/templates/stressng)
* [uperf](http://uperf.org/): running uperf workload in Pod, Kata or VM with [Configuration](benchmark_runner/common/template_operations/templates/uperf)
* [vdbench](https://wiki.lustre.org/VDBench/): running vdbench workload in Pod, Kata or VM with [Configuration](benchmark_runner/common/template_operations/templates/vdbench)
* [bootstorm](https://en.wiktionary.org/wiki/boot_storm): calculate VMs boot load time [Configuration](benchmark_runner/common/template_operations/templates/bootstorm)
** For hammerdb mssql must run once [permission](https://github.com/redhat-performance/benchmark-runner/blob/main/benchmark_runner/common/ocp_resources/custom/template/02_mssql_patch_template.sh)
Benchmark-runner grafana dashboard example:
Reference:
* The benchmark-runner package is located in [PyPi](https://pypi.org/project/benchmark-runner)
* The benchmark-runner container image is located in [Quay.io](https://quay.io/repository/ebattat/benchmark-runner)
## Documentation
Documentation is available at [benchmark-runner.readthedocs.io](https://benchmark-runner.readthedocs.io/en/latest/)
_**Table of Contents**_
<!-- TOC -->
- [Benchmark-Runner](#benchmark-runner)
- [Documentation](#documentation)
- [Run workload using Podman or Docker](#run-workload-using-podman-or-docker)
- [Run workload in Pod using Kubernetes or OpenShift](#run-workload-in-pod-using-kubernetes-or-openshift)
- [Grafana dashboards](#grafana-dashboards)
- [Inspect Prometheus Metrics](#inspect-prometheus-metrics)
- [How to develop in benchmark-runner](#how-to-develop-in-benchmark-runner)
<!-- /TOC -->
## Run workload using Podman or Docker
The following options may be passed via command line flags or set in the environment:
**mandatory:** WORKLOAD=$WORKLOAD
**mandatory:** KUBEADMIN_PASSWORD=$KUBEADMIN_PASSWORD
**mandatory:** $KUBECONFIG [ kubeconfig file path]
Choose one from the following list:
`['stressng_pod', 'stressng_vm', 'stressng_kata', 'uperf_pod', 'uperf_vm', 'uperf_kata', 'hammerdb_pod_mariadb', 'hammerdb_vm_mariadb', 'hammerdb_kata_mariadb', 'hammerdb_pod_mariadb_lso', 'hammerdb_vm_mariadb_lso', 'hammerdb_kata_mariadb_lso', 'hammerdb_pod_postgres', 'hammerdb_vm_postgres', 'hammerdb_kata_postgres', 'hammerdb_pod_postgres_lso', 'hammerdb_vm_postgres_lso', 'hammerdb_kata_postgres_lso', 'hammerdb_pod_mssql', 'hammerdb_vm_mssql', 'hammerdb_kata_mssql', 'hammerdb_pod_mssql_lso', 'hammerdb_vm_mssql_lso', 'hammerdb_kata_mssql_lso', 'vdbench_pod', 'vdbench_kata', 'vdbench_vm', 'clusterbuster', 'bootstorm_vm']`
** clusterbuster workloads: cpusoaker, files, fio, uperf. for more details [see](https://github.com/RobertKrawitz/OpenShift4-tools)
**auto:** NAMESPACE=benchmark-operator [ The default namespace is benchmark-operator ]
**auto:** ODF_PVC=True [ True=ODF PVC storage, False=Ephemeral storage, default True ]
**auto:** EXTRACT_PROMETHEUS_SNAPSHOT=True [ True=extract Prometheus snapshot into artifacts, false=don't, default True ]
**auto:** SYSTEM_METRICS=False [ True=collect metric, False=not collect metrics, default False ]
**auto:** RUNNER_PATH=/tmp [ The default work space is /tmp ]
**optional:** PIN_NODE_BENCHMARK_OPERATOR=$PIN_NODE_BENCHMARK_OPERATOR [node selector for benchmark operator pod]
**optional:** PIN_NODE1=$PIN_NODE1 [node1 selector for running the workload]
**optional:** PIN_NODE2=$PIN_NODE2 [node2 selector for running the workload, i.e. uperf server and client, hammerdb database and workload]
**optional:** ELASTICSEARCH=$ELASTICSEARCH [ elasticsearch service name]
**optional:** ELASTICSEARCH_PORT=$ELASTICSEARCH_PORT
**optional:** CLUSTER=$CLUSTER [ set CLUSTER='kubernetes' to run workload on a kubernetes cluster, default 'openshift' ]
**optional:scale** SCALE=$SCALE [For Vdbench/Bootstorm: Scale in each node]
**optional:scale** SCALE_NODES=$SCALE_NODES [For Vdbench/Bootstorm: Scale's node]
**optional:scale** REDIS=$REDIS [For Vdbench only: redis for scale synchronization]
**optional:** LSO_DISK_ID=$LSO_DISK_ID [LSO_DISK_ID='scsi-<replace_this_with_your_actual_disk_id>' For using Local Storage Operator in hammerdb]
**optional:** WORKER_DISK_IDS=$WORKER_DISK_IDS [WORKER_DISK_IDS For ODF/LSO workloads hammerdb/vdbench]
For example:
```sh
podman run --rm --workload=$WORKLOAD --kubeadmin-password=$KUBEADMIN_PASSWORD --pin-node-benchmark-operator=$PIN_NODE_BENCHMARK_OPERATOR --pin-node1=$PIN_NODE1 --pin-node2=$PIN_NODE2 --elasticsearch=$ELASTICSEARCH --elasticsearch-port=$ELASTICSEARCH_PORT -v $KUBECONFIG:/root/.kube/config --privileged quay.io/ebattat/benchmark-runner:latest
```
or
```sh
podman run --rm -e WORKLOAD=$WORKLOAD -e KUBEADMIN_PASSWORD=$KUBEADMIN_PASSWORD -e PIN_NODE_BENCHMARK_OPERATOR=$PIN_NODE_BENCHMARK_OPERATOR -e PIN_NODE1=$PIN_NODE1 -e PIN_NODE2=$PIN_NODE2 -e ELASTICSEARCH=$ELASTICSEARCH -e ELASTICSEARCH_PORT=$ELASTICSEARCH_PORT -e log_level=INFO -v $KUBECONFIG:/root/.kube/config --privileged quay.io/ebattat/benchmark-runner:latest
```
SAVE RUN ARTIFACTS LOCAL:
1. add `-e SAVE_ARTIFACTS_LOCAL='True'` or `--save-artifacts-local=true`
2. add `-v /tmp:/tmp`
3. git clone -b v1.0.2 https://github.com/cloud-bulldozer/benchmark-operator /tmp/benchmark-operator
### Run vdbench workload in Pod using OpenShift
### Run vdbench workload in Pod using Kubernetes
## Run workload in Pod using Kubernetes or OpenShift
[TBD]
## Grafana dashboards
There are 2 grafana dashboards templates:
1. [grafana/func/benchmark-runner-ci-status-report.json](grafana/func/benchmark-runner-ci-status-report.json)
2. [grafana/func/benchmark-runner-report.json](grafana/func/benchmark-runner-report.json)
** After importing json in grafana, you need to configure elasticsearch data source. (for more details: see [HOW_TO.md](HOW_TO.md))
## Inspect Prometheus Metrics
The CI jobs store snapshots of the Prometheus database for each run as part of the artifacts. Within the artifact directory is a Prometheus snapshot directory named:
```
promdb-YYYY_MM_DDTHH_mm_ss+0000_YYYY_MM_DDTHH_mm_ss+0000.tar
```
The timestamps are for the start and end of the metrics capture; they
are stored in UTC time (`+0000`). It is possible to run containerized
Prometheus on it to inspect the metrics. *Note that Prometheus
requires write access to its database, so it will actually write to
the snapshot.* So for example if you have downloaded artifacts for a
run named `hammerdb-vm-mariadb-2022-01-04-08-21-23` and the Prometheus
snapshot within is named
`promdb_2022_01_04T08_21_52+0000_2022_01_04T08_45_47+0000`, you could run as follows:
```
$ local_prometheus_snapshot=/hammerdb-vm-mariadb-2022-01-04-08-21-23/promdb_2022_01_04T08_21_52+0000_2022_01_04T08_45_47+0000
$ chmod -R g-s,a+rw "$local_prometheus_snapshot"
$ sudo podman run --rm -p 9090:9090 -uroot -v "$local_prometheus_snapshot:/prometheus" --privileged prom/prometheus --config.file=/etc/prometheus/prometheus.yml --storage.tsdb.path=/prometheus --storage.tsdb.retention.time=100000d --storage.tsdb.retention.size=1000PB
```
and point your browser at port 9090 on your local system, you can run queries against it, e.g.
```
sum(irate(node_cpu_seconds_total[2m])) by (mode,instance) > 0
```
It is important to use the `--storage.tsdb.retention.time` option to
Prometheus, as otherwise Prometheus may discard the data in the
snapshot. And note that you must set the time bounds on the
Prometheus query to fit the start and end times as recorded in the
name of the promdb snapshot.
## How to develop in benchmark-runner
see [HOW_TO.md](HOW_TO.md)
%package -n python3-benchmark-runner
Summary: Benchmark Runner Tool
Provides: python-benchmark-runner
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-benchmark-runner
# Benchmark-Runner: Running benchmarks
[](https://github.com/redhat-performance/benchmark-runner/actions)
[](https://pypi.org/project/benchmark-runner/)
[](https://quay.io/repository/ebattat/benchmark-runner?tab=tags)
[](https://coveralls.io/github/redhat-performance/benchmark-runner?branch=main&kill_cache=1)
[](https://benchmark-runner.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.org/project/benchmark-runner)
[](https://github.com/redhat-performance/benchmark-runner/blob/main/LICENSE)
## What is it?
**benchmark-runner** is a containerized Python lightweight and flexible framework for running benchmark workloads
on Kubernetes/OpenShift runtype kinds Pod, kata and VM.
This framework support the following embedded workloads:
* [hammerdb](https://hammerdb.com/): running hammerdb workload on the following databases: MSSQL, Mariadb, Postgresql in Pod, Kata or VM with [Configuration](benchmark_runner/common/template_operations/templates/hammerdb)
* [stressng](https://wiki.ubuntu.com/Kernel/Reference/stress-ng): running stressng workload in Pod, Kata or VM [Configuration](benchmark_runner/common/template_operations/templates/stressng)
* [uperf](http://uperf.org/): running uperf workload in Pod, Kata or VM with [Configuration](benchmark_runner/common/template_operations/templates/uperf)
* [vdbench](https://wiki.lustre.org/VDBench/): running vdbench workload in Pod, Kata or VM with [Configuration](benchmark_runner/common/template_operations/templates/vdbench)
* [bootstorm](https://en.wiktionary.org/wiki/boot_storm): calculate VMs boot load time [Configuration](benchmark_runner/common/template_operations/templates/bootstorm)
** For hammerdb mssql must run once [permission](https://github.com/redhat-performance/benchmark-runner/blob/main/benchmark_runner/common/ocp_resources/custom/template/02_mssql_patch_template.sh)
Benchmark-runner grafana dashboard example:
Reference:
* The benchmark-runner package is located in [PyPi](https://pypi.org/project/benchmark-runner)
* The benchmark-runner container image is located in [Quay.io](https://quay.io/repository/ebattat/benchmark-runner)
## Documentation
Documentation is available at [benchmark-runner.readthedocs.io](https://benchmark-runner.readthedocs.io/en/latest/)
_**Table of Contents**_
<!-- TOC -->
- [Benchmark-Runner](#benchmark-runner)
- [Documentation](#documentation)
- [Run workload using Podman or Docker](#run-workload-using-podman-or-docker)
- [Run workload in Pod using Kubernetes or OpenShift](#run-workload-in-pod-using-kubernetes-or-openshift)
- [Grafana dashboards](#grafana-dashboards)
- [Inspect Prometheus Metrics](#inspect-prometheus-metrics)
- [How to develop in benchmark-runner](#how-to-develop-in-benchmark-runner)
<!-- /TOC -->
## Run workload using Podman or Docker
The following options may be passed via command line flags or set in the environment:
**mandatory:** WORKLOAD=$WORKLOAD
**mandatory:** KUBEADMIN_PASSWORD=$KUBEADMIN_PASSWORD
**mandatory:** $KUBECONFIG [ kubeconfig file path]
Choose one from the following list:
`['stressng_pod', 'stressng_vm', 'stressng_kata', 'uperf_pod', 'uperf_vm', 'uperf_kata', 'hammerdb_pod_mariadb', 'hammerdb_vm_mariadb', 'hammerdb_kata_mariadb', 'hammerdb_pod_mariadb_lso', 'hammerdb_vm_mariadb_lso', 'hammerdb_kata_mariadb_lso', 'hammerdb_pod_postgres', 'hammerdb_vm_postgres', 'hammerdb_kata_postgres', 'hammerdb_pod_postgres_lso', 'hammerdb_vm_postgres_lso', 'hammerdb_kata_postgres_lso', 'hammerdb_pod_mssql', 'hammerdb_vm_mssql', 'hammerdb_kata_mssql', 'hammerdb_pod_mssql_lso', 'hammerdb_vm_mssql_lso', 'hammerdb_kata_mssql_lso', 'vdbench_pod', 'vdbench_kata', 'vdbench_vm', 'clusterbuster', 'bootstorm_vm']`
** clusterbuster workloads: cpusoaker, files, fio, uperf. for more details [see](https://github.com/RobertKrawitz/OpenShift4-tools)
**auto:** NAMESPACE=benchmark-operator [ The default namespace is benchmark-operator ]
**auto:** ODF_PVC=True [ True=ODF PVC storage, False=Ephemeral storage, default True ]
**auto:** EXTRACT_PROMETHEUS_SNAPSHOT=True [ True=extract Prometheus snapshot into artifacts, false=don't, default True ]
**auto:** SYSTEM_METRICS=False [ True=collect metric, False=not collect metrics, default False ]
**auto:** RUNNER_PATH=/tmp [ The default work space is /tmp ]
**optional:** PIN_NODE_BENCHMARK_OPERATOR=$PIN_NODE_BENCHMARK_OPERATOR [node selector for benchmark operator pod]
**optional:** PIN_NODE1=$PIN_NODE1 [node1 selector for running the workload]
**optional:** PIN_NODE2=$PIN_NODE2 [node2 selector for running the workload, i.e. uperf server and client, hammerdb database and workload]
**optional:** ELASTICSEARCH=$ELASTICSEARCH [ elasticsearch service name]
**optional:** ELASTICSEARCH_PORT=$ELASTICSEARCH_PORT
**optional:** CLUSTER=$CLUSTER [ set CLUSTER='kubernetes' to run workload on a kubernetes cluster, default 'openshift' ]
**optional:scale** SCALE=$SCALE [For Vdbench/Bootstorm: Scale in each node]
**optional:scale** SCALE_NODES=$SCALE_NODES [For Vdbench/Bootstorm: Scale's node]
**optional:scale** REDIS=$REDIS [For Vdbench only: redis for scale synchronization]
**optional:** LSO_DISK_ID=$LSO_DISK_ID [LSO_DISK_ID='scsi-<replace_this_with_your_actual_disk_id>' For using Local Storage Operator in hammerdb]
**optional:** WORKER_DISK_IDS=$WORKER_DISK_IDS [WORKER_DISK_IDS For ODF/LSO workloads hammerdb/vdbench]
For example:
```sh
podman run --rm --workload=$WORKLOAD --kubeadmin-password=$KUBEADMIN_PASSWORD --pin-node-benchmark-operator=$PIN_NODE_BENCHMARK_OPERATOR --pin-node1=$PIN_NODE1 --pin-node2=$PIN_NODE2 --elasticsearch=$ELASTICSEARCH --elasticsearch-port=$ELASTICSEARCH_PORT -v $KUBECONFIG:/root/.kube/config --privileged quay.io/ebattat/benchmark-runner:latest
```
or
```sh
podman run --rm -e WORKLOAD=$WORKLOAD -e KUBEADMIN_PASSWORD=$KUBEADMIN_PASSWORD -e PIN_NODE_BENCHMARK_OPERATOR=$PIN_NODE_BENCHMARK_OPERATOR -e PIN_NODE1=$PIN_NODE1 -e PIN_NODE2=$PIN_NODE2 -e ELASTICSEARCH=$ELASTICSEARCH -e ELASTICSEARCH_PORT=$ELASTICSEARCH_PORT -e log_level=INFO -v $KUBECONFIG:/root/.kube/config --privileged quay.io/ebattat/benchmark-runner:latest
```
SAVE RUN ARTIFACTS LOCAL:
1. add `-e SAVE_ARTIFACTS_LOCAL='True'` or `--save-artifacts-local=true`
2. add `-v /tmp:/tmp`
3. git clone -b v1.0.2 https://github.com/cloud-bulldozer/benchmark-operator /tmp/benchmark-operator
### Run vdbench workload in Pod using OpenShift
### Run vdbench workload in Pod using Kubernetes
## Run workload in Pod using Kubernetes or OpenShift
[TBD]
## Grafana dashboards
There are 2 grafana dashboards templates:
1. [grafana/func/benchmark-runner-ci-status-report.json](grafana/func/benchmark-runner-ci-status-report.json)
2. [grafana/func/benchmark-runner-report.json](grafana/func/benchmark-runner-report.json)
** After importing json in grafana, you need to configure elasticsearch data source. (for more details: see [HOW_TO.md](HOW_TO.md))
## Inspect Prometheus Metrics
The CI jobs store snapshots of the Prometheus database for each run as part of the artifacts. Within the artifact directory is a Prometheus snapshot directory named:
```
promdb-YYYY_MM_DDTHH_mm_ss+0000_YYYY_MM_DDTHH_mm_ss+0000.tar
```
The timestamps are for the start and end of the metrics capture; they
are stored in UTC time (`+0000`). It is possible to run containerized
Prometheus on it to inspect the metrics. *Note that Prometheus
requires write access to its database, so it will actually write to
the snapshot.* So for example if you have downloaded artifacts for a
run named `hammerdb-vm-mariadb-2022-01-04-08-21-23` and the Prometheus
snapshot within is named
`promdb_2022_01_04T08_21_52+0000_2022_01_04T08_45_47+0000`, you could run as follows:
```
$ local_prometheus_snapshot=/hammerdb-vm-mariadb-2022-01-04-08-21-23/promdb_2022_01_04T08_21_52+0000_2022_01_04T08_45_47+0000
$ chmod -R g-s,a+rw "$local_prometheus_snapshot"
$ sudo podman run --rm -p 9090:9090 -uroot -v "$local_prometheus_snapshot:/prometheus" --privileged prom/prometheus --config.file=/etc/prometheus/prometheus.yml --storage.tsdb.path=/prometheus --storage.tsdb.retention.time=100000d --storage.tsdb.retention.size=1000PB
```
and point your browser at port 9090 on your local system, you can run queries against it, e.g.
```
sum(irate(node_cpu_seconds_total[2m])) by (mode,instance) > 0
```
It is important to use the `--storage.tsdb.retention.time` option to
Prometheus, as otherwise Prometheus may discard the data in the
snapshot. And note that you must set the time bounds on the
Prometheus query to fit the start and end times as recorded in the
name of the promdb snapshot.
## How to develop in benchmark-runner
see [HOW_TO.md](HOW_TO.md)
%package help
Summary: Development documents and examples for benchmark-runner
Provides: python3-benchmark-runner-doc
%description help
# Benchmark-Runner: Running benchmarks
[](https://github.com/redhat-performance/benchmark-runner/actions)
[](https://pypi.org/project/benchmark-runner/)
[](https://quay.io/repository/ebattat/benchmark-runner?tab=tags)
[](https://coveralls.io/github/redhat-performance/benchmark-runner?branch=main&kill_cache=1)
[](https://benchmark-runner.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.org/project/benchmark-runner)
[](https://github.com/redhat-performance/benchmark-runner/blob/main/LICENSE)
## What is it?
**benchmark-runner** is a containerized Python lightweight and flexible framework for running benchmark workloads
on Kubernetes/OpenShift runtype kinds Pod, kata and VM.
This framework support the following embedded workloads:
* [hammerdb](https://hammerdb.com/): running hammerdb workload on the following databases: MSSQL, Mariadb, Postgresql in Pod, Kata or VM with [Configuration](benchmark_runner/common/template_operations/templates/hammerdb)
* [stressng](https://wiki.ubuntu.com/Kernel/Reference/stress-ng): running stressng workload in Pod, Kata or VM [Configuration](benchmark_runner/common/template_operations/templates/stressng)
* [uperf](http://uperf.org/): running uperf workload in Pod, Kata or VM with [Configuration](benchmark_runner/common/template_operations/templates/uperf)
* [vdbench](https://wiki.lustre.org/VDBench/): running vdbench workload in Pod, Kata or VM with [Configuration](benchmark_runner/common/template_operations/templates/vdbench)
* [bootstorm](https://en.wiktionary.org/wiki/boot_storm): calculate VMs boot load time [Configuration](benchmark_runner/common/template_operations/templates/bootstorm)
** For hammerdb mssql must run once [permission](https://github.com/redhat-performance/benchmark-runner/blob/main/benchmark_runner/common/ocp_resources/custom/template/02_mssql_patch_template.sh)
Benchmark-runner grafana dashboard example:
Reference:
* The benchmark-runner package is located in [PyPi](https://pypi.org/project/benchmark-runner)
* The benchmark-runner container image is located in [Quay.io](https://quay.io/repository/ebattat/benchmark-runner)
## Documentation
Documentation is available at [benchmark-runner.readthedocs.io](https://benchmark-runner.readthedocs.io/en/latest/)
_**Table of Contents**_
<!-- TOC -->
- [Benchmark-Runner](#benchmark-runner)
- [Documentation](#documentation)
- [Run workload using Podman or Docker](#run-workload-using-podman-or-docker)
- [Run workload in Pod using Kubernetes or OpenShift](#run-workload-in-pod-using-kubernetes-or-openshift)
- [Grafana dashboards](#grafana-dashboards)
- [Inspect Prometheus Metrics](#inspect-prometheus-metrics)
- [How to develop in benchmark-runner](#how-to-develop-in-benchmark-runner)
<!-- /TOC -->
## Run workload using Podman or Docker
The following options may be passed via command line flags or set in the environment:
**mandatory:** WORKLOAD=$WORKLOAD
**mandatory:** KUBEADMIN_PASSWORD=$KUBEADMIN_PASSWORD
**mandatory:** $KUBECONFIG [ kubeconfig file path]
Choose one from the following list:
`['stressng_pod', 'stressng_vm', 'stressng_kata', 'uperf_pod', 'uperf_vm', 'uperf_kata', 'hammerdb_pod_mariadb', 'hammerdb_vm_mariadb', 'hammerdb_kata_mariadb', 'hammerdb_pod_mariadb_lso', 'hammerdb_vm_mariadb_lso', 'hammerdb_kata_mariadb_lso', 'hammerdb_pod_postgres', 'hammerdb_vm_postgres', 'hammerdb_kata_postgres', 'hammerdb_pod_postgres_lso', 'hammerdb_vm_postgres_lso', 'hammerdb_kata_postgres_lso', 'hammerdb_pod_mssql', 'hammerdb_vm_mssql', 'hammerdb_kata_mssql', 'hammerdb_pod_mssql_lso', 'hammerdb_vm_mssql_lso', 'hammerdb_kata_mssql_lso', 'vdbench_pod', 'vdbench_kata', 'vdbench_vm', 'clusterbuster', 'bootstorm_vm']`
** clusterbuster workloads: cpusoaker, files, fio, uperf. for more details [see](https://github.com/RobertKrawitz/OpenShift4-tools)
**auto:** NAMESPACE=benchmark-operator [ The default namespace is benchmark-operator ]
**auto:** ODF_PVC=True [ True=ODF PVC storage, False=Ephemeral storage, default True ]
**auto:** EXTRACT_PROMETHEUS_SNAPSHOT=True [ True=extract Prometheus snapshot into artifacts, false=don't, default True ]
**auto:** SYSTEM_METRICS=False [ True=collect metric, False=not collect metrics, default False ]
**auto:** RUNNER_PATH=/tmp [ The default work space is /tmp ]
**optional:** PIN_NODE_BENCHMARK_OPERATOR=$PIN_NODE_BENCHMARK_OPERATOR [node selector for benchmark operator pod]
**optional:** PIN_NODE1=$PIN_NODE1 [node1 selector for running the workload]
**optional:** PIN_NODE2=$PIN_NODE2 [node2 selector for running the workload, i.e. uperf server and client, hammerdb database and workload]
**optional:** ELASTICSEARCH=$ELASTICSEARCH [ elasticsearch service name]
**optional:** ELASTICSEARCH_PORT=$ELASTICSEARCH_PORT
**optional:** CLUSTER=$CLUSTER [ set CLUSTER='kubernetes' to run workload on a kubernetes cluster, default 'openshift' ]
**optional:scale** SCALE=$SCALE [For Vdbench/Bootstorm: Scale in each node]
**optional:scale** SCALE_NODES=$SCALE_NODES [For Vdbench/Bootstorm: Scale's node]
**optional:scale** REDIS=$REDIS [For Vdbench only: redis for scale synchronization]
**optional:** LSO_DISK_ID=$LSO_DISK_ID [LSO_DISK_ID='scsi-<replace_this_with_your_actual_disk_id>' For using Local Storage Operator in hammerdb]
**optional:** WORKER_DISK_IDS=$WORKER_DISK_IDS [WORKER_DISK_IDS For ODF/LSO workloads hammerdb/vdbench]
For example:
```sh
podman run --rm --workload=$WORKLOAD --kubeadmin-password=$KUBEADMIN_PASSWORD --pin-node-benchmark-operator=$PIN_NODE_BENCHMARK_OPERATOR --pin-node1=$PIN_NODE1 --pin-node2=$PIN_NODE2 --elasticsearch=$ELASTICSEARCH --elasticsearch-port=$ELASTICSEARCH_PORT -v $KUBECONFIG:/root/.kube/config --privileged quay.io/ebattat/benchmark-runner:latest
```
or
```sh
podman run --rm -e WORKLOAD=$WORKLOAD -e KUBEADMIN_PASSWORD=$KUBEADMIN_PASSWORD -e PIN_NODE_BENCHMARK_OPERATOR=$PIN_NODE_BENCHMARK_OPERATOR -e PIN_NODE1=$PIN_NODE1 -e PIN_NODE2=$PIN_NODE2 -e ELASTICSEARCH=$ELASTICSEARCH -e ELASTICSEARCH_PORT=$ELASTICSEARCH_PORT -e log_level=INFO -v $KUBECONFIG:/root/.kube/config --privileged quay.io/ebattat/benchmark-runner:latest
```
SAVE RUN ARTIFACTS LOCAL:
1. add `-e SAVE_ARTIFACTS_LOCAL='True'` or `--save-artifacts-local=true`
2. add `-v /tmp:/tmp`
3. git clone -b v1.0.2 https://github.com/cloud-bulldozer/benchmark-operator /tmp/benchmark-operator
### Run vdbench workload in Pod using OpenShift
### Run vdbench workload in Pod using Kubernetes
## Run workload in Pod using Kubernetes or OpenShift
[TBD]
## Grafana dashboards
There are 2 grafana dashboards templates:
1. [grafana/func/benchmark-runner-ci-status-report.json](grafana/func/benchmark-runner-ci-status-report.json)
2. [grafana/func/benchmark-runner-report.json](grafana/func/benchmark-runner-report.json)
** After importing json in grafana, you need to configure elasticsearch data source. (for more details: see [HOW_TO.md](HOW_TO.md))
## Inspect Prometheus Metrics
The CI jobs store snapshots of the Prometheus database for each run as part of the artifacts. Within the artifact directory is a Prometheus snapshot directory named:
```
promdb-YYYY_MM_DDTHH_mm_ss+0000_YYYY_MM_DDTHH_mm_ss+0000.tar
```
The timestamps are for the start and end of the metrics capture; they
are stored in UTC time (`+0000`). It is possible to run containerized
Prometheus on it to inspect the metrics. *Note that Prometheus
requires write access to its database, so it will actually write to
the snapshot.* So for example if you have downloaded artifacts for a
run named `hammerdb-vm-mariadb-2022-01-04-08-21-23` and the Prometheus
snapshot within is named
`promdb_2022_01_04T08_21_52+0000_2022_01_04T08_45_47+0000`, you could run as follows:
```
$ local_prometheus_snapshot=/hammerdb-vm-mariadb-2022-01-04-08-21-23/promdb_2022_01_04T08_21_52+0000_2022_01_04T08_45_47+0000
$ chmod -R g-s,a+rw "$local_prometheus_snapshot"
$ sudo podman run --rm -p 9090:9090 -uroot -v "$local_prometheus_snapshot:/prometheus" --privileged prom/prometheus --config.file=/etc/prometheus/prometheus.yml --storage.tsdb.path=/prometheus --storage.tsdb.retention.time=100000d --storage.tsdb.retention.size=1000PB
```
and point your browser at port 9090 on your local system, you can run queries against it, e.g.
```
sum(irate(node_cpu_seconds_total[2m])) by (mode,instance) > 0
```
It is important to use the `--storage.tsdb.retention.time` option to
Prometheus, as otherwise Prometheus may discard the data in the
snapshot. And note that you must set the time bounds on the
Prometheus query to fit the start and end times as recorded in the
name of the promdb snapshot.
## How to develop in benchmark-runner
see [HOW_TO.md](HOW_TO.md)
%prep
%autosetup -n benchmark-runner-1.0.465
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-benchmark-runner -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.465-1
- Package Spec generated
|
