• "Benchmarking" page of the CloudNativePG open source project
• "Benchmarking" page for EDB Postgres for Kubernetes

cnp-bench provides a bundle of helm charts designed for benchmarking a PostgreSQL deployment in Kubernetes, in a controlled, non-production environment. This guide is currently designed and tested to run cnp-bench on a Cloud Native PostgreSQL (CNP) Cluster by EDB.

Benchmarking is focused on two aspects:

• the storage, by relying on fio
• the database, by relying on either pgbench, PostgreSQL's default benchmarking application, or HammerDB.

IMPORTANT: cnp-bench must be run in a staging or pre-production environment. Do not use cnp-bench in a production environment, as it might have catastrophic consequences on your databases and the other workloads/applications that run in the same shared environment.

• A running Kubernetes cluster;
• A working connection to the Kubernetes cluster via kubectl;
• Helm should be installed locally (see the "Installing Helm" documentation page);
• Access, within Kubernetes, to the PostgreSQL cluster you want to benchmark.

As far as the latter is concerned, you might need to install the CNP operator if you are planning to run benchmarks on disposable Postgres clusters that exist for the sole duration of the test. Consider that, if you *install cnp-sandbox, the CNP operator will be installed together with the monitoring stack comprising Grafana, Prometheus and default metrics. This is our recommended approach.

• The above installation method applies to Kubernetes clusters and not Openshift Clusters.

First, you need to clone the main repository:

git clone git@github.com:EnterpriseDB/cnp-bench.git
cd cnp-bench

You can install a chart by defining a "Release Name" that will be used to identify the resources and running:

helm install RELEASE_NAME path/to/chart/dir

Please note that each benchmark is managed by a different chart that is located in a different folder.

For example, to run the fio benchmark, you need to install the chart located in the fio-benchmark folder. Please refer to the specific sections below for details on each scenario.

You can override the default settings included in the values.yaml file of a chart using the --values or --set options of helm install or by passing the whole yaml configuration file with the--values argument. You can obtain a configuration file by copying the values.yml contained in your chosen chart and use it as a starting point. More details are available in the Helm install documentation. See the README.md file included in each chart for the available parameters.

You can verify the installed helm charts with:

helm list

Resources created by a chart can be removed running:

helm uninstall RELEASE_NAME

cnp-sandbox will deploy in your selected Kubernetes cluster Prometheus, Grafana, CNP, as well as a sample dashboard for Granana based on a sample set of metrics exposed by CNP Clusters.

For more information, please refer to the main Github repository of cnp-sandbox.

The chart is contained in the fio-benchmark directory.

It will:

1. Create a PVC;
2. Create a ConfigMap representing the configuration of a fio job;
3. Create a fio deployment composed by a single Pod, which will run fio on the PVC, create graphs after completing the benchmark and start serving the generated files with a webserver. We use the fio-tools image for that.

The pod created by the deployment will be ready when it starts serving the results. You can forward the port of the pod created by the deployment

kubectl port-forward -n NAMESPACE deployment/RELEASE_NAME 8000

and then use a browser and connect to http://localhost:8000/ to get the data.

The default 8k block size has been chosen to emulate a PostgreSQL workload. Disks that cap the amount of available IOPS can show very different throughput values changing this parameter.

Below is an example of diagram of sequential writes on a local disk mounted on a Standard_E8ds_v4 dedicated Kubernetes node on Azure (1 hour benchmark):

pgbench is the default benchmarking application for PostgreSQL. The chart for pgbench is contained in the pgbench-benchmark directory.

You can run a pgbench benchmark on:

• a disposable PostgreSQL cluster created by the CNP operator specifically for the benchmark
• an existing PostgreSQL cluster, by providing connection information (host, port, database name, and user)

The cnp.existingCluster option is the one that controls the above behavior.

While running a job on a cluster that lives for the sole duration of the test is useful, we recommend that you first create your PostgreSQL cluster, possibly with cnp-sandbox installed, and then run pgbench on that cluster as explained in the "Running pgbench on an existing Postgres cluster" section below.

When cnp.existingCluster is set to false (default), the chart will:

1. Create a CNP cluster based on the user-defined values;
2. Execute a user-defined pgbench job on it.

You can gather the results after the job is completed running:

kubectl logs -n NAMESPACE job/RELEASE_NAME-pgbench

You can use the kubectl wait command to wait until the job is complete:

kubectl wait --for=condition=complete -n NAMESPACE job/RELEASE_NAME-pgbench

It is suggested to label nodes and use node selectors to avoid pgbench and PostgreSQL pods running on the same node. By default, the chart expects the nodes on which pgbench can run to be labelled with workload: pgbench and the node for CNP instances to be labelled with workload: postgres.

kubectl label node/NODE_NAME workload:pgbench
kubectl label node/OTHER_NODE_NAME workload:postgresql

Below is an example of pgbench output on the same Standard_E8ds_v4 dedicated Kubernetes node on Azure with local disks as in the previous section (1 hour benchmark):

starting vacuum...end.
transaction type: <builtin: TPC-B (sort of)>
scaling factor: 12000
query mode: simple
number of clients: 16
number of threads: 8
duration: 3600 s
number of transactions actually processed: 23022969
latency average = 2.502 ms
tps = 6395.218137 (including connections establishing)
tps = 6395.231977 (excluding connections establishing)

CNP has native support for the PgBouncer pooler. You can create a database access layer with PgBouncer by managing the cnp.pooler section of the values file. By default, PgBouncer will be placed on those nodes with the workload: pooler label.

Look at the pgbench-benchmark/values.yaml for an example, as well as the CNP documentation for more information on the PgBouncer implementation.

Suppose you already have your PostgreSQL database setup (not necessarily with CNP). You can use cnp-bench to run a pgbench test.

cnp:
  existingCluster: true
  # Name of the host (or service in K8s) or IP address where Postgres is running
  existingHost: mydb
  # You need to point `existingCredentials` to a Kubernetes `basic-auth`secret
  # containing username and password to connect to the database
  existingCredentials: mydb-app
  # Name of the database on which to run pgbench
  existingDatabase: pgbench

pgbench:
  # Node where to run pgbench
  nodeSelector:
    workload: pgbench
  initialize: true
  scaleFactor: 1
  time: 60
  clients: 1
  jobs: 1
  skipVacuum: false
  reportLatencies: false

The cnp section above, points to the existing database.

The pgbench setion contains the parameters you can use to run the pgbench job. For example, you can create a job that initializes only the pgbench database for a given scale (e.g. 7000, corresponding to roughly 95GB-100GB of database size), then create another one that only runs the job, with different settings of clients, time and jobs.

HammerDB is the leading benchmarking and load testing software for the world's most popular databases supporting Oracle Database, SQL Server, IBM Db2, MySQL, MariaDB and PostgreSQL.

cnp-bench by default will run the TPROC-C benchmark, that is the OLTP workload implemented in HammerDB derived from the TPC-C specification with modification to make running HammerDB straightforward and cost-effective on any of the supported database environments. The HammerDB TPROC-C workload is an open source workload derived from the TPC-C Benchmark Standard and as such is not comparable to published TPC-C results, as the results comply with a subset rather than the full TPC-C Benchmark Standard. The name for the HammerDB workload TPROC-C means "Transaction Processing Benchmark derived from the TPC "C" specification".

Similarly to the case of pgbench, you can run HammerDB on a disposable CNP cluster. We won't report instructions here as they are identical to the pgbench case. Also in this case we recommend that you run the test on a previously created Postgres cluster (with CNP or another operator/method).

Make a local copy of the hammerdb-benchmark/values.yaml file, and make sure you set the existingSuperuserCredentials option to the Kubernetes secret containing the superuser password.

cnp:
  existingCluster: true
  existingSuperuserCredentials: mydb-superuser
  existingHost: mydb

Then look at the hammerdb section on possible ways to customize the test. In particular, modify the scripts in hammerdb.pgschemabuild and hammerdb.pgrun, by taking care of the following options:

• pg_count_ware
• pg_num_vu
• pg_rampup
• pg_duration
• the value n in vuset vu n
• the value x in runtimer x

As reported in the HammerDB documentation, a good starting rule is to set pg_num_vu and vu to the number of cores available on Postgres, while pg_count_ware to 4/5 times the same value.

It is suggested to label nodes and use node selectors to avoid HammerDB and PostgreSQL pods running on the same node. By default, the chart expects the nodes on which pgbench can run to be labelled with workload: hammerdb and the node for CNP instances to be labelled with workload: postgresql.

kubectl label node/NODE_NAME workload=hammerdb
kubectl label node/OTHER_NODE_NAME workload=postgresql

Please read the code of conduct and the guidelines to contribute to the project.

cnp-bench is open source software and comes "as is". Please carefully read the license before you use this software, in particular the "Disclaimer of Warranty" and "Limitation of Liability" items.

Benchmarking is an activity that must be done before you deploy a system in production. Do not run cnp-bench in a production environment, unless you are aware of the impact of this operation with other services that are hosted on the same environment.

cnp-bench is distributed under Apache License 2.0.

Copyright (C) 2021 EnterpriseDB Corporation.