> Note: This scenario assumes you already have an OpenShift cluster or have followed the instructions in the Deploying OpenShift Container Storage 4 to OpenShift 4 Blog to set up an OpenShift Container Platform 4.2.14+ cluster using OpenShift Container Storage 4.
PostgreSQL has been the fastest growing open source RDBMS over the past decade. It has a solid community and has been around for many years adding more and more features. PostgreSQL features ACID (Atomicity, Consistent, Isolation and Durability) properties. It has indexes (primary/unique), updatable views, triggers, foreign keys (FKs) and even stored procedures (SPs). PostgreSQL also features built-in replication via shipping the WAL (Write Ahead Log) to a number of different database replicas. These replicas can be used in read-only mode. It also has a synchronous replication, where the master waits for at least one replica to have written the data before ACKing.
1.1. This Blog will show you how to:
Create a PostgreSQL template that uses Red Hat OpenShift Container Storage 4 persistent storage and learn about the template variables.
Deploy a PostgreSQL via
oc new-app using our new template
pgbench basics and deploy a
Run performance testing to measure the performance of your database
Do all this from the command line!
- Make sure you have a running OCP cluster.
- A copy of this git repo.
- NOTE: The scripts in this lab will work on Linux (various distributions) and MacOS. They have not been tested on any Windows OS.
- Please git clone this repository, you’ll find all the scripts needed in the ocs4jenkins directory:
git clone https://github.com/red-hat-storage/ocs-training.git
3. Deploying PostgreSQL
3.1. Our PostgreSQL example
This example will deploy a PostgreSQL database that will use OpenShift Container Storage persistent storage and then run
pgbench on the newly deployed database.
pgbench is maintained together with PostgreSQL. It is used to create data and also to stress the database. It has many options and variations, but we are going to use the default workload scenario which is loosely based on TCP-B (a more write oriented workload). We will also learn about the pgbench output.
3.2. Creating a PostgreSQL template that uses OpenShift Container Storage 4
OpenShift Container Platform comes pre-configured with two PostgreSQL templates to use:
oc get templates -n openshift -o custom-columns=NAME:.metadata.name|grep -i ^postgres
We are going to create a new template based on the
postgresql-persistent template. To do this, we run the
The script parameters (
NEW_TEMPLATE_NAME) are self explanatory. You can edit the script and change them, but remember that going forward, some sections might reference the default value of
NEW_TEMPLATE_NAME. The script will perform the following tasks:
- Change the name of the template (so it can co-exists with the one we copied from).
- Add the storage class (sc) we want to use in the template (the jenkins-persistent template just uses the default storage class in OCP).
- Add/change the size of the Persistent Volume (PV) we want for the PostgreSQL database.
- Run the `oc create` command and the create the new template.
$ bash create_ocs_postgresql_template
After running the script, you should see another jenkins template:
oc get templates -n openshift -o custom-columns=NAME:.metadata.name|grep -i ^jenkins
The last jenkins template
postgresql-persistent-ocs is the one that we are going to use.
3.3. Creating our project
Let’s create a project that will run our example scenario:
oc new-project my-postgresql
Once the project is created (oc will automatically use it), lets create postgresql database:
oc new-app --name=postgresql --template=postgresql-persistent-ocs
Pay close attention to the output of the "new-app" command, especially to the section below "* With parameters:" as it contains the randomly created username ("PostgreSQL Connection Username=") and password ("PostgreSQL Connection Username") to connect to the database, for example:
* With parameters:
* Memory Limit=512Mi
* Database Service Name=postgresql
* PostgreSQL Connection Username=user8CK # generated
* PostgreSQL Connection Password=DmoXvvuh6PetIG5V # generated
* PostgreSQL Database Name=sampledb
* Volume Capacity=50Gi
* Version of PostgreSQL Image=10
You can monitor the creation of the PostgreSQL pod using
oc get pods and you can see that two pods are being created, a sidecar named
postgresql--deploy, and the actual pod running the database named,
The output should similar to this:
$ oc get pods
NAME READY STATUS RESTARTS AGE
postgresql-1-deploy 0/1 Completed 0 65s
postgresql-1-ptcdm 1/1 Running 0 57s
So once the PostgreSQL pods is running and ready (in the above output the name is
postgresql-1-ptcdm), we have a running database. Now we can create a pod that will contain pgbench, to do so we will use a container I’ve created and used for all my PostgreSQL tests.
The yaml file looks like this:
- image: quay.io/sagyvolkov/pgbench-container:0.1
You can copy/paste this yaml to a file (lets call it
pgbench.yaml) and then run:
oc -n my-postgresql apply -f pgbench.yaml
Once the pgbench pod is up, let’s make sure we can connect to our PostgreSQL database.
First we need the username and password that the
oc new-app command output has returned. We also need the service IP address that was created when we ran "oc new-app". To get the service IP, run:
oc get svc -o custom-columns=CLUSTER-IP:.spec.clusterIP
now that we have all the information to test connectivity, we can
rsh into the
oc rsh pgbench
and then once inside the pod, run:
psql -U -h sampledb
psql -U user8CK -h 172.30.126.152 sampledb
Once you can see that you can login to the sampledb database, just type
exit to leave psql.
Now, we can load data via
pgbench container holds a wrapper script to run pgbench (as I wrote, this container is used for performance testing). It is out of the scope of this Blog to go over all the parameters of the
run_pgbench script, but feel free to
cat the script once you
rsh to the
To load our data, the command will be (again, this is running from within the
./run_pgbench init 10 1 1 simple time 60 yes no sampledb 10
> NOTE: Please leave the parameters that are not enclosed with <> as they are.
./run_pgbench init 172.30.126.152 user8CK 10 1 1 simple time 60 yes no DmoXvvuh6PetIG5V sampledb 5
One of the parameters in this script is the scale factor of the data, in this case it is set to 10 (4th parameter) which will create a very small database, you can use a much larger scale factor to create bigger database (for example, a scale factor of 5350 is about 75GB in database size). When the load is done, we can now use the same script to run the workload:
./run_pgbench workload 172.30.126.152 user8CK 10 1 1 simple time 60 yes no DmoXvvuh6PetIG5V sampledb 5
With these variables used by the
run_pgbench we are going to run
pgbench for 60 seconds, using 2 threads and 2 jobs and the output will be sampled every 5 seconds.
The output will be similar to this:
Running pgbench workload ...
progress: 5.0 s, 589.6 tps, lat 3.390 ms stddev 2.279
progress: 10.0 s, 613.2 tps, lat 3.261 ms stddev 2.026
progress: 15.0 s, 623.6 tps, lat 3.207 ms stddev 2.399
progress: 20.0 s, 624.2 tps, lat 3.204 ms stddev 4.685
progress: 25.0 s, 690.2 tps, lat 2.898 ms stddev 1.555
progress: 30.0 s, 681.8 tps, lat 2.933 ms stddev 2.599
progress: 35.0 s, 632.4 tps, lat 3.141 ms stddev 7.810
progress: 40.0 s, 628.4 tps, lat 3.204 ms stddev 5.069
progress: 45.0 s, 568.6 tps, lat 3.517 ms stddev 3.696
progress: 50.0 s, 601.8 tps, lat 3.323 ms stddev 2.555
progress: 55.0 s, 583.4 tps, lat 3.429 ms stddev 3.358
progress: 60.0 s, 623.0 tps, lat 3.211 ms stddev 1.025
scaling factor: 10
query mode: simple
number of clients: 2
number of threads: 2
duration: 60 s
number of transactions actually processed: 37303
latency average = 3.217 ms
latency stddev = 3.716 ms
tps = 621.691291 (including connections establishing)
tps = 621.719866 (excluding connections establishing)
What we can see here is that we achieved 37303 transactions during our 60 seconds test with an average tps (transaction per seconds) of roughly 621 and latency average 3.217 ms.
As previously stated, you can play with parameters of the
run_pgbench script to run a heavier, longer workload or to create a bigger database.
Resources and Feedback
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