Storage v1
Storage is the most critical component in a database workload. Storage should be always available, scale, perform well, and guarantee consistency and durability. The same expectations and requirements that apply to traditional environments, such as virtual machines and bare metal, are also valid in container contexts managed by Kubernetes.
Important
Kubernetes has its own specificities, when it comes to dynamically provisioned storage. These include storage classes, persistent volumes, and persistent volume claims. You need to own these concepts, on top of all the valuable knowledge you have built over the years in terms of storage for database workloads on VMs and physical servers.
There are two primary methods of access to storage:
- network: either directly or indirectly (think of an NFS volume locally mounted on a host running Kubernetes)
- local: directly attached to the node where a Pod is running (this also includes directly attached disks on bare metal installations of Kubernetes)
Network storage, which is the most common usage pattern in Kubernetes, presents the same issues of throughput and latency that you can experience in a traditional environment. These can be accentuated in a shared environment, where I/O contention with several applications increases the variability of performance results.
Local storage enables shared-nothing architectures, which is more suitable for high transactional and Very Large DataBase (VLDB) workloads, as it guarantees higher and more predictable performance.
Warning
Benchmarking EDB Postgres for Kubernetes
EDB maintains cnp-bench, an open source set of guidelines and Helm charts for benchmarking EDB Postgres for Kubernetes in a controlled Kubernetes environment, before deploying the database in production.
Briefly, cnp-bench
is designed to operate at two levels:
- measuring the performance of the underlying storage using
fio
, with relevant metrics for database workloads such as throughput for sequential reads, sequential writes, random reads and random writes - measuring the performance of the database using the default benchmarking tool
distributed along with PostgreSQL:
pgbench
Important
Measuring both the storage and database performance is an activity that must be done before the database goes in production. However, such results are extremely valuable not only in the planning phase (e.g., capacity planning), but also in the production lifecycle, especially in emergency situations (when we don't have the luxury anymore to run this kind of tests). Databases indeed change and evolve over time, so does the distribution of data, potentially affecting performance: knowing the theoretical maximum throughput of sequential reads or writes will turn out to be extremely useful in those situations. Especially in shared-nothing contexts, where results do not vary due to the influence of external workloads. Know your system, benchmark it.
Persistent Volume Claim
The operator creates a persistent volume claim (PVC) for each PostgreSQL
instance, with the goal to store the PGDATA
, and then mounts it into each Pod.
Additionally, it supports the creation of clusters with a separate PVC on which to store PostgreSQL Write-Ahead Log (WAL), as explained in the "Volume for WAL" section below.
In EDB Postgres for Kubernetes, the volumes attached to a single PostgreSQL instance are defined as PVC group.
Configuration via a storage class
The easier way to configure the storage for a PostgreSQL class is to just request storage of a certain size, like in the following example:
Using the previous configuration, the generated PVCs will be satisfied by the default storage class. If the target Kubernetes cluster has no default storage class, or even if you need your PVCs to be satisfied by a known storage class, you can set it into the custom resource:
Important
EDB Postgres for Kubernetes has been designed to be storage class agnostic. As usual, our recommendation is to properly benchmark the storage class in a controlled environment, before hitting production.
Configuration via a PVC template
To further customize the generated PVCs, you can provide a PVC template inside the Custom Resource, like in the following example:
Volume for WAL
By default, PostgreSQL stores all its data in the so-called PGDATA
(a directory).
One of the core directories inside PGDATA
is pg_wal
(historically
known as pg_xlog
in PostgreSQL), which contains the log of transactional
changes occurred in the database, in the form of segment files.
Info
Normally, each segment is 16 MB in size, but the size can be configured
through the walSegmentSize
option, applied at cluster initialization time, as
described in "Bootstrap an empty cluster".
While in most cases, having pg_wal
on the same volume where PGDATA
resides is fine, there are a few benefits from having WALs stored in a separate
volume:
I/O performance: by storing WAL files on different storage than
PGDATA
, PostgreSQL can exploit parallel I/O for WAL operations (normally sequential writes) and for data files (tables and indexes for example), thus improving vertical scalabilitymore reliability: by reserving dedicated disk space to WAL files, you can always be sure that exhaustion of space on the
PGDATA
volume will never interfere with WAL writing, ensuring that your PostgreSQL primary is correctly shut down.finer control: you can define the amount of space dedicated to both
PGDATA
andpg_wal
, fine tune WAL configuration and checkpoints, even use a different storage class for cost optimizationbetter I/O monitoring: you can constantly monitor the load and disk usage on both
PGDATA
andpg_wal
, and set proper alerts that notify you in case, for example,PGDATA
requires resizing
Write-Ahead Log (WAL)
Please refer to the "Reliability and the Write-Ahead Log" page from the official PostgreSQL documentation for more information.
You can add a separate volume for WAL through the .spec.walStorage
option,
which follows the same rules described for the storage
field and provisions a
dedicated PVC. For example:
Important
Removing walStorage
is not supported: once added, a separate volume for
WALs cannot be removed from an existing Postgres cluster.
Volume expansion
Kubernetes exposes an API allowing expanding PVCs
that is enabled by default but needs to be supported by the underlying StorageClass
.
To check if a certain StorageClass
supports volume expansion, you can read the allowVolumeExpansion
field for your storage class:
Using the volume expansion Kubernetes feature
Given the storage class supports volume expansion, you can change the size requirement
of the Cluster
, and the operator will apply the change to every PVC.
If the StorageClass
supports online volume resizing
the change is immediately applied to the Pods. If the underlying Storage Class doesn't support
that, you will need to delete the Pod to trigger the resize.
The best way to proceed is to delete one Pod at a time, starting from replicas and waiting for each Pod to be back up.
Expanding PVC volumes on AKS
At the moment, Azure is not able to resize the PVC's volume without restarting the pod.
EDB Postgres for Kubernetes has overcome this limitation through the
ENABLE_AZURE_PVC_UPDATES
environment variable in the
operator configuration.
When set to 'true'
, EDB Postgres for Kubernetes triggers a rolling update of the
Postgres cluster.
Alternatively, you can follow the workaround below to manually resize the volume in AKS.
Workaround for volume expansion on AKS
You can manually resize a PVC on AKS by following these procedures. As an example, let's suppose you have a cluster with 3 replicas:
An Azure disk can only be expanded while in "unattached" state, as described in the docs. This means, that to resize a disk used by a PostgreSQL cluster, you will need to perform a manual rollout, first cordoning the node that hosts the Pod using the PVC bound to the disk. This will prevent the Operator to recreate the Pod and immediately reattach it to its PVC before the background disk resizing has been completed.
First step is to edit the cluster definition applying the new size, let's say "2Gi", as follows:
Assuming the cluster-example-1
Pod is the cluster's primary, we can proceed with the replicas first.
For example start with cordoning the kubernetes node that hosts the cluster-example-3
Pod:
Then delete the cluster-example-3
Pod:
Run the following command:
Wait until you see the following output:
Then, you can uncordon the node:
Wait for the Pod to be recreated correctly and get in Running and Ready state:
Now verify the PVC expansion by running the following command, which should return "2Gi" as configured:
So, you can repeat these steps for the remaining Pods.
Important
Please leave the resizing of the disk associated with the primary instance as last disk,
after promoting through a switchover a new resized Pod, using kubectl cnp promote
(e.g. kubectl cnp promote cluster-example 3
to promote cluster-example-3
to primary).
Recreating storage
If the storage class does not support volume expansion, you can still regenerate your cluster on different PVCs, by allocating new PVCs with increased storage and then move the database there. This operation is feasible only when the cluster contains more than one node.
While you do that, you need to prevent the operator from changing the existing PVC
by disabling the resizeInUseVolumes
flag, like in the following example:
In order to move the entire cluster to a different storage area, you need to recreate all the PVCs and all the Pods. Let's suppose you have a cluster with three replicas like in the following example:
To recreate the cluster using different PVCs, you can edit the cluster definition to disable
resizeInUseVolumes
, and then recreate every instance in a different PVC.
As an example, to recreate the storage for cluster-example-3
you can:
Important
In case you have created a dedicated WAL volume, both PVCs will have to be deleted during this process.
Additionally, the same procedure applies in case you want to regenerate the WAL volume PVC, which can be done
by disabling resizeInUseVolumes
also for the .spec.walStorage
section.
For example (in case a PVC dedicated to WAL storage is present):
Having done that, the operator will orchestrate the creation of another replica with a resized PVC: