One of the great things about PostgreSQL is its reliability: it is very stable and typically “just works.” However, there are certain things that can happen in the environment that PostgreSQL is deployed in that can affect its uptime, including:
- The database storage disk fails or some other hardware failure occurs
- The network on which the database resides becomes unreachable
- The host operating system becomes unstable and crashes
- A key database file becomes corrupted
- A data center is lost
There may also be downtime events that are due to the normal case of operations, such as performing a minor upgrade, security patching of operating system, hardware upgrade, or other maintenance.
Fortunately, PGO, the Postgres Operator from Crunchy Data, is prepared for this.
The Crunchy PostgreSQL Operator supports a distributed-consensus based high availability (HA) system that keeps its managed PostgreSQL clusters up and running, even if the PostgreSQL Operator disappears. Additionally, it leverages Kubernetes specific features such as Pod Anti-Affinity to limit the surface area that could lead to a PostgreSQL cluster becoming unavailable. The PostgreSQL Operator also supports automatic healing of failed primaries and leverages the efficient pgBackRest “delta restore” method, which eliminates the need to fully reprovision a failed cluster!
The Crunchy PostgreSQL Operator also maintains high availability during a routine task such as a PostgreSQL minor version upgrade.
For workloads that are sensitive to transaction loss, PGO supports PostgreSQL synchronous replication.
The high availability backing for your PostgreSQL cluster is only as good as your high availability backing for Kubernetes. To learn more about creating a high availability Kubernetes cluster, please review the Kubernetes documentation or consult your systems administrator.
The Crunchy Postgres Operator High Availability Algorithm
A critical aspect of any production-grade PostgreSQL deployment is a reliable and effective high availability (HA) solution. Organizations want to know that their PostgreSQL deployments can remain available despite various issues that have the potential to disrupt operations, including hardware failures, network outages, software errors, or even human mistakes.
The key portion of high availability that the PostgreSQL Operator provides is that it delegates the management of HA to the PostgreSQL clusters themselves. This ensures that the PostgreSQL Operator is not a single-point of failure for the availability of any of the PostgreSQL clusters that it manages, as the PostgreSQL Operator is only maintaining the definitions of what should be in the cluster (e.g. how many instances in the cluster, etc.).
Each HA PostgreSQL cluster maintains its availability using concepts that come from the Raft algorithm to achieve distributed consensus. The Raft algorithm (“Reliable, Replicated, Redundant, Fault-Tolerant”) was developed for systems that have one “leader” (i.e. a primary) and one-to-many followers (i.e. replicas) to provide the same fault tolerance and safety as the PAXOS algorithm while being easier to implement.
For the PostgreSQL cluster group to achieve distributed consensus on who the primary (or leader) is, each PostgreSQL cluster leverages the distributed etcd key-value store that is bundled with Kubernetes. After it is elected as the leader, a primary will place a lock in the distributed etcd cluster to indicate that it is the leader. The “lock” serves as the method for the primary to provide a heartbeat: the primary will periodically update the lock with the latest time it was able to access the lock. As long as each replica sees that the lock was updated within the allowable automated failover time, the replicas will continue to follow the leader.
The “log replication” portion that is defined in the Raft algorithm is handled by PostgreSQL in two ways. First, the primary instance will replicate changes to each replica based on the rules set up in the provisioning process. For PostgreSQL clusters that leverage “synchronous replication,” a transaction is not considered complete until all changes from those transactions have been sent to all replicas that are subscribed to the primary.
In the above section, note the key word that the transaction are sent to each replica: the replicas will acknowledge receipt of the transaction, but they may not be immediately replayed. We will address how we handle this further down in this section.
During this process, each replica keeps track of how far along in the recovery process it is using a “log sequence number” (LSN), a built-in PostgreSQL serial representation of how many logs have been replayed on each replica. For the purposes of HA, there are two LSNs that need to be considered: the LSN for the last log received by the replica, and the LSN for the changes replayed for the replica. The LSN for the latest changes received can be compared amongst the replicas to determine which one has replayed the most changes, and an important part of the automated failover process.
The replicas periodically check in on the lock to see if it has been updated by the primary within the allowable automated failover timeout. Each replica checks in at a randomly set interval, which is a key part of Raft algorithm that helps to ensure consensus during an election process. If a replica believes that the primary is unavailable, it becomes a candidate and initiates an election and votes for itself as the new primary. A candidate must receive a majority of votes in a cluster in order to be elected as the new primary.
There are several cases for how the election can occur. If a replica believes that a primary is down and starts an election, but the primary is actually not down, the replica will not receive enough votes to become a new primary and will go back to following and replaying the changes from the primary.
In the case where the primary is down, the first replica to notice this starts an election. Per the Raft algorithm, each available replica compares which one has the latest changes available, based upon the LSN of the latest logs received. The replica with the latest LSN wins and receives the vote of the other replica. The replica with the majority of the votes wins. In the event that two replicas’ logs have the same LSN, the tie goes to the replica that initiated the voting request.
Once an election is decided, the winning replica is immediately promoted to be a primary and takes a new lock in the distributed etcd cluster. If the new primary has not finished replaying all of its transactions logs, it must do so in order to reach the desired state based on the LSN. Once the logs are finished being replayed, the primary is able to accept new queries.
At this point, any existing replicas are updated to follow the new primary.
When the old primary tries to become available again, it realizes that it has been deposed as the leader and must be healed. The old primary determines what kind of replica it should be based upon the CRD, which allows it to set itself up with appropriate attributes. It is then restored from the pgBackRest backup archive using the “delta restore” feature, which heals the instance and makes it ready to follow the new primary, which is known as “auto healing.”
How The Crunchy PostgreSQL Operator Uses Pod Anti-Affinity
Kubernetes has two types of Pod anti-affinity:
- Preferred: With preferred (
preferredDuringSchedulingIgnoredDuringExecution) Pod anti-affinity, Kubernetes will make a best effort to schedule Pods matching the anti-affinity rules to different Nodes. However, if it is not possible to do so, then Kubernetes may schedule one or more Pods to the same Node.
- Required: With required (
requiredDuringSchedulingIgnoredDuringExecution) Pod anti-affinity, Kubernetes mandates that each Pod matching the anti-affinity rules must be scheduled to different Nodes. However, a Pod may not be scheduled if Kubernetes cannot find a Node that does not contain a Pod matching the rules.
There is a tradeoff with these two types of pod anti-affinity: while “required” anti-affinity will ensure that all the matching Pods are scheduled on different Nodes, if Kubernetes cannot find an available Node, your Postgres instance may not be scheduled. Likewise, while “preferred” anti-affinity will make a best effort to scheduled your Pods on different Nodes, Kubernetes may compromise and schedule more than one Postgres instance of the same cluster on the same Node.
By understanding these tradeoffs, the makeup of your Kubernetes cluster, and your requirements, you can choose the method that makes the most sense for your Postgres deployment. We’ll show examples of both methods below!
For an example for how pod anti-affinity works with PGO, please see the high availability tutorial.
Synchronous Replication: Guarding Against Transactions Loss
Clusters managed by the Crunchy PostgreSQL Operator can be deployed with synchronous replication, which is useful for workloads that are sensitive to losing transactions, as PostgreSQL will not consider a transaction to be committed until it is committed to all synchronous replicas connected to a primary. This provides a higher guarantee of data consistency and, when a healthy synchronous replica is present, a guarantee of the most up-to-date data during a failover event.
This comes at a cost of performance: PostgreSQL has to wait for a transaction to be committed on all synchronous replicas, and a connected client will have to wait longer than if the transaction only had to be committed on the primary (which is how asynchronous replication works). Additionally, there is a potential impact to availability: if a synchronous replica crashes, any writes to the primary will be blocked until a replica is promoted to become a new synchronous replica of the primary.
Kubernetes Node Affinity can be used to scheduled Pods to specific Nodes within a Kubernetes cluster. This can be useful when you want your PostgreSQL instances to take advantage of specific hardware (e.g. for geospatial applications) or if you want to have a replica instance deployed to a specific region within your Kubernetes cluster for high availability purposes.
For an example for how node affinity works with PGO, please see the high availability tutorial.
Kubernetes Tolerations can help with the scheduling of Pods to appropriate nodes. There are many reasons that a Kubernetes administrator may want to use tolerations, such as restricting the types of Pods that can be assigned to particular Nodes. Reasoning and strategy for using taints and tolerations is outside the scope of this documentation.
You can configure the tolerations for your Postgres instances on the
postgresclusters custom resource.
Pod Topology Spread Constraints
Kubernetes Pod Topology Spread Constraints can also help you efficiently schedule your workloads by ensuring your Pods are not scheduled in only one portion of your Kubernetes cluster. By spreading your Pods across your Kubernetes cluster among your various failure-domains, such as regions, zones, nodes, and other user-defined topology domains, you can achieve high availability as well as efficient resource utilization.
For an example of how pod topology spread constraints work with PGO, please see the high availability tutorial.
During the lifecycle of a PostgreSQL cluster, there are certain events that may
require a planned restart, such as an update to a “restart required” PostgreSQL
configuration setting (e.g.
or a change to a Kubernetes Deployment template (e.g. changing the memory request). Restarts can be disruptive in a high availability deployment, which is
why many setups employ a “rolling update” strategy
(aka a “rolling restart”) to minimize or eliminate downtime during a planned
Because PostgreSQL is a stateful application, a simple rolling restart strategy will not work: PostgreSQL needs to ensure that there is a primary available that can accept reads and writes. This requires following a method that will minimize the amount of downtime when the primary is taken offline for a restart.
The PostgreSQL Operator uses the following algorithm to perform the rolling restart to minimize any potential interruptions:
Each replica is updated in sequential order. This follows the following process:
The replica is explicitly shut down to ensure any outstanding changes are flushed to disk.
If requested, the PostgreSQL Operator will apply any changes to the Deployment.
The replica is brought back online. The PostgreSQL Operator waits for the replica to become available before it proceeds to the next replica.
The above steps are repeated until all of the replicas are restarted.
A controlled switchover is performed. The PostgreSQL Operator determines which replica is the best candidate to become the new primary. It then demotes the primary to become a replica and promotes the best candidate to become the new primary.
The former primary follows a process similar to what is described in step 1.
The downtime is thus constrained to the amount of time the switchover takes.
PGO will automatically detect when to apply a rolling update.