Previously, it was set to the lifecycle-status:ready, breaking a
lot of minikube deployments. Also it was not possible befor to run
with this label set to an empty value.
Document the effect of the label in the new section of the
documentation.
Previously, the operator started to move the pods off the nodes to be
decomissioned by watching the eol_node_label value. Every new postgres
pod has been created with the anti-affinity to that label, making sure
that the pods being moved won't land on another to be decomissioned
node.
The changes introduce another label that indicates the ready node. The
new pod affinity will esnure that the pod is only scheduled to the node
marked as ready, discarding the previous anti-affinity. That way the
nodes can transition from the pending-decomission to the other statuses
(drained, terminating) without having pods suddently scaled to them.
In addition, rename the label that triggers the start of the upgrade
process to node_eol_label (for consistency with node_readiness_label)
and set its default vvalue to lifecycle-status:pending-decomission.
Add options to the PgUser structure, potentially allowing to set
per-role options in the cluster definition as well.
Introduce api_roles_configuration operator option with the default
of log_statement=all
removing parts of config.
* chaning secret name pattern to make things shorter.
* Move section on self building docker image.
* Fix typo.
* Bump image.
* bump version for pdb fix.
* Changes in regards to review.
* Fix xhyve driver link.
* Move to new api, remove service account, not needed for minikube.
* Changed minimal manifest and example to use right file.
* Added service account for operator again, it is needed in pods anyways later.
Allow cloning clusters from the operator.
The changes add a new JSON node `clone` with possible values `cluster`
and `timestamp`. `cluster` is mandatory, and setting a non-empty
`timestamp` triggers wal-e point in time recovery. Spilo and Patroni do
the whole heavy-lifting, the operator just defines certain variables and
gathers some data about how to connect to the host to clone or the
target S3 bucket.
As a minor change, set the image pull policy to IfNotPresent instead
of Always to simplify local testing.
Change the default replication username to standby.
* Deny all requests to the load balancer by default.
* Operator-wide toggle for the load-balancer.
* Define per-cluster useLoadBalancer option.
If useLoadBalancer is not set - then operator-wide defaults take place. If it
is true - the load balancer is created, otherwise a service type clusterIP is
created.
Internally, we have to completely replace the service if the service type
changes. We cannot patch, since some fields from the old service that will
remain after patch are incompatible with the new one, and handling them
explicitly when updating the service is ugly and error-prone. We cannot
update the service because of the immutable fields, that leaves us the only
option of deleting the old service and creating the new one. Unfortunately,
there is still an issue of unnecessary removal of endpoints associated with
the service, it will be addressed in future commits.
* Revert the unintended effect of go fmt
* Recreate endpoints on service update.
When the service type is changed, the service is deleted and then
the one with the new type is created. Unfortnately, endpoints are
deleted as well. Re-create them afterwards, preserving the original
addresses stored in them.
* Improve error messages and comments. Use generate instead of gen in names.
Run operations concerning multiple clusters in parallel. Each cluster gets its
own worker in order to create, update, sync or delete clusters. Each worker
acquires the lock on a cluster. Subsequent operations on the same cluster
have to wait until the current one finishes. There is a pool of parallel
workers, configurable with the `workers` parameter in the configmap and set by
default to 4. The cluster-related tasks are assigned to the workers based on
a cluster name: the tasks for the same cluster will be always assigned to the
same worker. There is no blocking between workers, although there is a chance
that a single worker will become a bottleneck if too many clusters are
assigned to it; therefore, for large-scale deployments it might be necessary
to bump up workers from the default value.
Sergey Dudoladov
Oleksii Kliukin
Jan Mussler
Alexander Kukushkin
Murat Kabilov
Murat Kabilov