The old strategy was waiting for 1 second and hoping that we will get an update event from the WATCH connection.
Unfortunately, it didn't work well in practice. Instead, we will get the current value from the API by performing an explicit read request.
Close https://github.com/zalando/patroni/issues/1767
When running on K8s Patroni is communicating with API via the `kubernetes` service, which is address is exposed via the
`KUBERNETES_SERVICE_HOST` environment variable. Like any other service, the `kubernetes` service is handled by `kube-proxy`, that depending on configuration is either relying on userspace program or `iptables` for traffic routing.
During K8s upgrade, when master nodes are replaced, it is possible that `kube-proxy` doesn't update the service configuration in time and as a result Patroni fails to update the leader lock and demotes postgres.
In order to improve the user experience and get more control on the problem we make it possible to bypass the `kubernetes` service and connect directly to API nodes.
The strategy is very simple:
1. Resolve list IPs of API nodes from the kubernetes endpoint on every iteration of HA loop.
2. Stick to one of these IPs for API requests
3. Switch to a different IP if connected to IP is not from the list
4. If the request fails, switch to another IP and retry
Such a strategy is already used for Etcd and proven to work quite well.
In order to enable the feature, you need either to set to `true` `kubernetes.bypass_api_service` in the Patroni configuration file or `PATRONI_KUBERNETES_BYPASS_API_SERVICE` environment variable.
If for some reason `GET /default/endpoints/kubernetes` isn't allowed Patroni will disable the feature.
The official python kubernetes client contains a lot of auto-generated code and therefore very heavy, but we need only a little fraction of it.
The naive implementation, that covers all API methods we use, takes about 250 LoC, and about half of it is responsible for the handling of configuration files.
Disadvantage: If somebody was using the `patronictl` outside of the pod (on his machine), it might not work anymore (depending on the environment).
1. Between get_cluster() and update_leader() calls the K8s leader object might be updated from outside and therefore the resource version will not match (error code=409). Since we are watching for all changes, the ObjectCache likely will have the most up-to-date version and we will take advantage of that. There is still a chance to hit a race-condition, but it would be smaller than before. Actually, other DCS are free of this issue. Etcd - update is based on the value comparison, Zookeeper and Consul are relying on session mechanism.
2. If the update still failed - recheck the resource version of the leader object and that the current node is still the leader there and repeat the call.
P.S. The leader race is still relying on the version of the leader object as it was during the get_cluster() call.
In addition to that fixed handling of K8s API errors, we should retry on 500, not on 502.
Close https://github.com/zalando/patroni/issues/1589
* Implement proper tests for `multiprocessing.set_start_method()`
* Exclude some watchdog code from coverage (it is used only for behave tests)
* properly use os.path.join for windows compatibility
* import DCS modules in `features/environment.py` on demand. It allows to run behave tests against chosen DCS without installing all dependencies.
* remove some unused behave code
* fix some minor issues in the dcs.kubernetes module
There is an opinion that LIST requests with labelSelector to K8s API are expensive and Patroni was doing two such requests per HA loop (LIST pods and LIST endpoints/configmaps).
To efficiently detect object changes we will switch to the LIST+WATCH approach.
The initial LIST request populates the ObjectCache and events from the WATCH request update it.
In addition to that, the ObjectCache will be updated after performing the UPDATE operations on the K8s objects. To avoid race conditions, all operations on ObjectCache are performed after comparing the resource_version of the old and the new objects and rejected if the new resource_version value is smaller than the old one.
The disadvantage of such an approach is that it will require keeping three connections to the K8s API from each Patroni Pod (previously it was two).
Yesterday I deployed this feature branch on our biggest K8s cluster, with ~300 Patroni pods.
The CPU Utilization on K8s master nodes immediately dropped from ~20% to ~10% (two times), and the incoming traffic on master nodes dropped ~7-8 times!
Last, but not least, we get more or less the same impact on etcd cluster behind K8s master nodes, the CPU Utilization dropped nearly twice and outgoing traffic ~7-8 times.
Watch requests to K8s API either streaming the data or close connection by timeout. In any case it requires a second connection open, but opening a new connection every 10 seconds is more expensive for both, Patroni and K8s API.
Switching to the streaming model also brings other benefits: we can watch not only on leader object, but also on config and wake up Patroni main thread if the config was changed.
`dcs.cluster` and `dcs.get_cluster()` are using the same lock resource and therefore when get_cluster call is slow due to the slowness of DCS it was also affecting the `dcs.cluster` call, which in return was making health-check requests slow.
if there is no service defined k8s assumes that endpoint is orphaned and removes it.
Patroni tries to create the service only in case if use_endpoints is enabled if the following cases:
1. Upon start
2. When it tries to (re-)create the config endpoint
If for some reason creation of the service has failed, Patroni will retry it on every cycle of HA loop. Usually it fails due to lack of permissions and if you don't want to give such permissions to the service account used by Patroni, you can create the service explicitly in the deployment manifest.
Permanent replication slots are preserved on failover/switchover, that is Patroni on the new primary will create configured replication slots right after doing promote.
Slots could be configured with the help of `patronictl edit-config`.
The initial configuration could be also done in the `bootstrap.dcs`
```yaml
slots:
permanent_physical_1:
type: physical
permanent_logical_1:
type: logical
database: foo
plugin: pgoutput
```
It is the responsibility of the operator to make sure that there are no clashes in names between replication slots automatically created by Patroni for members and permanent replication slots.
Closes https://github.com/zalando/patroni/issues/656
Output exception trace to the logs when http status code == 403, something is wrong with permissions.
When http status code == 409 -- such error could be ignored, because object probably was created or updated by another process.
For all other http status codes it will also produce stack traces.
I hope it will help to debug issues similar to the https://github.com/zalando/patroni/issues/606
It is very easy to get current timeline on the master by executing
```sql
SELECT ('x' || SUBSTR(pg_walfile_name(pg_current_wal_lsn()), 1, 8))::bit(32)::int
```
Unfortunately the same method doesn't work when postgres is_in_recovery. Therefore we will use replication connection for that on the replicas. In order to avoid opening and closing replication connection on every HA loop we will cache the result if its value matches with the timeline of the master.
Also this PR introduces a new key in DCS: `/history`. It will contain a json serialized object with timeline history in a format similar to the usual history files. The differences are:
* Second column is the absolute wal position in bytes, instead of LSN
* Optionally there might be a fourth column - timestamp, (mtime of history file)
* Use ConfigMaps or Endpoins for leader elections and to keep cluster state
* Label pods with a postgres role
* change behavior of pip install. From now on it will not install all dependencies, you have to specify explicitly DCS you want to use Patroni with: `pip install patroni[etcd,zookeeper,kubernetes]`
