From 5eb7322d08e93912653dd6ba02a4507e80c50aec Mon Sep 17 00:00:00 2001 From: Vlad Lazar Date: Wed, 28 Aug 2024 14:56:14 +0100 Subject: [PATCH] docs: rolling storage controller restarts RFC (#8310) ## Problem Storage controller upgrades (restarts, more generally) can cause multi-second availability gaps. While the storage controller does not sit on the main data path, it's generally not acceptable to block management requests for extended periods of time (e.g. https://github.com/neondatabase/neon/issues/8034). ## Summary of changes This RFC describes the issues around the current storage controller restart procedure and describes an implementation which reduces downtime to a few milliseconds on the happy path. Related https://github.com/neondatabase/neon/issues/7797 --- docs/rfcs/037-storage-controller-restarts.md | 259 +++++++++++++++++++ 1 file changed, 259 insertions(+) create mode 100644 docs/rfcs/037-storage-controller-restarts.md diff --git a/docs/rfcs/037-storage-controller-restarts.md b/docs/rfcs/037-storage-controller-restarts.md new file mode 100644 index 000000000000..bad422344fd3 --- /dev/null +++ b/docs/rfcs/037-storage-controller-restarts.md @@ -0,0 +1,259 @@ +# Rolling Storage Controller Restarts + +## Summary + +This RFC describes the issues around the current storage controller restart procedure +and describes an implementation which reduces downtime to a few milliseconds on the happy path. + +## Motivation + +Storage controller upgrades (restarts, more generally) can cause multi-second availability gaps. +While the storage controller does not sit on the main data path, it's generally not acceptable +to block management requests for extended periods of time (e.g. https://github.com/neondatabase/neon/issues/8034). + +### Current Implementation + +The storage controller runs in a Kubernetes Deployment configured for one replica and strategy set to [Recreate](https://kubernetes.io/docs/concepts/workloads/controllers/deployment/#recreate-deployment). +In non Kubernetes terms, during an upgrade, the currently running storage controller is stopped and, only after, +a new instance is created. + +At start-up, the storage controller calls into all the pageservers it manages (retrieved from DB) to learn the +latest locations of all tenant shards present on them. This is usually fast, but can push into tens of seconds +under unfavourable circumstances: pageservers are heavily loaded or unavailable. + +## Prior Art + +There's probably as many ways of handling restarts gracefully as there are distributed systems. Some examples include: +* Active/Standby architectures: Two or more instance of the same service run, but traffic is only routed to one of them. +For fail-over, traffic is routed to one of the standbys (which becomes active). +* Consensus Algorithms (Raft, Paxos and friends): The part of consensus we care about here is leader election: peers communicate to each other +and use a voting scheme that ensures the existence of a single leader (e.g. Raft epochs). + +## Requirements + +* Reduce storage controller unavailability during upgrades to milliseconds +* Minimize the interval in which it's possible for more than one storage controller +to issue reconciles. +* Have one uniform implementation for restarts and upgrades +* Fit in with the current Kubernetes deployment scheme + +## Non Goals + +* Implement our own consensus algorithm from scratch +* Completely eliminate downtime storage controller downtime. Instead we aim to reduce it to the point where it looks +like a transient error to the control plane + +## Impacted Components + +* storage controller +* deployment orchestration (i.e. Ansible) +* helm charts + +## Terminology + +* Observed State: in-memory mapping between tenant shards and their current pageserver locations - currently built up +at start-up by quering pageservers +* Deployment: Kubernetes [primitive](https://kubernetes.io/docs/concepts/workloads/controllers/deployment/) that models +a set of replicas + +## Implementation + +### High Level Flow + +At a very high level the proposed idea is to start a new storage controller instance while +the previous one is still running and cut-over to it when it becomes ready. The new instance, +should coordinate with the existing one and transition responsibility gracefully. While the controller +has built in safety against split-brain situations (via generation numbers), we'd like to avoid such +scenarios since they can lead to availability issues for tenants that underwent changes while two controllers +were operating at the same time and require operator intervention to remedy. + +### Kubernetes Deployment Configuration + +On the Kubernetes configuration side, the proposal is to update the storage controller `Deployment` +to use `spec.strategy.type = RollingUpdate`, `spec.strategy.rollingUpdate.maxSurge=1` and `spec.strategy.maxUnavailable=0`. +Under the hood, Kubernetes creates a new replica set and adds one pod to it (`maxSurge=1`). The old replica set does not +scale down until the new replica set has one replica in the ready state (`maxUnavailable=0`). + +The various possible failure scenarios are investigated in the [Handling Failures](#handling-failures) section. + +### Storage Controller Start-Up + +This section describes the primitives required on the storage controller side and the flow of the happy path. + +#### Database Table For Leader Synchronization + +A new table should be added to the storage controller database for leader synchronization during startup. +This table will always contain at most one row. The proposed name for the table is `leader` and the schema +contains two elements: +* `hostname`: represents the hostname for the current storage controller leader - should be addressible +from other pods in the deployment +* `start_timestamp`: holds the start timestamp for the current storage controller leader (UTC timezone) - only required +for failure case handling: see [Previous Leader Crashes Before New Leader Readiness](#previous-leader-crashes-before-new-leader-readiness) + +Storage controllers will read the leader row at start-up and then update it to mark themselves as the leader +at the end of the start-up sequence. We want compare-and-exchange semantics for the update: avoid the +situation where two concurrent updates succeed and overwrite each other. The default Postgres isolation +level is `READ COMMITTED`, which isn't strict enough here. This update transaction should use at least `REPEATABLE +READ` isolation level in order to [prevent lost updates](https://www.interdb.jp/pg/pgsql05/08.html). Currently, +the storage controller uses the stricter `SERIALIZABLE` isolation level for all transactions. This more than suits +our needs here. + +``` +START TRANSACTION ISOLATION LEVEL REPEATABLE READ +UPDATE leader SET hostname=, start_timestamp= +WHERE hostname=, start_timestampt=; +``` + +If the transaction fails or if no rows have been updated, then the compare-and-exchange is regarded as a failure. + +#### Step Down API + +A new HTTP endpoint should be added to the storage controller: `POST /control/v1/step_down`. Upon receiving this +request the leader cancels any pending reconciles and goes into a mode where it replies with 503 to all other APIs +and does not issue any location configurations to its pageservers. The successful HTTP response will return a serialized +snapshot of the observed state. + +If other step down requests come in after the initial one, the request is handled and the observed state is returned (required +for failure scenario handling - see [Handling Failures](#handling-failures)). + +#### Graceful Restart Happy Path + +At start-up, the first thing the storage controller does is retrieve the sole row from the new +`leader` table. If such an entry exists, send a `/step_down` PUT API call to the current leader. +This should be retried a few times with a short backoff (see [1]). The aspiring leader loads the +observed state into memory and the start-up sequence proceeds as usual, but *without* querying the +pageservers in order to build up the observed state. + +Before doing any reconciliations or persistence change, update the `leader` database table as described in the [Database Table For Leader Synchronization](database-table-for-leader-synchronization) +section. If this step fails, the storage controller process exits. + +Note that no row will exist in the `leaders` table for the first graceful restart. In that case, force update the `leader` table +(without the WHERE clause) and perform with the pre-existing start-up procedure (i.e. build observed state by querying pageservers). + +Summary of proposed new start-up sequence: +1. Call `/step_down` +2. Perform any pending database migrations +3. Load state from database +4. Load observed state returned in step (1) into memory +5. Do initial heartbeat round (may be moved after 5) +7. Mark self as leader by updating the database +8. Reschedule and reconcile everything + +Some things to note from the steps above: +* The storage controller makes no changes to the cluster state before step (5) (i.e. no location config +calls to the pageserver and no compute notifications) +* Ask the current leader to step down before loading state from database so we don't get a lost update +if the transactions overlap. +* Before loading the observed state at step (3), cross-validate against the database. If validation fails, +fall back to asking the pageservers about their current locations. +* Database migrations should only run **after** the previous instance steps down (or the step down times out). + + +[1] The API call might fail because there's no storage controller running (i.e. [restart](#storage-controller-crash-or-restart)), +so we don't want to extend the unavailability period by much. We still want to retry since that's not the common case. + +### Handling Failures + +#### Storage Controller Crash Or Restart + +The storage controller may crash or be restarted outside of roll-outs. When a new pod is created, its call to +`/step_down` will fail since the previous leader is no longer reachable. In this case perform the pre-existing +start-up procedure and update the leader table (with the WHERE clause). If the update fails, the storage controller +exists and consistency is maintained. + +#### Previous Leader Crashes Before New Leader Readiness + +When the previous leader (P1) crashes before the new leader (P2) passses the readiness check, Kubernetes will +reconcile the old replica set and create a new pod for it (P1'). The `/step_down` API call will fail for P1' +(see [2]). + +Now we have two cases to consider: +* P2 updates the `leader` table first: The database update from P1' will fail and P1' will exit, or be terminated +by Kubernetes depending on timings. +* P1' updates the `leader` table first: The `hostname` field of the `leader` row stays the same, but the `start_timestamp` field changes. +The database update from P2 will fail (since `start_timestamp` does not match). P2 will exit and Kubernetes will +create a new replacement pod for it (P2'). Now the entire dance starts again, but with P1' as the leader and P2' as the incumbent. + +[2] P1 and P1' may (more likely than not) be the same pod and have the same hostname. The implementation +should avoid this self reference and fail the API call at the client if the persisted hostname matches +the current one. + +#### Previous Leader Crashes After New Leader Readiness + +The deployment's replica sets already satisfy the deployment's replica count requirements and the +Kubernetes deployment rollout will just clean up the dead pod. + +#### New Leader Crashes Before Pasing Readiness Check + +The deployment controller scales up the new replica sets by creating a new pod. The entire procedure is repeated +with the new pod. + +#### Network Partition Between New Pod and Previous Leader + +This feels very unlikely, but should be considered in any case. P2 (the new aspiring leader) fails the `/step_down` +API call into P1 (the current leader). P2 proceeds with the pre-existing startup procedure and updates the `leader` table. +Kubernetes will terminate P1, but there may be a brief period where both storage controller can drive reconciles. + +### Dealing With Split Brain Scenarios + +As we've seen in the previous section, we can end up with two storage controller running at the same time. The split brain +duration is not bounded since the Kubernetes controller might become partitioned from the pods (unlikely though). While these +scenarios are not fatal, they can cause tenant unavailability, so we'd like to reduce the chances of this happening. +The rest of this section sketches some safety measure. It's likely overkill to implement all of them however. + +### Ensure Leadership Before Producing Side Effects + +The storage controller has two types of side effects: location config requests into pageservers and compute notifications into the control plane. +Before issuing either, the storage controller could check that it is indeed still the leader by querying the database. Side effects might still be +applied if they race with the database updatem, but the situation will eventually be detected. The storage controller process should terminate in these cases. + +### Leadership Lease + +Up until now, the leadership defined by this RFC is static. In order to bound the length of the split brain scenario, we could require the leadership +to be renewed periodically. Two new columns would be added to the leaders table: +1. `last_renewed` - timestamp indicating when the lease was last renewed +2. `lease_duration` - duration indicating the amount of time after which the lease expires + +The leader periodically attempts to renew the lease by checking that it is in fact still the legitimate leader and updating `last_renewed` in the +same transaction. If the update fails, the process exits. New storage controller instances wishing to become leaders must wait for the current lease +to expire before acquiring leadership if they have not succesfully received a response to the `/step_down` request. + +### Notify Pageserver Of Storage Controller Term + +Each time that leadership changes, we can bump a `term` integer column in the `leader` table. This term uniquely identifies a leader. +Location config requests and re-attach responses can include this term. On the pageserver side, keep the latest term in memory and refuse +anything which contains a stale term (i.e. smaller than the current one). + +### Observability + +* The storage controller should expose a metric which describes it's state (`Active | WarmingUp | SteppedDown`). +Per region alerts should be added on this metric which triggers when: + + no storage controller has been in the `Active` state for an extended period of time + + more than one storage controllers are in the `Active` state + +* An alert that periodically verifies that the `leader` table is in sync with the metric above would be very useful. +We'd have to expose the storage controller read only database to Grafana (perhaps it is already done). + +## Alternatives + +### Kubernetes Leases + +Kubernetes has a [lease primitive](https://kubernetes.io/docs/concepts/architecture/leases/) which can be used to implement leader election. +Only one instance may hold a lease at any given time. This lease needs to be periodically renewed and has an expiration period. + +In our case, it would work something like this: +* `/step_down` deletes the lease or stops it from renewing +* lease acquisition becomes part of the start-up procedure + +The kubert crate implements a [lightweight lease API](https://docs.rs/kubert/latest/kubert/lease/struct.LeaseManager.html), but it's still +not exactly trivial to implement. + +This approach has the benefit of baked in observability (`kubectl describe lease`), but: +* We offload the responsibility to Kubernetes which makes it harder to debug when things go wrong. +* More code surface than the simple "row in database" approach. Also, most of this code would be in +a dependency not subject to code review, etc. +* Hard to test. Our testing infra does not run the storage controller in Kubernetes and changing it do +so is not simple and complictes and the test set-up. + +To my mind, the "row in database" approach is straightforward enough that we don't have to offload this +to something external.