When a Nomad client that is running an allocation with
`max_client_disconnect` set misses a heartbeat the Nomad server will
update its status to `disconnected`.
Upon reconnecting, the client will make three main RPC calls:
- `Node.UpdateStatus` is used to set the client status to `ready`.
- `Node.UpdateAlloc` is used to update the client-side information about
allocations, such as their `ClientStatus`, task states etc.
- `Node.Register` is used to upsert the entire node information,
including its status.
These calls are made concurrently and are also running in parallel with
the scheduler. Depending on the order they run the scheduler may end up
with incomplete data when reconciling allocations.
For example, a client disconnects and its replacement allocation cannot
be placed anywhere else, so there's a pending eval waiting for
resources.
When this client comes back the order of events may be:
1. Client calls `Node.UpdateStatus` and is now `ready`.
2. Scheduler reconciles allocations and places the replacement alloc to
the client. The client is now assigned two allocations: the original
alloc that is still `unknown` and the replacement that is `pending`.
3. Client calls `Node.UpdateAlloc` and updates the original alloc to
`running`.
4. Scheduler notices too many allocs and stops the replacement.
This creates unnecessary placements or, in a different order of events,
may leave the job without any allocations running until the whole state
is updated and reconciled.
To avoid problems like this clients must update _all_ of its relevant
information before they can be considered `ready` and available for
scheduling.
To achieve this goal the RPC endpoints mentioned above have been
modified to enforce strict steps for nodes reconnecting:
- `Node.Register` does not set the client status anymore.
- `Node.UpdateStatus` sets the reconnecting client to the `initializing`
status until it successfully calls `Node.UpdateAlloc`.
These changes are done server-side to avoid the need of additional
coordination between clients and servers. Clients are kept oblivious of
these changes and will keep making these calls as they normally would.
The verification of whether allocations have been updates is done by
storing and comparing the Raft index of the last time the client missed
a heartbeat and the last time it updated its allocations.
Upon dequeuing an evaluation workers snapshot their state store at the
eval's wait index or later. This ensures we process an eval at a point
in time after it was created or updated. Processing an eval on an old
snapshot could cause any number of problems such as:
1. Since job registration atomically updates an eval and job in a single
raft entry, scheduling against indexes before that may not have the
eval's job or may have an older version.
2. The older the scheduler's snapshot, the higher the likelihood
something has changed in the cluster state which will cause the plan
applier to reject the scheduler's plan. This could waste work or
even cause eval's to be failed needlessly.
However, the workers run in parallel with a new server pulling the
cluster state from a peer. During this time, which may be many minutes
long, the state store is likely far behind the minimum index required
to process evaluations.
This PR addresses this by adding an additional long backoff period after
an eval is nacked. If the scheduler's indexes catches up within the
additional backoff, it will unblock early to dequeue the next eval.
When the server shuts down we'll get a `context.Canceled` error from the state
store method. We need to bubble this error up so that other callers can detect
it. Handle this case separately when waiting after dequeue so that we can warn
on shutdown instead of throwing an ambiguous error message with just the text
"canceled."
While there may be more precise ways to block scheduling until the
server catches up, this approach adds little risk and covers additional
cases where a server may be temporarily behind due to a spike in load or
a saturated network.
For testing, we make the `raftSyncLimit` into a parameter on the worker's `run` method
so that we can run backoff tests without waiting 30+ seconds. We haven't followed thru
and made all the worker globals into worker parameters, because there isn't much
use outside of testing, but we can consider that in the future.
Co-authored-by: Tim Gross <tgross@hashicorp.com>
This change adds a new table that will store ACL binding rule
objects. The two indexes allow fast lookups by their ID, or by
which auth method they are linked to. Snapshot persist and
restore functionality ensures this table can be saved and
restored from snapshots.
In order to write and delete the object to state, new Raft messages
have been added.
All RPC request and response structs, along with object functions
such as diff and canonicalize have been included within this work
as it is nicely separated from the other areas of work.
* scheduler: create placements for non-register MRD
For multiregion jobs, the scheduler does not create placements on
registration because the deployment must wait for the other regions.
Once of these regions will then trigger the deployment to run.
Currently, this is done in the scheduler by considering any eval for a
multiregion job as "paused" since it's expected that another region will
eventually unpause it.
This becomes a problem where evals not triggered by a job registration
happen, such as on a node update. These types of regional changes do not
have other regions waiting to progress the deployment, and so they were
never resulting in placements.
The fix is to create a deployment at job registration time. This
additional piece of state allows the scheduler to differentiate between
a multiregion change, where there are other regions engaged in the
deployment so no placements are required, from a regional change, where
the scheduler does need to create placements.
This deployment starts in the new "initializing" status to signal to the
scheduler that it needs to compute the initial deployment state. The
multiregion deployment will wait until this deployment state is
persisted and its starts is set to "pending". Without this state
transition it's possible to hit a race condition where the plan applier
and the deployment watcher may step of each other and overwrite their
changes.
* changelog: add entry for #15325
After Deployments were added in Nomad 0.6.0, the `AllocUpdateRequestType` raft
log entry was no longer in use. Mark this as deprecated, remove the associated
dead code, and remove references to the metrics it emits from the docs. We'll
leave the entry itself just in case we encounter old raft logs that we need to
be able to safely load.
During unusual outage recovery scenarios on large clusters, a backlog of
millions of evaluations can appear. In these cases, the `eval delete` command can
put excessive load on the cluster by listing large sets of evals to extract the
IDs and then sending larges batches of IDs. Although the command's batch size
was carefully tuned, we still need to be JSON deserialize, re-serialize to
MessagePack, send the log entries through raft, and get the FSM applied.
To improve performance of this recovery case, move the batching process into the
RPC handler and the state store. The design here is a little weird, so let's
look a the failed options first:
* A naive solution here would be to just send the filter as the raft request and
let the FSM apply delete the whole set in a single operation. Benchmarking with
1M evals on a 3 node cluster demonstrated this can block the FSM apply for
several minutes, which puts the cluster at risk if there's a leadership
failover (the barrier write can't be made while this apply is in-flight).
* A less naive but still bad solution would be to have the RPC handler filter
and paginate, and then hand a list of IDs to the existing raft log
entry. Benchmarks showed this blocked the FSM apply for 20-30s at a time and
took roughly an hour to complete.
Instead, we're filtering and paginating in the RPC handler to find a page token,
and then passing both the filter and page token in the raft log. The FSM apply
recreates the paginator using the filter and page token to get roughly the same
page of evaluations, which it then deletes. The pagination process is fairly
cheap (only abut 5% of the total FSM apply time), so counter-intuitively this
rework ends up being much faster. A benchmark of 1M evaluations showed this
blocked the FSM apply for 20-30ms at a time (typical for normal operations) and
completes in less than 4 minutes.
Note that, as with the existing design, this delete is not consistent: a new
evaluation inserted "behind" the cursor of the pagination will fail to be
deleted.
This PR implements ACLAuthMethod type, acl_auth_methods table schema and crud state store methods. It also updates nomadSnapshot.Persist and nomadSnapshot.Restore methods in order for them to work with the new table, and adds two new Raft messages: ACLAuthMethodsUpsertRequestType and ACLAuthMethodsDeleteRequestType
This PR is part of the SSO work captured under ☂️ ticket #13120.
When replication of a single key fails, the replication loop breaks early and
therefore keys that fall later in the sorting order will never get
replicated. This is particularly a problem for clusters impacted by the bug that
caused #14981 and that were later upgraded; the keys that were never replicated
can now never be replicated, and so we need to handle them safely.
Included in the replication fix:
* Refactor the replication loop so that each key replicated in a function call
that returns an error, to make the workflow more clear and reduce nesting. Log
the error and continue.
* Improve stability of keyring replication tests. We no longer block leadership
on initializing the keyring, so there's a race condition in the keyring tests
where we can test for the existence of the root key before the keyring has
been initialize. Change this to an "eventually" test.
But these fixes aren't enough to fix#14981 because they'll end up seeing an
error once a second complaining about the missing key, so we also need to fix
keyring GC so the keys can be removed from the state store. Now we'll store the
key ID used to sign a workload identity in the Allocation, and we'll index the
Allocation table on that so we can track whether any live Allocation was signed
with a particular key ID.
The `Eval.Delete` endpoint has a helper that takes a list of jobs and allocs and
determines whether the eval associated with those is safe to delete (based on
their state). Filtering improvements to the `Eval.Delete` endpoint are going to
need this check to run in the state store itself for consistency.
Refactor to push this check down into the state store to keep the eventual diff
for that work reasonable.
This changes adds ACL role creation and deletion to the event
stream. It is exposed as a single topic with two types; the filter
is primarily the role ID but also includes the role name.
While conducting this work it was also discovered that the events
stream has its own ACL resolution logic. This did not account for
ACL tokens which included role links, or tokens with expiry times.
ACL role links are now resolved to their policies and tokens are
checked for expiry correctly.
An ACL roles name must be unique, however, a bug meant multiple
roles of the same same could be created. This fixes that problem
with checks in the RPC handler and state store.
The original design for workload identities and ACLs allows for operators to
extend the automatic capabilities of a workload by using a specially-named
policy. This has shown to be potentially unsafe because of naming collisions, so
instead we'll allow operators to explicitly attach a policy to a workload
identity.
This changeset adds workload identity fields to ACL policy objects and threads
that all the way down to the command line. It also a new secondary index to the
ACL policy table on namespace and job so that claim resolution can efficiently
query for related policies.
ACL Roles along with policies and global token will be replicated
from the authoritative region to all federated regions. This
involves a new replication loop running on the federated leader.
Policies and roles may be replicated at different times, meaning
the policies and role references may not be present within the
local state upon replication upsert. In order to bypass the RPC
and state check, a new RPC request parameter has been added. This
is used by the replication process; all other callers will trigger
the ACL role policy validation check.
There is a new ACL RPC endpoint to allow the reading of a set of
ACL Roles which is required by the replication process and matches
ACL Policies and Tokens. A bug within the ACL Role listing RPC has
also been fixed which returned incorrect data during blocking
queries where a deletion had occurred.
ACL tokens can now utilize ACL roles in order to provide API
authorization. Each ACL token can be created and linked to an
array of policies as well as an array of ACL role links. The link
can be provided via the role name or ID, but internally, is always
resolved to the ID as this is immutable whereas the name can be
changed by operators.
When resolving an ACL token, the policies linked from an ACL role
are unpacked and combined with the policy array to form the
complete auth set for the token.
The ACL token creation endpoint handles deduplicating ACL role
links as well as ensuring they exist within state.
When reading a token, Nomad will also ensure the ACL role link is
current. This handles ACL roles being deleted from under a token
from a UX standpoint.
Move conflict resolution implementation into the state store with a new Apply RPC.
This also makes the RPC for secure variables much more similar to Consul's KV,
which will help us support soft deletes in a post-1.4.0 version of Nomad.
Reimplement quotas in the state store functions.
Co-authored-by: Charlie Voiselle <464492+angrycub@users.noreply.github.com>
New ACL Role RPC endpoints have been created to allow the creation,
update, read, and deletion of ACL roles. All endpoints require a
management token; in the future readers will also be allowed to
view roles associated to their ACL token.
The create endpoint in particular is responsible for deduplicating
ACL policy links and ensuring named policies are found within
state. This is done within the RPC handler so we perform a single
loop through the links for slight efficiency.
This commit includes the new state schema for ACL roles along with
state interaction functions for CRUD actions.
The change also includes snapshot persist and restore
functionality and the addition of FSM messages for Raft updates
which will come via RPC endpoints.
Move the secure variables quota enforcement calls into the state store to ensure
quota checks are atomic with quota updates (in the same transaction).
Switch to a machine-size int instead of a uint64 for quota tracking. The
ENT-side quota spec is described as int, and negative values have a meaning as
"not permitted at all". Using the same type for tracking will make it easier to
the math around checks, and uint64 is infeasibly large anyways.
Add secure vars to quota HTTP API and CLI outputs and API docs.
When we delete a namespace, we check to ensure that there are no non-terminal
jobs or CSI volume, which also covers evals, allocs, etc. Secure variables are
also namespaces, so extend this check to them as well.
When we delete a namespace, we check to ensure that there are no non-terminal
jobs, which effectively covers evals, allocs, etc. CSI volumes are also
namespaced, so extend this check to cover CSI volumes.
Two new periodic core jobs have been added which handle removing
expired local and global tokens from state. The local core job is
run on every leader; the global core job is only run on the leader
within the authoritative region.
When applying a raft log to expire ACL tokens, we need to use a
timestamp provided by the leader so that the result is deterministic
across servers. Use leader's timestamp from RPC call
When secure variables are updated, we were adding the update to the
existing quota tracking without first checking whether it was an
update to an existing variable. In that case we need to add/subtract
only the difference between the new and existing quota usage.
The ACL token state schema has been updated to utilise two new
indexes which track expiration of tokens that are configured with
an expiration TTL or time. A new state function allows listing
ACL expired tokens which will be used by internal garbage
collection.
The ACL endpoint has been modified so that all validation happens
within a single function call. This is easier to understand and
see at a glance. The ACL token validation now also includes logic
for expiry TTL and times. The ACL endpoint upsert tests have been
condensed into a single, table driven test.
There is a new token canonicalize which provides a single place
for token canonicalization, rather than logic spread in the RPC
handler.
Plan rejections occur when the scheduler work and the leader plan
applier disagree on the feasibility of a plan. This may happen for valid
reasons: since Nomad does parallel scheduling, it is expected that
different workers will have a different state when computing placements.
As the final plan reaches the leader plan applier, it may no longer be
valid due to a concurrent scheduling taking up intended resources. In
these situations the plan applier will notify the worker that the plan
was rejected and that they should refresh their state before trying
again.
In some rare and unexpected circumstances it has been observed that
workers will repeatedly submit the same plan, even if they are always
rejected.
While the root cause is still unknown this mitigation has been put in
place. The plan applier will now track the history of plan rejections
per client and include in the plan result a list of node IDs that should
be set as ineligible if the number of rejections in a given time window
crosses a certain threshold. The window size and threshold value can be
adjusted in the server configuration.
To avoid marking several nodes as ineligible at one, the operation is rate
limited to 5 nodes every 30min, with an initial burst of 10 operations.
When the `Full` flag is passed for key rotation, we kick off a core
job to decrypt and re-encrypt all the secure variables so that they
use the new key.
* SV: CAS
* Implement Check and Set for Delete and Upsert
* Reading the conflict from the state store
* Update endpoint for new error text
* Updated HTTP api tests
* Conflicts to the HTTP api
* SV: structs: Update SV time to UnixNanos
* update mock to UnixNano; refactor
* SV: encrypter: quote KeyID in error
* SV: mock: add mock for namespace w/ SV
We need to track per-namespace storage usage for secure variables even
in Nomad OSS so that a cluster can be seamlessly upgraded from OSS to
ENT without having to re-calculate quota usage.
Provide a hook in the upsert RPC for enforcement of quotas in
ENT. This will be a no-op in Nomad OSS.
* Add Path only index for SecureVariables
* Add GetSecureVariablesByPrefix; refactor tests
* Add search for SecureVariables
* Add prefix search for secure variables
This PR splits SecureVariable into SecureVariableDecrypted and
SecureVariableEncrypted in order to use the type system to help
verify that cleartext secret material is not committed to file.
* Make Encrypt function return KeyID
* Split SecureVariable
Co-authored-by: Tim Gross <tgross@hashicorp.com>
After internal design review, we decided to remove exposing algorithm
choice to the end-user for the initial release. We'll solve nonce
rotation by forcing rotations automatically on key GC (in a core job,
not included in this changeset). Default to AES-256 GCM for the
following criteria:
* faster implementation when hardware acceleration is available
* FIPS compliant
* implementation in pure go
* post-quantum resistance
Also fixed a bug in the decoding from keystore and switched to a
harder-to-misuse encoding method.
