The Distributed Area contains indexing and coordination systems.
The index path stretches from the user REST command through shard routing down to each individual shard's translog and storage
engine. Reindexing is effectively reading from a source index and writing to a destination index (perhaps on different nodes).
The coordination side includes cluster coordination, shard allocation, cluster autoscaling stats, task management, and cross
cluster replication. Less obvious coordination systems include networking, the discovery plugin system, the snapshot/restore
logic, and shard recovery.
A guide to the general Elasticsearch components can be found [here](https://github.com/elastic/elasticsearch/blob/main/docs/internal/GeneralArchitectureGuide.md).
See the [Javadocs for `ActionListener`](https://github.com/elastic/elasticsearch/blob/main/server/src/main/java/org/elasticsearch/action/ActionListener.java)
(Managed by the TransportService, TransportActions must be registered there, too)
(Executing a TransportAction (either locally via NodeClient or remotely via TransportService) is where most of the authorization & other security logic runs)
(What actions, and why, are registered in TransportService but not NodeClient?)
### Direct Node to Node Transport Layer
(TransportService maps incoming requests to TransportActions)
(Sketch of important classes? Might inform more sections to add for details.)
(A NodeB can coordinate a search across several other nodes, when NodeB itself does not have the data, and then return a result to the caller. Explain this coordinating role)
### Node Roles
### Master Nodes
### Master Elections
(Quorum, terms, any eligibility limitations)
### Cluster Formation / Membership
(Explain joining, and how it happens every time a new master is elected)
#### Discovery
### Master Transport Actions
### Cluster State
#### Master Service
#### Cluster State Publication
(Majority concensus to apply, what happens if a master-eligible node falls behind / is incommunicado.)
#### Cluster State Application
(Go over the two kinds of listeners -- ClusterStateApplier and ClusterStateListener?)
[through the CapacityResponseCache]: https://github.com/elastic/elasticsearch/blob/v8.13.2/x-pack/plugin/autoscaling/src/main/java/org/elasticsearch/xpack/autoscaling/action/TransportGetAutoscalingCapacityAction.java#L97
### Where the data comes from
The Deciders each pull data from different sources as needed to inform their decisions. The
[DiskThresholdMonitor][] is one such data source. The Monitor runs on the master node and maintains
lists of nodes that exceed various disk size thresholds. [DiskThresholdSettings][] contains the
threshold settings with which the `DiskThresholdMonitor` runs.
The `ReactiveStorageDeciderService` tracks information that demonstrates storage limitations are causing
problems in the cluster. It uses [an algorithm defined here][]. Some examples are
- information from the `DiskThresholdMonitor` to find out whether nodes are exceeding their storage capacity
- number of unassigned shards that failed allocation because of insufficient storage
- the max shard size and minimum node size, and whether these can be satisfied with the existing infrastructure
[an algorithm defined here]: https://github.com/elastic/elasticsearch/blob/v8.13.2/x-pack/plugin/autoscaling/src/main/java/org/elasticsearch/xpack/autoscaling/storage/ReactiveStorageDeciderService.java#L158-L176
The `ProactiveStorageDeciderService` maintains a forecast window that [defaults to 30 minutes][]. It only
runs on data streams (ILM, rollover, etc), not regular indexes. It looks at past [index changes][] that
took place within the forecast window to [predict][] resources that will be needed shortly.
[defaults to 30 minutes]: https://github.com/elastic/elasticsearch/blob/v8.13.2/x-pack/plugin/autoscaling/src/main/java/org/elasticsearch/xpack/autoscaling/storage/ProactiveStorageDeciderService.java#L32
The tasks infrastructure is used to track currently executing operations in the Elasticsearch cluster. The [Task management API] provides an interface for querying, cancelling, and monitoring the status of tasks.
Each individual task is local to a node, but can be related to other tasks, on the same node or other nodes, via a parent-child relationship.
### Task tracking and registration
Tasks are tracked in-memory on each node in the node's [TaskManager], new tasks are registered via one of the [TaskManager#register] methods.
Registration of a task creates a [Task] instance with a unique-for-the-node numeric identifier, populates it with some metadata and stores it in the [TaskManager].
The [register][TaskManager#register] methods will return the registered [Task] instance, which can be used to interact with the task. The [Task] class is often sub-classed to include task-specific data and operations. Specific [Task] subclasses are created by overriding the [createTask][TaskAwareRequest#createTask] method on the [TaskAwareRequest] passed to the [TaskManager#register] methods.
When a task is completed, it must be unregistered via [TaskManager#unregister].
#### A note about task IDs
The IDs given to a task are numeric, supplied by a counter that starts at zero and increments over the life of the node process. So while they are unique in the individual node process, they would collide with IDs allocated after the node restarts, or IDs allocated on other nodes.
To better identify a task in the cluster scope, a tuple of persistent node ID and task ID is used. This is represented in code using the [TaskId] class and serialized as the string `{node-ID}:{local-task-ID}` (e.g. `oTUltX4IQMOUUVeiohTt8A:124`). While [TaskId] is safe to use to uniquely identify tasks _currently_ running in a cluster, it should be used with caution as it can collide with tasks that have run in the cluster in the past (i.e. tasks that ran prior to a cluster node restart).
The purpose of tasks is to provide management and visibility of the cluster workload. There is some overhead involved in tracking a task, so they are best suited to tracking non-trivial and/or long-running operations. For smaller, more trivial operations, visibility is probably better implemented using telemetry APIs.
Some examples of operations that are tracked using tasks include:
All [ThreadPool] threads have an associated [ThreadContext]. The [ThreadContext] contains a map of headers which carry information relevant to the operation currently being executed. For example, a thread spawned to handle a REST request will include the HTTP headers received in that request.
When threads submit work to an [ExecutorService] from the [ThreadPool], those spawned threads will inherit the [ThreadContext] of the thread that submitted them. When [TransportRequest]s are dispatched, the headers from the sending [ThreadContext] are included and then loaded into the [ThreadContext] of the thread handling the request. In these ways, [ThreadContext] is preserved across threads involved in an operation, both locally and on remote nodes.
[X-Opaque-Id API DOC]:https://www.elastic.co/guide/en/elasticsearch/reference/current/tasks.html#_identifying_running_tasks
When a task is registered by a thread, a subset (defined by [Task#HEADERS_TO_COPY] and any [ActionPlugin][ActionPlugin#getTaskHeaders]s loaded on the node) of the headers from the [ThreadContext] are copied into the [Task]'s set of headers.
One such header is `X-Opaque-Id`. This is a string that [can be submitted on REST requests][X-Opaque-Id API DOC], and it will be associated with all tasks created on all nodes in the course of handling that request.
Another way to track the operations of a task is by following the parent/child relationships. When registering a task it can be optionally associated with a parent task. Generally if an executing task initiates sub-tasks, the ID of the executing task will be set as the parent of any spawned tasks (see [ParentTaskAssigningClient], [TransportService#sendChildRequest] and [TaskAwareRequest#setParentTask] for how this is implemented for [TransportAction]s).
Some long-running tasks are implemented to be cancel-able. Cancellation of a task and its descendants can be done via the [Cancel Task REST API] or programmatically using [TaskManager#cancelTaskAndDescendants]. Perhaps the most common use of cancellation you will see is cancellation of [TransportAction]s dispatched from the REST layer when the client disconnects, to facilitate this we use the [RestCancellableNodeClient].
In order to support cancellation, the [Task] instance associated with the task must extend [CancellableTask]. It is the job of any workload tracked by a [CancellableTask] to periodically check whether it has been cancelled and, if so, finish early. We generally wait for the result of a cancelled task, so tasks can decide how they complete upon being cancelled, typically it's exceptionally with [TaskCancelledException].
When a [Task] extends [CancellableTask] the [TaskManager] keeps track of it and any child tasks that it spawns. When the task is cancelled, requests are sent to any nodes that have had child tasks submitted to them to ban the starting of any further children of that task, and any cancellable child tasks already running are themselves cancelled (see [BanParentRequestHandler]).
When a cancellable task dispatches child requests through the [TransportService], it registers a proxy response handler that will instruct the remote node to cancel that child and any lingering descendants in the event that it completes exceptionally (see [UnregisterChildTransportResponseHandler]). A typical use-case for this is when no response is received within the time-out, the sending node will cancel the remote action and complete with a timeout exception.
A list of tasks currently running in a cluster can be requested via the [Task management API], or the [cat task management API]. The former returns each task represented using [TaskResult], the latter returning a more compact [CAT] representation.
Some [ActionRequest]s allow the results of the actions they spawn to be stored upon completion for later retrieval. If [ActionRequest#getShouldStoreResult] returns true, a [TaskResultStoringActionListener] will be inserted into the chain of response listeners. [TaskResultStoringActionListener] serializes the [TaskResult] of the [TransportAction] and persists it in the `.tasks` index using the [TaskResultsService].
The [Task management API] also exposes an endpoint where a task ID can be specified, this form of the API will return currently running tasks, or completed tasks whose results were persisted. Note that although we use [TaskResult] to return task information from all the JSON APIs, the `error` or `response` fields will only ever be populated for stored tasks that are already completed.
Up until now we have discussed only ephemeral tasks. If we want a task to survive node failures, it needs to be registered as a persistent task at the cluster level.
Plugins can register persistent tasks definitions by implementing [PersistentTaskPlugin] and returning one or more [PersistentTasksExecutor] instances. These are collated into a [PersistentTasksExecutorRegistry] which is provided to [PersistentTasksNodeService] active on each node in the cluster, and a [PersistentTasksClusterService] active on the master.
The [PersistentTasksClusterService] runs on the master to manage the set of running persistent tasks. It periodically checks that all persistent tasks are assigned to live nodes and handles the creation, completion, removal and updates-to-the-state of persistent task instances in the cluster state (see [PersistentTasksCustomMetadata]).
The [PersistentTasksNodeService] monitors the cluster state to:
- Start any tasks allocated to it (tracked in the local [TaskManager] by an [AllocatedPersistentTask])
- Cancel any running tasks that have been removed ([AllocatedPersistentTask] extends [CancellableTask])
If a node leaves the cluster while it has a persistent task allocated to it, the master will re-allocate that task to a surviving node. To do this, it creates a new [PersistentTasksCustomMetadata.PersistentTask] entry with a higher `#allocationId`. The allocation ID is included any time the [PersistentTasksNodeService] communicates with the [PersistentTasksClusterService] about the task, it allows the [PersistentTasksClusterService] to ignore persistent task messages originating from stale allocations.
Some examples of the use of persistent tasks include:
- [ShardFollowTasksExecutor]: Defined by [cross-cluster replication](#cross-cluster-replication-ccr) to poll a remote cluster for updates
- [HealthNodeTaskExecutor]: Used to schedule work related to monitoring cluster health
- [SystemIndexMigrationExecutor]: Manages the migration of system indices after an upgrade
Tasks are integrated with the ElasticSearch APM infrastructure. They implement the [Traceable] interface, and [spans][APM Spans] are published to represent the execution of each task.
