elasticsearch/docs/internal/DistributedArchitectureGuide.md

Distributed Area Internals

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.

Networking

ThreadPool

(We have many thread pools, what and why)

ActionListener

See the Javadocs for ActionListener

(TODO: add useful starter references and explanations for a range of Listener classes. Reference the Netty section.)

Chunk Encoding

XContent

Performance

Netty

(long running actions should be forked off of the Netty thread. Keep short operations to avoid forking costs)

Work Queues

RestClient

The RestClient is primarily used in testing, to send requests against cluster nodes in the same format as would users. There are some uses of RestClient, via RestClientBuilder, in the production code. For example, remote reindex leverages the RestClient internally as the REST client to the remote elasticsearch cluster, and to take advantage of the compatibility of RestClient requests with much older elasticsearch versions. The RestClient is also used externally by the Java API Client to communicate with Elasticsearch.

Cluster Coordination

(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

The Metadata of a ClusterState is persisted on disk and comprises information from two categories:

1. Cluster scope information such as clusterUUID, CoordinationMetadata
2. Project scope information (ProjectMetadata) such as indices and templates belong to each project.

Some concepts are applicable to both cluster and project scopes, e.g. persistent tasks. The state of a persistent task is therefore stored accordingly depending on the task's scope.

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?)

Persistence

(Sketch ephemeral vs persisted cluster state.)

(what's the format for persisted metadata)

Replication

(More Topics: ReplicationTracker concepts / highlights.)

What is a Shard

Primary Shard Selection

(How a primary shard is chosen)

Versioning

(terms and such)

How Data Replicates

(How an index write replicates across shards -- TransportReplicationAction?)

Consistency Guarantees

(What guarantees do we give the user about persistence and readability?)

Locking

(rarely use locks)

ShardLock

Translog / Engine Locking

Lucene Locking

Engine

(What does Engine mean in the distrib layer? Distinguish Engine vs Directory vs Lucene)

(High level explanation of how translog ties in with Lucene)

(contrast Lucene vs ES flush / refresh / fsync)

Refresh for Read

(internal vs external reader manager refreshes? flush vs refresh)

Reference Counting

Store

(Data lives beyond a high level IndexShard instance. Continue to exist until all references to the Store go away, then Lucene data is removed)

Translog

(Explain checkpointing and generations, when happens on Lucene flush / fsync)

(Concurrency control for flushing)

(VersionMap)

Translog Truncation

Direct Translog Read

Index Version

Lucene

(copy a sketch of the files Lucene can have here and explain)

(Explain about SearchIndexInput -- IndexWriter, IndexReader -- and the shared blob cache)

(Lucene uses Directory, ES extends/overrides the Directory class to implement different forms of file storage. Lucene contains a map of where all the data is located in files and offsites, and fetches it from various files. ES doesn't just treat Lucene as a storage engine at the bottom (the end) of the stack. Rather ES has other information that works in parallel with the storage engine.)

Segment Merges

Recovery

(All shards go through a 'recovery' process. Describe high level. createShard goes through this code.)

(How is the translog involved in recovery?)

Create a Shard

Local Recovery

Peer Recovery

Snapshot Recovery

Recovery Across Server Restart

(partial shard recoveries survive server restart? reestablishRecovery? How does that work.)

How a Recovery Method is Chosen

Data Tiers

(Frozen, warm, hot, etc.)

Allocation

Indexes and Shards

Each index consists of a fixed number of primary shards. The number of primary shards cannot be changed for the lifetime of the index. Each primary shard can have zero-to-many replicas used for data redundancy. The number of replicas per shard can be changed dynamically.

The allocation assignment status of each shard copy is tracked by its ShardRoutingState. The RoutingTable and RoutingNodes objects are responsible for tracking the data nodes to which each shard in the cluster is allocated: see the routing package javadoc for more details about these structures.

Core Components

The DesiredBalanceShardsAllocator is what runs shard allocation decisions. It leverages the DesiredBalanceComputer to produce DesiredBalance instances for the cluster based on the latest cluster changes (add/remove nodes, create/remove indices, load, etc.). Then the DesiredBalanceReconciler is invoked to choose the next steps to take to move the cluster from the current shard allocation to the latest computed DesiredBalance shard allocation. The DesiredBalanceReconciler will apply changes to a copy of the RoutingNodes, which is then published in a cluster state update that will reach the data nodes to start the individual shard recovery/deletion/move work.

The DesiredBalanceReconciler is throttled by cluster settings, like the max number of concurrent shard moves and recoveries per cluster and node: this is why the DesiredBalanceReconciler will make, and publish via cluster state updates, incremental changes to the cluster shard allocation. The DesiredBalanceShardsAllocator is the endpoint for reroute requests, which may trigger immediate requests to the DesiredBalanceReconciler, but asynchronous requests to the DesiredBalanceComputer via the ContinuousComputation component. Cluster state changes that affect shard balancing (for example index deletion) all call some reroute method interface that reaches the DesiredBalanceShardsAllocator to run reconciliation and queue a request for the DesiredBalancerComputer, leading to desired balance computation and reconciliation actions. Asynchronous completion of a new DesiredBalance will also invoke a reconciliation action, as will cluster state updates completing shard moves/recoveries (unthrottling the next shard move/recovery).

The ContinuousComputation saves the latest desired balance computation request, which holds the cluster information at the time of that request, and a thread that runs the DesiredBalanceComputer. The ContinuousComputation thread takes the latest request, with the associated cluster information, feeds it into the DesiredBalanceComputer and publishes a DesiredBalance back to the DesiredBalanceShardsAllocator to use for reconciliation actions. Sometimes the ContinuousComputation thread's desired balance computation will be signalled to exit early and publish the initial DesiredBalance improvements it has made, when newer rebalancing requests (due to cluster state changes) have arrived, or in order to begin recovery of unassigned shards as quickly as possible.

Rebalancing Process

There are different priorities in shard allocation, reflected in which moves the DesiredBalancerReconciler selects to do first given that it can only move, recover, or remove a limited number of shards at once. The first priority is assigning unassigned shards, primaries being more important than replicas. The second is to move shards that violate any rule (such as node resource limits) as defined by an AllocationDecider. The AllocationDeciders holds a group of AllocationDecider implementations that place hard constraints on shard allocation. There is a decider, DiskThresholdDecider, that manages disk memory usage thresholds, such that further shards may not be allowed assignment to a node, or shards may be required to move off because they grew to exceed the disk space; or another, FilterAllocationDecider, that excludes a configurable list of indices from certain nodes; or MaxRetryAllocationDecider that will not attempt to recover a shard on a certain node after so many failed retries. The third priority is to rebalance shards to even out the relative weight of shards on each node: the intention is to avoid, or ease, future hot-spotting on data nodes due to too many shards being placed on the same data node. Node shard weight is based on a sum of factors: disk memory usage, projected shard write load, total number of shards, and an incentive to distribute shards within the same index across different nodes. See the WeightFunction and NodeAllocationStatsAndWeightsCalculator classes for more details on the weight calculations that support the DesiredBalanceComputer decisions.

Inter-Node Communicaton

The elected master node creates a shard allocation plan with the DesiredBalanceShardsAllocator and then selects incremental shard movements towards the target allocation plan with the DesiredBalanceReconciler. The results of the DesiredBalanceReconciler is an updated RoutingTable. The RoutingTable is part of the cluster state, so the master node updates the cluster state with the new (incremental) desired shard allocation information. The updated cluster state is then published to the data nodes. Each data node will observe any change in shard allocation related to itself and take action to achieve the new shard allocation by: initiating creation of a new empty shard; starting recovery (copying) of an existing shard from another data node; or removing a shard. When the data node finishes a shard change, a request is sent to the master node to update the shard as having finished recovery/removal in the cluster state. The cluster state is used by allocation as a fancy work queue: the master node conveys new work to the data nodes, which pick up the work and report back when done.

• See DesiredBalanceShardsAllocator#submitReconcileTask for the master node's cluster state update post-reconciliation.
• See IndicesClusterStateService#doApplyClusterState for the data node hook to observe shard changes in the cluster state.
• See ShardStateAction#sendShardAction for the data node request to the master node on completion of a shard state change.

Autoscaling

The Autoscaling API in ES (Elasticsearch) uses cluster and node level statistics to provide a recommendation for a cluster size to support the current cluster data and active workloads. ES Autoscaling is paired with an ES Cloud service that periodically polls the ES elected master node for suggested cluster changes. The cloud service will add more resources to the cluster based on Elasticsearch's recommendation. Elasticsearch by itself cannot automatically scale.

Autoscaling recommendations are tailored for the user based on user defined policies, composed of data roles (hot, frozen, etc.) and deciders. There's a public webinar on autoscaling, as well as the public Autoscaling APIs docs.

Autoscaling's current implementation is based primary on storage requirements, as well as memory capacity for ML and frozen tier. It does not yet support scaling related to search load. Paired with ES Cloud, autoscaling only scales upward, not downward, except for ML nodes that do get scaled up and down.

Plugin REST and TransportAction entrypoints

Autoscaling is a plugin. All the REST APIs can be found in autoscaling/rest/. GetAutoscalingCapacityAction is the capacity calculation operation REST endpoint, as opposed to the other rest commands that get/set/delete the policies guiding the capacity calculation. The Transport Actions can be found in autoscaling/action/, where TransportGetAutoscalingCapacityAction is the entrypoint on the master node for calculating the optimal cluster resources based on the autoscaling policies.

How cluster capacity is determined

AutoscalingMetadata implements Metadata.ClusterCustom in order to persist autoscaling policies. Each Decider is an implementation of AutoscalingDeciderService. The AutoscalingCalculateCapacityService is responsible for running the calculation.

TransportGetAutoscalingCapacityAction.computeCapacity is the entry point to AutoscalingCalculateCapacityService.calculate, which creates a AutoscalingDeciderResults for each autoscaling policy. AutoscalingDeciderResults.toXContent then determines the maximum required capacity to return to the caller. AutoscalingCapacity is the base unit of a cluster resources recommendation.

The TransportGetAutoscalingCapacityAction response is cached to prevent concurrent callers overloading the system: the operation is expensive. TransportGetAutoscalingCapacityAction contains a CapacityResponseCache. TransportGetAutoscalingCapacityAction.masterOperation calls through the CapacityResponseCache, into the AutoscalingCalculateCapacityService, to handle concurrent callers.

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.

Deciders

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

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.

There are several more Decider Services, implementing the AutoscalingDeciderService interface.

Snapshot / Restore

(We've got some good package level documentation that should be linked here in the intro)

(copy a sketch of the file system here, with explanation -- good reference)

Snapshot Repository

Creation of a Snapshot

(Include an overview of the coordination between data and master nodes, which writes what and when)

(Concurrency control: generation numbers, pending generation number, etc.)

(partial snapshots)

Deletion of a Snapshot

Restoring a Snapshot

Detecting Multiple Writers to a Single Repository

Task Management / Tracking

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.

Note

The Task management API is experimental/beta, its status and outstanding issues can be tracked here.

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 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 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).

What Tasks Are Tracked

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:

• Execution of TransportActions
  • NodeClient#executeLocally invokes TaskManager#registerAndExecute
  • RequestHandlerRegistry#processMessageReceived registers tasks for actions that are spawned to handle TransportRequests
• Publication of cluster state updates

Tracking a Task Across Threads and Nodes

ThreadContext

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 TransportRequests 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.

Headers

When a task is registered by a thread, a subset (defined by Task#HEADERS_TO_COPY and any ActionPlugins 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, and it will be associated with all tasks created on all nodes in the course of handling that request.

Parent/child relationships

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 TransportActions).

Kill / Cancel A Task

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 TransportActions 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.

Publishing Task Results

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 ActionRequests 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.

Persistent Tasks

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. A PersistentTasksExecutor can declare either project or cluster scope, but not both. A project scope task is not able to access data on a different project.

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 and ClusterPersistentTasksCustomMetadata).

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 to poll a remote cluster for updates
• HealthNodeTaskExecutor: Used to schedule work related to monitoring cluster health. This is currently the only example of a cluster scope persistent task.
• SystemIndexMigrationExecutor: Manages the migration of system indices after an upgrade

Integration with APM

Tasks are integrated with the ElasticSearch APM infrastructure. They implement the Traceable interface, and spans are published to represent the execution of each task.

Cross Cluster Replication (CCR)

(Brief explanation of the use case for CCR)

(Explain how this works at a high level, and details of any significant components / ideas.)

Cross Cluster Search

Indexing / CRUD

(Explain that the Distributed team is responsible for the write path, while the Search team owns the read path.)

(Generating document IDs. Same across shard replicas, _id field)

(Sequence number: different than ID)

Reindex

Locking

(what limits write concurrency, and how do we minimize)

Soft Deletes

Refresh

(explain visibility of writes, and reference the Lucene section for more details (whatever makes more sense explained there))

Server Startup

Server Shutdown

Closing a Shard

(this can also happen during shard reallocation, right? This might be a standalone topic, or need another section about it in allocation?...)