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Any time that you start an instance of Elasticsearch, you are starting a node. Acollection of connected nodes is called a cluster. If youare running a single node of Elasticsearch, then you have a cluster of one node.

As the cluster grows and in particular if you have large machine learning jobs orcontinuous transforms, consider separating dedicated master-eligible nodes fromdedicated data nodes, machine learning nodes, and transform nodes.

The master node is responsible for lightweight cluster-wide actions such ascreating or deleting an index, tracking which nodes are part of the cluster,and deciding which shards to allocate to which nodes. It is important forcluster health to have a stable master node.

Master nodes must have a path.data directory whose contentspersist across restarts, just like data nodes, because this is where thecluster metadata is stored. The cluster metadata describes how to read the datastored on the data nodes, so if it is lost then the data stored on the datanodes cannot be read.

It is important for the health of the cluster that the elected master node hasthe resources it needs to fulfill its responsibilities. If the elected masternode is overloaded with other tasks then the cluster will not operate well. Themost reliable way to avoid overloading the master with other tasks is toconfigure all the master-eligible nodes to be dedicated master-eligible nodeswhich only have the master role, allowing them to focus on managing thecluster. Master-eligible nodes will still also behave ascoordinating nodes that route requests from clients tothe other nodes in the cluster, but you should not use dedicated master nodesfor this purpose.

A small or lightly-loaded cluster may operate well if its master-eligible nodeshave other roles and responsibilities, but once your cluster comprises morethan a handful of nodes it usually makes sense to use dedicated master-eligiblenodes.

It may seem confusing to use the term "master-eligible" to describe avoting-only node since such a node is not actually eligible to become the masterat all. This terminology is an unfortunate consequence of history:master-eligible nodes are those nodes that participate in elections and performcertain tasks during cluster state publications, and voting-only nodes have thesame responsibilities even if they can never become the elected master.

High availability (HA) clusters require at least three master-eligible nodes, atleast two of which are not voting-only nodes. Such a cluster will be able toelect a master node even if one of the nodes fails.

Voting-only master-eligible nodes may also fill other roles in your cluster.For instance, a node may be both a data node and a voting-only master-eligiblenode. A dedicated voting-only master-eligible nodes is a voting-onlymaster-eligible node that fills no other roles in the cluster. To create adedicated voting-only master-eligible node, set:

Data nodes hold the shards that contain the documents you have indexed. Datanodes handle data related operations like CRUD, search, and aggregations.These operations are I/O-, memory-, and CPU-intensive. It is important tomonitor these resources and to add more data nodes if they are overloaded.

In a multi-tier deployment architecture, you use specialized data roles toassign data nodes to specific tiers: data_content,data_hot, data_warm,data_cold, or data_frozen. A node can belong to multiple tiers, but a nodethat has one of the specialized data roles cannot have the generic data role.

Hot data nodes are part of the hot tier.The hot tier is the Elasticsearch entry point for time series data and holds your most-recent,most-frequently-searched time series data.Nodes in the hot tier need to be fast for both reads and writes,which requires more hardware resources and faster storage (SSDs).For resiliency, indices in the hot tier should be configured to use one or more replicas.

Cold data nodes are part of the cold tier.When you no longer need to search time series data regularly, it can move fromthe warm tier to the cold tier. While still searchable, this tier is typicallyoptimized for lower storage costs rather than search speed.

Ingest nodes can execute pre-processing pipelines, composed of one or moreingest processors. Depending on the type of operations performed by the ingestprocessors and the required resources, it may make sense to have dedicatedingest nodes, that will only perform this specific task.

If you take away the ability to be able to handle master duties, to hold data,and pre-process documents, then you are left with a coordinating node thatcan only route requests, handle the search reduce phase, and distribute bulkindexing. Essentially, coordinating only nodes behave as smart load balancers.

Coordinating only nodes can benefit large clusters by offloading thecoordinating node role from data and master-eligible nodes. They join thecluster and receive the full cluster state, like every othernode, and they use the cluster state to route requests directly to theappropriate place(s).

A remote-eligible node acts as a cross-cluster client and connects toremote clusters. Once connected, you can searchremote clusters using cross-cluster search. You can also syncdata between clusters using cross-cluster replication.

The remote_cluster_client role is optional but strongly recommended.Otherwise, cross-cluster search fails when used in machine learning jobs or datafeeds. If you use cross-cluster search inyour anomaly detection jobs, the remote_cluster_client role is also required on allmaster-eligible nodes. Otherwise, the datafeed cannot start. See Remote-eligible node.

Each node checks the contents of its data path at startup. If it discoversunexpected data then it will refuse to start. This is to avoid importingunwanted dangling indices which can leadto a red cluster health. To be more precise, nodes without the data role willrefuse to start if they find any shard data on disk at startup, and nodeswithout both the master and data roles will refuse to start if they have anyindex metadata on disk at startup.

The contents of the path.data directory must persist across restarts, becausethis is where your data is stored. Elasticsearch requires the filesystem to act as if itwere backed by a local disk, but this means that it will work correctly onproperly-configured remote block devices (e.g. a SAN) and remote filesystems(e.g. NFS) as long as the remote storage behaves no differently from localstorage. You can run multiple Elasticsearch nodes on the same filesystem, but each Elasticsearchnode must have its own data path.

Elasticsearch has a powerful _search API that allows it to search against all indices on the local cluster. We recently released Elasticsearch 5.3.0 including a new functionality called Cross-Cluster Search that allows users to compose searches not just across local indices but also across clusters. This means that one can search against data that belongs to other remote clusters. Historically, the Tribe Node was used when searches needed to span multiple clusters, yet it works very differently. In this blog post, we will cover why we implemented Cross-Cluster Search, how it works and compares to the Tribe Node, and why we are convinced it is a step in the right direction for the future of federated search using Elasticsearch.

When we sat down to try and redesign the next-generation Tribe Node, we re-evaluated the problems that it was trying to solve. The goal was to make federated search possible without all the limitations that the Tribe Node provides, and without adding a specific API for it, so that maintaining such feature would also be easier. We realized that some of the features that the Tribe Node offers besides federated search are commodities. The Tribe Node supports many Elasticsearch APIs allowing, for instance, to retrieve the cluster state or nodes stats via a Tribe Node, which will return the information collected from all the remote clusters and merged into a single view. Merging information coming from different sources is nothing complicated though, and is easily performed on the client side by sending multiple requests to the different clusters. The hard problem that must be addressed on the server side is federated search. It involves a distributed scatter and gather to be executed on nodes belonging to multiple clusters as well as the result merging and reduction requiring internal knowledge. That is why we decided to focus on solving this specific problem in a sustainable and robust way by adding support for Cross-Cluster Search to the existing _search API.

The _search API allows Elasticsearch to execute searches, queries, aggregations, suggestions, and more against multiple indices, each one composed by one or more shards. When a Client sends a search request to an Elasticsearch cluster, the node that receives the request acts as the coordinating node for the whole execution of the request. It identifies which indices, shards, and nodes the search has to be executed against. While executing, all data nodes holding a shard that is queried receive requests in parallel, then each node executes the query phase locally and sends the results back to the coordinating node. The coordinating node waits for all shards to respond in order to reduce the results down to the top-N hits that need to be fetched from the shards. A second execution phase then fetches the top-N documents from the shards in order to return the results back to the Client.

Since Elasticsearch 5.3.0, it is possible to register remote clusters through the cluster update settings API under the search.remote namespace. Each cluster is identified by a cluster alias and a list of seed nodes that are used to discover other nodes belonging to the remote cluster, as follows:

Whenever a search request expands to an index on a remote cluster, the coordinating node resolves the shards of these indices on the remote cluster by sending one _search_shards request per cluster. Once the shards and remote data nodes are fetched, searches are executed just like any other search on the local cluster explained above, using the exact same code-paths which improves testability and robustness dramatically. 2b1af7f3a8