Analysis is the process of converting full text to terms. Depending on which analyzer is used, these phrases:
foo,barwill probably all result in the terms
bar. These terms are what is actually stored in the index.
A full text query (not a term query) for
FoO:bARwill also be analyzed to the terms
barand will thus match the terms stored in the index.
It is this process of analysis (both at index time and at search time) that allows Elasticsearch to perform full text queries.
- A cluster consists of one or more nodes which share the same cluster name. Each cluster has a single master node which is chosen automatically by the cluster and which can be replaced if the current master node fails.
- cross-cluster replication (CCR)
- The cross-cluster replication feature enables you to replicate indices in remote clusters to your local cluster. For more information, see Cross-cluster replication.
- cross-cluster search (CCS)
- The cross-cluster search feature enables any node to act as a federated client across multiple clusters. See Search across clusters.
A document is a JSON object (also known in other languages as a hash / hashmap / associative array) which contains zero or more fields, or key-value pairs.
The original JSON document that is indexed will be stored in the
_sourcefield, which is returned by default when getting or searching for a document.
A document contains a list of fields, or key-value pairs. The value can be a simple (scalar) value (eg a string, integer, date), or a nested structure like an array or an object. A field is similar to a column in a table in a relational database.
The mapping for each field has a field type (not to be confused with document type) which indicates the type of data that can be stored in that field, eg
object. The mapping also allows you to define (amongst other things) how the value for a field should be analyzed.
- A filter is a non-scoring query, meaning that it does not score documents. It is only concerned about answering the question - "Does this document match?". The answer is always a simple, binary yes or no. This kind of query is said to be made in a filter context, hence it is called a filter. Filters are simple checks for set inclusion or exclusion. In most cases, the goal of filtering is to reduce the number of documents that have to be examined.
- follower index
- Follower indices are the target indices for cross-cluster replication. They exist in your local cluster and replicate leader indices.
- force merge
- Manually trigger a merge to reduce the number of segments in each shard of an index and free up the space used by deleted documents. You should not force merge indices that are actively being written to. Merging is normally performed automatically, but you can use force merge after rollover to reduce the shards in the old index to a single segment. See the force merge API.
- Make an index read-only and minimize its memory footprint. Frozen indices can be searched without incurring the overhead of of re-opening a closed index, but searches are throttled and might be slower. You can freeze indices to reduce the overhead of keeping older indices searchable before you are ready to archive or delete them. See the freeze API.
The ID of a document identifies a document. The
index/idof a document must be unique. If no ID is provided, then it will be auto-generated. (also see routing)
- index alias
An index alias is a secondary name used to refer to one or more existing indices.
Most Elasticsearch APIs accept an index alias in place of an index name.
See Add index alias.
See Add index alias.
- index template
Defines settings and mappings to apply to new indexes that match a simple naming pattern, such as logs-*. An index template can also attach a lifecycle policy to the new index. Index templates are used to automatically configure indices created during rollover.
- leader index
- Leader indices are the source indices for cross-cluster replication. They exist on remote clusters and are replicated to follower indices.
A mapping can either be defined explicitly, or it will be generated automatically when a document is indexed.
A node is a running instance of Elasticsearch which belongs to a cluster. Multiple nodes can be started on a single server for testing purposes, but usually you should have one node per server.
At startup, a node will use unicast to discover an existing cluster with the same cluster name and will try to join that cluster.
- primary shard
You cannot change the number of primary shards in an index, once the index is created. However, an index can be split into a new index using the split API.
See also routing
A request for information from Elasticsearch. You can think of a query as a question, written in a way Elasticsearch understands. A search consists of one or more queries combined.
There are two types of queries: scoring queries and filters. For more information about query types, see Query and filter context.
Recovery automatically occurs during the following processes:
- To cycle through some or all documents in one or more indices, re-writing them into the same or new index in a local or remote cluster. This is most commonly done to update mappings, or to upgrade Elasticsearch between two incompatible index versions.
- replica shard
Each primary shard can have zero or more replicas. A replica is a copy of the primary shard, and has two purposes:
- increase failover: a replica shard can be promoted to a primary shard if the primary fails
increase performance: get and search requests can be handled by primary or replica shards.
By default, each primary shard has one replica, but the number of replicas can be changed dynamically on an existing index. A replica shard will never be started on the same node as its primary shard.
Redirect an alias to begin writing to a new index when the existing index reaches a certain age, number of docs, or size. The new index is automatically configured according to any matching index templates. For example, if you’re indexing log data, you might use rollover to create daily or weekly indices. See the rollover index API.
When you index a document, it is stored on a single primary shard. That shard is chosen by hashing the
routingvalue. By default, the
routingvalue is derived from the ID of the document or, if the document has a specified parent document, from the ID of the parent document (to ensure that child and parent documents are stored on the same shard).
+ Other than defining the number of primary and replica shards that an index should have, you never need to refer to shards directly. Instead, your code should deal only with an index.
- Reduce the number of primary shards in an index. You can shrink an index to reduce its overhead when the request volume drops. For example, you might opt to shrink an index once it is no longer the write index. See the shrink index API.
- source field
By default, the JSON document that you index will be stored in the
_sourcefield and will be returned by all get and search requests. This allows you access to the original object directly from search results, rather than requiring a second step to retrieve the object from an ID.
A term is an exact value that is indexed in Elasticsearch. The terms
FOOare NOT equivalent. Terms (i.e. exact values) can be searched for using term queries.
Text fields need to be analyzed at index time in order to be searchable as full text, and keywords in full text queries must be analyzed at search time to produce (and search for) the same terms that were generated at index time.
A type used to represent the type of document, e.g. an
user, or a
tweet. Types are deprecated and are in the process of being removed. See Removal of mapping types.
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