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org.elasticsearch.plugin:analysis-kuromoji
Advanced tools
The Japanese (kuromoji) Analysis plugin integrates Lucene kuromoji analysis module into elasticsearch.
= Elasticsearch
Elasticsearch is a distributed search and analytics engine, scalable data store and vector database optimized for speed and relevance on production-scale workloads. Elasticsearch is the foundation of Elastic's open Stack platform. Search in near real-time over massive datasets, perform vector searches, integrate with generative AI applications, and much more.
Use cases enabled by Elasticsearch include:
... and more!
To learn more about Elasticsearch's features and capabilities, see our https://www.elastic.co/products/elasticsearch[product page].
To access information on https://www.elastic.co/search-labs/blog/categories/ml-research[machine learning innovations] and the latest https://www.elastic.co/search-labs/blog/categories/lucene[Lucene contributions from Elastic], more information can be found in https://www.elastic.co/search-labs[Search Labs].
[[get-started]] == Get started
The simplest way to set up Elasticsearch is to create a managed deployment with https://www.elastic.co/cloud/as-a-service[Elasticsearch Service on Elastic Cloud].
If you prefer to install and manage Elasticsearch yourself, you can download the latest version from https://www.elastic.co/downloads/elasticsearch[elastic.co/downloads/elasticsearch].
=== Run Elasticsearch locally
//// IMPORTANT: This content is replicated in the Elasticsearch guide. If you make changes, you must also update setup/set-up-local-dev-deployment.asciidoc. ////
To try out Elasticsearch on your own machine, we recommend using Docker and running both Elasticsearch and Kibana. Docker images are available from the https://www.docker.elastic.co[Elastic Docker registry].
NOTE: Starting in Elasticsearch 8.0, security is enabled by default.
The first time you start Elasticsearch, TLS encryption is configured automatically,
a password is generated for the elastic
user,
and a Kibana enrollment token is created so you can connect Kibana to your secured cluster.
For other installation options, see the https://www.elastic.co/guide/en/elasticsearch/reference/current/install-elasticsearch.html[Elasticsearch installation documentation].
Start Elasticsearch
. Install and start https://www.docker.com/products/docker-desktop[Docker Desktop]. Go to Preferences > Resources > Advanced and set Memory to at least 4GB.
<1> Replace {version} with the version of Elasticsearch you want to run.
+
When you start Elasticsearch for the first time, the generated elastic
user password and
Kibana enrollment token are output to the terminal.
+
NOTE: You might need to scroll back a bit in the terminal to view the password
and enrollment token.
. Copy the generated password and enrollment token and save them in a secure location. These values are shown only when you start Elasticsearch for the first time. You'll use these to enroll Kibana with your Elasticsearch cluster and log in.
Start Kibana
Kibana enables you to easily send requests to Elasticsearch and analyze, visualize, and manage data interactively.
<1> Replace {version} with the version of Kibana you want to run. + When you start Kibana, a unique URL is output to your terminal.
. To access Kibana, open the generated URL in your browser.
.. Paste the enrollment token that you copied when starting Elasticsearch and click the button to connect your Kibana instance with Elasticsearch.
.. Log in to Kibana as the elastic
user with the password that was generated
when you started Elasticsearch.
Send requests to Elasticsearch
You send data and other requests to Elasticsearch through REST APIs. You can interact with Elasticsearch using any client that sends HTTP requests, such as the https://www.elastic.co/guide/en/elasticsearch/client/index.html[Elasticsearch language clients] and https://curl.se[curl]. Kibana's developer console provides an easy way to experiment and test requests. To access the console, go to Management > Dev Tools.
Add data
You index data into Elasticsearch by sending JSON objects (documents) through the REST APIs.
Whether you have structured or unstructured text, numerical data, or geospatial data,
Elasticsearch efficiently stores and indexes it in a way that supports fast searches.
For timestamped data such as logs and metrics, you typically add documents to a data stream made up of multiple auto-generated backing indices.
To add a single document to an index, submit an HTTP post request that targets the index.
This request automatically creates the customer
index if it doesn't exist,
adds a new document that has an ID of 1, and
stores and indexes the firstname
and lastname
fields.
The new document is available immediately from any node in the cluster. You can retrieve it with a GET request that specifies its document ID:
To add multiple documents in one request, use the _bulk
API.
Bulk data must be newline-delimited JSON (NDJSON).
Each line must end in a newline character (\n
), including the last line.
Search
Indexed documents are available for search in near real-time.
The following search matches all customers with a first name of Jennifer
in the customer
index.
Explore
You can use Discover in Kibana to interactively search and filter your data. From there, you can start creating visualizations and building and sharing dashboards.
To get started, create a data view that connects to one or more Elasticsearch indices, data streams, or index aliases.
. Go to Management > Stack Management > Kibana > Data Views. . Select Create data view. . Enter a name for the data view and a pattern that matches one or more indices, such as customer. . Select Save data view to Kibana.
To start exploring, go to Analytics > Discover.
[[upgrade]] == Upgrade
To upgrade from an earlier version of Elasticsearch, see the https://www.elastic.co/guide/en/elasticsearch/reference/current/setup-upgrade.html[Elasticsearch upgrade documentation].
[[build-source]] == Build from source
Elasticsearch uses https://gradle.org[Gradle] for its build system.
Distributions are output to distribution/archives
.
To run the test suite, see xref:TESTING.asciidoc[TESTING].
[[docs]] == Documentation
For the complete Elasticsearch documentation visit https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html[elastic.co].
For information about our documentation processes, see the xref:docs/README.asciidoc[docs README].
[[examples]] == Examples and guides
The https://github.com/elastic/elasticsearch-labs[`elasticsearch-labs`] repo contains executable Python notebooks, sample apps, and resources to test out Elasticsearch for vector search, hybrid search and generative AI use cases.
[[contribute]] == Contribute
For contribution guidelines, see xref:CONTRIBUTING.md[CONTRIBUTING].
[[questions]] == Questions? Problems? Suggestions?
To report a bug or request a feature, create a https://github.com/elastic/elasticsearch/issues/new/choose[GitHub Issue]. Please ensure someone else hasn't created an issue for the same topic.
Need help using Elasticsearch? Reach out on the https://discuss.elastic.co[Elastic Forum] or https://ela.st/slack[Slack]. A fellow community member or Elastic engineer will be happy to help you out.
FAQs
Unknown package
We found that org.elasticsearch.plugin:analysis-kuromoji demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 0 open source maintainers collaborating on the project.
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