diff --git a/docs/reference/intro.asciidoc b/docs/reference/intro.asciidoc index 3fc23b44994a7..cd9c126e7b1fd 100644 --- a/docs/reference/intro.asciidoc +++ b/docs/reference/intro.asciidoc @@ -1,42 +1,70 @@ [[elasticsearch-intro]] == What is {es}? -_**You know, for search (and analysis)**_ - -{es} is the distributed search and analytics engine at the heart of -the {stack}. {ls} and {beats} facilitate collecting, aggregating, and -enriching your data and storing it in {es}. {kib} enables you to -interactively explore, visualize, and share insights into your data and manage -and monitor the stack. {es} is where the indexing, search, and analysis -magic happens. - -{es} provides near real-time search and analytics for all types of data. Whether you -have structured or unstructured text, numerical data, or geospatial data, -{es} can efficiently store and index it in a way that supports fast searches. -You can go far beyond simple data retrieval and aggregate information to discover -trends and patterns in your data. And as your data and query volume grows, the -distributed nature of {es} enables your deployment to grow seamlessly right -along with it. - -While not _every_ problem is a search problem, {es} offers speed and flexibility -to handle data in a wide variety of use cases: - -* Add a search box to an app or website -* Store and analyze logs, metrics, and security event data -* Use machine learning to automatically model the behavior of your data in real - time -* Use {es} as a vector database to create, store, and search vector embeddings -* Automate business workflows using {es} as a storage engine -* Manage, integrate, and analyze spatial information using {es} as a geographic - information system (GIS) -* Store and process genetic data using {es} as a bioinformatics research tool - -We’re continually amazed by the novel ways people use search. But whether -your use case is similar to one of these, or you're using {es} to tackle a new -problem, the way you work with your data, documents, and indices in {es} is -the same. + +{es-repo}[{es}] is a distributed search and analytics engine, scalable data store, and vector database built on Apache Lucene. +It's optimized for speed and relevance on production-scale workloads. +Use {es} to search, index, store, and analyze data of all shapes and sizes in near real time. + +[TIP] +==== +{es} has a lot of features. Explore the full list on the https://www.elastic.co/elasticsearch/features[product webpage^]. +==== + +{es} is the heart of the {estc-welcome-current}/stack-components.html[Elastic Stack] and powers the Elastic https://www.elastic.co/enterprise-search[Search], https://www.elastic.co/observability[Observability] and https://www.elastic.co/security[Security] solutions. + +{es} is used for a wide and growing range of use cases. Here are a few examples: + +* *Monitor log and event data*. Store logs, metrics, and event data for observability and security information and event management (SIEM). +* *Build search applications*. Add search capabilities to apps or websites, or build enterprise search engines over your organization's internal data sources. +* *Vector database*. Store and search vectorized data, and create vector embeddings with built-in and third-party natural language processing (NLP) models. +* *Retrieval augmented generation (RAG)*. Use {es} as a retrieval engine to augment Generative AI models. +* *Application and security monitoring*. Monitor and analyze application performance and security data effectively. +* *Machine learning*. Use {ml} to automatically model the behavior of your data in real-time. + +This is just a sample of search, observability, and security use cases enabled by {es}. +Refer to our https://www.elastic.co/customers/success-stories[customer success stories] for concrete examples across a range of industries. +// Link to demos, search labs chatbots + +[discrete] +[[elasticsearch-intro-elastic-stack]] +.What is the Elastic Stack? +******************************* +{es} is the core component of the Elastic Stack, a suite of products for collecting, storing, searching, and visualizing data. +https://www.elastic.co/guide/en/starting-with-the-elasticsearch-platform-and-its-solutions/current/stack-components.html[Learn more about the Elastic Stack]. +******************************* +// TODO: Remove once we've moved Stack Overview to a subpage? + +[discrete] +[[elasticsearch-intro-deploy]] +=== Deployment options + +To use {es}, you need a running instance of the {es} service. +You can deploy {es} in various ways: + +* <>. Get started quickly with a minimal local Docker setup. +* {cloud}/ec-getting-started-trial.html[*Elastic Cloud*]. {es} is available as part of our hosted Elastic Stack offering, deployed in the cloud with your provider of choice. Sign up for a https://cloud.elastic.co/registration[14 day free trial]. +* {serverless-docs}/general/sign-up-trial[*Elastic Cloud Serverless* (technical preview)]. Create serverless projects for autoscaled and fully managed {es} deployments. Sign up for a https://cloud.elastic.co/serverless-registration[14 day free trial]. + +**Advanced deployment options** + +* <>. Install, configure, and run {es} on your own premises. +* {ece-ref}/Elastic-Cloud-Enterprise-overview.html[*Elastic Cloud Enterprise*]. Deploy Elastic Cloud on public or private clouds, virtual machines, or your own premises. +* {eck-ref}/k8s-overview.html[*Elastic Cloud on Kubernetes*]. Deploy Elastic Cloud on Kubernetes. + +[discrete] +[[elasticsearch-next-steps]] +=== Learn more + +Here are some resources to help you get started: + +* <>. A beginner's guide to deploying your first {es} instance, indexing data, and running queries. +* https://elastic.co/webinars/getting-started-elasticsearch[Webinar: Introduction to {es}]. Register for our live webinars to learn directly from {es} experts. +* https://www.elastic.co/search-labs[Elastic Search Labs]. Tutorials and blogs that explore AI-powered search using the latest {es} features. +** Follow our tutorial https://www.elastic.co/search-labs/tutorials/search-tutorial/welcome[to build a hybrid search solution in Python]. +** Check out the https://github.com/elastic/elasticsearch-labs?tab=readme-ov-file#elasticsearch-examples--apps[`elasticsearch-labs` repository] for a range of Python notebooks and apps for various use cases. [[documents-indices]] -=== Data in: documents and indices +=== Documents and indices {es} is a distributed document store. Instead of storing information as rows of columnar data, {es} stores complex data structures that have been serialized @@ -65,8 +93,7 @@ behavior makes it easy to index and explore your data--just start indexing documents and {es} will detect and map booleans, floating point and integer values, dates, and strings to the appropriate {es} data types. -Ultimately, however, you know more about your data and how you want to use it -than {es} can. You can define rules to control dynamic mapping and explicitly +You can define rules to control dynamic mapping and explicitly define mappings to take full control of how fields are stored and indexed. Defining your own mappings enables you to: @@ -89,7 +116,7 @@ used at search time. When you query a full-text field, the query text undergoes the same analysis before the terms are looked up in the index. [[search-analyze]] -=== Information out: search and analyze +=== Search and analyze While you can use {es} as a document store and retrieve documents and their metadata, the real power comes from being able to easily access the full suite @@ -160,27 +187,8 @@ size 70 needles, you’re displaying a count of the size 70 needles that match your users' search criteria--for example, all size 70 _non-stick embroidery_ needles. -[discrete] -[[more-features]] -===== But wait, there’s more - -Want to automate the analysis of your time series data? You can use -{ml-docs}/ml-ad-overview.html[machine learning] features to create accurate -baselines of normal behavior in your data and identify anomalous patterns. With -machine learning, you can detect: - -* Anomalies related to temporal deviations in values, counts, or frequencies -* Statistical rarity -* Unusual behaviors for a member of a population - -And the best part? You can do this without having to specify algorithms, models, -or other data science-related configurations. - [[scalability]] -=== Scalability and resilience: clusters, nodes, and shards -++++ -Scalability and resilience -++++ +=== Scalability and resilience {es} is built to be always available and to scale with your needs. It does this by being distributed by nature. You can add servers (nodes) to a cluster to @@ -209,7 +217,7 @@ interrupting indexing or query operations. [discrete] [[it-depends]] -==== It depends... +==== Shard size and number of shards There are a number of performance considerations and trade offs with respect to shard size and the number of primary shards configured for an index. The more @@ -237,7 +245,7 @@ testing with your own data and queries]. [discrete] [[disaster-ccr]] -==== In case of disaster +==== Disaster recovery A cluster's nodes need good, reliable connections to each other. To provide better connections, you typically co-locate the nodes in the same data center or @@ -257,7 +265,7 @@ secondary clusters are read-only followers. [discrete] [[admin]] -==== Care and feeding +==== Security, management, and monitoring As with any enterprise system, you need tools to secure, manage, and monitor your {es} clusters. Security, monitoring, and administrative features @@ -265,3 +273,5 @@ that are integrated into {es} enable you to use {kibana-ref}/introduction.html[{ as a control center for managing a cluster. Features like <> and <> help you intelligently manage your data over time. + +Refer to <> for more information. \ No newline at end of file diff --git a/docs/reference/search/search-your-data/near-real-time.asciidoc b/docs/reference/search/search-your-data/near-real-time.asciidoc index 46a996c237c38..47618ecd9fd7a 100644 --- a/docs/reference/search/search-your-data/near-real-time.asciidoc +++ b/docs/reference/search/search-your-data/near-real-time.asciidoc @@ -2,7 +2,7 @@ [[near-real-time]] === Near real-time search -The overview of <> indicates that when a document is stored in {es}, it is indexed and fully searchable in _near real-time_--within 1 second. What defines near real-time search? +When a document is stored in {es}, it is indexed and fully searchable in _near real-time_--within 1 second. What defines near real-time search? Lucene, the Java libraries on which {es} is based, introduced the concept of per-segment search. A _segment_ is similar to an inverted index, but the word _index_ in Lucene means "a collection of segments plus a commit point". After a commit, a new segment is added to the commit point and the buffer is cleared.