From 56cfb44370c2c89f7d952aff023e00ee88d5c8ff Mon Sep 17 00:00:00 2001 From: Liam Thompson <32779855+leemthompo@users.noreply.github.com> Date: Tue, 27 Aug 2024 10:33:14 +0200 Subject: [PATCH] Use attributes MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: István Zoltán Szabó --- docs/reference/intro.asciidoc | 4 ++-- docs/reference/modules/shard-ops.asciidoc | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/reference/intro.asciidoc b/docs/reference/intro.asciidoc index 1aa3cc03d16b2..ce8ad57c6f065 100644 --- a/docs/reference/intro.asciidoc +++ b/docs/reference/intro.asciidoc @@ -9,7 +9,7 @@ Use {es} to search, index, store, and analyze data of all shapes and sizes in ne {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 the <> 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 the heart of the the <> 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: @@ -18,7 +18,7 @@ Use {es} to search, index, store, and analyze data of all shapes and sizes in ne * *Vector database*. Store and search vectorized data, create vector embeddings with built-in and third-party 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 machine learning to automatically model the behavior of your data in real time. +* *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 industry verticals. diff --git a/docs/reference/modules/shard-ops.asciidoc b/docs/reference/modules/shard-ops.asciidoc index fdca5c898a3f7..7854633e8be9f 100644 --- a/docs/reference/modules/shard-ops.asciidoc +++ b/docs/reference/modules/shard-ops.asciidoc @@ -1,7 +1,7 @@ [[shard-allocation-relocation-recovery]] === Shard allocation, relocation, and recovery -Each index in Elasticsearch is divided into one or more <>. +Each index in {es} is divided into one or more <>. Each document in an index belongs to a single shard. A cluster can contain multiple copies of a shard. Each shard has one distinguished shard copy called the _primary_, and zero or more non-primary copies called _replicas_. The primary shard copy serves as the main entry point for all indexing operations. The operations on the primary shard copy are then forwarded to its replicas.