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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 happen.

{es} provides 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:

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.

== Data in: 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 as JSON documents. When you have multiple {es} nodes in a cluster, stored documents are distributed across the cluster and can be accessed immediately from any node.

When a document is stored, it is indexed and fully searchable in near real-time—​within 1 second. {es} uses a data structure called an inverted index that supports very fast full-text searches. An inverted index lists every unique word that appears in any document and identifies all of the documents each word occurs in.

An index can be thought of as an optimized collection of documents and each document is a collection of fields, which are the key-value pairs that contain your data. By default, {es} indexes all data in every field and each indexed field has a dedicated, optimized data structure. For example, text fields are stored in inverted indices, and numeric and geo fields are stored in BKD trees. The ability to use the per-field data structures to assemble and return search results is what makes {es} so fast.

{es} also has the ability to be schema-less, which means that documents can be indexed without explicitly specifying how to handle each of the different fields that might occur in a document. When dynamic mapping is enabled, {es} automatically detects and adds new fields to the index. This default 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} datatypes.

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 define mappings to take full control of how fields are stored and indexed.

Defining your own mappings enables you to:

  • Distinguish between full-text string fields and exact value string fields

  • Perform language-specific text analysis

  • Optimize fields for partial matching

  • Use custom date formats

  • Use data types such as geo_point and geo_shape that cannot be automatically detected

It’s often useful to index the same field in different ways for different purposes. For example, you might want to index a string field as both a text field for full-text search and as a keyword field for sorting or aggregating your data. Or, you might choose to use more than one language analyzer to process the contents of a string field that contains user input.

The analysis chain that is applied to a full-text field during indexing is also 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.

== Information out: 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 of search capabilities built on the Apache Lucene search engine library.

{es} provides a simple, coherent REST API for managing your cluster and indexing and searching your data. For testing purposes, you can easily submit requests directly from the command line or through the Developer Console in {kib}. From your applications, you can use the {es} client for your language of choice: Java, JavaScript, Go, .NET, PHP, Perl, Python or Ruby.

Searching your data

The {es} REST APIs support structured queries, full text queries, and complex queries that combine the two. Structured queries are similar to the types of queries you can construct in SQL. For example, you could search the gender and age fields in your employee index and sort the matches by the hire_date field. Full-text queries find all documents that match the query string and return them sorted by relevance—how good a match they are for your search terms.

In addition to searching for individual terms, you can perform phrase searches, similarity searches, and prefix searches, and get autocomplete suggestions.

Have geospatial or other numerical data that you want to search? {es} indexes non-textual data in optimized data structures that support high-performance geo and numerical queries.

You can access all of these search capabilities using {es}'s comprehensive JSON-style query language (Query DSL). You can also construct SQL-style queries to search and aggregate data natively inside {es}, and JDBC and ODBC drivers enable a broad range of third-party applications to interact with {es} via SQL.

Analyzing your data

{es} aggregations enable you to build complex summaries of your data and gain insight into key metrics, patterns, and trends. Instead of just finding the proverbial “needle in a haystack”, aggregations enable you to answer questions like:

  • How many needles are in the haystack?

  • What is the average length of the needles?

  • What is the median length of the needles, broken down by manufacturer?

  • How many needles were added to the haystack in each of the last six months?

You can also use aggregations to answer more subtle questions, such as:

  • What are your most popular needle manufacturers?

  • Are there any unusual or anomalous clumps of needles?

Because aggregations leverage the same data-structures used for search, they are also very fast. This enables you to analyze and visualize your data in real time. Your reports and dashboards update as your data changes so you can take action based on the latest information.

What’s more, aggregations operate alongside search requests. You can search documents, filter results, and perform analytics at the same time, on the same data, in a single request. And because aggregations are calculated in the context of a particular search, you’re not just displaying a count of all size 7 needles, you’re displaying a count of the size 7 needles that match your users' search criteria—​for example, all size 7 non-stick embroidery needles.

But wait, there’s more

Want to automate the analysis of your time-series data? You can use {ml-docs}/ml-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 and resilience: clusters, nodes, and shards

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 increase capacity and {es} automatically distributes your data and query load across all of the available nodes. No need to overhaul your application, {es} knows how to balance multi-node clusters to provide scale and high availability. The more nodes, the merrier.

How does this work? Under the covers, an {es} index is really just a logical grouping of one or more physical shards, where each shard is actually a self-contained index. By distributing the documents in an index across multiple shards, and distributing those shards across multiple nodes, {es} can ensure redundancy, which both protects against hardware failures and increases query capacity as nodes are added to a cluster. As the cluster grows (or shrinks), {es} automatically migrates shards to rebalance the cluster.

There are two types of shards: primaries and replicas. Each document in an index belongs to one primary shard. A replica shard is a copy of a primary shard. Replicas provide redundant copies of your data to protect against hardware failure and increase capacity to serve read requests like searching or retrieving a document.

The number of primary shards in an index is fixed at the time that an index is created, but the number of replica shards can be changed at any time, without interrupting indexing or query operations.

It depends…​

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 shards, the more overhead there is simply in maintaining those indices. The larger the shard size, the longer it takes to move shards around when {es} needs to rebalance a cluster.

Querying lots of small shards makes the processing per shard faster, but more queries means more overhead, so querying a smaller number of larger shards might be faster. In short…​it depends.

As a starting point:

  • Aim to keep the average shard size between a few GB and a few tens of GB. For use cases with time-based data, it is common to see shards in the 20GB to 40GB range.

  • Avoid the gazillion shards problem. The number of shards a node can hold is proportional to the available heap space. As a general rule, the number of shards per GB of heap space should be less than 20.

The best way to determine the optimal configuration for your use case is through testing with your own data and queries.

In case of disaster

For performance reasons, the nodes within a cluster need to be on the same network. Balancing shards in a cluster across nodes in different data centers simply takes too long. But high-availability architectures demand that you avoid putting all of your eggs in one basket. In the event of a major outage in one location, servers in another location need to be able to take over. Seamlessly. The answer? {ccr-cap} (CCR).

CCR provides a way to automatically synchronize indices from your primary cluster to a secondary remote cluster that can serve as a hot backup. If the primary cluster fails, the secondary cluster can take over. You can also use CCR to create secondary clusters to serve read requests in geo-proximity to your users.

{ccr-cap} is active-passive. The index on the primary cluster is the active leader index and handles all write requests. Indices replicated to secondary clusters are read-only followers.

Care and feeding

As with any enterprise system, you need tools to secure, manage, and monitor your {es} clusters. Security, monitoring, and administrative features that are integrated into {es} enable you to use {kibana-ref}/introduction.html[{kib}] as a control center for managing a cluster. Features like data rollups and index lifecycle management help you intelligently manage your data over time.