Why elasticsearch

Elasticsearch uses a data structure called an inverted index , which is designed to allow 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. During the indexing process, Elasticsearch stores documents and builds an inverted index to make the document data searchable in near real-time. Logstash, one of the core products of the Elastic Stack, is used to aggregate and process data and send it to Elasticsearch.

Logstash is an open source, server-side data processing pipeline that enables you to ingest data from multiple sources simultaneously and enrich and transform it before it is indexed into Elasticsearch.

Kibana is a data visualization and management tool for Elasticsearch that provides real-time histograms, line graphs, pie charts, and maps. Kibana also includes advanced applications such as Canvas, which allows users to create custom dynamic infographics based on their data, and Elastic Maps for visualizing geospatial data.

Elasticsearch is fast. Because Elasticsearch is built on top of Lucene, it excels at full-text search. Elasticsearch is also a near real-time search platform, meaning the latency from the time a document is indexed until it becomes searchable is very short — typically one second. As a result, Elasticsearch is well suited for time-sensitive use cases such as security analytics and infrastructure monitoring.

Elasticsearch is distributed by nature. The documents stored in Elasticsearch are distributed across different containers known as shards , which are duplicated to provide redundant copies of the data in case of hardware failure.

The distributed nature of Elasticsearch allows it to scale out to hundreds or even thousands of servers and handle petabytes of data.

Elasticsearch comes with a wide set of features. In addition to its speed, scalability, and resiliency, Elasticsearch has a number of powerful built-in features that make storing and searching data even more efficient, such as data rollups and index lifecycle management. The Elastic Stack simplifies data ingest, visualization, and reporting.

Integration with Beats and Logstash makes it easy to process data before indexing into Elasticsearch. And Kibana provides real-time visualization of Elasticsearch data as well as UIs for quickly accessing application performance monitoring APM , logs, and infrastructure metrics data. Additional free features are available under the Elastic License, and paid subscriptions provide access to support as well as advanced features such as alerting and machine learning.

The official distribution of Elasticsearch is available on the Elastic website. Elasticsearch is an free and open project managed by Elastic. A cluster is a collection of related nodes that, together, contain all of the data from an index. We clearly cannot store the entire index there.

However, we have the option of adding another node, so that Elasticsearch can store data on both nodes. To do this, Elasticsearch uses sharding, a mechanism to separate the index in multiple pieces. This is how Elasticsearch horizontally scales the data. Note that an index contains a single shard by default, but we can configure the number of shards. Elasticsearch can scale up to thousands of servers and accommodate petabytes of data.

Its enormous capacity results from its elaborate, distributed architecture. However, things can still go wrong from time to time. So, how does Elasticsearch handle those cases? Replicated shards are called primary shards.

Replica shards can serve search requests just like a primary shard. Similar to sharding, the number of replicas is configurable. Elasticsearch places replica shards on a different node than the primary shard they belong to, which is why we need a minimum of two nodes to use replication. It depends! We should consider the use case, resources, data, and many other factors.

However, guidelines come in handy. First, decide how critical the data is. Second, ask yourself: can I restore the data from other sources? And if so, is downtime acceptable while re-indexing the data? When you load a document into Elasticsearch, its text fields go through an analysis process.

An analyzer is composed of character filters, tokenizers, and token filters. The standard analyzer does not perform character filtering. It uses a tokenizer that splits by whitespace, removes common symbols such as punctuation marks, and uses a token filter that lowercases the words. Note that we can use a custom analyzer fully adapted to our needs, but in most cases, the standard analyzer is enough.

Once the analyzer finishes this process, the result is stored in something called the inverted index. This structure represents a mapping between the terms and which documents contain those terms. By looking in the inverted index instead of the JSON documents, Elasticsearch manages to perform a fast-full text search.

Elasticsearch allows us to store and search large volumes of data very quickly. It can also handle typos and we can easily write complex queries to search by any criteria we want. It also allows us to aggregate data to obtain statistics. It scales data horizontally, has a failover mechanism in place, and can do so much more! Elasticsearch can provide search-as-you-type functionality and use machine learning to detect anomalies in the data and make predictions.

However, there is a steep learning curve for implementing this product and in most organizations. This is especially true in cases where companies have multiple data sources besides Elasticsearch—since Kibana only works with Elasticsearch data.

A good alternative is Knowi , an analytics platform that natively integrates with Elasticsearch and allows even non-technical business users to create visualizations and perform analytics on Elasticsearch data without prior knowledge or expertise of the ELK Stack. Netflix relies on the ELK Stack across various use cases to monitor and analyze customer service operations and security logs. For example, Elasticsearch is the underlying engine behind their messaging system.

In addition, the company chose Elasticsearch for its automatic sharding and replication, flexible schema, nice extension model, and ecosystem with many plugins. Netflix has steadily increased their use of Elasticsearch from a few isolated deployments to over a dozen clusters consisting of several hundred nodes.

Walmart utilizes the Elastic Stack to reveal the hidden potential of its data to gain insights about customer purchasing patterns, track store performance metrics, and holiday analytics — all in near real-time. So what is Elasticsearch? Happy searching! Skip to content. March 7, Ralf Abueg. Updated: September 21, Before we jump into it, if you have a project and are trying to visualize your Elasticsearch data, take a look at our Elasticsearch Analytics page.

What is Elasticsearch? How does Elasticsearch work? Logical Concepts Documents Documents are the basic unit of information that can be indexed in Elasticsearch expressed in JSON, which is the global internet data interchange format. Indices An index is a collection of documents that have similar characteristics. Visual Representation of an Inverted Index Backend Components Cluster An Elasticsearch cluster is a group of one or more node instances that are connected together.

Node A node is a single server that is a part of a cluster. Shards Elasticsearch provides the ability to subdivide the index into multiple pieces called shards.

The Elastic Stack ELK Elasticsearch is the central component of the Elastic Stack, a set of open-source tools for data ingestion, enrichment, storage, analysis, and visualization. Kibana Kibana is a data visualization and management tool for Elasticsearch that provides real-time histograms, line graphs, pie charts, and maps. Logstash Logstash is used to aggregate and process data and send it to Elasticsearch.

Beats Beats is a collection of lightweight, single-purpose data shipping agents used to send data from hundreds or thousands of machines and systems to Logstash or Elasticsearch.

What is Elasticsearch used for? Primary Use Cases Application search —- For applications that rely heavily on a search platform for the access, retrieval, and reporting of data. Company Use Cases Netflix Netflix relies on the ELK Stack across various use cases to monitor and analyze customer service operations and security logs.

Walmart Walmart utilizes the Elastic Stack to reveal the hidden potential of its data to gain insights about customer purchasing patterns, track store performance metrics, and holiday analytics — all in near real-time. Hey, I hope my article was useful. Share This Post. Share on facebook.