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. You can also set up a 15 minute call with a member of our team to see if Knowi may be a good BI solution for your project.
When people ask, “what is Elasticsearch?”, some may answer that it’s “an index”, “a search engine”, an “analytics database”, “a big data solution”, that “it’s fast and scalable”, or that “it’s kind of like Google”. Depending on your level of familiarity with this technology, these answers may either bring you closer to an ah-ha moment or further confuse you. But the truth is, all of these answers are correct and that’s part of the appeal of Elasticsearch. Over the years, Elasticsearch and the ecosystem of components that’s grown around it called the “Elastic Stack” has been used for a growing number of use cases, from simple search on a website or document, collecting and analyzing log data, to a business intelligence tool for data analysis and visualization. So how did a simple search engine created by Elastic co-founder Shay Bannon for his wife’s cooking recipes grow to become today’s most popular enterprise search engine and one of the 10 most popular DBMS? We’ll answer that in this post by understanding what Elasticsearch is, how it works, and how it’s used. Let’s dive in.
What is Elasticsearch?
At its core, you can think of Elasticsearch as a server that can process JSON requests and give you back JSON data.
Elasticsearch is a distributed, open-source search and analytics engine built on Apache Lucene and developed in Java. It started as a scalable version of the Lucene open-source search framework then added the ability to horizontally scale Lucene indices. Elasticsearch allows you to store, search, and analyze huge volumes of data quickly and in near real-time and give back answers in milliseconds. It’s able to achieve fast search responses because instead of searching the text directly, it searches an index. It uses a structure based on documents instead of tables and schemas and comes with extensive REST APIs for storing and searching the data. At its core, you can think of Elasticsearch as a server that can process JSON requests and give you back JSON data.
How does Elasticsearch work?
To better understand how Elasticsearch works, let’s cover some basic concepts of how it organizes data and its backend components.
Documents are the basic unit of information that can be indexed in Elasticsearch expressed in JSON, which is the global internet data interchange format. You can think of a document like a row in a relational database, representing a given entity — the thing you’re searching for. In Elasticsearch, a document can be more than just text, it can be any structured data encoded in JSON. That data can be things like numbers, strings, and dates. Each document has a unique ID and a given data type, which describes what kind of entity the document is. For example, a document can represent an encyclopedia article or log entries from a web server.
An index is a collection of documents that have similar characteristics. An index is the highest level entity that you can query against in Elasticsearch. You can think of the index as being similar to a database in a relational database schema. Any documents in an index are typically logically related. In the context of an e-commerce website, for example, you can have an index for Customers, one for Products, one for Orders, and so on. An index is identified by a name that is used to refer to the index while performing indexing, search, update, and delete operations against the documents in it.
An index in Elasticsearch is actually what’s called an inverted index, which is the mechanism by which all search engines work. It is a data structure that stores a mapping from content, such as words or numbers, to its locations in a document or a set of documents. Basically, it is a hashmap-like data structure that directs you from a word to a document. An inverted index doesn’t store strings directly and instead splits each document up to individual search terms (i.e. each word) then maps each search term to the documents those search terms occur within. For example, in the image below, the term “best” occurs in document 2, so it is mapped to that document. This serves as a quick look-up of where to find search terms in a given document. By using distributed inverted indices, Elasticsearch quickly finds the best matches for full-text searches from even very large data sets.
An Elasticsearch cluster is a group of one or more node instances that are connected together. The power of an Elasticsearch cluster lies in the distribution of tasks, searching, and indexing, across all the nodes in the cluster.
A node is a single server that is a part of a cluster. A node stores data and participates in the cluster’s indexing and search capabilities. An Elasticsearch node can be configured in different ways:
Master Node — Controls the Elasticsearch cluster and is responsible for all cluster-wide operations like creating/deleting an index and adding/removing nodes.
Data Node — Stores data and executes data-related operations such as search and aggregation.
Client Node — Forwards cluster requests to the master node and data-related requests to data nodes.
Elasticsearch provides the ability to subdivide the index into multiple pieces called shards. Each shard is in itself a fully-functional and independent “index” that can be hosted on any node within a cluster. By distributing the documents in an index across multiple shards, and distributing those shards across multiple nodes, Elasticsearch can ensure redundancy, which both protects against hardware failures and increases query capacity as nodes are added to a cluster.
Elasticsearch allows you to make one or more copies of your index’s shards which are called “replica shards” or just “replicas”. Basically, a replica shard is a copy of a primary shard. Each document in an index belongs to one 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 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. It is commonly referred to as the “ELK” stack after its components Elasticsearch, Logstash, and Kibana and now also includes Beats. Although a search engine at its core, users started using Elasticsearch for log data and wanted a way to easily ingest and visualize that data.
Kibana is a data visualization and management tool for Elasticsearch that provides real-time histograms, line graphs, pie charts, and maps. It lets you visualize your Elasticsearch data and navigate the Elastic Stack. You can select the way you give shape to your data by starting with one question to find out where the interactive visualization will lead you. For example, since Kibana is often used for log analysis, it allows you to answer questions about where your web hits are coming from, your distribution URLs, and so on. If you’re not building your own application on top of Elasticsearch, Kibana is a great way to search and visualize your index with a powerful and flexible UI. However, a major drawback is that every visualization can only work against a single index/index pattern. So if you have indices with strictly different data, you’ll have to create separate visualizations for each. For more advanced use cases, Knowi is a good option. It allows you to join your Elasticsearch data across multiple indexes and blend it with other SQL/NoSQL/REST-API data sources, then create visualizations from it in a business-user friendly UI.
Logstash is used to aggregate and process data and send it to Elasticsearch. It is an open-source, server-side data processing pipeline that ingests data from a multitude of sources simultaneously, transforms it, and then sends it to collect. It also transforms and prepares data regardless of format by identifying named fields to build structure, and transform them to converge on a common format. For example, since data is often scattered across different systems in various formats, Logstash allows you to tie different systems together like web servers, databases, Amazon services, etc. and publish data to wherever it needs to go in a continuous streaming fashion.
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. Beats are great for gathering data as they can sit on your servers, with your containers, or deploy as functions then centralize data in Elasticsearch. For example, Filebeat can sit on your server, monitor log files as they come in, parses them, and import into Elasticsearch in near-real-time.
What is Elasticsearch used for?
Now that we have a general understanding of what Elasticsearch is, the logical concepts behind it, and its architecture, we have a better sense of why and how it can be used for a variety of use cases. Below, we’ll examine some of Elasticsearch’s primary use cases and provide examples of how companies are using it today.
Primary Use Cases
Application search —- For applications that rely heavily on a search platform for the access, retrieval, and reporting of data.
Website search —- Websites which store a lot of content find Elasticsearch a very useful tool for effective and accurate searches. It’s no surprise that Elasticsearch is steadily gaining ground in the site search domain sphere.
Enterprise search —- Elasticsearch allows enterprise-wide search that includes document search, E-commerce product search, blog search, people search, and any form of search you can think of. In fact, it has steadily penetrated and replaced the search solutions of most of the popular websites we use on a daily basis. From a more enterprise-specific perspective, Elasticsearch is used to great success in company intranets.
Logging and log analytics —- As we’ve discussed, Elasticsearch is commonly used for ingesting and analyzing log data in near-real-time and in a scalable manner. It also provides important operational insights on log metrics to drive actions.
Infrastructure metrics and container monitoring —- Many companies use the ELK stack to analyze various metrics. This may involve gathering data across several performance parameters that vary by use case.
Security analytics —- Another major analytics application of Elasticsearch is security analysis. Access logs and similar logs concerning system security can be analyzed with the ELK stack, providing a more complete picture of what’s going on across your systems in real-time.
Business analytics —- Many of the built-in features available within the ELK Stack makes it a good option as a business analytics tool. 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.
Company Use Cases
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.
With countless business-critical text search and analytics use cases that utilize Elasticsearch as the backbone, eBay has created a custom ‘Elasticsearch-as-a-Service’ platform to allow easy Elasticsearch cluster provisioning on their internal OpenStack-based cloud platform.
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. It also leverages ELK’s security features for security with SSO, alerting for anomaly detection, and monitoring for DevOps
So what is Elasticsearch? In this post, we attempted to answer that question through the lens of understanding what it is, how it works, and how it’s used and we’re still only barely scratching the surface of learning everything there is about it. But based on what we’ve covered, we can briefly summarize that Elasticsearch is at its core a search engine, whose underlying architecture and components makes it fast and scalable, sitting at the heart of an ecosystem of complementary tools that together can be used for many uses cases including search, analytics, and data processing and storage. If you’re interested in learning more about Elasticsearch and trying it out for yourself, you can get started here. And for more advanced use cases in which you need to join and blend your Elasticsearch data across multiple indexes and other SQL/NoSQL/REST-API data sources, check out Knowi, an analytics platform that natively integrates with Elasticsearch and is accessible to both technical and non-technical users. Happy searching!
I'm Ralf, a solutions engineer here at Knowi.
If you're looking for an analytics or BI solution I'd be happy to hop on a 15 min call and chat about your use-case.