ElasticSearch, an open-source search server project, has evolved into a powerhouse in the field of search solutions since its inception by Shay Banon in February 2010. With its distributed nature and real-time capabilities, many have come to use ElasticSearch as a document database.
In this article, we’ll explore into the fundamental concepts of ElasticSearch to help you understand its key components.
An index in ElasticSearch is the fundamental unit where data is stored. If you’re coming from a relational database background, you can think of an ElasticSearch index as analogous to a table. However, ElasticSearch’s indexes are optimized for fast and efficient full-text searching, and they don’t store the original data values.
For those familiar with NoSQL databases, an ElasticSearch index is similar to a collection in MongoDB or a database in CouchDB.
The primary entity stored in ElasticSearch is a document. In a relational database analogy, a document is like a row in a table. However, unlike a relational database, ElasticSearch documents can have different structures, though they must maintain consistent data types for common fields.
Documents consist of fields (similar to columns in a table), and each field may have multiple values, making it “multivalued.” Each field has a data type (e.g., text, number, date, string, integer) that informs ElasticSearch how to perform operations such as comparison or sorting.
The flexibility of ElasticSearch allows documents to have varying structures, and fields do not need to be known during application development. Nonetheless, you can enforce a document structure using a schema if needed.
3. Document Type
In ElasticSearch, one index can serve multiple purposes and store different types of objects. For instance, a blog application may store articles and comments within the same index. Document types enable the easy differentiation of these objects. While ElasticSearch allows documents to have varying structures, employing document types helps streamline data manipulation.
Keep in mind that different document types cannot set different data types for the same property, so some limitations do exist.
4. Node and Cluster
ElasticSearch can function as a standalone, single-search server. However, to process large datasets and achieve fault tolerance, ElasticSearch can be deployed on multiple cooperating servers. Collectively, these servers form a cluster, with each server being referred to as a node.
Large datasets can be distributed across multiple nodes through a process known as index sharding, which divides the data into smaller individual parts. Better availability and performance are achieved through the use of replicas, which are copies of index parts.
When dealing with a vast number of documents, a single node might not suffice due to constraints such as RAM limitations and hard disk capacity. In such cases, data can be divided into smaller units called shards, where each shard functions as a separate Apache Lucene index.
These shards can be placed on different servers, allowing data to be distributed across the cluster. When a query is made to an index composed of multiple shards, ElasticSearch sends the query to each relevant shard and merges the results seamlessly, abstracting the concept of shards from your application.
To increase query throughput and enhance high availability, shard replicas can be employed. The primary shard is the place where operations that modify the index are directed. A replica is essentially an exact copy of the primary shard, and each shard can have zero or more replicas.
In the event of a primary shard failure, such as when the server holding the shard data becomes unavailable, a cluster can promote a replica to serve as the new primary shard, ensuring data availability and fault tolerance.
In summary, ElasticSearch is a versatile and powerful search and data storage solution, well-suited for various use cases, including search engines, document databases, and analytics platforms. Its distributed architecture, support for real-time search, and dynamic schema make it a popular choice for organizations dealing with large and diverse datasets.
Understanding the core concepts of ElasticSearch is essential for harnessing its capabilities and making the most of its features in your applications.