What is Elasticsearch?

Elasticsearch (ES) is an open source, enterprise-grade search engine which is readily scalable and broadly distributable. Released back in 2010, it is based on Apache Lucene and has rapidly become the most popular search engine.

Elasticsearch has an HTTP web interface and schema-free JSON documents. Elasticsearch is accessible through extensive Application Programming Interfaces (API) and can support rapid searches which support data discovery applications.

Product information retrieval from a huge database takes a long time, which directs to poor user experience and thus missing a potential customer. The problem is mainly due to a relational database in which the data is scattered among multiple tables. Retrieving relevant user information that demands fetching data is a time-consuming process.

In the overcrowded digital market, businesses are in search of alternatives for data storage, thereby facilitating a quick retrieval. This can be achieved by adopting a NoSQL data distribution instead of RDBMS for storing data.

Elasticsearch is one such efficient NoSQL distributed database that is based on flexible data models for building and updating visitors’ profiles. This can efficiently manage the increased workload and low latency necessary for real-time engagement.


How does Elasticsearch work?

Elasticsearch has a query domain specific language of its own, that you can define the data in JSON format using an application programming interface (or using some ingestion tools like Logstash). ES adds a searchable reference to the document in the clusters index and automatically stores the original document. Now you can search and retrieve the documents using an Elasticsearch API.

Elasticsearch APIs are directly related to Lucene and uses the same name as that of Lucene operations. Interactive dashboards can be built by integrating other suitable open source visualization tools with Elasticsearch.

Elasticsearch can achieve rapid search responses because it searches an index (the added searchable reference) instead of the complete data available. This is very much similar to retrieving the pages in a textbook with reference to a keyword using the index behind, rather searching for the keyword in each and every page. This type of indexing is called an inverted index and Elasticsearch uses Apache Lucene to develop and manage the inverted index.

Elasticsearch basic concepts

1. Near Real Time: This means that Elasticsearch regularly schedules a fresh state of searchable documents. It is one state per second, respectively. Hence, there is a slight delay for a document to be searchable from the moment you index it.

2. Index: A collection of documents with similar characteristics is referred to as an Index. For example, one index can be customer data and another for product details. It stores data using SQL analogies in one or more indexes. They are used to read and store documents. Indexes are identified by a unique name all in lower cases. In a single cluster, there can be n number of indexes.

3. Cluster: A collection of servers that hold the entire data and allows associated indexing and searching options across the servers.

4. Document: A Document is basic information which we can index. Each document comprises of different fields identified by its name and contains one or more values. They are schema free and have a JSON (JavaScript Object Notation). An Index can store n number of documents.

5. Type: Documents with a common set of fields. More than one type can be defined within an index.

6. Node: A single specimen of the server that stores the data. It plays an active role in searching and indexing capabilities of a cluster. A random Universally Unique IDentifier (UUID) is tagged to the node at the beginning. This is used for administration purposes. These names help you identify which servers correspond to which nodes in your Elasticsearch Server.

7. Shards: Indexes are subdivided to shards to solve the problem of storing huge volumes of information that exceeds the limit of a single server. Shard requirement is defined while creating an index. Each shard can function as a fully functional and independent index that can be hosted in any node within any cluster.

8. Replicas: An additional copy of a shard as a fail over mechanism. They can be used as original shards for any query.

Elastic Search Features

1. High Performance

The distributed nature of Elasticsearch enables a rapid, incisive search from a huge volume of data. Elasticsearch design is very much simpler and sleeker than a conventional database limited by table, fields, columns, and schemas.

On the same hardware, queries that would take more than 10 seconds using SQL will return results in under 10 milliseconds in Elasticsearch.

2. Easy, fast, direct access

As Elasticsearch is not a relational database, DBMS concepts will not work. Do remember to denormalize your data as ES do not allow joins or sub-queries. Full-text searches are superfast as the document storage is in close contiguity to the corresponding metadata. This significantly brings down the number of data reads, and ES limits the index growth rate by keeping it compressed. Elasticsearch is the best suitable for near real-time use cases like application monitoring and anomaly detection.

3. Built based on Lucene

This ensures robust full-text search capabilities.

4. Easy application development

ES supports various languages like Java, Python, PHP, JavaScript, Node.js, Ruby, and many more which enables an easy application development. ES is featured by simple REST-based APIs, a simple HTTP interface, and uses schema-free JSON documents, making it user-friendly and easily build applications for a variety of use-cases.

5. Complimentary plugins and tools

Elasticsearch is integrated with Kibana, a well-known visualization and reporting tool, and also provides integration with tools like Beats and Logstash. You can add functionality to the applications with ES plugins.

6. Broadly distributable and highly scalable

The highly distributed Elasticsearch architecture keeps it powerful enough to scale up to thousands of servers and accommodate petabytes of data. The complex distributed design comes with automated management.

Elasticsearch Limitations

  1. When your data queries are very simple like to search user names, yet you overlooked your primary datastore abilities.
  2. When you overestimate your data size, assuming that your primary database will not be able to handle the querying.
  3. The data you index is searchable only after 1 sec. It does not support SQL like joins.
  4. Transactions and updates are expensive in a distributed system.
  5. Data might be lost due to network partitions or multiple nodes going down at the same time.

Elasticsearch versus Couchbase

The primary database model of a Couchbase is documented store and that of an ES is a Search Engine. Both are open search platforms and schema-free, whereas the implementation language for ES is Java and for Couchbase is C, C++, Go and Erlang.

Get your answers instantly with ES and change your relationship with the data and easily afford to iterate and cover more ground. Be enviably fast with ES as everything is carefully indexed and you are no more left with index envy. Leverage and access all the huge data at ludicrously awesome speeds!

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