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Elasticsearch is an open-source, highly scalable analytics and search engine. It is developed in Java and is basically a wrapper on Apache Lucene Library. It consists of an HTTP web API interface. It has no schema with JSON documents where all the data is stored. Lucene is the current big thing in the data word but it is a library with very efficient and powerful APIs. Most current search engines like elastic search are wrappers around the library. Various applications based on their requirements need different types of search engines. although elasticsearch caters to most of their needs, There are many open-source alternatives to Elasticsearch.

Features of Elasticsearch

• Implemented in Java.

• Uses the Vector space model, Information Retrieval, etc.. to match the most likely results.

• Has REST API Interface(Supports HTTP).

• Kibana is a visualization tool used to visualize the logs to understand the operations.

• Used mostly in applications related to the?Internet of Things(IoT),?Mobile, Cloud Computing, etc.

• Indexing is done by the Lucene Indexer.

Top 6 Alternatives of Elasticsearch

The following are the alternatives of elasticsearch are:

1. Solr

Solr (pronounced as solar) is an open-source search engine built on Apache Lucene Library which is in Java. It is developed in Java. It consists of HTTP/XML web API interfaces. Solr queries are in the form of JSON documents.

• Solr’s focus is mainly on text search and related operations.

• Solr supports JSON but this feature was added recently.when Solr was built initially, its main language was XML.

• Solr has been in the industry since 2004 and it is safe to say that it is a mature product and it has a huge community. Elasticsearch is relatively new to the industry. Solr is truly open source in the sense that anyone can contribute to the product.

• Scalable only with the help of SolrCloud and zookeeper.

• Solr is a bit old school. Although it has extensive documentation, setting up and experimenting on Solr is a bit tedious. Solr is now working on making it more under friendly.

2. InfluxDB

InfluxDB is a TSDB (Time-series database). It is an open-source and developed by Influxdata.InfluxDB is developed in the Go language. It is used mainly in high-availability storage applications and applications that require time-series data like in the fields of real-time analytics and sensor data for the internet of things.

• InfluxDB uses SQL queries to retrieve data which is stored in a key-value pair fashion.

• InfluxDB supports data via HTTP, TCP, and UDP protocols.

• InfluxDB is a time-based database, therefore it can be used in applications that require data monitoring for a certain period like sensor data.

3. TypeSense

Typesense is an open-source search engine that is fast and compact. It is built from scratch and is typo tolerant. It is better suited for low scale systems with high performance. Typesense supports Ruby, Python, JavaScript, and shell.

• Supports instant search.

• Supports auto-suggest.

• Supports faceted search.

• Minimal effort in setting up and using it.

4. Algolia

Algolia is a search engine that offers web searches via the Saas model, (software as a service). Algolia also provides services via an externally hosted search engine. It consists of mainly two functionalities, search analytics, and search implementation. Algolia is entirely developed in c++.

• Supports back-end APIs in various languages.

• Along with the APIs, it also offers front end widget for better user experience. Algolia is better in terms of human-machine interface compared to the others as it has a dashboard with various filters that can be configured by the users to view user behavior/history or any data for that matter in a much-simplified way.

• Rich and extensive documentation that helps in better usage/development of applications.

5. Sphinx

Sphinx is an open-source full-text search engine providing full-text search to its clients.

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It can be used as a storage engine (sphinxSE) for the SQL based database. It can also be used as a standalone server. Sphinx daemon supports MySQL and the clients can access using SQL APIs like INSERT, SELECT, etc. Sphinx like any other SQL database works with a schema. It can also extend its features for MariaDB along with SQL.

• Sphinx contains attributes to the data it stores/operates. Supported types include integers, strings, JSON,, floating-point values, UNIX timestamps, and multi-value attributes.

• Sphinx finds the inquired data based on its index. Its indexer creates a full-index on all the data that it hosts.

• APIs are supported currently for python, ruby, Perl, Java, and PHP. It also supports third-party programs.

• Integrating the engine with the user application is very easy.

6. Amazon Cloud Search

Amazon cloud search is a service offered by Amazon.Amazon cloud search like Elasticsearch is also provided by Amazon but it is completely developed and managed by AWS(Amazon Web Services). This is much easier as setting up Amazon cloud search is very simple and easy. It provides inbuilt indexing and basic search filters. Users can configure or add extra search filters according to their application requirements.

• Amazon cloud search by default takes care of the backup process. It saves snapshots automatically without any manual intervention.

• Amazon cloud search provides security via the Identity Access Management (IAM) service provided by AWS.

• It is highly scalable as it is hosted entirely in the cloud. When an Amazon cloud server reaches its maximum threshold it automatically scales up to the next server and vice-versa. This helps in storage management and saves a lot of money as the users pay only for what they use.

• It has an Amazon cloudwatch which is a monitoring system, integrated within. It can monitor requests, documents, data partitioning, etc. and send alerts based on the preconditions set.

• Amazon cloud search supports RESTful APIs and many SDKs.They are supported in java, python, Ruby PHP, .Net, and Noje.js.

Studies on performance search engines are ongoing. The results from the existing studies are not concrete. Choosing a search engine is entirely dependent on the organizations and their applications. The description of the pros and cons is given above to get a brief understanding.