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Pros & Cons of Elasticsearch

Data analysts often need to search and analyze vast amounts of data. Finding the tools to conduct these data-intensive operations were complicated until a new open-source tool arrived on the market in 2019. Elasticsearch is an open-source distributed RESTful search and analytics engine based on Lucene that views data in real-time. It runs fast searches and returns responses within one second. You can start with a single node and scale horizontally by adding more nodes. Elasticsearch builds clients in several development languages, including Java, .Net, Groovy and Python. There are several use cases, including web search, application search, logging analytics, application performance monitoring and geospatial analysis.

Elasticsearch is packaged with a log-parsing engine called Logstash and Kibana, a visualization platform. The three tools are known as the Elastic Stack (aka the ELK stack). Logstash is an open-source data processing tool that enables you to curate data from multiple sources. Kibana, a data visualization tool, provides real-time histograms, line charts, pie charts and custom infographics.

Elasticsearch can be deployed on a cloud service like Amazon Web Services, Microsoft Azure and Google Cloud. It also supports multiple development languages, including Java, Javascript (Node.js), .Net, Perl, PHP, Python and Ruby.

Financeonline.com ranks Elasticsearch in its Top 20 Site Search Solution products with an 8.9 rating and a 95% customer satisfaction rating.

Pros of Elasticsearch

Elasticsearch is fast since it uses inverted indices to provide instant answers to your queries. It also allows you to run and combine multiple searches with either structured or unstructured data, geodata and metric searches. Another top feature is scalability. Elasticsearch runs as well on a single personal computer or multiple servers running petabytes of data. One of the best aspects of Elasticsearch is its simplicity and it can search a variety of unstructured documents. Search results are stored in JSON format enabling the data to be shared by other applications via API integrations. Elasticsearch is multilingual, supports auto-completion and stores data in documents versus structured databases.

Cons of Elasticsearch

Elasticsearch is an excellent search and analytics tool, but there are better options, such as Manticore Search, an alternative open-source search engine. Manticore is an SQL-first search engine and provides developers with different ways of communicating with databases and provides additional flexibility that can be helpful when designing more complex projects.

Elasticsearch Integrations

Since Elasticsearch is built in JAVA and deployed under the Apache license, many standard integrations are available, including AWS, Microsoft Azure, Kubernetes, and Box, to name a few. Elasticsearch integrates with many leading database applications and development tools, including InfluxDB and Prisma 2.0. The Telegraf Input Plugin gathers Elasticsearch health statistics clusters and queries endpoints to gather cluster-health or cluster-stats metrics. The Telegraf Output Plugin writes to Elasticsearch via HTTP using Elastic, an Elasticsearch client for Go programming.

Conclusion

Elasticsearch is an excellent tool for performing real-time search and analysis on streaming data. It’s unique data structure organizes and searches data in documents and makes them searchable in nearly real-time. The open-source application handles multiple languages and a lot of unstructured data in document formats. It doesn’t manage time-series data and databases developed for that purpose, like InfluxDB and MongoDB.

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