When to Use Elasticsearch and How?

Nowadays, there are plenty of various add-ons, plugins, APIs(for instance REstful APIs), and other additional software and content, that are used to simplify the developers' lives, save their time, or enable new features, that were not planned before. However, the number is not equal to quality. For instance, some unnecessary add-ons can cause system overload or simply waste your system's resources. Also, some have alternatives, that can provide the same or even more advanced features and settings for less effort.

Therefore, in case you are trying to choose the tech stack for your future project, or trying to figure out how to improve the already existing one, it would be a great idea to do market research, trying to find some reserve stack.

Today we will try to show an example of how to do so. Hence, let's consider Elasticsearch, what it is, how it works, and what can we use it for.

What is Elasticsearch?

Frankly speaking, there is a lot of various statements about this software. However, some of them are not exactly right.

For instance, someone can claim that it is like Google because it is also an advanced search engine. Clearly, it is not quite correct. As a matter of fact, Elasticsearch use cases stay somewhere between the industries. Even though it is defined as an advanced search engine, we strongly believe, that it is more than that. It is something in between an independent full-fledged database and an advanced search engine with a limited list of features and possibilities.

Elasticsearch is relatively new software, based on the Apache Lucene library, which was developed and is used to simplify the search of various data or documents. However, the main problem is that Elasticsearch uses some specific ways to treat the data, therefore Elastic integration into the existing database is almost impossible. At least, it is impossible without some additional processing of this data. Exactly for this reason The Elastic Stack, also known as ELK stack, is used.

Elastic Stack

In fact, all of the needed features are possible thanks to the cooperation between the parts of this collection. The ELK is an abbreviation for Elasticseatch, Logstash, and Kibana.

• Elasticsearch - obviously, it is the main tool we are interested in. It is the search engine itself, that is capable of finding the data and is used exactly for it. However, due to the specifics of the data transformation, it is almost impossible to use it without two other components.
• Logstash - is a log-parsing engine or data collection pipeline. It is used to gather the data, transform it according to the needed specifics, and send it to the final destination point. It is the second crucial component, that enables the possibility to search for the data and change it.
• Kibana - is used for the analysis and visualization of the data.
• And the last component, which is used for the correct work of the Elasticsearch is known as Beats. They simply play the role of shippers, transferring the data to Logstash or Elasticsearch. Also, each beat provides a single purpose. In other words, there are various types of beats, each of which provides a one-and-only function. These types correspond with the type of the file, for instance, there are filebeats, audiobeats, metricbeats, etc.

How to Use the Elasticsearch?

One of the most common questions, related to Elasticsearch is how to use one. Actually, elasticsearch requires downloading. As was mentioned before, it is almost impossible to integrate it into the existing database. Therefore, the person who wants to use Elasticsearch has to create the needed conditions for data gathering and operating. Honestly, downloading is just one of the needed steps.

An additional fact, worth knowing is that all interactions are performed by coding. As a result, the person who is going to use Elasticsearch has to have at least some coding background. Yet, even if the user has some coding experience and related knowledge, it has to adapt to the Elasticsearch coding specifics. Believe it or not, Elasticsearch uses a modified version of the Java programming language, which can cause some misconceptions.

The setup process is quite easy: the user has to download Elasticsearch from their website and install it. After these actions, it is possible to start the program itself and start the configuration. There are numerous ways to do so, depending on the final usage purpose.

Despite the fact, that Elasticseach is unable to be integrated into another database, at least its major version, it is still possible to use it as a part of other applications. For instance, it can be used as a basic search engine inside the app or website. Nevertheless, as was mentioned before, it requires the setup database in order to work correctly.

The developer can struggle with some compatibility issues. For example, some Elasticserach versions are unable to cooperate directly. So, in case you had a project, based on the outdated OlderElasticsearch version, and want to transfer it to the updated one, you will have to use RESTful APIs. Only RESTful API can help you with updating the project. 

When to use Elasticsearch?

Frankly speaking, there are a few great features with hidden potential, that make Elasticsearch a great tool, that has no actual alternatives on the market. First of all, let's consider the previously mentioned specifics. Actually, Elasticseatch works with JSON files. However, it operates them with some additional modifications.

For instance, the fast-response structure is possible thanks to indexing. In order to simplify the searching process, Elasticsearch indexes all of the files it has access to. Moreover, each type of file, also known as beat has its own type of index. Therefore, elasticsearch creates a highly-structured system of various files, with the possibility to access them in the fastest way possible.

Also, there are some additional features related to the text files. As a matter of fact, Elasticksearch uses additional indexes for various parts of the text file. It results in the possibility to find the concrete part of the text immediately. Therefore, if you are looking for a fast and powerful search engine to work with the text - Elasticsearch is probably the best possible solution, that fits you the best.

Frankly speaking, it seems like one of the most essential elasticsearch use cases was exactly to work with the text. It is the only reason why there are so many various search options possible. In addition to the previously mentioned extra indexing, the are also various types of text search. For instance, besides the regular searching, Elasticsearch allows "fuzzy search". In this case, the search engine will look for additional similarities. As a result, even if the user misspelled the word, the search engine can still find the needed file or sentence. 

As we can see, the text search is extremely advanced: on the one hand, there is a "regular" or simple search option, which is surprisingly fast thanks to the indexing feature. On the flip side, there is a "fuzzy" search, which helps to find the result even despite the misspelling. However, this option is very CPU-intensive, therefore it is highly recommended to avoid using it on a regular basis. 

Moreover, Elasticsearch provides some additional useful features like autocompletion. Actually, it is a common feature in most search engines. It helps to make the search process faster, trying to predict or complete the sentence, corresponding with the available data. Obviously, it can be used both for regular and fuzzy searches. Yet, it is a more powerful tool in the case of regular search, because it can predict the whole sentence and make the search faster. 

Additionally, Elasticsearch is capable of processing and converting various text documents, whether it is Word, PDF, etc into plain text. It has to be done in order to index it later. It is also a CPU-intensive process, however, can be done in the background.

Eventually, Elasticsearch can also support out-of-box solutions. For instance, it can be used as a cache for sample code from other projects. It can store and sort the data, as well as find it in a very fast way. Therefore, the developer can use it to store various code blocks or other inputs. In such a case, the developer will always have the needed code near at hand. Also, do not forget about its log analytics engine, which can be used as an additional calculation method if needed.

Summing up all the above, we can assume, that the elasticsearch use cases are:

• Using it as an advanced search engine with fuzzy search capabilities.
• Basic search work with full-text searches.
• Additional cache database, used for saving and fast searching for the source code.
• To log files or code for integration in the future.

End Line

It is hard to tell exactly when to use elasticsearch because the variety of elasticsearch use cases and purposes is enormous. Clearly, the best elasticsearch use cases are related to advanced search. Obviously, it was developed as an instrument for an advanced search. Yet, despite the original purpose, conceived by its developers, Elasticsearch provides a variety of various possibilities.

Finally, it can become a full-fledged search engine for various products and websites. The main drawback, though, is that it is database-dependent. In other words, it can't be integrated into another system, but can be integrated into any project as a part of Elastic Stack.

Hence, as a part of Elastic Stack, it provides all the needed functions without the need for any additional software. It can play the role of an advanced search engine, or a "middle data" database, meaning that it is not powerful enough to be a part of the Big Data market, but is too big to be considered a regular database as well. 

In case you struggle with some issues that can appear during the development or even optimization stages, no matter if they are related to Elasticsearch or not, contact us. You can be sure that our experienced developers are ready to help you with any of your projects, no matter what software or programming language they are based on.