If you ask of the easiest terms that could define what exactly ElasticSearch is, it is a search engine that is dedicatedly used to search data which is generally stored in the forms of documents. Being an open sourced service that is distributed in the real time, the concepts of elastic search are majorly implemented in the areas that involve a lot of data management. One such example is big data. However, if you are planning to take up ElasticSearch for Developers Training from any of the leading Big Data Training Institutes, check out some of the important concepts related to it that are important to develop the basics.
Let us begin understanding the elastic search by understand what exactly the nodes are. Node is like a single point server where you store a specific type of data. Individually every node has a functionality of holding a special type of data so that when a query is run, the node having that data displays its files. But when they come together as a cluster, the task of data indexing is also done in order to simplify the way data is searched.
The next core area of the elastic search is the cluster. As the name suggests, it is basically something that is used for keeping the data intact. Since we are talking about innumerable terabytes of data, there are a lot of nodes which are used to hold the data. So when you bring all these nodes together, they are defined as clusters. To put it in simple words, what a cluster does is gathers all the type of data that is similar in nature or usage together.
The next thing that demands our attention when talking about the elastic search is the concept of NRT; i.e. Near Real Time. The entire designing of the elastic search is done on the base of NRT. The main aim behind using NRT here is to provide the needed latency to the process of data search. For instance, if you have entered a query for searching a specific data, what will happen is a list would be displayed in a few seconds. So these few seconds that you have to wait is the latent time.
We have frequently used a term indexing and you would continue to do that whenever you talk about data. Index is a process where you bring all the documents of similar type and characteristics together. One important point that you should know before we proceed further is that the only way elastic search would be able to display results or run its search would be through the documents. So the process of indexing becomes very important here. Depending upon the size and type of the data, there may be any possible number of indexes.
At times when the data is too large to be indexed, another concept of type is introduced. Through this, you can further classify different indexes as well thus making the process much simplified for elastic search.