From your enormous database, if your customers are able to search for their preferred product/service, only then your business can be known for providing superior customer services. Poor customer service arises from the delay in retrieving information, which may also lead to the loss of a potential client. The backbone of a successful and efficient organization is in their service providing the easy handling of data and providing information faster.
So now we come to the main question. Where does Elasticsearch come into the picture? But before that, what is Elasticsearch?
Elasticsearch is a search engine based on the distributed document orientation. It has the potential to store, retrieve and handle structured, unstructured, semi-structured, textual, numerical, and geospatial data. The use of Lucene StandardAnalyzer helps it in automatic type guessing and writing with more precision. To grow and update its visitor’s profile, it relies upon the flexible data models which help to meet the growing workload and low latency which is needed for real-time engagement. In Elasticsearch, the data is stored in JSON document form.
Every feature of Elasticsearch is exposed as a REST API:
- Index API– Used to document the Index
- Get API– Used to retrieve the document
- Search API– Used to submit your query and get the result
- Put Mapping API– Used to override default choices and define our mapping.
Elasticsearch provides its Query Domain Specific Language, specifically in JSON format, while also keeping in mind other queries based on your needs. Research holds a major part of real projects. They have some specific conditions, different weights, recent documents, values of some predefined fields. All such complexities being expressed in a single query. Just like Elasticsearch, the query DSL is also linked with Lucene, which is specially designed to handle real-world queries.
Basic terminologies related to Elasticsearch
- Index- The document collection with similar characteristics. Just like having an index for customer data or production information. It has a unique name, which is referred to while performing an indexing search. In elaticsearch index is considered to be of the same meaning as the database schema in RDBMS (Relational Database Management system).
- Shards- A group of shards, constitute an index. A shard can be classified under the documents of the index.
- Cluster: A collection of servers that together hold the entire data. It provides federated indexing and search capabilities across all the servers. For example, Db is the node for Relational Database. Then there can be N nodes with the same cluster name.
- NRT (Near Real-Time): Elasticsearch is a near real-time search platform, which means that there is a slight gap, from the time you index a document until it becomes searchable.
- Node: A single server holding some data and participates in the cluster’s index and query. It can be configured to join a specific cluster with a particular cluster name. A single cluster can have nodes as per your desire. A node is simply one Elasticsearch instance.
When to use Elasticsearch?
After knowing what is elasticsearch, it is equally important to know, when to use the elasticsearch. Customers do not focus on only one thing while searching. They require diverse use of cases, ranging from appending tiny log-line documents to indexing web-scale collections of large documents and maximizing indexing. Elasticsearch helps them achieve such common and important goals. The various uses of elasticsearch are mentioned below.
- Geo-Search– Elasticsearch is also good at finding any geo-localized product. Like the search query may be: ‘all the restaurants that serve burgers within 30 minutes’ can use Elasticsearch to display information of the relevant restaurants immediately.
- Auto-Suggest– After typing some initial words/characters. Elasticsearch auto-generates the nest words. It gives them a list of suggested queries as they type.
- Auto-Complete– Elasticsearch database also has the option of auto-completing the partially typed words. It completes the query by completing a search box based on previous searches.
- Metrics & Analytics – Elasticsearch also analyzes a lot of dashboards which consist of various emails, logs, databases, and syslogs, that help businesses make sense of their data and provide actionable insights.
- Textual Search(searches containing pure texts) – Elasticsearch is generally used where there is lots of text and a user wants to search any data that will be the best match with a specific phrase that they are searching for.
- Product Search– Elasticsearch is also used to facilitate faster product searches by using their properties and names ( whether textual search or structured data ).
- Data Aggregation– The aggregation’s framework provides aggregated data based on a search query, based on simple building blocks called aggregations, which are composed to build complex summaries of the data. It is seen as a unit of work that builds analytic information over a set of documents.
Advantages of Elasticsearch
With the large number of benefits that the Elasticsearch provides, it is leading to its growing popularity with huge organizations like testifies. Still, it is being upgraded and new functionalities are being added, but its core peculiarity of offering high-performance search results and consistency is what makes it stand out. Some of the key benefits are listed below:
- Schema Free- It means, Elasticsearch doesn’t require any data definition, and it uses its defaults to index the date unless any specific data type is provided. It accepts the JSON format documents and detects the data type, indexes, the records and makes them searchable.
- Distributed Architecture – ElasticSearch can handle large volumes of data with its distributed architecture. This approach divides the indices into shards, which are capable of creating innumerable replicas. As and when new documents go on being added, routing and rebalancing operations are conducted automatically.
- High Performance- ElasticSearch delivers extremely fast searches by using distributed inverted indices. It caches all the queries for the result set, so for every search query that contains a cached filter, it searches the result from the cache.
- Easily Scalable- ElasticSearch can be scaled up to thousands of servers and can also store thousands of gigabytes of data. It can run smoothly on any system or in any cluster containing several nodes. When you grow from a small cluster to a big cluster and then a very big cluster, the process is nearly automatic and requires a little planning, and is painless.
Although Elasticsearch is not new, it is growing at a faster rate. To manage and scale your Elasticsearch environment and make the most out of it for your business, we at Augmento Labs are here at your service. With our vast expertise in the IT industry, we are here to share our experience and help you. We will deliver you cutting-edge solutions, and our team of experts will help you move up the chain and use the latest technologically assert to benefit your business.