Products that involve e-commerce and search engines with huge databases are facing issues such as product information retrieval taking too long. This leads to poor user experience and in turn turns off potential customers.
Lag in search is attributed to the relational database used for the product design, where the data is scattered among multiple tables — and the successful retrieval of meaningful user information requires fetching the data from these tables. The relational database works comparatively slow when it comes to huge data and fetching search results through database queries. Businesses nowadays are looking for alternatives where the data stored to promote quick retrieval. This can be achieved by adopting NoSQL rather than RDBMS for storing data. Elasticsearch (ES) is one such NoSQL distributed database. Elasticsearch relies on flexible data models to build and update visitor profiles to meet the demanding workloads and low latency required for real-time engagement.
Let’s understand what is so significant about Elasticsearch. ES is a document-oriented database designed to store, retrieve, and manage document-oriented or semi-structured data. When you use Elasticsearch, you store data in JSON document form. Then, you query them for retrieval. It is schema-less, using some defaults to index the data unless you provide mapping as per your needs. Elasticsearch uses Lucene StandardAnalyzer for indexing for automatic type guessing and for high precision.
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 a result.
Put Mapping API: Used to override default choices and define the mapping.
Elasticsearch has its own query domain-specific language in which you specify the query in JSON format. You can also nest other queries based on your needs. Real-world projects require search on different fields by applying some conditions, different weights, recent documents, values of some predefined fields, and so on. All such complexity can be expressed through a single query. The query DSL is powerful and is designed to handle real-world query complexity through a single query. Elasticsearch APIs are directly related to Lucene and use the same name as Lucene operations. Query DSL also uses the Lucene TermQuery to execute it.
The Basic Concepts of Elasticsearch
Let's take a look at the basic concepts of Elasticsearch: clusters, near real-time search, indexes, nodes, shards, mapping types, and more.
A cluster is a collection of one or more servers that together hold entire data and give federated indexing and search capabilities across all servers. For relational databases, the node is DB Instance. There can be N nodes with the same cluster name.
Elasticsearch is a near-real-time search platform. There is a slight from the time you index a document until the time it becomes searchable.
The index is a collection of documents that have similar characteristics. For example, we can have an index for customer data and another one for a product information. An index is identified by a unique name that refers to the index when performing indexing search, update, and delete operations. In a single cluster, we can define as many indexes as we want. Index = database schema in an RDBMS (relational database management system) — similar to a database or a schema. Consider it a set of tables with some logical grouping. In Elasticsearch terms: index = database; type = table; document = row.
A node is a single server that holds some data and participates on the cluster’s indexing and querying. A node can be configured to join a specific cluster by the particular cluster name. A single cluster can have as many nodes as we want. A node is simply one Elasticsearch instance. Consider this a running instance of MySQL. There is one MySQL instance running per machine on different a port, while in Elasticsearch, generally, one Elasticsearch instance runs per machine. Elasticsearch uses distributed computing, so having separate machines would help, as there would be more hardware resources.
A shard is a subset of documents of an index. An index can be divided into many shards.
Mapping type = database table in an RDBMS.
Elasticsearch uses document definitions that act as tables. If you
PUT (“index”) a document in Elasticsearch, you will notice that it automatically tries to determine the property types. This is like inserting a JSON blob in MySQL, and then MySQL determining the number of columns and column types as it creates the database table.
Do you want to know more about what Elasticsearch is and when to use it? Some of the use cases of Elasticsearch can be found here. Elasticsearch users have delightfully diverse use cases, ranging from appending tiny log-line documents to indexing web-scale collections of large documents and maximizing indexing throughput.
Sometimes, we have more than one way to index or query documents. And with the help of Elasticsearch, we can do it better. Elasticsearch is not new, though it is evolving rapidly. Still, the core product is consistent and can help achieve faster performance with search results for your search engine.