Building an Advanced Search Capability with ElasticSearch

13 min read

Every website needs a simple, convenient, and strong search system so that users can easily find what they are looking for. However, only 15% companies pay attention towards this fact. 42% don’t even think about optimizing their search space.

This is the reason why information retrieval remains one of the biggest problems all the time and the reason why many businesses lose to their competitors.

So, if you are looking forward to have a website, no matter if it’s an online store, product website, knowledge-base, or a marketplace, you should pay attention towards building an advanced search capability. By doing so, you can easily surpass 85% of your competitors.

Elastic search can help you in building an advanced search capability. For those who don’t know, it is a search engine based on the Lucene library and a full text search application which allows a fast and flexible search.

Elastic search is:

Fast: Search results returned almost instantly, providing a responsive user experience.

Flexible: We can easily modify how the search is performed, in order to optimize for different datasets and use cases.

Full-Text: We can search everything in our data store (including large text fields) for a match.

Elasticsearch Use Cases

Here are some examples of real-world Elasticsearch use cases from the official Elastic website.

  • Wikipedia uses Elasticsearch to provide full-text search with highlighted search snippets, and search-as-you-type and did-you-mean suggestions.
  • The Guardian uses Elasticsearch to combine visitor logs with social -network data to provide real-time feedback to its editors about the public’s response to new articles.
  • Stack Overflow combines full-text search with geo-location queries and uses more-like-this to find related questions and answers.
  • GitHub uses Elasticsearch to query 130 billion lines of code.

How is Elasticsearch different from other search engines?

Inverted indices are what make Elasticsearch different from other search engines.

An “index” is a data structure to allow for ultra-fast data query and retrieval operations in databases. Databases generally index entries by storing an association of fields with the matching table rows. By storing the index in a searchable data structure (often a B-Tree), databases can achieve sub-linear time on optimized queries (such as “Find the row with ID = 5”).

We can think of a database index like an old-school library card catalog – it tells you precisely where the entry that you’re searching for is located, if you already know the title and author of the book. Database tables generally have multiple indices in order to speed up queries on specific fields (i.e. an index on the name column would greatly speed up queries for rows with a specific name).

Inverted indexes work in a substantially different manner. The content of each row (or document) is split up, and each individual entry (in this case each word) point back to any documents that it was found within.

This inverted-index data structure allows us to very quickly find, say, all the documents where “football” was mentioned. Using a heavily optimized in-memory inverted index, Elasticsearch enables us to perform some very powerful and customizable full-text searches on our stored data.

How Elastic Search Benefits Businesses?

Here are some of the business benefits of Elastic search:

1. Lots of search options

The best thing about Elasticsearch is that it comes packed with a long list of features like customized splitting text into words, fuzzy search, customized stemming, faceted search, full-text search, auto-completion, and instant search. This means you have a vast range of search options and you can easily find what you are looking for even if you are entering a misspelled word.

2. Document-oriented

Elasticsearch stories all the information as structured JSON documents so that you can easily find the relevant information. It also enables high performance.

3. High Speed

Elasticsearch can execute complex queries very fast as it caches almost all the queries so that they need to be executed only once.

4. Scalability

Elasticsearch is a distributed system by nature. This means it can be easily scaled horizontally, thus providing the ability to extend resources or balance loads between the nodes in a cluster.

5. Minimum Chances of Data Loss

Elasticsearch keeps a detailed record of all the changes made in transactions logs on multiple nodes in the cluster. This reduces the chances of data loss.

6. You can fine-tune queries

Elastic search is made of a powerful JSON-based DSL which allows development teams to construct complex queries and fine tune them to receive accurate search results. It proves helpful especially in ranking and grouping results.

7. Capable of using RESTful APIs

Since Elasticsearch is API-driven, actions can be easily performed using simple RESTful APIs.

8. A distributed approach

In Elasticsearch, indices can be divided into shards in which each shard can have any number of replicas. This ensures routing and re-balancing operations can be done automatically when new documents are added.

9. Multi-tenancy

With the multi-tenancy feature, Elasticsearch ensures that users are not able to search documents that do not belong to them. This ensures safety and privacy.

How Elasticsearch has helped Softobiz deliver high performance?

At Softobiz, we have been using the microservice approach for developing high-performing apps since a long time. While the trend helped us deliver high-quality products at faster rate, it also brought along some serious challenges.

One such challenge was the lack of communication between two individual services. We all know that in the microservice architecture, an application is developed as a collection of multiple individual microservices where each microservice has its own database, and no two microservices can communicate with each other.

While the problem of communication was resolved by creating events and streaming them through open-source streaming platform, one problem remained. In an application, you would want to see data being fetched from databases so that you know that your application is performing.

Doing so is possible with even the normal database management systems (DBMS) like SQL but individually creating event for each microservice and then fetching it could result in the server overload. For this reason, we had to look for a better option.

After trying out several options like Cassandra, we finally settled on Elastic Search which suited our business needs and helped us become more productive. Following are changes we witnessed after adopting ElasticSearch into our business process:

With ElasticSearch, we have aggregated all the data at a single place which makes it easy for us to manage the entire data of our process from a single source. Since all the information related to a microservice is stored in the database related to that microservice, ElasticSearch has also made it possible to efficiently manage data so that we can easily find it whenever needed. Since ElasticSearch fetches data quickly, the application we have develop are fast and quick to respond. Since ElasticSearch creates multiple replicas of each instance on multiple nodes, there is no single-point failure in the apps we create. This has also given us assurance that the apps we develop are always active and running.

Overall, ElasticSearch helped us deliver high-performance by enhancing the search capability of our apps. Consistent performance and reliability of apps are no longer the concern for us. Also, we have witnessed the performance gains we were looking for.

With ElasticSearch, we have found a solution for easily and cost-effectively scaling a search application to meet growing volumes of data, ensure excellent search along with discovery experience, and manage operational complexities.

Want to know about Elastic Search and its role in business growth?

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