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Magento Sphinx Search vs Magento Elasticsearch: Pros and Cons


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If you want to rocket conversions and increase revenue in your Magento-based e-commerce store, then it’s a must for you to integrate an incredible search engine.

The primary goal of a search engine is to bring the most relevant search results to visitors’ questions. In fact, if your website doesn’t provide quick and precise search results, visitors are likely to leave your site and find another alternative.

When it refers to e-commerce search engines, there are plenty of options available to select from. However, in this article, we’ll dig deeper into the 2 most widely-used e-commerce search engines in the market, which are Sphinx Search and Elasticsearch.

How Does Magento Search Work?

How Does Magento Search Work?
How Does Magento Search Work?

When a search engine is installed, it instantly works by indexing content on your website. For e-commerce stores, this means generating a “list” of items along with their attributes. This indexing process is constant. Whenever an update is created on your website (a new item added), the “list” will need to be refreshed.

For instance, you may include a few smartphones in your store. The search engine will then index things like its title, description, and price. Besides, it will index attributes you have specified, such as memory space.

As a user enters a search question, the search engine will sort through the list of items and their attributes and then come back with the most related. Different search engines handle this in different ways and have various features for interpreting and filtering more complicated data.

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No matter what search engine you’re going to use, there are six features that you should concentrate on:

  • How long will the indexing and results compiling processes take? The longer these last, the slower your search will be.
  • What languages can the search engine support? Determine what language is perfect for your store first and then ensure your search engine offers support for it.
  • What are natural language processing features? This isn’t just essential now, and it will be critical for voice search too.
  • How does facet search work and how detailed can the filters be?How precise is the search engine?
  • It’s worth experiencing the search engine first to figure out whether it really brings accurate results.
  • Synonym management (absolutely crucial for niche stores)

Magento Elasticsearch

Magento Elasticsearch
Magento Elasticsearch

Elasticsearch is presently the most widely-used and the default option for Magento search.

Being a java-based document store, Elasticsearch is designed to store great numbers of JSON documents and speak to them natively. Therefore, in addition to handling text-based queries, it can also comprehend advanced analytical questions too, covering interpreting numeric and geodata.

Where Elasticsearch really excels is in its full support for Apache Lucene’s real-time search. From a customer’s viewpoint, this means Elasticsearch can offer faster and more related search experiences. For merchants, this means faster conversions.

Advantages of Elasticsearch

1. Great scalability

If you’re looking to expand your products database, then you might probably encounter difficulties while looking up and seeking relevant items.

However, Elasticsearch can help improve your search capabilities in line with your products database to decrease search relevancy and speed.

For example, Expedia - one of the most dominant airline ticket and hotel aggregator - uses Elasticsearch to offer seeking through for about 1 TB per data along with 300K events every second.

In fact, with Elasticsearch, they have succeeded in dramatically enhancing their customers’ booking experience contentment.

2. Data visualization

Data visualization is one of the most popular and in-demand features in e-commerce websites and it’s been impeccably implemented in Elasticsearch.

The mix of Logstash, Kibana plugins, and Elasticsearch builds an incredible tool for analytics. It enables not only real-time tracking of web traffic, but also monitors critical metrics such as the number of unique visitors, total visitors, most searched questions, browser used, and IP addresses. All these data will be visualized in the dashboard with colorful tables, charts, and maps.

With the data visualization feature of Elasticsearch, The Guardian runs a robust analytics system, which deals with more than 40 million documents per day to understand how their content is being absorbed by visitors.

Moreover, Netflix also leverages Elasticsearch for real-time analytics to keep tabs on events such as performance, UI activities, error logs, video viewing activities, and so on.

3. Near real-time indexing

Elasticsearch has the ability to index quickly changing data in near real-time (less than 1 second).

Therefore, if your e-commerce store needs updating the database immediately, Elasticsearch is a perfect option for you.

Uber, for example, utilizes Elasticsearch to accumulate its business metrics depending on dynamic metrics such as pricing and supply positioning. Consequently, Uber can manage more than 1,000 questions every second at peak times, all thanks to Elasticsearch.

4. Security analytics

Elasticsearch also provides an incredible security analytics tool. Its visualization and near real-time analytics allow you to explore all types of security threats like attack locations, broken links, attempts for unauthorized access, and problems with your web server.

In fact, Dell used Elasticsearch some time back and since then, their security has greatly improved, while their number of servers has decreased by 25-30%.

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5. Amazon Elasticsearch service

Elasticsearch also comes with an Amazon Elasticsearch service, which basically supports easy and rapid setup, operates, and scales Elasticsearch directly in the cloud without configuring your own servers.

Disadvantages of Elasticsearch

1. Language constraint

To manage requests and responses, Elasticsearch doesn’t come with multi-language support. It only supports a JSON format, while other tools like Apache Solr supports CSV, XML, and JSON format.

2. SSD’s requirement

Elasticsearch requires a group of servers having 64 GB of RAM to work effectively. Or else, if we have too many small servers, it generates overhead. And if we use a few robust servers, there is a chance of failover.

Besides, queries run faster if data is stored in SSDs instead of rotating disks. Nevertheless, SSDs are way more expensive, which makes the infrastructure overpriced.

Magento Sphinx Search
Magento Sphinx Search

Sphinx is a SQL-based full-text search tool that tailors search API for several scripting languages like PHP, Python, Perl, and Ruby.

Sphinx is defined as an open-source search engine that is written in C++. Sphinx allows you to batch index and search data saved in an SQL database, or index and find data on the fly. A host of text processing features from fine-tuning Sphinx will satisfy your specific application requirements. This search engine also comes with a variety of relevance functions, making sure you can tweak search quality easily.

1. Fast and robust

For several years, Sphinx Search has developed dramatically and gained the ability to offer a near real-time search.

Its search speed has increased up to 500+ queries per second against 1,000,000 documents, with the most enormous registered number of indexing at over 25 billion documents.

Craigslist, which has more than 50 billion page views per month, can serve 300+ million users’ queries every day.

Moreover, Infegy also indexes more than 22 billion Facebook, Twitter, and assorted blog posts to offer insightful social media monitoring and analytic queries.

2. Creating ResultSet in Parallel

One of Sphinx Search Engine’s outstanding benefits is that it allows you to generate different results from the same data at the same time by leveraging a fixed amount of memory.

In contrast, a traditional SQL method usually either produces a temporary table for each search result set or runs two queries.

Meanwhile, Sphinx utilizes a multi-query mechanism to implement this task. In other words, rather than implementing one query after another, Sphinx makes a host of multiple queries at once and submits them in a single request.

Sphinx is not perfect for e-commerce stores that need to manage unstructured data such as Docs, MP3s, or PDFs. This is because Sphinx requires many developers’ time to configure, which ultimately makes Sphinx less convenient to use than Elasticsearch.

Compare Magento Sphinx Search with Magento Elasticsearch

Search Relevance

Magento Sphinx Search

  • Utilizes synonyms, highlighting, and a spell checker called suggest for search relevance.

  • Supports limited language analyzers and morphological preprocessors for stemming and lemmatization.

  • Provides essential search relevance features but might require more manual configuration for fine-tuning.

Magento Elasticsearch

  • Offers a comprehensive set of tools for search relevance, including typo-tolerance, synonyms, highlights, suggestions, and over 34 language analyzers.

  • Employs Lucene under the hood, enabling real-time results and powerful search capabilities.

  • Dynamic field typing during indexing might impact search relevance without manual mapping.

Magento Sphinx Search

  • Supports facets to some extent but lacks robustness compared to Elasticsearch or similar solutions.
  • Faceted search functionality might be limited, requiring additional configurations and multiple search calls for complex queries.

Magento Elasticsearch

  • Allows the creation of basic facets for search, but implementing complex queries might demand good technical knowledge.

  • Offers more advanced and flexible facet creation options, providing a smoother and more comprehensive faceted search experience.

Search Analytics And Designing

Both Elasticsearch and Sphinx Search in Magento lack built-in analytics tools, requiring businesses to manually implement telemetry and external BI (Business Intelligence) tools for data analysis. Similarly, when it comes to designing the search frontend, both systems necessitate intervention from the business side.

For both Sphinx Search and Elasticsearch within Magento, it’s the responsibility of the business to design and deploy the search frontend. This involves setting up databases, indexing data, crafting queries, and constructing the user interface. The process entails various tasks such as database preparation, data indexing, query development, UI design, and ensuring the entire project is ready for production.

Creating the frontend interface and implementing analytic tools both demand manual intervention by the business, without inherent integration features available in either Sphinx Search or Elasticsearch when integrated into Magento.


If you think that your store has begun to take a while to retrieve the search results, it basically requires integrating a more robust search engine.

While the discrepancies between Sphinx and Elasticsearch are not too considerable, you should implement a comprehensive analysis of your specific demands before making any decision.

We hope that this article will somehow give you some insights into these two search engines, helping you pick the right option for your e-commerce store.

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