Magento Sphinx Search vs Magento Elasticsearch
Last Updated | May 4, 2023
Magento Sphinx Search vs Magento Elasticsearch
More than 100,000 online stores have been made on Magento. The platform code has been downloaded more than 2.5 million times, and $155 billion worth of merchandise was sold through Magento-based frameworks in 2019. Magento is an open-source e-commerce platform written in PHP. It accounted for approximately 30% of the entire e-commerce market share in the previous 2 years.
It is preferable to use the services of Magento development companies if you look to make your business successful in the long term in the field of e-commerce. They can manage your Magento e-commerce platform whilst you focus on the product side. If you aim to boost your revenue stream on your Magento store, you can integrate your platform with a robust search engine that can also increase user satisfaction levels.
Why choose a search engine?
A robust search engine will fetch the most relevant results for the user, which allows for better human interaction with your e-commerce store. This will make the users stay in your store longer and interact with goods they are interested in. Some important functionalities which are present on your search engines are:
- Correct spellings of user input in case of errors.
- Task migration, such as autocomplete terms whilst typing, reduces the burden on the user.
- Visual feedback, such as highlighting words, allows your users to find what they need.
- Allow search of items by providing suggestions to them based on their activities.
- Display results concerning segmentation such as geographical positions, cultures, and social trends of the country the user resides in.
- Allow search by providing synonyms in case the user cannot recall the exact words of the product.
- Real-time updates of product prices, stock availability, and promotional packages.
- Provide suggestions to the user of your platform.
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One of the most used search engines is the ElasticSearch for Magento 2. You can hire the services of professional companies to provide Magento support services for your platform. It is preferable to hire these companies as your store needs to be perfect, or your store may be outclassed by your competitors.
What is ElasticSearch?
ElasticSearch is a search engine built upon the Apache Lucene library. It gives a fully capable text search engine alongside an HTTP web interface. Elasticsearch is free and services as a search and analytics engine for all sorts of information. Be it numbered, geospatial, organized, or unorganized. ElasticSearch was initially released in 2010 by ElasticSearch N.V. (presently known as Elastic).
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The search engine, best known for its basic REST APIs, distributed nature, scalability on huge data sets, and versatility. ElasticSearch includes a set of free and open devices for information processing, analysis, capacity, examination, and mapping.
What are the features of Elasticsearch?
There are features provided by ElasticSearch for Magento 2. They are as follows:
Flexibility in data storage.
- Elasticsearch supports a number of variable data types such as text, numbers, shapes, vectors, histograms, structured and unstructured data, flattened fields, and date/time series. This allows users to analyze, store, and utilize the data as they see fit regardless of the type of data.
- Elasticsearch makes use of a structure, namely the inverted index. This allows very quick full-text searches.
- Unstructured documents can also be stored as it is not necessary that only structured data is ingested or analyzed. This design allows the design to be simple and also makes the search engine to be an effective document store, even after not being a NoSQL database.
- Data can even be stored in columnar forms. Document values are on-disk data structure in ElasticSearch, which makes this data access pattern possible. This allows ElasticSearch to excel at time series and metrics analysis.
Security of data.
- ElasticSearch provides API security on-field and document levels. This means some users are unable to have access to restricted documents and fields.
- The Elastic stack recommends disk-level encryption be configured on all the host machines. Also, snapshot targets ensure that the data at rest is encrypted and cannot be accessed from unprivileged users.
- Clustered indices are used. These are a collection on, or more nodes that altogether hold data and provide indexing and searching functionalities across servers in collections called clusters. This architecture allows data to be scaled horizontally. REST APIs and user interfaces allow a user to manage their cluster architecture.
- A backup snapshot of your cluster can be taken, which can then be stored on a repository on a shared file. This backs up the data in another location.
- If you want to preserve your memory by half, you can take source-only data snapshots. They contain stored fields and index metadata and omit indexes, and are not searchable when they are restored.
- Historical data can be kept for data analysis, but it is avoided due to financial costs. So the rollup indices feature provides a summary and stores the data so that it can be utilized for analysis at a fraction of the cost for storage of raw data.
- Data analyzer functionality allows the text to be converted for searching using tokenizers. The tokenizers break up a stream of characters into word tokens which can then be used to record the order or position of terms.
- In case of errors in a string, character filters can pre-process the stream and transform the stream by adding, deleting, or changing the characters for custom analyzers.
Search and analyze words.
- ElasticSearch provides auto-complete as a navigational feature to guide users to relevant and precise results. This feature is provided by the completion suggestions.
- A phrase suggester also adds the did-you-mean functionality to our search by providing additional logic alongside the term suggesters and helping us select correct phrases. This allows the search engine to make efficient decisions about which tokens to store on the basis of frequencies and occurrences, in other words.
- Correction on our spellings is also a feature of ElasticSearch. The provided suggested text is analyzed before terms are suggested. The suggested terms are provided per the analyzed suggested text token.
What is Sphinx Search?
SphinxSearch is an open-source full-text look server designed by considering significance focused on integrational robustness. It’s composed in the C++ programming language and works on operating systems such as Linux, RedHat, Ubuntu, Windows, Mac, Solaris, and FreeBSD. Sphinx allows you to either group records and look for information composed inside SQL databases or traverse through records rapidly and effortlessly.
A wide range of content handling empowers fine-tuning Sphinx for your specific application necessities. Relevant functionalities guarantee that you’ll enhance search quality as well. Searching by utilizing SphinxAPI is as straightforward as a couple of lines of code. Querying by employing SphinxQL is indeed easier, thanks to SQL search queries. SphinxSearch powers top websites as well, such as Craigslist, Groupon, and Living Social.
What are the features of Sphinx Search?
SphinxSearch provides several features which allow the functionality of the search engine to be efficient. Magento 2 development companies quote these respective features, which are utilized in their Magento 2 stores.
Indexing the databases of SQL
This allows the Sphinx search engine to directly have access and index the data present in SQL storage engines such as Oracle, Drizzle, Microsoft SQL Server, and PostgreSQL. Sphinx is able to interact with all ODBC (Open Database Connectivity) APIs for accessing the databases.
Searching which can be distributed
This means that our queries can be divided across several machines, which allow horizontal processing and high availability.
Versatile text processing
The Sphinx indexing feature compliments full support for SBDC (single-byte character set) alongside UTF-8, which is a variable-width character encoding used in electronic communication. Subsequently, we can process text in all the world’s languages. Morphology and synonyms are processed through the word form dictionaries and the words they stem from.
Furthermore, exceptions and blended characters are addressed as well, making the text processing versatile.
A Competitive relevance ranking.
In contrast to other search engines, Sphinx relied on keyword frequencies instead of statistical rankings of words decades ago. Sphinx also analyzes the proximity of keywords and then displays results that have higher traffic and are ranked on the top in terms of visitors. Additionally, the ranking can be sorted accordingly as we see fit.
The advanced syntax for full-text searches.
The SphinxSearch engine supports queries that include Boolean operators, strict order, phrases, proximity, field and position limits, and searching for substrings.
Effective integration for applications.
SphinxSearch is equipped with three distinctive APIs, SphinxAPI, SphinxSE, and SphinxQL. SphinxAPI is a local library that is accessible for languages such as Java, PHP, Python, Perl, C, etc. SphinxSE empowers gigantic result sets to be transported specifically to MySQL server for post-processing. SphinxQL allows the application to query Sphinx by utilizing standard libraries and inquiry syntax.
Querying arithmetic features.
Sphinx allows users to compute arithmetic expressions as well, add conditional statements, represent data in sorted orders, use aggregates such as average, sum, maximum, and minimum as well.
Non SQL storage features.
Data can also be streamed into a batch indexer in simple XML format by an XMLpipe or inserted into an incremental RT index directly.
Supporting non-text attributes.
Arbitrary attributes such as product IDs, company names, company prices can be stored in the indexes and used for retrieval instead of accessing the database repeatedly. This results in an efficient Sphinx side search result.
What are the Pros & Cons of Elasticsearch?
Variable search options.
ElasticSearch makes use of features when it comes to searching customized sections of words, customized word stemming, full-text searching, auto-completion of words, and instantaneous search. Moreover, you’ll discover what you’re looking for in spite of the fact that you’ve got a spelling mistake.
Auto-completion and instant searches refer to searching whilst the user is in the process of typing. It can be simple suggestions of existing labels, attempting to foresee a word based on the history of the user, or doing a new search for a new keyword.
The search engines are storing new data every second. To keep up with this exponential growth, it is necessary for search engines to be greatly scalable. A scalable data structure will be able to handle a greater number of queries and advanced queries in an instant.
ElasticSearch can distribute its systems horizontally, which provides the ability to extend the resource fetching and balance the query handling inside the nodes in a cluster.
Multiple users have their separate collections of documents as they should not be able to search the documents that do not belong to them. This means that Elastic search has a design where every user has their index. Even though the number of indexes overall increases, but one large ElasticSearch index is much better.
Fine-tuning the queries.
ElasticSearch incorporates a powerful JSON-based DSL that permits development groups to construct complex queries and fine-tune them to get the most precise results from a search. It also provides a way of positioning and gathering results.
Data visualization and real-time updates.
ElasticSearch rapidly updates its data which ensures that your Magento store will have the most recent data. For example, Uber makes use of ElasticSearch and can tackle up to 1000 queries a second and even update location, pricing, and distance, all thanks to ElasticSearch.
Also, the search engine makes use of plugins that can be used for analytics. It can monitor real-time traffic and track metrics such as the number of visitors, most searched queries, unique visitors, and IP addresses for locations. This is ideal as ElasticSearch for Magento 2 will allow users to stay updated and relevant to the situation of their Magento store.
Strong Security protocols.
ElasticSearch provides security features that make it extremely secure. Real-time analytics allow you to discover real-time threats such as attack locations, broken links, web server problems, and unauthorized access attempts. For example, when Dell migrated to this search engine, it reported that their security improved drastically.
ElasticSearch is on the top of the line in search engines operating on e-commerce stores. It is relatively new and misses a few features which have to be added by installing plugins. The arguments have to be fine-tuned for maximum efficiency. This requires a well-managed hierarchy and manually sorted clusters that change rapidly.
The hardware requirements for ElasticSearch are also dependent on large servers instead of small ones. Similarly, solid-state drives (SSD’s) can handle queries better, but they are expensive as compared to rotating disks (HDDs).
What are the Pros & Cons of Sphinx Search?
Fast performance and great scalability
- SphinxSearch can index up to 10-15 MB of text per second on a single CPU processor core. This results in 60+ MegaBytes per second on each serveron a dedicated indexing machine.
- It can search through approximately 1,000,000-documents. Data amounting to 1.2 GigaBytes for text collection that we approximately use for everyday testing and development runs at least 500+ queries per secondon a dual-core desktop machine with 2 GB of RAM. It makes use of less memory.
- The biggest known Sphinx cluster as of yet includes indexes of 25+ billion documents, resulting in over 9TB of data. Craigslist, which makes use of Sphinx search, serves 300+ million queries approximately every day. The search engine is highly scalable.
Great relevance for searching non-text attributes.
- Since SphinxSearch makes use of features that allow you to search data in all sorts of forms. You can add Boolean operators, phrases, and proximity fields. This means that you can even query arithmetic functions such as maximum, minimum and average, etc.
- This smart search also allows you to find substrings of larger search strings relevantly. You can search for product IDs, prices, company names, etc.
Great Integration with programming applications.
SphinxSearch integrates very well with the major applications due to its distinctive APIs. Local libraries are accessible for programming languages such as Java, PHP, Python, Perl, C, etc. The APIs also empower result sets to be transported specifically to the MySQL server for post-processing.
One of the major cons of SphinxSearch is that it is not suitable for customized and unstructured data such as MP4s, MP3s, and PDFs. This is major because the search engine is very resource-consuming in terms of development perspective, which gives ElasticSearch the edge in this case.
As the traffic on your store expands, the fetching time for the store will increase. To maintain your store’s quality standards, you should integrate your Magento 2 store with a search engine. To decide which search engine will be most suitable, it would be preferable to hire Magento development companies. These companies also help bring your Magento 2 cost down by using efficient and established structures which leave very small room for error.
1. Is Sphinx search good?
The SphinxSearch engine is more than just good. It is one of the most experienced and one of the most popular search engines available in the market. The powerful functionalities that are provided in the search engine allow for layered navigations, which have been achieved by extensive experience and extensive updates of this search engine.
Sphinx is highly scalable but not as compared to ElasticSearch. SphinxSearch consumes minimal memory as compared to ElasticSearch. Sphinx also makes use of their own fine-tuned index files; these are the reason SphinxSearch can fetch much faster results than MySQL alone. For increased scalability, set up a cluster of Sphinx servers.
Sphinx is actively used for databases with millions of documents, which span on terabytes and terabytes of disk memory.
2. Is Elasticsearch better than Sphinx?
ElasticSearch is classified under the category of tools in the “Search as a service.” Whereas SphinxSearch is listed under “search engines.”
Some of the most important features that are offered by ElasticSearch are:
- A highly available and well-managed search engine.
- Multi-Tenant facilities with multiple data types.
- Fine-tuning the queries which allow for efficient searches.
- Strong and live security protocols which track threats in real-time.
- Real-time updates on databases.
- A wide range of search options as queries.
On the other hand, SphinxSearch provides the following crucial key features:
- SphinxSearch has the fastest performance and in terms of speed. It can access data very quickly.
- It consumes the least memory as compared to the other search engines.
- It has the largest cluster of data due to its great repository management.
- SphinxSearch has intricate query searches. It can include Boolean operators, arithmetic operations. Searches can be based on customer requirements such as company name etc.
- It can easily integrate with major programming applications such as Python, C, Java, PHP, and Perl, etc.
If we consider the best search for your Magento 2 store, we would recommend ElasticSearch for Magento 2 as it is best suited for what it offers as compared to SphinxSearch in the domain of e-commerce. If you worry about your maintenance of the store, you can hire the services of professional companies to provide Magento support services for your platform.
3. What is Magento 2 search criteria builder?
A search criteria builder is a criterion that is used to filter desired results from the data repository. For Magento 2, we make use of filters to help create criteria. We create the dependencies in data. We can select certain fields, conditions, and the data values for which we require data filtered out of the repository.
We also implement Boolean operators in our criteria. In and, all conditions must be true, and as for or, either one condition can be true for the data being filtered. We can even sort the elements required for comparisons as per requirement. We can group related data and filter them out of the repository by filtering the data which fulfill our required conditions.
We can even apply further operations to the filtered data. So we can have criteria that will be based on the dependencies of the data.
4. What are some alternatives to Elasticsearch and Sphinx?
There are several alternates in search engines for ElasticSearch and SphinxSearch.
- DataDog: It is a leading service for monitoring servers, databases, tools, and services through a SaaS-based data analytics platform. Some notable features are the automation of tools, monitoring, and instrumentation of the data in real-time. A data source can be controlled, and bug tracking is enabled to make the code most safe. Databases and common server components are all listed integrations supported by Datadog.
- Solr is another alternate. It is an open-source enterprise-search platform written in Java. Its important features implement full-text search, highlighting, faceted search, indexing in real-time, making clusters dynamically, integration with databases, NoSQL features, and document handling.
- MongoDB is another source-available, cross-platform, document-oriented database software. It is listed as a NoSQL database software. MongoDB uses JSON-like documents with optional schemas. MongoDB is a highly scalable, flexible NoSqldocument database platform. It has been designed to overcome the challenges proposed by relational databases and the limitations of other NoSQL solutions. MongoDB is reputed for its horizontal scaling and load balancing abilities, which give the application developers an unprecedented level of flexibility and scalability.
Azure Cognitive Search is another contender. It is the only cloud search service that is available and has built-in artificial intelligence capabilities that help a user identify and explore relevant data. It utilizes features such as cognitive skills for vision, speech, and language or uses machine learning models to provide insights from all fields of data.
Azure Cognitive Search also offers semantic search capability, which makes use of advanced machine learning techniques which can understand user intentions and subsequently rank the most relevant search results. It allows you to spend more time innovating solutions and less time in maintaining a complex cloud search solution.
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