Google’s Google site search offered an easy way for business owners to add search to their websites using their product Google site search. They charged a monthly fee for this and businesses got access to an ad-free search experience that came along with crawling capabilities. This helped websites to offer a Google-like search experience on their website without building their own website from scratch.
Google discontinued their Google site search product in April 2017 and replaced it with Google’s custom search engine, a search engine that came along with ads and offered it for free. Maybe Google wanted to increase the number of places they wanted to show ads and made this move. When this happened a lot of websites were not ready to accept Google custom search as a ready-made replacement to Google site search since they were afraid that the intrusive ads inside Google custom search would lead to a bad customer experience or in the worst-case scenario lead their hard-earned visitors to fall into competitors hands.
Here are the top 10 Google site search alternatives. This list has been compiled keeping in mind the cost, ease of implementation, and search performance. At Expertrec, we help customers migrate from Google site search. If you are interested, drop us an email at email@example.com
Expertrec is the best low-cost Google site search alternative in the market. Expertrec comes with a powerful crawler and is a paid hosted SaaS solution, just like Google site search. Expertrec can index PDF, .xls, docs, .ppt, and other file formats with ease. It also comes with the capability of crawling behind login pages. Prices begin at 9 USD per month for 2000 pages. No ads. No developer bandwidth required, All you have to do is add a piece of code to your website and that’s it you have replaced your Google site search. An additional feature that expertrec offers is Voice search. In short, expertrec is a feature-rich site search engine at a budget price. Expertrec is used by companies such as Scylladb, NATO OTAN, and more..
Elastic search hosted version (open source)
Hosted search as a service pricing begins at 79 USD per month. Requires developer bandwidth, as this only handles the search part. It does not provide you with a ready crawler and the capability to index PDF and other file types.
Solr is used by many fortune 500 companies across the globe. Completely free- you could run it on your own server, but requires developer bandwidth for implementation. You would also require a crawler for getting this working (you could use nutch or scrapy). There are a lot of resources available on the internet to help you out in implementing this.
Its major features include full-text search, hit highlighting, faceted search, real-time indexing, dynamic clustering, database integration, NoSQL features, and rich document (e.g., Word, PDF) handling. Providing distributed search and index replication, Solr is designed for scalability and fault tolerance. Solr is widely used for enterprise search and analytics use cases and has an active development community and regular releases.
Elastic search (Open source)
You could run it on your server, crawling has to be set up by you. Elasticsearch is a search engine based on the Lucene library. It provides a distributed, multi tenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elasticsearch is the most popular enterprise search engine followed by Apache Solr, also based on Lucene.
Google Custom Search Engine, the successor to Google Site Search, allows website owners to create a custom search engine that can be added to their websites.
Benefits of Google custom search
Google custom search is free to use. The best part about Google custom search is that it doesn’t cost any money to run per month. Instead, if you are looking to monetize your website, it is a good option.
- Google custom search comes with a control panel where you can choose what pages are displayed in your search results, add synonyms, promote search results, and more.
- It has a simple to use UI editor where you can choose different layouts and colors for the search engine to match the look and feel of your website.
- Google custom search also has support for developers. Developers can use the Google custom search API to build their own custom search experiences without having to rely on the default search UI given by google custom search.
Disadvantages of Google custom search
- The biggest disadvantage of using Google custom search is ads. This can lead to visitors moving to competitor websites or getting distracted by seeing some other ad on your website and moving away without making a purchase on your website.
- The next biggest disadvantage is poor control over what is crawled on your site and when it is crawled. Since we don’t have much control over what content Google crawls on your website.
- The third biggest disadvantage is that Google is notorious for shutting down products. We never know when Google will discontinue Google custom search like Google site search. It is better to choose a good alternative right now and make sure that you are not left out in the fray looking for last-minute replacements when a shutdown is announced.
Free With Ads, but easy to implement- no coding required. Highly customizable. Go to https://site.yandex.ru/searches (make sure you have created an account already)->click “my search boxes” enter your website URL and get the code to be added. You will get a search box on your website similar to this.
It includes all the usual Yandex search functions: It understands various word forms and corrects mistakes and typos, as well as incorrect keyboard layouts.
Tipue Search is a site search jQuery plugin. It’s free, open-source, responsive, and fast. Tipue Search only needs a browser that supports jQuery. It doesn’t need MySQL or similar. It doesn’t even need a web server. The issue with this approach is since it is purely run on the client-side, it works fine on desktop browsers. It will not perform well on mobile browsers due to high computation load. If the site size is larger than 100 documents, then this is not a recommended approach.
Sphinx is an open-source full-text search server
It is built in C++, hence offers great speed. Does not come with a crawler, hence you will have to set up a crawler, meaning more developer bandwidth.
Sphinx lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with Sphinx pretty much as with a database server.
A variety of text processing features enable fine-tuning Sphinx for your particular application requirements, and a number of relevance functions ensure you can tweak search quality as well.
Searching via SphinxAPI is as simple as 3 lines of code, and querying via SphinxQL is even simpler, with search queries expressed in good old SQL.
Sphinx clusters scale up to tens of billions of documents and hundreds of millions of search queries per day, powering top websites such as Craigslist, Living Social, MetaCafe, and Groupon.
And last but not least, it’s licensed under GPLv2.
Performance and scalability
- Indexing performance. Sphinx indexes up to 10-15 MB of text per second per single CPU core, which is 60+ MB/sec per server (on a dedicated indexing machine).
- Searching performance. Searching through 1,000,000-document, 1.2 GB text collection that we use for everyday development and testing runs at 500+ queries/sec on a 2-core desktop machine with 2 GB of RAM.
- Scalability. The biggest known Sphinx cluster indexes 25+ billion documents, resulting in over 9TB of data. The busiest known one is Craigslist, serving 300+ million search queries/day.
- Batch and Real-Time full-text indexes. Two indexes back end that support both efficient offline index construction and incremental on-the-fly index updates are available.
- Non-text attributes support. An arbitrary number of attributes (product IDs, company names, prices, etc) can be stored in the index and used either just for retrieval (to avoid hitting the DB), or for efficient Sphinx-side search result set post-processing.
- SQL database indexing. Sphinx can directly access and index data stored in MySQL (all storage engines are supported), PostgreSQL, Oracle, Microsoft SQL Server, SQLite, Drizzle, and anything else that supports ODBC.
- Non-SQL storage indexing. Data can also be streamed to batch indexer in a simple XML format called XMLpipe, or inserted directly into an incremental RT index.
- Easy application integration. Sphinx comes with three different APIs, SphinxAPI, SphinxSE, and SphinxQL. SphinxAPI is a native library available for Java, PHP, Python, Perl, C, and other languages. SphinxSE, a pluggable storage engine for MySQL, enables huge result sets to be shipped directly to the MySQL server for post-processing. SphinxQL lets the application query Sphinx using standard MySQL client library and query syntax.
- Advanced full-text searching syntax. The querying engine supports arbitrarily complex queries combining boolean operators, phrase, proximity, strict order, and quorum matching, field and position limits, exact keyword form matching, substring searches, etc.
- Rich database-like querying features. Sphinx does not limit you to just keyword searching. On top of the full-text search result set, you can compute arbitrary arithmetic expressions, add WHERE conditions, do ORDER BY, GROUP BY, use MIN/MAX/AVG/SUM, aggregates, etc. Essentially, full-blown SQL SELECT is supported.
- Better relevance ranking. Unlike many other engines, Sphinx does not solely rely on a 30-year-old statistical ranking that only considers keyword frequencies, nor limits you to it. By default, Sphinx additionally analyzes keyword proximity and ranks closer phrase matches higher, with perfect matches ranked on top. Also, ranking is flexible: you can choose from some built-in relevance functions, tweak their weights by using expressions or develop new ones.
- Flexible text processing. Sphinx indexing features include full support for SBCS and UTF-8 encoding (meaning that effectively all world’s languages are supported); stop word removal and optional hit position removal (hitless indexing); morphology and synonym processing through word forms dictionaries and stemmers; exceptions and blended characters; and many more.
- Distributed searching. Searches can be distributed across multiple machines, enabling horizontal scale-out and HA (High Availability).
Xapian is a highly adaptable toolkit that allows developers to easily add advanced indexing and search facilities to their own applications. It supports the Probabilistic Information Retrieval model and also supports a rich set of boolean query operators.
DataparkSearch Engine is a full-featured open-source web-based search engine and can be used to search within a website, group of websites, intranet, or local system.
The open-source project Strus provides a collection of libraries and command-line tools written in C++ for building a competitive, scalable full-text search engine.
Do drop in any more alternatives that you might know of in the comments section and we will add them to the post here.
Some of the open-source Google site search alternatives would require a crawler to set up to index your site content. You can utilize open-source tools for crawling such as scrapy (easy to set up if you have knowledge in python), heiritrix (Java-based), or Apache Nutch (Java-based). Needless to say, these require developer bandwidth to do this.
The best alternative to Google site search is Expertrec that comes with a plethora of features and is priced at 9 USD per month.