Even though the 21st century gave us gigabit communication channels and cat videos on YouTube in 4K and 60 FPS on our home screens, text data still remains the basis of the Internet. This is the reason why text search is one of the most important things in data management. You should always know how to find something you needed in tons of data. In this topic, you will get to know about full-text search and its engines.
Definition of full-text search
Full-text search is an advanced way to search a database. It is a technique that allows doing both title and content search through documents and databases. Unlike other search methods, full-text search analyzes all the words in the document, and not only the description or the metadata. This provides a fast and relevant search for information.
The full-text search helps to quickly find all the instances of a term (word) in a table without having to scan its rows and columns. Full-text search relies on text indexes.
A text index item is some part of a string: a word, a sentence, or just a letter. For example, in the sentence "London is the capital of Great Britain", "London" can be an indexed item. So when we type "London" in the search bar, we will find the whole sentence.
Full-text search engines
A full-text search engine is a program that scans through text data. Full-text search engines are great solutions for fast and efficient searching through big amounts of text data. You can use full-text search for unstructured data, semi-structured content, or structured data. But it is mostly used for unstructured data, because of its benefits. Such engines can also categorize the acquired information based on specific values within the data (alphabetical order, price range, region, color, size, file type, author).
Here are some of the reasons to use a full-text search engine:
- Full-text search engines can be configured with ease to suit the needs of each developer and each project.
- These engines are open, so you can implement your own search algorithm and use it.
- They can also have some enhancements (plugins, modules). Full-text search systems can conduct a search even through non-text or constrained text fields.
Many full-text search engines can be used in various situations and projects. Here are the most popular and powerful search engines:
- Elasticsearch provides a distributed and highly available search engine and an HTTP RESTful API that makes development more comfortable.
- ClickHelp provides an easy and quick migration that ensures working with different text formats (MS Word, HTML, Web Help, CHM, etc).
- Sphinx Search guarantees a fast speed of indexation and search, any encoding support, and other features.
You need to remember that developers always choose search engines based on their project architecture. There're a lot of advantages and disadvantages to every full-text search engine, so you need to be very careful when choosing one.
Benefits of full-text search engines
There are numerous and varied reasons for implementing a full-text search system. Here are just a few advantages users can derive from such solutions:
- High query-processing speed. Full-text search engines provide high-speed query processing that makes quick searching possible through large amounts of text data.
- Great accuracy and precision. Thanks to the algorithms of full-text search engines, they ensure the most relevant search results and great search experience in every project.
- Support of websites and mobile applications. Full-text search engines can comfortably work with websites and mobile applications. In these cases, full-text search engines are a great solution to provide fast and useful search to make the user experience much better.
When full-text search is helpful
Full-text search can be useful in a lot of cases, for example:
- Searching for phrases in a large amount of text;
- Implementing the autocomplete feature;
- Searching in a regular expression;
- Implementing user suggestion algorithms.
Full-text search can provide a better user experience and make data finding easier. Nowadays, a lot of big companies use full-text search systems in their projects. For example, GitHub queries billions of lines of code with a search engine, Wikipedia's search provider relies on full-text search, and StackOverflow uses full-text search for geolocation queries and source-related questions/answers.
Summary
As you can see, searching for something is not as simple as it may seem, but it is essential for modern realities when we have large databases with lots of data. Let's repeat some key facts that we've learned in this topic:
- Full-text search is a technique that helps to search for any content in large amounts of text data.
- Full-text search relies on text indexes.
- There're a lot of benefits to using full-text search engines.
Now let's see what you've learned so far. Time for some practice!