How does the full text search work?

How does the full text search work?

Full-text search works by indexing text content and applying various algorithms to find relevant results based on user queries. The process can be divided into three main steps: Preparation, Execution and Optimization.

Preparing the database for the full-text search

Before a full-text search can be carried out, the data must be prepared accordingly. This step involves indexing the text content in order to maximize search efficiency.

1. tokenization

Tokenization is the process by which text is broken down into smaller units, so-called tokens. These tokens can be words, phrases or even individual characters. For example, the sentence “The quick brown vixen jumps over the lazy dog” is tokenized into the words “the”, “quick”, “brown”, “vixen”, “jumps”, “over”, “the”, “lazy” and “dog”. This step is crucial as it forms the basis for indexing and determines how the search engine analyzes the text. The tokenization can be adjusted depending on the language and specific requirements in order to recognize multi-word expressions or special terms.

2. removal of stop words

Stop words are frequently occurring words that usually have no significant meaning for the search, such as “and”, “the”, “is” and “on”. Removing these words from the index helps to reduce the size of the index and increase the search speed. For example, after removing the stop words, the sentence would be reformatted as “quick brown vixen jumps lazy dog”. This step ensures that the search engine focuses on the more relevant parts of the text and delivers more accurate results.

3. stemming and lemmatization

Stemming and lemmatization are techniques for normalizing words to their basic forms. Stemming reduces words to their root form by removing suffixes, e.g. “laufend” becomes “lauf”. Lemmatization goes one step further by finding the grammatically correct base form, e.g. “better” becomes “good”. These techniques help to consolidate different forms of the same word, improving search accuracy. For example, “laufen”, “läuft” and “lief” would all be indexed as “laufen”, ensuring that a search for “laufen” covers all relevant variations.

4. creating indexes

Creating indexes is a crucial step in improving the speed of full-text searches. An index is a data structure that allows search queries to be executed quickly by eliminating the need to search through every entry in the database. There are different types of indexes used in full-text search:

• Inverse index: Lists each word together with the documents in which it appears. For example, the word “cat” could appear in documents 1, 3 and 5.
• B-tree index: A tree-like structure that is suitable for areas of text data.
• Trigram index: Uses three-letter sequences to search for words with small variations.

In PostgreSQL, indexes can be created using commands such as CREATE INDEX index_name ON table USING gin(column), which significantly improves the performance of search queries.

Executing the search query

Once the data has been indexed, the search query can be executed efficiently. This step includes parsing the search query, applying ranking algorithms and returning the results.

1. parsing the search query

Parsing the search query involves interpreting the user’s input to find relevant documents. This process includes tokenizing the search terms, removing stop words and applying stemming or lemmatization. For example, a search for “fast foxes” would be parsed as “fast” and “fox”. In addition, search engines can support operators such as AND, OR and NOT to enable complex queries. For example, “cat AND dog” would find documents containing both words, while “cat NOT dog” would find documents containing “cat” but not “dog”.

2. ranking of the results

Ranking the results is crucial to ensure that the most relevant documents are displayed first. Various algorithms are used to determine the relevance of a document based on factors such as word frequency and document structure. A common algorithm is TF-IDF (Term Frequency-Inverse Document Frequency), which gives a higher weighting to words that appear frequently in the search context but are rare in other documents. For example, a document in which the search term appears several times would be ranked higher than one in which it appears only once. Factors such as the proximity of the search terms and the importance of the fields (e.g. title vs. main text) can also influence the ranking.

3. return the results

The final results are presented to the user in a sorted format based on their relevance scores. This step involves retrieving the matching documents from the database and displaying them in a user-friendly format. For example, a search for “web development” might return a list of articles, tutorials and resources related to web development, with the most relevant at the top. The presentation of results can include features such as snippets, highlighting and pagination to enhance the user experience.

Optimization of search performance

Optimizing search performance is essential to ensure that full-text search remains efficient and effective, especially as data volumes grow. Various strategies can be implemented to improve search speed and accuracy.

1st index maintenance

Regular maintenance of the indexes is necessary to maintain their efficiency. This includes updating indexes when new data is added and reindexing when significant changes occur. In PostgreSQL, the REINDEX command can be used to rebuild an index, and VACUUM helps to optimize the database by removing dead tuples. Proper index maintenance ensures that search queries remain fast and do not consume system resources unnecessarily.

2. caching

Caching is a powerful technique for improving search performance by storing the results of frequent search queries. When a similar query is made again, the system can retrieve the results from the cache instead of performing the search again, saving time and computing resources. For example, a search for “weather today” could be cached so that subsequent queries return immediate results. Caching can be implemented at different levels, including query results, index data and application data.

3. load distribution

In high traffic scenarios, load balancing can help distribute the search load across multiple servers to maintain optimal performance. By distributing search queries across different servers, the system can handle a larger number of concurrent users without sacrificing performance. Search engines such as Elasticsearch support load balancing natively, making them suitable for large-scale applications. Load balancing not only improves performance, but also increases reliability by ensuring that the system remains functional even in the event of server failures.

Simple search vs. full text search

When it comes to retrieving data, it’s crucial to understand the differences between simple search techniques and full-text search. Each method has its own strengths and areas of application that can affect how effectively you can retrieve the information you need.

Simple search

Simple search refers to basic query methods that use exact matches or simple pattern recognition to find data. Common methods include:

LIKE operator: Used in SQL to search for patterns within text fields. For example, SELECT * FROM articles WHERE title LIKE ‘%database%’ finds titles that contain “database”.

Regular expressions: Allow more complex pattern matching, such as searching for variations of a word.

These methods are easy to implement and work well for small data sets or simple search requirements. However, they are limited to exact matches and cannot handle more complex search scenarios.

Full text search

Full-text search is a more advanced technique that enables large amounts of unstructured text data to be searched efficiently. It supports functions such as:

• Ranking: Sorts the results based on their relevance to the search query.
• Stemming: Recognizes different forms of a word, such as “run”, “running” and “ran”.
• Synonym support: Finds words with a similar meaning.
• Phrase search: Finds exact phrases within the text.

Full-text search is better suited to applications that require complex search functions across large data sets, such as content management systems or e-commerce platforms.

Main differences

To illustrate the differences between a simple search and a full-text search, see the following list:

Features Search with full text search:

• Fast search speed for large data sets with indexes
• Relevance of the ranking supported
• Stemming and synonyms supported
• Complex queries extended to a limited extent
• Implementation simply more complex

Performance comparison simple vs. full text search

Performance is a decisive factor when choosing between a simple search and a full-text search. The simple search may be sufficient for small data sets, but its limitations become clear when the data volume increases.

Consider a table with 1 million rows. Using the LIKE operator would require a full table scan, which leads to significant performance issues. In contrast, full-text search can use indexes to execute search queries efficiently, making it more suitable for larger data sets.

Here is a comparison to illustrate the differences in performance:

The full-text search behaves similarly to the simple search in terms of speed. However, it offers advanced features that improve search accuracy and user experience. It is the preferred choice for applications that require robust search capabilities over large amounts of unstructured data.

Full text search engines

Full-text search engines are specialized software solutions designed to provide efficient and effective search capabilities over large amounts of text data. They play a critical role in supporting applications that require fast and accurate search results, such as content management systems, e-commerce platforms and big data analytics. These engines are designed to process complex search queries, rank results and provide features such as autocomplete and fault tolerance.

Overview of full-text search engines

Full-text search engines can be divided into two main categories: relational databases with built-in search functions and dedicated search engines. Each category has its own strengths and is suitable for different use cases.

Relational databases with full-text search

Several relational databases offer built-in support for full-text search, allowing developers to implement search functionality directly within their existing database infrastructure. Notable examples include:

• MySQL: Offers full-text search functions in the InnoDB and MyISAM storage engines. It supports natural language queries, Boolean queries and queries with query expansion. MySQL is popular for small to medium sized applications due to its simplicity and effectiveness.
• MariaDB: A fork of MySQL, MariaDB extends the full-text search capabilities by offering additional integrations such as the Sphinx search engine. It is known for its performance and additional features that go beyond what MySQL offers.
• PostgreSQL: Known for its robust implementation of full-text search, PostgreSQL offers features such as multi-language support, custom dictionaries and advanced ranking algorithms. It is highly customizable and suitable for applications that have complex search requirements.

These databases are ideal for applications that already store their data in relational databases and do not require search functions on a very large scale.

Dedicated search engines

Dedicated search engines are often used for applications that require extensive search functions across massive data sets. Popular options include:

• Elasticsearch: A distributed search and analytics engine based on Apache Lucene. Elasticsearch is known for its scalability, speed and real-time analytics capabilities. It is often used in scenarios where large amounts of data need to be searched and analyzed quickly.
• Apache Solr: Also built on Apache Lucene, Solr is an open source search platform that offers features such as distributed search, faceting and rich document processing. It is often used in companies that require robust search functions.
• Algolia: A proprietary search and discovery API for developers that focuses on speed and relevance. Algolia offers features such as autocomplete, synonym management and personalized search, making it ideal for user-centric applications.

These search engines are designed to handle large-scale search requirements and provide advanced features that go beyond what relational databases can offer.

Comparison of search engines

When choosing a full-text search engine, it is important to compare its features, performance and suitability for your specific requirements. The following table provides a comparison of popular search engines: