Optimizing SQLite Search Queries with Multiple LIKE Conditions

Slow Performance in Multi-Column LIKE Searches with OR Conditions

When working with SQLite, one common challenge is optimizing search queries that involve multiple columns with LIKE conditions combined using OR. This issue often arises in scenarios where the database schema includes several text-based columns, and the application requires searching across all these columns for a given pattern. The problem is exacerbated when the dataset grows, as the query performance degrades significantly due to the inherent limitations of LIKE operations and the lack of indexing support for such patterns.

In the provided scenario, the user has a table named sqlDiaryData with ten key fields (fldKey0 to fldKey9), each containing text data. The goal is to search across all these fields for a specific pattern and return records that match any of the fields. The initial query uses a series of OR conditions to achieve this, but the performance is suboptimal, especially when dealing with larger datasets. The user also wants to refine the search by adding an additional AND condition to filter the results further, but struggles with the syntax and performance implications.

The core issue here is the inefficiency of using multiple LIKE conditions with OR in a single query. Each LIKE operation requires a full table scan unless indexed, and combining multiple such conditions compounds the performance overhead. Additionally, the lack of schema normalization and the use of serialized field names (fldKey0, fldKey1, etc.) suggest that the database design could be improved to better support such queries.

Inefficient Query Structure and Lack of Indexing

The primary cause of the slow performance is the structure of the query itself. The use of multiple LIKE conditions with OR forces SQLite to evaluate each condition sequentially for every row in the table. Since LIKE operations with wildcards (%) at the beginning of the pattern cannot utilize indexes, the query effectively results in a full table scan for each condition. This is computationally expensive, especially as the number of rows and columns increases.

Another contributing factor is the schema design. The table sqlDiaryData contains ten key fields, each storing text data. This design is not normalized, as it repeats the same type of data across multiple columns. In a normalized schema, these key fields would be stored in a separate table, allowing for more efficient indexing and querying. The current design also makes it difficult to add or remove key fields without altering the table structure, which can be problematic as the application evolves.

Furthermore, the user’s attempt to add an AND condition to refine the search results introduces additional complexity. The AND condition requires that both sets of LIKE conditions be satisfied, which further increases the computational load. Without proper indexing or schema optimization, this approach is unlikely to yield satisfactory performance.

Implementing Schema Normalization and Query Optimization

To address the performance issues, the first step is to normalize the database schema. This involves restructuring the data into separate tables to eliminate redundancy and improve query efficiency. In this case, the sqlDiaryData table should be split into two tables: one for diary entries and another for keywords. This allows for a many-to-many relationship between entries and keywords, which can be efficiently queried using joins.

The normalized schema would look like this:

CREATE TABLE entry (
    id INTEGER PRIMARY KEY,
    content TEXT
);

CREATE TABLE keyword (
    id INTEGER PRIMARY KEY,
    word TEXT COLLATE NOCASE UNIQUE
);

CREATE TABLE entry_keyword (
    entry_id INTEGER,
    key_id INTEGER,
    UNIQUE(entry_id, key_id),
    FOREIGN KEY (entry_id) REFERENCES entry(id),
    FOREIGN KEY (key_id) REFERENCES keyword(id)
);

With this schema, each diary entry is stored in the entry table, and each keyword is stored in the keyword table. The entry_keyword table establishes the relationship between entries and keywords, allowing for efficient querying using joins.

The next step is to optimize the search query. Instead of using multiple LIKE conditions with OR, the query can be rewritten to leverage the normalized schema and use joins to filter the results. For example, to search for entries that contain specific keywords, the following query can be used:

SELECT e.content 
FROM keyword k 
JOIN entry_keyword ek ON (k.id = ek.key_id) 
JOIN entry e ON (e.id = ek.entry_id) 
WHERE k.word LIKE '%search_term%';

This query is more efficient because it uses indexed columns (k.word) to filter the results, reducing the need for full table scans. Additionally, the use of joins allows for more complex queries, such as searching for entries that match multiple keywords:

SELECT e.content 
FROM keyword k1 
JOIN entry_keyword ek1 ON (k1.id = ek1.key_id) 
JOIN entry e ON (e.id = ek1.entry_id) 
JOIN keyword k2 ON (k2.id = ek1.key_id) 
WHERE k1.word LIKE '%search_term1%' 
AND k2.word LIKE '%search_term2%';

This query searches for entries that match both search_term1 and search_term2, using the normalized schema to efficiently filter the results.

In cases where schema normalization is not feasible, another approach is to use a virtual table with full-text search capabilities. SQLite supports full-text search (FTS) through virtual tables, which can be used to index and search text data more efficiently than LIKE conditions. For example, an FTS virtual table can be created as follows:

CREATE VIRTUAL TABLE fts_entry USING fts5(content);

This table can then be populated with the text data from the sqlDiaryData table, and queries can be performed using the MATCH operator:

SELECT * FROM fts_entry WHERE content MATCH 'search_term';

This approach is particularly useful for large datasets, as it leverages SQLite’s full-text search capabilities to provide faster and more accurate search results.

In conclusion, the performance issues with multi-column LIKE searches in SQLite can be addressed through schema normalization, query optimization, and the use of full-text search. By restructuring the database schema and using efficient query techniques, it is possible to achieve significant performance improvements and support more complex search requirements.