Optimizing Slow JOINs with LIKE Conditions on Concatenated Provider Descriptions in SQLite

Issue Overview: Slow Performance in JOIN Using LIKE with Growing Data

The core challenge arises when attempting to join two tables where one table contains concatenated provider descriptions without a consistent separator, requiring the use of a LIKE operator with a wildcard suffix (%). The Classifications table stores providers and their classifications, while the AllTransactions table contains transaction details with a ProviderDescription field that combines provider names and additional descriptors in an unstructured format. A view (ClassifiedTransactions) joins these tables using a LEFT JOIN with the condition AllTransactions.ProviderDescription LIKE Classifications.Provider || '%'. As the dataset grows, this approach leads to significant performance degradation—queries taking over a minute compared to sub-second times when using equality joins. The root inefficiency stems from the inability of SQLite to leverage indexes effectively for prefix-matching LIKE conditions in joins, compounded by the lack of structured delimiters in the concatenated ProviderDescription field.

The LIKE operator with a leading wildcard (%) or suffix wildcard (...%) prevents index utilization unless specific collation or indexing strategies are employed. In this case, the concatenated Classifications.Provider || '%' dynamically constructs the search pattern, further complicating query optimization. The absence of a deterministic method to extract the provider name from ProviderDescription forces a full scan of the Classifications table for each transaction row, resulting in a combinatorial explosion of comparisons. Additionally, the use of a view materializes this inefficient join logic, exacerbating latency as data volume increases. Attempts to use Full-Text Search (FTS) for acceleration are hindered by FTS’s limitation to WHERE clauses, making it unsuitable for direct use in joins.

Possible Causes: Inefficient Pattern Matching and Indexing Limitations

1. Unstructured Concatenation in ProviderDescription
The ProviderDescription field combines provider names and descriptors without a consistent separator, making it impossible to reliably split the provider name using substring operations or regular expressions. Arbitrary word counts and formatting in provider names prevent deterministic parsing, necessitating flexible pattern matching with LIKE. This lack of structure forces the join condition to evaluate every possible prefix of ProviderDescription against all Provider values in Classifications.

2. Ineffective Index Utilization for LIKE Conditions
SQLite cannot use standard B-tree indexes for LIKE conditions unless the pattern is a prefix match (e.g., LIKE 'abc%') and the indexed column uses the TEXT data type with COLLATE NOCASE or BINARY collation. In this scenario, the LIKE condition compares ProviderDescription (from AllTransactions) against a dynamically generated pattern (Classifications.Provider || '%'). Since the pattern is not a fixed string, SQLite cannot leverage an index on ProviderDescription or Provider efficiently. Even if an index exists on Classifications.Provider, the concatenation with % invalidates its use for prefix scanning.

3. Full-Table Scans and Quadratic Complexity
Without index support, the join degenerates into a nested loop join: for each row in AllTransactions, SQLite scans the entire Classifications table to find matching providers. If AllTransactions has N rows and Classifications has M rows, the operation has O(N×M) time complexity. As both tables grow, the cost increases quadratically. For example, 10,000 transactions and 1,000 providers result in 10 million comparisons—a substantial load for SQLite’s query engine.

4. View Materialization Overheads
Views in SQLite are not materialized by default; each query referencing the view re-executes the underlying SELECT statement. Repeated execution of the inefficient join amplifies performance issues, especially when the view is used in larger queries or application logic.

5. FTS Limitations in Join Contexts
While FTS virtual tables accelerate text searches via inverted indexes, they are designed for MATCH operations in WHERE clauses, not for joining tables. FTS does not support standard relational joins, and attempting to force it into this role requires convoluted subqueries or temporary tables, negating performance benefits.

Troubleshooting Steps, Solutions & Fixes: Structured Data Extraction and Indexed Joins

Step 1: Normalize ProviderDescription with Generated Columns

Add a derived column to AllTransactions that extracts the provider name prefix from ProviderDescription, enabling equality-based joins. Use SQLite’s generated columns to compute this value once and persist it (if stored) or compute it on-the-fly (if virtual).

-- Add a generated column to split the provider prefix
ALTER TABLE AllTransactions
ADD COLUMN ProviderPrefix TEXT GENERATED ALWAYS AS (
  SUBSTR(ProviderDescription, 1, INSTR(ProviderDescription || ' ', ' ') - 1)
) STORED;

-- Create an index for fast lookups
CREATE INDEX idx_alltransactions_providerprefix ON AllTransactions(ProviderPrefix);

This example assumes the provider name is the first word in ProviderDescription. Adjust the substring logic based on actual data patterns (e.g., using INSTR to find a delimiter).

Step 2: Precompute and Index Provider Lengths in Classifications

To handle variable-length provider names, precompute the length of each provider and use it for exact substring matches.

-- Add a column to store provider length
ALTER TABLE Classifications
ADD COLUMN ProviderLength INTEGER GENERATED ALWAYS AS (LENGTH(Provider)) STORED;

-- Create a covering index for joins
CREATE INDEX idx_classifications_provider_with_length ON Classifications(Provider, ProviderLength);

Modify the ProviderPrefix generation in AllTransactions to use Classifications.ProviderLength:

CREATE VIEW ClassifiedTransactions AS
SELECT 
  at.*, 
  c.Classification
FROM AllTransactions at
LEFT JOIN Classifications c
  ON at.ProviderPrefix = c.Provider
  AND LENGTH(at.ProviderPrefix) = c.ProviderLength;

Step 3: Leverage Triggers for Dynamic Provider Matching

If generated columns cannot capture the provider prefix reliably (e.g., multi-word providers), use triggers to update a ProviderKey column in AllTransactions whenever ProviderDescription changes.

-- Add a column to store the extracted provider key
ALTER TABLE AllTransactions
ADD COLUMN ProviderKey TEXT;

-- Create a trigger to update ProviderKey on insert/update
CREATE TRIGGER trg_alltransactions_extract_provider
AFTER INSERT ON AllTransactions
BEGIN
  UPDATE AllTransactions
  SET ProviderKey = (
    SELECT Provider FROM Classifications
    WHERE NEW.ProviderDescription LIKE Provider || '%'
    ORDER BY LENGTH(Provider) DESC
    LIMIT 1
  )
  WHERE TransactionID = NEW.TransactionID;
END;

This trigger selects the longest matching provider to handle overlaps (e.g., "ABC Corp" vs. "ABC Corp Limited"). Index Classifications.Provider for faster lookups:

CREATE INDEX idx_classifications_provider ON Classifications(Provider);

Step 4: Implement a Recursive Prefix Extraction Function

For complex provider patterns, use a user-defined SQL function (in application code) to recursively test possible prefixes against the Classifications table.

# Python example using SQLite UDF
import sqlite3

def extract_provider(description):
    conn = sqlite3.connect(':memory:')
    cursor = conn.cursor()
    cursor.execute("SELECT Provider FROM Classifications WHERE ? LIKE Provider || '%' ORDER BY LENGTH(Provider) DESC LIMIT 1", (description,))
    result = cursor.fetchone()
    return result[0] if result else None

conn = sqlite3.connect('your_database.db')
conn.create_function("extract_provider", 1, extract_provider)

# Query using the UDF
cursor = conn.execute("""
    SELECT 
      AllTransactions.*,
      Classifications.Classification
    FROM AllTransactions
    LEFT JOIN Classifications
      ON Classifications.Provider = extract_provider(AllTransactions.ProviderDescription)
""")

Step 5: Optimize with Covering Indexes and Query Hints

Force SQLite to use indexed columns by restructuring the view and providing query hints.

CREATE VIEW ClassifiedTransactions AS
SELECT 
  at.*,
  c.Classification
FROM AllTransactions at
LEFT JOIN Classifications c
  ON at.ProviderDescription >= c.Provider
  AND at.ProviderDescription < c.Provider || X'FFFF'
  AND at.ProviderDescription LIKE c.Provider || '%';

This exploits lexicographical ordering and index range scans. Create a collation-aware index:

CREATE INDEX idx_classifications_provider_nocase ON Classifications(Provider COLLATE NOCASE);

Step 6: Batch Processing with Temporary Tables

For static or infrequently updated data, precompute matches and store them in a temporary table.

-- Create a temporary table for provider-classification mappings
CREATE TEMP TABLE TempProviderMap AS
SELECT 
  at.TransactionID,
  c.Classification
FROM AllTransactions at
LEFT JOIN Classifications c
  ON at.ProviderDescription LIKE c.Provider || '%'
WHERE c.Provider IS NOT NULL;

-- Create indexes on the temporary table
CREATE INDEX idx_tempprovidermap_transactionid ON TempProviderMap(TransactionID);

-- Query using the temporary table
SELECT 
  at.*,
  tpm.Classification
FROM AllTransactions at
LEFT JOIN TempProviderMap tpm
  ON at.TransactionID = tpm.TransactionID;

Step 7: Schema Redesign for Long-Term Scalability

If feasible, restructure the AllTransactions table to store provider names and descriptions in separate columns during data import.

-- New schema with split columns
CREATE TABLE AllTransactions (
  TransactionID TEXT PRIMARY KEY,
  Date TEXT,
  ProviderName TEXT,  -- Extracted during import
  ProviderDescription TEXT,
  Credit TEXT,
  Debit TEXT
);

-- Join using ProviderName
CREATE VIEW ClassifiedTransactions AS
SELECT 
  at.*,
  c.Classification
FROM AllTransactions at
LEFT JOIN Classifications c
  ON at.ProviderName = c.Provider;

Step 8: Hybrid Approach with FTS and Lookup Tables

Use FTS to identify candidate providers and resolve classifications in a secondary step.

-- Create an FTS virtual table for Classifications
CREATE VIRTUAL TABLE ClassificationsFTS USING fts5(Provider);

INSERT INTO ClassificationsFTS (rowid, Provider)
SELECT rowid, Provider FROM Classifications;

-- Query FTS to find potential matches
CREATE VIEW ClassifiedTransactions AS
SELECT 
  at.*,
  (SELECT Classification FROM Classifications c WHERE c.Provider = fts.Provider) AS Classification
FROM AllTransactions at
LEFT JOIN (
  SELECT 
    at.TransactionID,
    highlight(ClassificationsFTS, 0, '', '') AS MatchedProvider
  FROM AllTransactions at
  JOIN ClassificationsFTS
    ON ClassificationsFTS.Provider = SUBSTR(at.ProviderDescription, 1, LENGTH(ClassificationsFTS.Provider))
) fts
ON at.TransactionID = fts.TransactionID;

Final Recommendation

Implement Step 1 (Generated Columns) or Step 7 (Schema Redesign) for sustainable performance. Use Step 3 (Triggers) if dynamic updates are critical. Avoid LIKE in joins entirely by precomputing provider keys, enabling indexed equality checks. For large datasets, combine generated columns with covering indexes to reduce I/O overhead. Regularly analyze query plans using EXPLAIN QUERY PLAN to verify index usage and refine strategies.