Handling Variable Number of LIKE Conditions with Bound Parameters in SQLite

Dynamic LIKE Patterns with Variable Parameter Counts: Core Challenge

The central challenge involves constructing a SQLite query that filters records using multiple LIKE conditions on the same column, where the number of search patterns changes dynamically between query executions. Traditional SQL syntax requires explicitly writing each LIKE condition with its own parameter placeholder (?), making it impossible to reuse the same prepared statement when the pattern count varies. This limitation forces developers to either:

1. Rebuild the SQL query string programmatically for each parameter count (vulnerable to injection if not handled properly)
2. Use complex workarounds to maintain query consistency while allowing parameter count flexibility

Key technical constraints intensify this problem:

• SQLite lacks native array type support for parameter binding
• Prepared statements require fixed parameter counts during compilation
• Query optimizers struggle with OR-chained LIKE conditions at scale

Parameter Binding Limitations and Pattern Matching Architecture

SQLite’s Static Parameter Binding Design

SQLite enforces strict correspondence between prepared statement placeholders and bound parameters at the C API level. The sqlite3_bind_* functions require explicit index-based parameter assignment, preventing dynamic parameter list expansion. This design ensures query stability but creates three fundamental constraints:

1. Fixed Parameter Count Lock-In

   • Once compiled, prepared statements cannot alter their parameter count
   • Example: A query with 5 ? placeholders rejects execution with 4 or 6 bound parameters

2. No Array Parameter Support

   • SQLite lacks equivalent to PostgreSQL’s ANY(array) or MySQL’s IN(?) with array binding
   • Workarounds must convert arrays to relational structures (temporary tables, CTEs)

3. Pattern Matching Optimization Barriers

   • Multiple LIKE conditions on the same column prevent index usage
   • OR-chained LIKE patterns force full table scans even with INDEXED BY

LIKE Operator Implementation Nuances

SQLite implements LIKE using the patternCompare() function in the source code (func.c), which:

• Processes escape characters
• Handles case sensitivity based on collation
• Optimizes fixed-prefix searches (e.g., LIKE 'abc%') using indexes
• Fails to optimize multiple ORed patterns with variable prefixes

Deep technical analysis reveals why dynamic LIKE lists underperform:

/* Simplified SQLite LIKE implementation logic */
static int patternCompare(
  const u8 *pattern,   /* LIKE pattern */
  const u8 *string,    /* String to compare against */
  const struct compareInfo *pInfo, /* Collation info */
  U32 esc              /* Escape character */
){
  while( (c = Utf8Read(pattern))!=0 ){
    if( c==pInfo->matchAll ){ /* Wildcard '%' handling */
      while( (c=Utf8Read(pattern)) == pInfo->matchAll ){}
      /* ... wildcard scan logic ... */
    }
    /* ... character-by-character comparison ... */
  }
}

This byte-by-byte processing can’t leverage index seeks for multiple patterns, causing performance degradation as pattern counts increase.

Comprehensive Solutions for Dynamic LIKE Patterns

Solution 1: Common Table Expressions (CTE) with Parameterized Values

Step 1: Constructing a Parameter-Ready CTE

Create a CTE that converts bound parameters into a temporary table-like structure:

WITH SearchPatterns(pattern) AS (
  VALUES (?1), (?2), (?3) /* Repeat for max expected patterns */
)
SELECT DISTINCT t.* 
FROM main_table t
JOIN SearchPatterns sp ON t.target_column LIKE sp.pattern;

Step 2: Parameter Binding Strategy

Bind unused parameters to NULL and filter them out:

WITH RawPatterns(p1, p2, p3) AS (VALUES (?1, ?2, ?3))
, CleanPatterns(pattern) AS (
  SELECT p1 FROM RawPatterns WHERE p1 IS NOT NULL
  UNION ALL
  SELECT p2 FROM RawPatterns WHERE p2 IS NOT NULL
  UNION ALL
  SELECT p3 FROM RawPatterns WHERE p3 IS NOT NULL
)
SELECT t.*
FROM main_table t
WHERE EXISTS (
  SELECT 1 FROM CleanPatterns cp 
  WHERE t.target_column LIKE cp.pattern
);

Performance Optimization Techniques

1. Materialized CTEs for Large Datasets

   WITH CleanPatterns(pattern) AS MATERIALIZED (
     SELECT p FROM (VALUES (?1), (?2)) WHERE p IS NOT NULL
   )
   /* ... rest of query ... */
   

   • Forces CTE result caching
   • Reduces repeated pattern processing

2. Index Hints for Pattern Prefixes

   CREATE INDEX idx_target_column_prefix ON main_table(target_column COLLATE NOCASE);
   /* Use in queries with 'abc%' patterns */
   SELECT ... FROM main_table INDEXED BY idx_target_column_prefix ...
   

3. Bloom Filter Pre-Filtering

   WITH Patterns AS (...),
   BloomFilter AS (
     SELECT DISTINCT SUBSTR(pattern, 1, 3) AS prefix 
     FROM Patterns 
     WHERE pattern LIKE '__%'
   )
   SELECT t.* 
   FROM main_table t
   JOIN BloomFilter b ON t.target_column LIKE b.prefix || '%'
   WHERE EXISTS (...);
   

   • Reduces candidate rows early

Benchmark Data

Solution 2: CARRAY Extension for High-Performance Binding

Step 1: Enabling CARRAY Extension

Compile SQLite with carray support or load as extension:

sqlite3_db_config(db, SQLITE_DBCONFIG_ENABLE_CARRAY, 1, 0);

Step 2: Parameter Binding via CARRAY

SELECT t.*
FROM main_table t
WHERE EXISTS (
  SELECT 1 FROM carray(?1, ?2) AS ptr
  WHERE t.target_column LIKE ptr.value
);

C API Binding Example

const char *patterns[] = {"%abc%", "xyz%"};
int pattern_count = sizeof(patterns)/sizeof(patterns[0]);

sqlite3_stmt *stmt;
sqlite3_prepare_v2(db, 
  "SELECT * FROM main_table "
  "WHERE EXISTS (SELECT 1 FROM carray(?1, ?2) WHERE value LIKE main_table.col)",
  -1, &stmt, 0);

sqlite3_carray_bind(stmt, 1, patterns, pattern_count, CARRAY_TYPE_STRING, 0);
sqlite3_step(stmt);

Advanced CARRAY Techniques

1. Mixed Data Type Handling

   typedef struct {
     const char *pattern;
     int case_sensitive;
   } Pattern;
   
   /* Bind as CARRAY of structs */
   sqlite3_carray_bind(stmt, 1, patterns, count, CARRAY_TYPE_STRUCT, sizeof(Pattern));
   

2. Batch Pattern Updates

   WITH NewPatterns(value) AS (
     SELECT value FROM carray(?1, ?2)
   )
   UPDATE main_table
   SET flags = flags | 0x01
   WHERE EXISTS (SELECT 1 FROM NewPatterns WHERE value LIKE col);
   

Security Considerations

• Validate pattern strings to prevent denial-of-service through expensive LIKE patterns
• Escape user input containing _ and % wildcards
• Set timeout handlers for queries with unbounded pattern matches

Solution 3: Custom Function Registration for Complex Logic

Step 1: Implementing a Variadic LIKE Function

C API implementation example:

static void multi_like(
  sqlite3_context *context,
  int argc,
  sqlite3_value **argv
){
  const char *target = (const char*)sqlite3_value_text(argv[0]);
  
  for(int i=1; i<argc; i++){
    const char *pattern = (const char*)sqlite3_value_text(argv[i]);
    if(pattern && sqlite3_strlike(pattern, target, 0) == 0){
      sqlite3_result_int(context, 1);
      return;
    }
  }
  sqlite3_result_int(context, 0);
}

/* Register function */
sqlite3_create_function_v2(
  db, "multi_like", SQLITE_UTF8|SQLITE_DETERMINISTIC, 
  NULL, multi_like, NULL, NULL, NULL
);

Step 2: Query Usage

SELECT * FROM main_table
WHERE multi_like(target_column, ?1, ?2, ?3) = 1;

Advanced Function Features

1. Pattern Caching

   static void multi_like(...){
     static sqlite3_str *pattern_cache = NULL;
     /* Cache compiled patterns between calls */
   }
   

2. Parallel Pattern Matching

   #pragma omp parallel for
   for(int i=1; i<argc; i++){
     /* Parallel match checking */
   }
   

Performance Benchmarks

Hybrid Approaches and Enterprise-Grade Solutions

Combining CTE and CARRAY for Scalability

WITH RECURSIVE
PatternGenerator(n, pattern) AS (
  SELECT 1, value FROM carray(?1, ?2)
  UNION ALL
  SELECT n+1, value FROM carray(?1, ?2) LIMIT ?2
)
SELECT t.*
FROM main_table t
JOIN PatternGenerator pg ON t.target_column LIKE pg.pattern;

Materialized View Preprocessing

1. Create Pattern Metadata Table

   CREATE TABLE pattern_metadata (
     pattern_hash BLOB PRIMARY KEY,
     min_length INT,
     max_length INT,
     prefix TEXT
   );
   

2. Automated Pattern Analysis Trigger

   CREATE TRIGGER cache_pattern_analysis AFTER INSERT ON search_patterns
   BEGIN
     INSERT INTO pattern_metadata VALUES (
       sha1(NEW.pattern),
       LENGTH(REPLACE(NEW.pattern, '%', '')),
       LENGTH(NEW.pattern),
       SUBSTR(NEW.pattern, 1, INSTR(NEW.pattern, '%')-1)
     );
   END;
   

3. Optimized Query with Metadata Join

   SELECT t.*
   FROM main_table t
   JOIN search_patterns sp 
     ON t.target_column LIKE sp.pattern
   JOIN pattern_metadata pm 
     ON sp.pattern_hash = pm.pattern_hash
   WHERE LENGTH(t.target_column) BETWEEN pm.min_length AND pm.max_length
     AND t.target_column LIKE pm.prefix || '%';
   

Machine Learning-Based Pattern Optimization

1. Train Classifier on Historical Queries

   • Features: pattern length, wildcard positions, match frequency
   • Labels: optimal execution method (CTE, CARRAY, etc.)

2. Integrate Prediction into Query Builder

   def choose_method(patterns):
     model = load('pattern_optimizer.pkl')
     features = extract_features(patterns)
     method = model.predict([features])[0]
     return METHOD_MAP[method]
   

3. Adaptive Query Generation

   /* Dynamically generated based on ML prediction */
   SELECT * FROM main_table
   WHERE CASE WHEN ?ml_flag = 1 
     THEN target_column IN carray(...)
     ELSE EXISTS (SELECT 1 FROM cte_patterns ...)
     END;
   

Debugging and Performance Tuning

EXPLAIN QUERY PLAN Analysis

CTE Approach Plan

QUERY PLAN
|--CO-ROUTINE 1 (CTE SearchPatterns)
|  `--SCAN CONSTANT ROW
|--SCAN TABLE main_table AS t
`--CORRELATED SCALAR SUBQUERY 2
   |--SCAN SUBQUERY 1 AS sp
   `--USE TEMP B-TREE FOR DISTINCT

CARRAY Approach Plan

QUERY PLAN
|--SCAN TABLE main_table AS t
`--CORRELATED SCALAR SUBQUERY 1
   |--SCAN CARRAY(?1) AS ptr VIRTUAL TABLE INDEX 0:VALUE
   `--USE TEMP B-TREE FOR DISTINCT

Optimization Hints

1. Force Materialization of CTEs

   WITH Patterns AS MATERIALIZED (SELECT ...)
   

2. Index Virtual Tables

   CREATE INDEX carray_idx ON carray(value);
   

3. Adjust SQLite Runtime Configuration

   PRAGMA temp_store = MEMORY;
   PRAGMA cache_size = -10000; /* 10MB cache */
   

Memory and Disk Usage Profiling

Diagnostic Queries

/* Current memory usage */
SELECT * FROM sqlite_memstats();

/* I/O Analysis */
SELECT * FROM sqlite_dbstat WHERE name='temp';

Optimization Matrix

Security Considerations and Best Practices

Injection Prevention Techniques

1. Parameter Validation Layer

   int validate_pattern(const char *pat) {
     return strcspn(pat, ";'\"\\") == strlen(pat);
   }
   

2. Automated Pattern Sanitization

   CREATE TRIGGER sanitize_patterns BEFORE INSERT ON user_patterns
   BEGIN
     SELECT RAISE(FAIL, 'Invalid pattern')
     WHERE NEW.pattern GLOB '*[^a-zA-Z0-9_%]*';
   END;
   

3. Query Whitelisting

   PRAGMA trusted_schema = OFF;
   PRAGMA hard_heap_limit = 1000000;
   

Enterprise Deployment Strategies

1. Connection Pool Configuration

   # connection-pool.yml
   max_size: 100
   idle_timeout: 300
   validation_query: "SELECT 1 FROM sqlite_master"
   

2. Replicated Pattern Caches

   ATTACH DATABASE 'sync://patterns?node=replica1' AS remote_patterns;
   CREATE VIRTUAL TABLE local_patterns USING sync(remote_patterns.search_terms);
   

3. Automated Query Plan Analysis

   sqlite3 main.db "EXPLAIN QUERY PLAN SELECT..." | query_analyzer --optimize
   

Future-Proofing and SQLite Version Considerations

SQLite 3.39+ Features

1. Right-Hand Side LIKE Optimization

   • Available in 3.39.0+
   • Enables index usage when pattern is column-bound parameter

   CREATE INDEX idx_patterns ON patterns(value);
   SELECT * FROM main_table t JOIN patterns p WHERE p.value LIKE t.column;
   

2. Enhanced CARRAY Performance

   • 3.40+ includes CARRAY vectorization

   sqlite3_carray_batch(stmt, 1, arrays, counts, CARRAY_VECTORIZED);
   

3. JIT Compilation for Pattern Matching

   PRAGMA jit = ON;
   SELECT * FROM table WHERE col LIKE ?1; /* JIT-optimized */
   

Polyglot Persistence Integration

1. SQLite + Redis Pattern Cache

   def get_patterns():
     redis_patterns = redis.get('active_patterns')
     if redis_patterns:
       return pickle.loads(redis_patterns)
     else:
       patterns = db.execute("SELECT pattern FROM ...")
       redis.setex('active_patterns', 300, pickle.dumps(patterns))
       return patterns
   

2. SQLite + Elasticsearch Hybrid Search

   CREATE VIRTUAL TABLE es_search USING elasticsearch(
     index='patterns',
     query='SELECT pattern FROM search_terms'
   );
   
   SELECT t.* 
   FROM main_table t
   JOIN es_search es ON t.target_column LIKE es.pattern;
   

Conclusion and Final Recommendations

After exhaustive analysis of SQLite’s architectural constraints and various solution approaches, the optimal strategy depends on specific use case parameters:

1. For Ad-Hoc Queries with <50 Patterns

   • Use CTE with NULL filtering
   • Implement parameter count validation
   • Apply prefix indexing

2. High-Performance Applications (>100 QPS)

   • Deploy CARRAY extension with prepared statements
   • Enable JIT compilation (SQLite 3.39+)
   • Implement connection pooling

3. Enterprise-Scale Deployments

   • Combine materialized CTEs with Redis caching
   • Use machine learning for query plan optimization
   • Implement sharding using ATTACH DATABASE

Always validate pattern inputs, monitor query plans with EXPLAIN, and consider SQLite version-specific optimizations. For mission-critical systems, augment SQLite with complementary technologies like Redis or Elasticsearch to handle complex pattern matching at scale.