Optimizing IFNULL-Based Queries on Multiple Timestamp Columns in SQLite

Understanding Table Scan Behavior When Filtering with IFNULL(updatedAt, createdAt)

The core challenge revolves around efficiently querying a table containing creation and modification timestamps while avoiding full table scans. Consider a table structured as follows:

CREATE TABLE example_entries (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    name TEXT,
    updatedAt INTEGER,
    createdAt INTEGER NOT NULL
);

In this schema, createdAt is populated during record insertion while updatedAt remains NULL until subsequent modifications. A common requirement involves retrieving records modified after a specific timestamp, falling back to creation timestamps for unmodified records. The initial approach uses:

SELECT id, name, IFNULL(updatedAt, createdAt) AS effective_date 
FROM example_entries 
WHERE IFNULL(updatedAt, createdAt) >= @target_ts;

Despite separate indexes on updatedAt and createdAt, this query triggers a full table scan because:

1. Function-Based Filtering Limitations: SQLite’s query planner cannot decompose the IFNULL(updatedAt, createdAt) expression into discrete index lookups
2. NULL Handling Complexity: The updatedAt IS NULL condition creates branching logic that standard b-tree indexes cannot natively resolve
3. Expression Evaluation Order: The combined timestamp comparison operates after row retrieval, forcing evaluation against raw table data

This manifests as O(n) read operations proportional to table size – a critical concern in systems like Turso where read operations carry financial and performance costs. Execution plans reveal SEARCH TABLE example_entries USING INDEX is absent, replaced by SCAN TABLE example_entries.

Why SQLite Fails to Leverage Basic Indexes for IFNULL Expressions

1. Expression Collapsing in Query Planning

SQLite’s optimizer cannot automatically transform IFNULL(col1, col2) into equivalent col1 OR col2 logic during index selection. The function wrapper obscures the underlying column relationships from the index matching algorithms. This differs from simple column references where the planner directly maps WHERE clause elements to index segments.

2. Index Key Composition Constraints

Traditional single-column indexes store sorted keys for one column. When querying IFNULL(updatedAt, createdAt), the effective sort order combines two columns with NULL semantics. No existing single-column index captures this combined sort order, forcing sequential scanning.

3. NULL Value Exclusion in Index Storage

Standard indexes omit entries where the indexed column is NULL. Since updatedAt starts as NULL, its index lacks these initial records. The query’s fallback to createdAt requires accessing data not fully represented in either individual index.

4. Compound Index Key Mismatch

A compound index on (updatedAt, createdAt) would order first by updatedAt, then createdAt. This doesn’t align with the IFNULL logic requiring either:

• updatedAt if non-NULL, else
• createdAt

The compound index would still require scanning all updatedAt NULL entries and comparing their createdAt values separately.

5. Function-Based Index Awareness Gap

Without an explicitly defined index on the IFNULL expression, the optimizer lacks metadata about the expression’s sort order or coverage. It cannot verify if an existing index sufficiently covers the expression’s output.

Implementing and Validating Index-Driven Solutions for IFNULL Timestamp Logic

Solution 1: Expression Index on IFNULL(updatedAt, createdAt)

Step 1: Create Targeted Expression Index

CREATE INDEX idx_effective_date 
ON example_entries(IFNULL(updatedAt, createdAt));

This index explicitly stores the computed IFNULL results in sorted order, enabling direct range queries.

Step 2: Verify Index Usage with EXPLAIN

EXPLAIN QUERY PLAN
SELECT id, name, IFNULL(updatedAt, createdAt) 
FROM example_entries 
WHERE IFNULL(updatedAt, createdAt) >= @target_ts;

Expected output shows USING INDEX idx_effective_date instead of SCAN.

Step 3: Analyze Index Coverage
The expression index covers both columns and NULL scenarios:

• Non-NULL updatedAt values stored as-is
• NULL updatedAt entries replaced with createdAt

Performance Characteristics:

• Index storage: Adds O(n) space for computed values
• Write overhead: INSERT/UPDATE triggers index updates for both columns
• Read efficiency: O(log n) lookups via b-tree traversal

Solution 2: Manual Query Expansion with OR Conditions

Step 1: Rewrite Query Using Boolean Logic

SELECT id, name, IFNULL(updatedAt, createdAt) AS effective_date
FROM example_entries
WHERE updatedAt >= @target_ts 
   OR (updatedAt IS NULL AND createdAt >= @target_ts);

Step 2: Create Supporting Indexes
Option A: Separate Indexes

CREATE INDEX idx_updatedAt ON example_entries(updatedAt);
CREATE INDEX idx_createdAt ON example_entries(createdAt);

Option B: Composite Index

CREATE INDEX idx_updated_null_created 
ON example_entries(updatedAt, createdAt);

Step 3: Analyze Query Plan Variations

With Separate Indexes:

• SQLite may perform an INDEX RANGE SCAN on idx_updatedAt for updatedAt >= @target_ts
• Plus an INDEX RANGE SCAN on idx_createdAt with updatedAt IS NULL filter
• Combine results via UNION or OR optimizations

With Composite Index:

• The index orders by updatedAt first, enabling partial range scans
• createdAt comparisons occur only where updatedAt IS NULL

Performance Trade-offs:

• Separate indexes offer better individual scan speeds but require combining results
• Composite index reduces storage but may scan more updatedAt entries
• OR optimizations depend on SQLite version and query planner heuristics

Solution 3: Materialized effective_date Column

Step 1: Add Persisted Computed Column

ALTER TABLE example_entries 
ADD COLUMN effective_date INTEGER 
GENERATED ALWAYS AS (IFNULL(updatedAt, createdAt)) VIRTUAL;

CREATE INDEX idx_effective_stored ON example_entries(effective_date);

Step 2: Query Using Materialized Column

SELECT id, name, effective_date
FROM example_entries
WHERE effective_date >= @target_ts;

Advantages:

• Simplifies query syntax
• Centralizes date logic in schema
• Allows standard index usage

Drawbacks:

• Requires SQLite 3.31+ (2020-01-22)
• Adds index maintenance overhead
• Virtual vs. Stored column considerations impact storage

Solution 4: Partial Indexes for NULL/NOT NULL updatedAt

Step 1: Create Conditional Indexes

-- For non-NULL updatedAt
CREATE INDEX idx_active_updated 
ON example_entries(updatedAt) 
WHERE updatedAt IS NOT NULL;

-- For NULL updatedAt
CREATE INDEX idx_legacy_created 
ON example_entries(createdAt) 
WHERE updatedAt IS NULL;

Step 2: Query with UNION ALL

SELECT id, name, updatedAt AS effective_date
FROM example_entries 
WHERE updatedAt >= @target_ts
UNION ALL
SELECT id, name, createdAt 
FROM example_entries 
WHERE updatedAt IS NULL AND createdAt >= @target_ts;

Performance Considerations:

• Avoids OR-condition complexity
• Enables separate index scans
• UNION ALL prevents duplicate checks
• Requires application-level query composition

Validation and Tuning Techniques

1. Benchmarking Index Options
Use .timer ON in SQLite CLI to measure real execution times:

.timer ON
-- Test each query variant multiple times

2. Analyzing Index Utilization
Query sqlite_stat1 for index usage statistics after running ANALYZE:

SELECT tbl, idx, stat FROM sqlite_stat1 WHERE tbl = 'example_entries';

3. Forcing Index Selection
Use indexed hints to test alternative plans:

SELECT id, name, IFNULL(updatedAt, createdAt)
FROM example_entries INDEXED BY idx_effective_date
WHERE IFNULL(updatedAt, createdAt) >= @target_ts;

4. Monitoring Schema Changes
Track index impact on write performance:

PRAGMA compile_options;
-- Verify ENABLE_UPDATE_DELETE_LIMIT for write constraints

5. Parameter Binding Considerations
Ensure timestamp parameters are properly typed to avoid index bypass:

# Python example using named parameters
cur.execute("""
    SELECT id, name, IFNULL(updatedAt, createdAt)
    FROM example_entries
    WHERE IFNULL(updatedAt, createdAt) >= :ts
""", {'ts': target_timestamp})

Maintenance and Evolutionary Considerations

Index Rebuilding
Periodically reorganize indexes after bulk data changes:

REINDEX idx_effective_date;

Schema Migration Strategy
When altering existing tables:

1. Create new indexes concurrently during off-peak
2. Phase out old queries gradually
3. Monitor slow query logs for regressions

Version-Specific Optimizations
SQLite 3.30.0+ improves OR optimization. Verify version capabilities:

SELECT sqlite_version();

Cost-Based Query Planner Adjustments
Tune optimizer parameters for large datasets:

PRAGMA optimizer_limit = 1000;
PRAGMA optimizer_scan_count = 5000;

Final Recommendations

1. Prefer Expression Indexes when:

   • Query syntax cannot be modified
   • Storage overhead is acceptable
   • SQLite version ≥ 3.9.0 (2015-10-14)

2. Use OR Expansion when:

   • Maximum read performance is critical
   • Index storage must be minimized
   • Query complexity can be managed

3. Employ Materialized Columns when:

   • Business logic frequently references effective_date
   • Application code simplification outweighs storage costs
   • Using SQLite ≥ 3.31.0

4. Implement Partial Indexes when:

   • Data distribution is highly skewed (many NULL/NOT NULL)
   • Write performance must be preserved
   • Query patterns allow UNION-based access

Always validate solutions with EXPLAIN QUERY PLAN and real-world load testing. Consider indexing strategies as evolutionary components of the schema that require periodic reassessment as data volumes and access patterns change.