Lag Function

How lag functions work

A lag function retrieves values from previous rows based on a specified offset within an ordered partition of data. For each row, LAG looks back a defined number of rows and returns that historical value, enabling direct comparison with the current row.

Applications in time-series analysis

Lag functions are particularly valuable in time-series analysis for:

1. Calculating period-over-period changes
2. Detecting trends and patterns
3. Computing moving averages
4. Identifying sequential relationships

For example, calculating price changes in financial data:

SELECT 
    timestamp,
    symbol,
    price,
    price - LAG(price) OVER (PARTITION BY symbol ORDER BY timestamp) as price_change
FROM trades
WHERE symbol = 'AAPL'
LIMIT 10;

Performance considerations

When using lag functions, consider these performance aspects:

1. Window frame size impact
2. Partitioning strategy
3. Memory requirements
4. Index utilization

The storage engine design significantly affects lag function performance, especially in time-series databases optimized for sequential access patterns.

Advanced usage patterns

Multiple lag comparisons

Lag functions can be combined to analyze multiple historical points:

SELECT 
    timestamp,
    price,
    LAG(price, 1) OVER (ORDER BY timestamp) as prev_1,
    LAG(price, 2) OVER (ORDER BY timestamp) as prev_2
FROM trades_OHLC_15m
WHERE symbol = 'AAPL'
LIMIT 5;

Handling null values

When LAG references rows before the start of the partition, it returns NULL. Common strategies include:

1. COALESCE to provide default values
2. Conditional logic to handle edge cases
3. Filtering out NULL results for analysis

This makes lag functions particularly useful in real-time analytics where data completeness varies.