Index Scan

How index scans work

When executing a query, the database engine can use an index scan to quickly locate relevant data by following the organized structure of an index, similar to using a book's index to find specific pages. This is particularly efficient for time-series data where timestamps serve as a natural index.

Advantages over full table scans

Index scans offer several benefits compared to full table scans:

1. Reduced I/O operations
2. Faster data retrieval for selective queries
3. Efficient range-based searches
4. Optimal for time-series queries with temporal conditions

For time-series databases, index scans are particularly valuable when querying specific time ranges:

SELECT *
FROM trades
WHERE timestamp >= '2023-01-01'
AND timestamp < '2023-01-02';

Performance considerations

The effectiveness of an index scan depends on several factors:

1. Index selectivity
2. Index coverage
3. Data distribution
4. Query conditions

When dealing with time-series data, proper time-based partitioning can enhance index scan performance by limiting the scan to relevant partitions.

Common use cases

Index scans are particularly valuable in:

• Time range queries
• Point queries on indexed columns
• Range-based aggregations
• Join operations using indexed columns

For example, in financial market analysis, index scans enable rapid access to specific trading periods:

SELECT symbol, avg(price)
FROM trades
WHERE timestamp BETWEEN '2023-01-01' AND '2023-01-31'
GROUP BY symbol;

The query optimizer typically chooses between an index scan and a full table scan based on statistics and cost estimates, aiming for optimal query performance.