Query Planner

How query planners work

Query planners perform complex cost-based analysis to determine the best way to execute a query. They break down queries into logical operations and evaluate different strategies for each step, considering:

• Table statistics and size
• Available indexes
• Join methods
• Data access patterns
• System resources

The planner generates multiple candidate execution plans and estimates their costs using internal metrics before selecting the optimal approach.

Key optimization techniques

Modern query planners employ several sophisticated optimization techniques:

Predicate pushdown

The planner pushes filtering conditions (predicate pushdown) as close to the data source as possible, reducing the amount of data that needs to be processed.

Join optimization

The planner determines the most efficient join strategy and order of operations, choosing between methods like nested loop join, hash join, and specialized temporal joins like ASOF join.

Partition elimination

For partitioned tables, the planner uses partition pruning to skip reading irrelevant partitions based on query conditions.

Time-series specific considerations

When dealing with time-series data, query planners must account for unique characteristics:

1. Temporal locality - Data is typically accessed in time order
2. High insert rates - New data constantly arrives
3. Range-based queries - Most queries filter on time ranges
4. Time-based partitioning - Data is often partitioned by time

Here's an example of how a planner might optimize a time-series query:

EXPLAIN SELECT avg(price) 
FROM trades 
WHERE timestamp BETWEEN '2023-01-01' AND '2023-01-31'
SAMPLE BY 1h;

Performance impact

The query planner's decisions directly impact query performance:

• Choosing the wrong join strategy can lead to exponential performance degradation
• Failing to use available indexes can result in full table scans
• Improper partition pruning can cause excessive I/O
• Suboptimal execution plans can consume unnecessary memory

Modern planners continuously adapt to changing data patterns and system conditions, using statistics and runtime feedback to improve plan selection.

Best practices for working with query planners

To help the query planner make optimal decisions:

1. Keep statistics up to date
2. Create appropriate indexes
3. Use explicit data types
4. Write clear, standard SQL
5. Review execution plans for complex queries
6. Consider partitioning strategies for large datasets

Query planners are essential for database performance, particularly in time-series systems where data volumes and access patterns create unique optimization challenges. Understanding how they work helps developers write more efficient queries and design better data models.