Query Plan
A query plan, also known as an execution plan, is a structured sequence of steps that a database engine uses to retrieve or modify data. It represents the database's strategy for executing a SQL query in the most efficient way possible, considering factors like table sizes, available indexes, and system resources.
How query plans work
Query plans break down complex SQL statements into a series of discrete operations, typically represented as a tree of execution steps. Each node in the tree represents an operation like:
- Table scans or index lookups
- Filtering and sorting operations
- Join operations between tables
- Aggregation calculations
When processing time-series data, query plans become especially important as they must handle large volumes of sequential data efficiently.
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Understanding query plan operations
Modern databases use sophisticated cost-based optimizers to generate efficient query plans. Key operations in time-series query plans often include:
- Time-range filtering
- Partition pruning
- Vectorized execution for performance
- Parallel processing across partitions
For example, a query plan for time-series analysis might look like:
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Query plan optimization
Query plans optimize performance through several key mechanisms:
Statistics-based decisions
The optimizer uses statistics about data distribution to make informed choices:
- Table sizes and row counts
- Column value distributions
- Index coverage and selectivity
Cost estimation
Each potential plan receives a cost score based on:
- I/O operations required
- CPU processing needed
- Memory usage
- Network transfer costs
The explain command reveals the query plan, showing how the database will:
- Apply the timestamp filter
- Use partition pruning
- Perform columnar scans
- Calculate aggregations
Monitoring and tuning
Effective query plan management requires:
- Regular monitoring of plan performance
- Identifying suboptimal plans
- Adjusting statistics and parameters
- Reviewing index usage
For time-series workloads, special attention should be paid to:
- Timestamp column optimization
- Partition selection efficiency
- Aggregation strategy
- Memory usage for window functions
This ongoing process ensures that query plans remain efficient as data volumes grow and patterns change.