Understanding DuckDB’s Query Execution Plans: A Comprehensive Guide

DuckDB is an open-source, columnar database management system designed to provide high-performance analytics on structured data. Known for its efficiency and simplicity, DuckDB is used by data analysts, data scientists, and developers for processing large datasets directly within a Python or R environment. One of its core features is its ability to optimize queries, ensuring that analytical operations are executed quickly and with minimal resource consumption.

Understanding how DuckDB executes queries is essential for anyone looking to leverage its full potential. In this blog, we will take an in-depth look at DuckDB’s query execution plans, explaining their importance, structure, and how to analyze and optimize them.

What is a Query Execution Plan?

A Query Execution Plan (QEP) is a roadmap that outlines the steps a database engine takes to execute a SQL query. The QEP is the result of query optimization, which ensures that the query is executed in the most efficient way possible. The QEP is generated by the query planner, a component of the database engine, and it plays a crucial role in how a database manages the resources required to process a query.

In the case of DuckDB, the QEP details the specific operations, such as scans, joins, aggregations, and sorting, that the system will perform. Understanding how DuckDB constructs and executes these plans can help you optimize your queries for better performance.

The Role of the Query Planner in DuckDB

DuckDB’s query planner is responsible for converting a high-level SQL query into an execution plan that can be efficiently run. The query planner follows a series of steps:

1. Parsing: The SQL query is first parsed to check its syntax and create an internal representation.
2. Logical Planning: During this phase, the planner generates a logical plan, which is an abstract representation of the operations to be performed on the data. This plan doesn’t specify how to execute the operations but outlines what needs to be done.
3. Logical Optimization: The logical plan undergoes optimization to eliminate unnecessary operations and combine steps where possible. This may include pushing filters down to scan operations or eliminating redundant joins.
4. Physical Planning: This phase converts the logical plan into a physical plan, which specifies how the operations should be executed. It includes decisions like the order of joins or the method used for sorting.
5. Execution: The physical plan is executed by the DuckDB engine.

DuckDB uses a combination of cost-based optimization and heuristics to choose the best execution plan. This approach allows the system to adapt to different workloads and data distributions.

Anatomy of a DuckDB Query Execution Plan

Let’s explore the components that make up a DuckDB query execution plan. When you query DuckDB, you can view the execution plan using the EXPLAIN statement. This will show you the step-by-step operations DuckDB will perform when executing your query.

Example Query

sql
EXPLAIN SELECT column1, column2 FROM my_table WHERE column3 > 100 ORDER BY column1;

The output might look something like this:

scss
┌──────────────────────────────────────────────────────────────────────────────┐
│ Plan                                                                         │
├──────────────────────────────────────────────────────────────────────────────┤
│ Project(Relation column1, column2) → OrderBy(column1) → Filter(column3 > 100) │
│                                ↓                                             │
│                          Scan(my_table)                                     │
└──────────────────────────────────────────────────────────────────────────────┘

This simple query returns two columns from my_table where column3 is greater than 100 and orders the result by column1. The execution plan describes the operations DuckDB will perform in the following order:

1. Scan(my_table): DuckDB first performs a scan on my_table to retrieve all the rows.
2. Filter(column3 > 100): After scanning the table, DuckDB filters the rows where column3 is greater than 100.
3. OrderBy(column1): Finally, the result is ordered by column1.

Let’s break down these operations further:

1. Scan (Table Scan)

A Scan operation refers to reading the data from the table. In this case, Scan(my_table) means DuckDB is reading the data from the my_table table. DuckDB uses a columnar storage model, so this operation is highly efficient, especially when only a subset of columns are required in the query.

2. Filter

The Filter operation applies a condition to the scanned data. In this case, DuckDB filters the rows where column3 > 100. Filters are often pushed down into the scan phase, meaning DuckDB will try to apply the filter as early as possible to reduce the number of rows processed in subsequent operations.

3. OrderBy

The OrderBy operation sorts the results according to the specified column, in this case, column1. Sorting is an expensive operation, so DuckDB will try to minimize the amount of data that needs to be sorted. If possible, DuckDB might use techniques like parallel sorting or in-memory sorting to speed up this operation.

Analyzing Query Execution Plans in DuckDB

To effectively use DuckDB and optimize your queries, understanding the structure of the execution plan is crucial. The plan will provide insights into the operations performed and help identify areas that might need optimization.

DuckDB’s query execution plans are hierarchical, meaning that complex queries will involve multiple nested operations. You can use the EXPLAIN keyword to visualize these plans for your queries.

Understanding Cost Metrics

Each operation in the execution plan is associated with a cost. This cost represents the estimated computational resources required to perform the operation. DuckDB uses a cost model to evaluate different execution strategies. Higher-cost operations, such as sorts or joins, may be performed later in the plan or optimized in various ways to reduce their impact.

Understanding the costs associated with each operation can help you optimize queries by reducing the need for expensive operations. For instance, if the cost of a sort operation is too high, you might consider removing unnecessary sorts or using indexed columns.

Optimizing Query Execution

DuckDB’s query optimizer performs a number of strategies to ensure that queries run as efficiently as possible. However, there are still several techniques you can use to optimize query performance:

1. Use Indexes: Indexes can significantly reduce the cost of table scans, especially for large tables. DuckDB supports primary keys and unique indexes that can be used to optimize query performance.

2. Limit Data Scanned: Always try to limit the amount of data that is scanned by your queries. For instance, use selective filters and conditions to avoid scanning unnecessary rows.

3. Avoid Unnecessary Joins: Joins are expensive operations, especially when working with large datasets. Minimize the number of joins by only joining tables when necessary.

4. Leverage Parallelism: DuckDB is designed to efficiently utilize multiple CPU cores. It uses parallel processing techniques for certain operations, such as aggregation and sorting, to speed up query execution.

5. Use Caching: DuckDB caches intermediate results to speed up subsequent queries. By reusing cached results, you can reduce the time required to perform similar operations.

DuckDB and the Cost-Based Optimizer

The query planner in DuckDB utilizes a cost-based optimizer (CBO), which assigns costs to various query execution strategies and selects the most efficient one. The cost model is based on several factors:

• I/O Costs: The cost of reading data from disk or memory.
• CPU Costs: The computational effort required to perform operations like filtering, joining, and sorting.
• Memory Usage: The amount of memory required to perform operations like sorting or aggregation.

DuckDB’s cost-based optimizer evaluates different query execution strategies and selects the one with the lowest overall cost. For example, if there are multiple ways to join tables, the optimizer will choose the strategy that minimizes the cost of reading data and performing operations.

Advanced Query Execution in DuckDB

As you become more familiar with DuckDB, you’ll encounter more advanced query execution features, such as:

1. Parallel Query Execution

DuckDB can parallelize certain queries, using multiple CPU cores to speed up processing. For example, large aggregation operations and joins on partitioned data can be executed in parallel to reduce execution time.

2. Vectorized Execution

DuckDB is optimized for vectorized execution, meaning it processes data in chunks rather than row-by-row. This allows DuckDB to take advantage of modern CPU architectures and perform operations much faster.

3. In-Memory Execution

DuckDB supports in-memory execution for certain queries, where it keeps the entire dataset in memory during processing. This significantly speeds up operations compared to reading from disk.

4. Predicate Pushdown

DuckDB performs predicate pushdown optimization, meaning it moves filters and conditions as close to the data source as possible. This reduces the number of rows processed and speeds up query execution.

Conclusion

DuckDB’s query execution plans are integral to understanding how queries are processed and how performance can be optimized. By leveraging the information in the execution plan, you can identify bottlenecks and apply optimizations like indexing, data filtering, and reducing costly operations. The query planner in DuckDB uses a combination of heuristics and cost-based optimization to ensure that queries are executed efficiently, making it a powerful tool for anyone working with large datasets.

Understanding DuckDB’s query execution plans will allow you to write more efficient queries, troubleshoot performance issues, and fully unlock the power of this remarkable analytical database.