Trino (formerly known as PrestoSQL) is an open-source distributed SQL query engine that provides high performance and scalability for querying large datasets across various data sources. One of its core strengths is its ability to execute distributed queries efficiently. In this article, we will delve into the inner workings of Trino’s distributed query execution, offering detailed insights and practical examples to help you understand the process.
Understanding Trino’s Query Execution Architecture:
Before we dive into the specifics of how Trino executes distributed queries, let’s briefly review its overall architecture:
- Coordinator Node: The coordinator node is responsible for parsing, optimizing, and planning queries. It also coordinates query execution across worker nodes.
- Worker Nodes: Worker nodes are responsible for executing the actual tasks associated with a query. They communicate with the coordinator node and retrieve data from various data sources.
- Query Execution Flow: Trino follows a distributed query execution model. When a query is submitted, the coordinator node breaks it down into smaller tasks, known as stages. These stages are then distributed to worker nodes for parallel execution.
Distributed Query Execution in Trino:
Now, let’s explore the key steps involved in the execution of a distributed query in Trino:
- Query Parsing: When a query is submitted, the coordinator node parses it to understand its structure and requirements. It identifies the tables and columns involved, as well as any relevant conditions and joins.
- Query Optimization: Trino employs a cost-based query optimizer that generates multiple query execution plans. These plans are evaluated based on factors such as data locality, distribution, and estimated costs. The optimizer selects the most efficient plan for execution.
Example:
Consider the following query:
SELECT customer_name, SUM(order_total)
FROM orders
GROUP BY customer_name
In this case, the optimizer will evaluate various ways to perform the aggregation and choose the plan that minimizes data movement and processing overhead.
- Query Planning: Once the optimal execution plan is determined, the coordinator node breaks the query into stages. Each stage represents a set of tasks that can be executed in parallel.
- Task Distribution: The coordinator node distributes the query tasks to worker nodes, taking into account the location of data and the available resources on each worker. This ensures that data-intensive tasks are performed closer to the data source, minimizing data transfer overhead.
Example:
If the “orders” table is distributed across multiple nodes, tasks related to different parts of the table will be assigned to the corresponding worker nodes.
- Parallel Execution: Worker nodes execute their assigned tasks in parallel. They retrieve data from the data sources, apply filters, perform joins, aggregations, and other operations as specified in the query.
Example:
Worker nodes might concurrently process different customer segments and calculate the sum of order totals for each.
- Intermediate Data Exchange: During query execution, intermediate results are exchanged between worker nodes as needed. This allows for efficient aggregation and further processing of data.
Example:
Worker nodes may exchange partial results for aggregation, ensuring that the final result is accurate.
- Final Result Assembly: Once all tasks are completed, the coordinator node assembles the final result from the intermediate results obtained from worker nodes.
Example Output:
The final result of our example query might look like this:
customer_name | SUM(order_total)
------------------------------------
Sachin | 1500.00
Rajesh | 2200.00
Griesly | 1800.00
...Trino’s distributed query execution mechanism is a complex and highly efficient process that enables it to handle large-scale data analysis tasks with ease. By breaking down queries into stages, optimizing execution plans, and leveraging parallelism, Trino can deliver fast query performance across diverse data sources.
