Trino’s Query Optimization: A Deep Dive into the Inner Workings

Trino, formerly known as Presto, is a distributed SQL query engine that excels at querying and analyzing data stored in various data sources. One of its key strengths lies in its sophisticated query optimization process, which enables it to execute complex queries efficiently. In this article, we will delve into the inner workings of Trino’s query optimization process, providing detailed insights along with real-world examples and outputs.

Understanding the Query Optimization Process:

Trino’s query optimization process consists of several crucial stages, each designed to transform and optimize the original SQL query for better performance. Let’s explore these stages in detail:

  1. Parsing:
    • Trino begins by parsing the SQL query provided by the user.
    • The parser breaks down the query into its constituent elements, such as keywords, table names, column names, and predicates.
    • Example:
      • Original Query: SELECT name, age FROM employees WHERE department = ‘Sales’;
      • Parsed Query: [SELECT, name, age, FROM, employees, WHERE, department, =, Sales]
  2. Semantic Analysis:
    • Trino performs semantic analysis to validate the query and ensure that table and column names are correctly referenced.
    • It checks for any type mismatches and resolves ambiguous expressions.
    • Example:
      • Semantic Analysis Output: Valid query with resolved table and column references.
  3. Logical Planning:
    • Trino constructs a logical query plan based on the parsed query.
    • It represents the query as a tree of logical operators, defining the sequence of operations required to retrieve the desired data.
    • Example:
      • Logical Query Plan: Project(name, age) -> Filter(department = ‘Sales’) -> TableScan(employees)
  4. Logical Optimization:
    • Trino applies various logical optimizations to the query plan, such as predicate pushdown, constant folding, and expression simplification.
    • These optimizations aim to reduce the amount of data that needs to be processed and improve query performance.
    • Example:
      • Optimized Logical Query Plan: TableScan(employees with department = ‘Sales’) -> Project(name, age)
  5. Physical Planning:
    • Trino generates a physical query plan that specifies how data will be retrieved from the underlying data sources.
    • It considers factors like data distribution, parallelism, and available connectors.
    • Example:
      • Physical Query Plan: DistributedJoinHash(employees, sales_data)
  6. Physical Optimization:
    • Trino applies physical optimizations, such as join reordering and partition pruning, to enhance query execution efficiency.
    • Example:
      • Optimized Physical Query Plan: DistributedJoinHash(sales_data, employees)
  7. Execution:
    • Trino executes the optimized query plan, fetching and processing data from the data sources.
    • It leverages distributed computing capabilities to parallelize and speed up the execution.
    • Example:
      • Execution Output: Retrieved data showing ‘name’ and ‘age’ for employees in the ‘Sales’ department.

Read more on Trino here

Author: user