Trino [1], formerly known as PrestoSQL, implements type inference [3] to manage data types efficiently across its distributed SQL query engine. The engine can query large datasets distributed over one or more data sources, supporting various data formats and storage systems.

Trino’s Type Inference

Type [2] inference in Trino is crucial in facilitating seamless operations across heterogeneous data sources, enabling optimizations, and ensuring correctness in query execution. Here’s how Trino implements type inference:

  • During Parsing and Analysis
    • Expression Analysis: Trino parses and analyzes SQL queries to understand the structure and the types of expressions involved. During this phase, it examines functions, operators, and literals to infer the types of expressions based on their usage within the query.
    • Implicit Coercions: Trino performs implicit coercions during type inference, automatically converting data types to match function signatures or operator requirements. For example, if a function expects a double but receives an integer, Trino will implicitly coerce the integer to double.
  • Using Signature Matching
    • Function Resolution: Trino uses signature matching for function calls to determine the most appropriate function version (overload) based on the argument types. It analyzes the provided arguments and their types, comparing them against the function signatures defined in the catalog to select the best match.
    • Type Coercion and Casting: Trino may perform type coercion or cast operations to resolve the best match when multiple overloads are available. This process is guided by the engine’s type system and the precedence of types, aiming to minimize information loss and ensure optimal execution.
  • Handling Complex Data Structures
    • Row and Array Types: For complex data types like rows (structs) and arrays, Trino infers the composite types based on the context of their usage within the query. It recursively analyzes the structure to ensure the inferred types align with the operations performed on the data.
    • Type Parameters and Generics: In cases involving parametric types or generics, Trino resolves the specific types based on the context, correctly inferring type parameters to maintain type safety and compatibility across operations.
  • Optimization and Planning
    • Plan Optimization: During query planning, Trino uses the inferred types to optimize the execution plan. Type information helps select the most efficient algorithms and data structures, considering data distribution and the capabilities of the underlying data sources.

Trino’s implementation of type inference is integral to its architecture, enabling it to efficiently process and analyze data across diverse environments. By intelligently inferring and coercing types, Trino ensures that queries are both flexible for the user and optimized for execution, highlighting the engine’s capability to handle complex analytical workloads at scale.

Trino’s Schema Inference

Trino employs sophisticated mechanisms for schema inference to handle diverse and dynamic data environments efficiently. While Trino primarily relies on predefined schemas within data sources or catalogs, its architecture and query processing capabilities include features that facilitate flexible and dynamic schema handling, mainly when interfacing with semi-structured data or data sources with evolving schemas. Here’s an overview of how Trino performs schema inference:

  • Connectors and Schema Discovery
    • Data Source Integration: Trino integrates with various data sources through connectors, including traditional relational databases, NoSQL databases, and data lakes. Each connector is responsible for interfacing with its data source, including schema discovery and metadata retrieval.
    • Schema Discovery: For many data sources, Trino connectors can dynamically discover schemas by querying the data source’s metadata. This discovery is particularly relevant for sources like Hive or Kafka, where tables or topics can have schemas defined externally (e.g., in an external schema registry).
  • Handling Semi-Structured Data
    • Semi-Structured Types: Trino supports querying semi-structured data (e.g., JSON, Avro, or Parquet files in HDFS or S3) that may not have a strictly defined schema. For these data types, Trino and its connectors can infer the schema dynamically based on the data structure itself.
    • Dynamic Schema Inference: Trino can infer column types on the fly when querying semi-structured data. For instance, when accessing JSON data, Trino can derive the schema directly from the data, allowing users to query the data without needing a predefined schema.
  • Schema Evolution
    • Support for Schema Evolution: Trino supports schema evolution, where the schema of a data source can change over time (e.g., adding new columns). Connectors like those for Hive can handle evolved schemas by reading the metadata from the data source and adjusting the query execution plan accordingly.
    • Column Addition and Deletion: In environments where schemas evolve by adding or removing columns, Trino can adapt to these changes. It queries the latest schema information and ensures that queries access only the available columns, providing flexibility in handling data changes.
  • Type Coercion
    • Type Coercion and Conversion: Trino implements type coercion mechanisms to convert data types automatically, facilitating operations across columns or tables with slightly differing data types. This feature is part of Trino’s broader schema inference capabilities, ensuring that queries can run smoothly even when data types do not match precisely across sources.

Limitations and Considerations While Trino excels at schema inference for many scenarios, it operates within the constraints of the data source’s capabilities and the connector’s implementation. For highly dynamic schemas or unstructured data, additional configuration or preprocessing might be necessary to query the data effectively. Trino’s approach to schema inference, leveraging connectors for schema discovery, supporting semi-structured data types, handling schema evolution, and implementing type coercion, underscores its flexibility and power in querying diverse data ecosystems. By abstracting the complexities of schema management, Trino enables analysts and data scientists to focus on deriving insights from data, regardless of its location or format.

Trino’s approach to schema inference, leveraging connectors for schema discovery, supporting semi-structured data types, handling schema evolution, and implementing type coercion, underscores its flexibility and power in querying diverse data ecosystems. By abstracting the complexities of schema management, Trino enables analysts and data scientists to focus on deriving insights from data, regardless of its location or format.

Summary

Trino’s type and schema inference capabilities represent a sophisticated approach to addressing the complexities of querying and analyzing data across many diverse data sources in real time. By dynamically inferring types and schemas, Trino not only optimizes query execution but also simplifies the user experience, eliminating the need for explicit schema definitions and extensive data type management. This flexibility makes Trino an invaluable tool in real-world applications where data landscapes are constantly evolving, such as big data analytics, multi-source data integration, and interactive data exploration. Organizations can leverage Trino to unify their data querying capabilities, providing analysts and data scientists with a powerful, unified interface to access and analyze data stored in various formats and locations. Trino’s ability to seamlessly adapt to changing schemas and data types and integration with diverse data storage systems underscores its role as a critical component in modern data architecture, enabling businesses to derive insights from their data with greater agility and efficiency.

References

  1. Trino Overview
  2. Trino Data Types
  3. Type and Schema Inference