ApacheFlinkUsingCalcite - eellpp/pubScratchpad GitHub Wiki

Apache Flink primarily uses Apache Calcite for SQL parsing, validation, logical planning, and optimization, but it does not rely on Calcite's execution engine or custom adapters for physical execution. Instead, Flink takes the optimized relational expressions (logical plan) produced by Calcite and translates them into its own physical execution plan. Here's a more detailed breakdown of how this works:

1. Calcite's Role in Flink

  • SQL Parsing and Logical Planning: Calcite parses SQL queries and generates a logical plan (a tree of relational algebra operators like Project, Filter, Join, etc.).
  • Optimization: Calcite applies rule-based optimizations (e.g., predicate pushdown, join reordering) to produce an optimized logical plan.
  • Relational Expressions: The output of Calcite is a set of optimized relational expressions, which Flink uses as input for its own physical planning.

2. Flink's Role in Physical Execution

  • Physical Plan Generation: Flink takes the optimized logical plan from Calcite and translates it into a physical execution plan. This involves mapping Calcite's relational operators (e.g., Join, Aggregate) to Flink's runtime operators (e.g., DataStream or Table API operators).
  • Execution Engine: Flink has its own runtime engine for executing the physical plan. It does not use Calcite's execution engine or adapters. Instead, Flink's runtime handles the actual computation, state management, and fault tolerance.

3. Why Flink Doesn't Use Calcite's Execution Engine

  • Streaming vs. Batch: Calcite is primarily designed for batch processing and relational query optimization. Flink, on the other hand, is a streaming-first engine with support for both batch and stream processing. Flink's runtime is optimized for low-latency, stateful, and fault-tolerant stream processing, which is outside Calcite's scope.
  • Custom Operators: Flink has its own set of operators and runtime constructs (e.g., windows, watermarks, stateful processing) that are specific to stream processing. These cannot be directly expressed or executed by Calcite.
  • Performance: Flink's runtime is highly optimized for distributed execution, and using Calcite's execution engine would introduce unnecessary overhead.

4. Flink's Integration with Calcite

  • Logical Plan Translation: Flink uses Calcite's relational algebra as an intermediate representation. The logical plan is optimized by Calcite and then handed off to Flink for physical planning.
  • Custom Extensions: Flink extends Calcite with its own rules and operators to support streaming-specific features (e.g., windowed aggregations, temporal joins).
  • Table API and SQL: Flink's Table API and SQL interface are built on top of Calcite, but the actual execution is handled by Flink's runtime.

5. Example Workflow

  1. A user submits a SQL query to Flink.
  2. Calcite parses the query and generates a logical plan.
  3. Calcite optimizes the logical plan using its rule-based optimizer.
  4. Flink takes the optimized logical plan and translates it into a physical execution plan (e.g., DataStream transformations or Table API operations).
  5. Flink's runtime executes the physical plan on the cluster.

Summary

Flink uses Calcite for SQL parsing, validation, logical planning, and optimization, but it does not use Calcite's execution engine or adapters. Instead, Flink translates the optimized relational expressions into its own physical execution plan, which is then executed by Flink's runtime. This separation allows Flink to leverage Calcite's strengths in query optimization while maintaining full control over the execution of streaming and batch workloads.

Apache Flink, a powerful stream processing framework, leverages Apache Calcite for query optimization and SQL support.

Here's how Flink integrates with Calcite:

  1. SQL Parsing and Validation:

    • Flink uses Calcite to parse SQL queries. Calcite's SQL parser converts SQL statements into an abstract syntax tree (AST).
    • Calcite also validates the AST to ensure the query is semantically correct, checking for things like table existence, column types, and function signatures.

  2. Logical Query Planning:

    • After parsing and validation, Calcite helps Flink generate a logical query plan. This plan represents the high-level operations (e.g., filters, joins, aggregations) that need to be performed to execute the query.

  3. Query Optimization:

    • Calcite's optimizer applies a set of rules to transform the logical query plan into an optimized logical plan. These rules include predicate pushdown, join reordering, and projection pruning, which aim to reduce the computational cost of the query.
    • Flink extends Calcite's optimizer with its own set of rules tailored for stream processing, such as handling windowed aggregations or stateful operations.

  4. Physical Query Planning:

    • Once the logical plan is optimized, Flink translates it into a physical execution plan. This involves mapping the logical operations to Flink's runtime operators (e.g., DataStream or Table API operators).
    • Calcite's relational algebra and optimization framework help ensure that the physical plan is efficient and aligned with Flink's execution model.

  5. Streaming SQL Support:

    • Flink's Table API and SQL interface rely heavily on Calcite to provide SQL support for both batch and streaming data. Calcite enables Flink to handle complex SQL queries over unbounded data streams, including windowed aggregations and temporal joins.
    • Calcite's extensibility allows Flink to define custom functions, operators, and optimizations specific to streaming use cases.

  6. Integration with Flink's Table API:

    • Flink's Table API is built on top of Calcite. When you write a Table API query, Flink internally converts it into a Calcite relational expression, which is then optimized and executed.
    • This integration allows Flink to unify its batch and stream processing capabilities under a single API.

In summary, Apache Calcite plays a critical role in Flink's SQL and Table API by providing a robust foundation for query parsing, optimization, and execution planning. This integration enables Flink to offer high-performance, scalable SQL processing for both batch and streaming workloads.