Understanding Spark's Query Planning: Parsed, Analyzed, and Optimized Logical Plans

Apache Spark is a powerful distributed computing framework that excels at processing large-scale data. One of its key strengths lies in its ability to optimize SQL queries and DataFrame operations through a sophisticated query planning process. At the heart of this process are three critical stages: the Parsed Logical Plan, the Analyzed Logical Plan, and the Optimized Logical Plan. In this article, we’ll break down what these plans are, how they work, and why they matter.

The Catalyst Optimizer: Spark’s Secret Sauce

Before diving into the logical plans, it’s important to understand the role of Spark’s Catalyst Optimizer. Catalyst is the query optimization engine that powers Spark SQL and DataFrames. It takes a high-level query (written in SQL or using the DataFrame API) and transforms it into an efficient execution plan. This process involves several stages, including parsing, analysis, optimization, and physical planning.

The logical plans we’re discussing today are part of the early stages of this process. They represent the query at different levels of abstraction and refinement before it is executed.

1. Parsed Logical Plan: The First Step

When you submit a SQL query or a DataFrame operation to Spark, the first thing it does is parse the query into a Parsed Logical Plan.

What is a Parsed Logical Plan?

• It’s a tree-like structure that represents the query in its raw, syntactic form.
• Spark uses a parser (like ANTLR) to convert the query string into an Abstract Syntax Tree (AST).
• At this stage, Spark doesn’t validate whether the tables, columns, or data types exist. It simply ensures that the query is syntactically correct.

Example:

Consider the following SQL query:

SELECT name FROM employees WHERE salary > 1000        

The Parsed Logical Plan might look like this:

• Root node: SELECT

This plan is purely syntactic and doesn’t yet understand the semantics of the query.

2. Analyzed Logical Plan: Resolving the Query

Once Spark has a Parsed Logical Plan, the next step is to resolve it into an Analyzed Logical Plan.

What is an Analyzed Logical Plan?

• It’s a validated version of the Parsed Logical Plan.
• Spark uses its Catalog (a metadata repository) to resolve table names, column names, and data types.
• This step ensures that the query is semantically correct. For example, it checks if the employees table exists and if it has a salary column.

What Happens During Analysis?

• Table and Column Resolution: Spark ensures that all tables and columns referenced in the query exist.
• Type Checking: Spark verifies that the data types are compatible (e.g., you can’t compare a string to a number).
• Function Validation: Spark checks that any functions used in the query are valid.

If anything is invalid (e.g., a missing table or column), Spark will throw an error at this stage.

Example:

For the query SELECT name FROM employees WHERE salary > 1000, the Analyzed Logical Plan resolves:

• The employees table exists.
• The name and salary columns exist.
• The salary column is numeric, so the comparison salary > 1000 is valid.

3. Optimized Logical Plan: Making the Query Efficient

With a validated Analyzed Logical Plan in hand, Spark’s Catalyst Optimizer goes to work to produce an Optimized Logical Plan.

What is an Optimized Logical Plan?

• It’s a more efficient version of the Analyzed Logical Plan.
• Spark applies a set of optimization rules to improve the performance of the query.
• These rules include predicate pushdown, constant folding, column pruning, and join reordering.

Common Optimizations:

• Predicate Pushdown: Push filters closer to the data source to reduce the amount of data read.
• Column Pruning: Remove unnecessary columns to reduce data processing.
• Constant Folding: Evaluate constant expressions at compile time instead of runtime.
• Join Reordering: Reorder joins to minimize data shuffling.

Example:

For the query SELECT name FROM employees WHERE salary > 1000, the optimizer might:

• Push the filter salary > 1000 closer to the data source.
• Remove any unused columns (if there were more columns in the SELECT clause).

The result is a streamlined plan that minimizes data processing and improves query performance.

From Logical to Physical: The Final Step

After the Optimized Logical Plan is generated, Spark creates a Physical Plan. This plan specifies how the query will be executed on the cluster. It includes details like:

• Which join algorithms to use (e.g., broadcast join, sort-merge join).
• How to partition the data.
• How to schedule tasks across the cluster.

The Physical Plan is the final step before the query is executed.

Why Does This Matter?

Understanding these logical plans is crucial for:

• Debugging Queries: If a query fails or performs poorly, you can examine the logical plans to identify the issue.
• Optimizing Performance: Knowing how Spark optimizes queries helps you write more efficient code.
• Learning Spark Internals: It provides insight into how Spark works under the hood, making you a better Spark developer.

How to View Logical Plans in Spark

You can easily view the logical plans for a query using the explain() method in Spark. For example:

df = spark.sql("SELECT name FROM employees WHERE salary > 1000")
df.explain(True)        

This will print the Parsed Logical Plan, Analyzed Logical Plan, and Optimized Logical Plan, along with the Physical Plan.

Conclusion

The journey of a Spark query from a high-level SQL statement to an optimized execution plan involves several stages of planning and optimization. The Parsed Logical Plan, Analyzed Logical Plan, and Optimized Logical Plan are key steps in this process. By understanding these stages, you can gain deeper insights into how Spark works and write more efficient queries.

Next time you run a query in Spark, take a moment to appreciate the sophisticated planning and optimization that happens behind the scenes!