Given a code – How Catalyst Optimizer Works in This Code?

You have the following code. Explain how the catalyst optimizer works in the code? Explain in detail

from pyspark.sql import SparkSession

# Initialize Spark session
spark = SparkSession.builder \
    .appName("SalesCustomerJoin") \
    .getOrCreate()

# Read sales data into df1
df1 = spark.read.csv("path/to/sales_data.csv", header=True, inferSchema=True)

# Read customer data into df2
df2 = spark.read.csv("path/to/customer_data.csv", header=True, inferSchema=True)

# Perform join operation on a common column (e.g., 'customer_id')
joined_df = df1.join(df2, df1.customer_id == df2.customer_id)

# Filter the joined DataFrame (e.g., filter for sales amount greater than 1000)
filtered_df = joined_df.filter(joined_df.sales_amount > 1000)

# Show the filtered DataFrame
filtered_df.show()

PySpark’s Catalyst Optimizer is a powerful query optimizer used by Spark SQL to enhance the execution of queries. It automatically applies various optimization techniques to improve performance and efficiency before executing the query.

How Catalyst Optimizer Works in This Code?

Your PySpark query goes through four phases in the Catalyst Optimizer:

1. Analysis phase

• Spark reads the CSV files into DataFrames(df1 and df2). and create the unresolved logical plan
• Then the catalog/optimizer reads this unresolved logical plan, resolves column names and datatype, and generates resolved logical plans
• It checks for missing columns, incorrect column references, and whether operations are semantically valid.

2. Logical optimization

• Spark builds a logical plan representing the sequence of operations (JOIN -> FILTER -> SHOW).
• It applies rule-based optimization such as:
  • Predicate Pushdown -> Moves filters as early as possible.
  • Projection Pruning -> Drops unnecessary columns.

Before Optimization (Initial Logical Plan)

SELECT *
FROM sales_data s
JOIN customer_data c
ON s.customer_id = c.customer_id
WHERE s.sales_amount > 1000

After Optimization (Logical Plan with Predicate Pushdown)

SELECT * 
FROM (
    SELECT * FROM sales_data WHERE sales_amount > 1000  -- Pushdown Filter
) s
JOIN customer_data c 
ON s.customer_id = c.customer_id

 Benefit: The sales_amount > 1000 filter is applied before the join, reducing the number of rows processed in the join.

3. Physical Planning

• Spark converts the Logical Plan into a Physical Plan based on cost analysis.
• It chooses the best join strategy based on dataset size:
  • Broadcast Hash Join (if one dataset is small).
  • Sort-Merge Join (if both datasets are large).
  • Shuffle Hash Join (for medium-sized datasets).
• It applies column pruning (removes unnecessary columns).

4. Code Generation (Whole-Stage Code Generation – WSCG)

• Spark compiles the optimized query plan into Java bytecode for efficient execution.
• It avoids Python overhead by using JVM-based execution.

Final Optimized Execution Plan

1. Filter (sales_amount > 1000) is applied first to reduce data size before the join.
2. Join strategy is optimized based on data size.
3. Unnecessary columns are removed to reduce memory usage.
4. Query is executed efficiently with minimal shuffling.

Conclusion

• Catalyst Optimizer ensures Spark processes only relevant data, reducing memory usage and execution time.
• Predicate pushdown and join optimizations significantly improve performance.
• The optimizer automatically selects the best execution strategy, reducing the need for manual tuning.

Understanding the Execution Plan

When you run explain(True), Spark will output something like this:

== Parsed Logical Plan ==
Filter (sales_amount#4 > 1000)
+- Join Inner, (customer_id#1 = customer_id#5)
   :- Relation[sales_data] csv
   +- Relation[customer_data] csv

== Analyzed Logical Plan ==
sales_amount: int, customer_id: int, ... (other columns)
Filter (sales_amount#4 > 1000)
+- Join Inner, (customer_id#1 = customer_id#5)
   :- Relation[sales_data] csv
   +- Relation[customer_data] csv

== Optimized Logical Plan ==
Join Inner, (customer_id#1 = customer_id#5)
:- Filter (sales_amount#4 > 1000) (Pushed down)
:- Relation[sales_data] csv
+- Relation[customer_data] csv

== Physical Plan ==
*(1) SortMergeJoin [customer_id#1], [customer_id#5], Inner
:- *(1) Filter (sales_amount#4 > 1000)
   +- *(1) FileScan csv [customer_id#1, sales_amount#4] ... (other columns)
+- *(2) FileScan csv [customer_id#5, ...] ... (other columns)

Breaking Down the Execution Plan

1. Parsed Logical Plan:
   This represents the initial query structure before optimization.
2. Analyzed Logical Plan:
   Spark checks column names, data types, and syntax correctness.
3. Optimized Logical Plan (Catalyst Optimizations Applied):
   Filter Pushdown: sales_amount > 1000 is applied before the join, reducing data size.
   Column Pruning: Only the required columns (customer_id, sales_amount) are read.
4. Physical Plan (Final Execution Plan):
   SortMergeJoin → Since both datasets are large, Spark chooses a Sort-Merge Join instead of a Broadcast Join.
   Parallel FileScan → Data is read in parallel using multiple executors.
   Filtering is done before the join, reducing computation.