In PySpark, DataFrame transformations and operations can be efficiently handled using two main approaches:

1️⃣ PySpark SQL API Programming (Temp Tables / Views)

Each transformation step can be written as a SQL query.
Intermediate results can be stored as temporary views (createOrReplaceTempView).
Queries can be executed using spark.sql(), avoiding direct DataFrame chaining.

Example:

df.createOrReplaceTempView("source_data")

# Step 1: Filter Data
filtered_df = spark.sql("""
    SELECT * FROM source_data WHERE status = 'active'
""")
filtered_df.createOrReplaceTempView("filtered_data")

# Step 2: Aggregate Data
aggregated_df = spark.sql("""
    SELECT category, COUNT(*) AS count 
    FROM filtered_data 
    GROUP BY category
""")

👉 Benefits:
✔️ Each transformation is saved as a temp table/view for easy debugging.
✔️ Queries become more readable and modular.
✔️ Avoids excessive DataFrame chaining, improving maintainability.

2️⃣ Common Table Expressions (CTEs) for Multi-Step Queries

Instead of multiple temp tables, each transformation step can be wrapped in a CTE.
The entire logic is written in a single SQL query.

Example using CTEs:

query = """
WITH filtered_data AS (
    SELECT * FROM source_data WHERE status = 'active'
),
aggregated_data AS (
    SELECT category, COUNT(*) AS count 
    FROM filtered_data 
    GROUP BY category
)
SELECT * FROM aggregated_data
"""
df_final = spark.sql(query)

👉 Benefits:
✔️ Eliminates the need for multiple temp views.
✔️ Improves query organization by breaking steps into CTEs.
✔️ Executes everything in one optimized SQL call, reducing shuffle costs.

Which Approach is Better?

Use SQL API with Temp Views when:
- You need step-by-step debugging.
- Your query logic is complex and needs intermediate storage.
- You want to break down transformations into separate queries.
Use CTEs when:
- You want a single optimized query execution.
- The logic is modular but doesn’t require intermediate views.
- You aim for better performance by reducing redundant reads.

Both approaches eliminate excessive DataFrame chaining and leverage PySpark’s SQL execution engine efficiently.

# Best Practice Template for PySpark SQL API & CTE-based ETL

from pyspark.sql import SparkSession

# Initialize Spark Session
spark = SparkSession.builder.appName("PySparkSQL_ETL").getOrCreate()

# Sample Data (Creating a DataFrame)
data = [(1, "A", "active", 100),
        (2, "B", "inactive", 200),
        (3, "A", "active", 150),
        (4, "C", "active", 120),
        (5, "B", "inactive", 300)]

columns = ["id", "category", "status", "amount"]
df = spark.createDataFrame(data, columns)

# Approach 1: Using Temp Views for Step-by-Step ETL

df.createOrReplaceTempView("source_data")

# Step 1: Filter Active Records
filtered_query = """
    SELECT * FROM source_data WHERE status = 'active'
"""
filtered_df = spark.sql(filtered_query)
filtered_df.createOrReplaceTempView("filtered_data")

# Step 2: Aggregation
aggregated_query = """
    SELECT category, SUM(amount) AS total_amount
    FROM filtered_data
    GROUP BY category
"""
aggregated_df = spark.sql(aggregated_query)

aggregated_df.show()

# Approach 2: Using CTE for Optimized Query Execution
cte_query = """
WITH filtered_data AS (
    SELECT * FROM source_data WHERE status = 'active'
),
aggregated_data AS (
    SELECT category, SUM(amount) AS total_amount
    FROM filtered_data
    GROUP BY category
)
SELECT * FROM aggregated_data
"""

cte_df = spark.sql(cte_query)
cte_df.show()

# Additional Example: Using Multiple CTEs for Complex Transformations
complex_query = """
WITH filtered_data AS (
    SELECT * FROM source_data WHERE status = 'active'
),
ranked_data AS (
    SELECT *, RANK() OVER (PARTITION BY category ORDER BY amount DESC) AS rank
    FROM filtered_data
)
SELECT * FROM ranked_data WHERE rank = 1
"""

ranked_df = spark.sql(complex_query)
ranked_df.show()

# Closing Spark Session
spark.stop()

Optimization in PySpark SQL API programming (using `spark.sql()`) Vs optimization in PySpark DataFrame API programming

Yes, the optimization in PySpark SQL API programming (using spark.sql()) differs from the optimization in PySpark DataFrame API programming (using .select(), .filter(), .groupBy(), etc.). Both approaches rely on Catalyst Optimizer and Tungsten Execution Engine, but they have some differences in how they optimize execution.

🔹 1. PySpark SQL API Optimization (`spark.sql()`)

How it Works:

Queries written using SQL syntax (spark.sql("SELECT ... FROM table")) are parsed and optimized by Catalyst Optimizer.
Catalyst applies logical and physical optimizations, such as:
- Predicate Pushdown – Filtering data early.
- Constant Folding – Precomputing expressions.
- Join Reordering – Choosing the best join strategy.
The optimized query plan is translated into DataFrame API operations before execution.

Example (SQL API with Temp Views)

df.createOrReplaceTempView("source_data")
query = """
    SELECT category, SUM(amount) AS total_amount
    FROM source_data
    WHERE status = 'active'
    GROUP BY category
"""
optimized_df = spark.sql(query)
optimized_df.explain(True)  # Show optimized execution plan

✅ Benefits:

Queries are optimized before execution.
Easy to write & modular (especially for SQL-heavy workloads).
Better for analysts who are comfortable with SQL.

🚫 Limitations:

May not be as flexible for complex operations like UDFs, iterative computations.
Harder debugging when issues occur in long SQL queries.

🔹 2. PySpark DataFrame API Optimization (`df.filter().groupBy()`)

How it Works:

PySpark’s DataFrame API provides a lazy execution model.
Operations are chained together, and PySpark optimizes the execution only when an action (show(), collect(), etc.) is triggered.
Catalyst applies optimizations similar to SQL API:
- Column Pruning – Only selects required columns.
- Pushdown Filters – Applies filters at the data source level.
- Rearranging Joins – Chooses broadcast joins when applicable.

Example (DataFrame API Approach)

optimized_df = df.filter(df.status == 'active') \
                 .groupBy("category") \
                 .agg({"amount": "sum"}) \
                 .alias("total_amount")

optimized_df.explain(True)  # Show optimized execution plan

✅ Benefits:

More flexibility (easier to use UDFs, complex transformations).
Better debugging (each transformation step is separate).
Easier integration with ML & advanced functions.

🚫 Limitations:

Slightly more verbose compared to SQL API.
Requires chaining multiple transformations.

🔥 Key Differences Between SQL API & DataFrame API Optimization

Feature	PySpark SQL API (`spark.sql()`)	PySpark DataFrame API (`df.select()`, `df.filter()`)
Optimization Engine	Catalyst Optimizer + Tungsten	Catalyst Optimizer + Tungsten
Execution Plan	SQL query is converted into a DataFrame plan before execution	DataFrame transformations are optimized lazily before execution
Readability	Easier for SQL users	More Pythonic & readable for programmers
Performance	Good for batch queries (pre-optimized execution)	Good for iterative, complex logic
Debugging	Harder to debug long SQL queries	Easier debugging step by step

🔹 Which One Should You Use?

1️⃣ Use SQL API (spark.sql()) when:

You are working with SQL-heavy transformations.
You need modular queries with temp views.
You want batch processing & pre-optimized queries.

2️⃣ Use DataFrame API (df.filter(), df.groupBy()) when:

You need more flexibility (e.g., UDFs, machine learning, complex logic).
You want to debug transformations easily.
You are working in an iterative pipeline (e.g., dynamic processing).

🚀 Best Practice: Combine Both

For optimized ETL workflows, you can mix both approaches:

Preprocess with DataFrame API (better control over steps).
Use SQL API for heavy aggregations (better optimization).

Example Hybrid Approach:

# Step 1: DataFrame API - Initial Filtering
filtered_df = df.filter(df.status == 'active')

# Step 2: Register Temp View & Use SQL API for Aggregation
filtered_df.createOrReplaceTempView("filtered_data")
query = "SELECT category, SUM(amount) AS total_amount FROM filtered_data GROUP BY category"
final_df = spark.sql(query)

✅ Optimizes both transformations & execution performance.

Both PySpark SQL API and DataFrame API are optimized by Catalyst, but their execution models differ:

SQL API optimizes before execution (good for queries & batch processing).
DataFrame API optimizes lazily during execution (good for step-by-step debugging).

Let’s compare performance using explain(True) on a sample dataset for both PySpark SQL API and PySpark DataFrame API.

🔹 Step 1: Create a Sample DataFrame

from pyspark.sql import SparkSession
from pyspark.sql.functions import col

# Initialize Spark Session
spark = SparkSession.builder.appName("PySpark_Optimization").getOrCreate()

# Sample Data
data = [(1, "A", "active", 100),
        (2, "B", "inactive", 200),
        (3, "A", "active", 150),
        (4, "C", "active", 120),
        (5, "B", "inactive", 300),
        (6, "A", "active", 180)]

columns = ["id", "category", "status", "amount"]
df = spark.createDataFrame(data, columns)

🔹 Step 2: SQL API Optimization (`spark.sql()`)

# Register temp view
df.createOrReplaceTempView("source_data")

# SQL Query with Filtering and Aggregation
query = """
    SELECT category, SUM(amount) AS total_amount
    FROM source_data
    WHERE status = 'active'
    GROUP BY category
"""

# Execute SQL Query
sql_df = spark.sql(query)

# Explain Execution Plan
sql_df.explain(True)

🔹 SQL API Execution Plan Output (Sample)

== Optimized Logical Plan ==
Aggregate [category#32], [category#32, sum(amount#34) AS total_amount#50]
+- Filter (isnotnull(status#33) && (status#33 = active))
   +- Relation[id#31,category#32,status#33,amount#34] parquet

✅ Optimizations Applied:

Filter Pushdown (WHERE status = 'active' applied before aggregation)
Column Pruning (Only category and amount are selected)
Aggregate Optimization (SUM(amount) grouped efficiently)

🔹 Step 3: DataFrame API Optimization (`df.filter().groupBy()`)

# DataFrame API Approach
df_filtered = df.filter(col("status") == "active") \
                .groupBy("category") \
                .sum("amount") \
                .withColumnRenamed("sum(amount)", "total_amount")

# Explain Execution Plan
df_filtered.explain(True)

🔹 DataFrame API Execution Plan Output (Sample)

== Physical Plan ==
*(2) HashAggregate(keys=[category#32], functions=[sum(amount#34)])
+- *(2) HashAggregate(keys=[category#32], functions=[partial_sum(amount#34)])
   +- *(1) Project [category#32, amount#34]
      +- *(1) Filter (isnotnull(status#33) && (status#33 = active))
         +- Scan parquet [id#31,category#32,status#33,amount#34]

✅ Optimizations Applied:

Filter Pushdown
Column Pruning
Partial Aggregation for Efficiency (partial_sum())

🔹 Step 4: Performance Comparison

Feature	SQL API (`spark.sql()`)	DataFrame API (`df.filter()`)
Execution Plan Type	Logical SQL to DataFrame Plan	Direct Logical DataFrame Plan
Optimization Applied	Predicate Pushdown, Column Pruning, Aggregation Optimization	Same Optimizations but in step-wise execution
Performance	✅ Optimized before execution (Batch processing)	✅ Optimized lazily (Step-by-step execution)
Use Case	Best for complex SQL transformations & analytics	Best for incremental processing & iterative transformations

🔹 Key Takeaways

1️⃣ Both SQL API & DataFrame API get optimized using Catalyst Optimizer.
2️⃣ Execution Plans are similar (both use Filter Pushdown, Column Pruning, Aggregation).
3️⃣ SQL API pre-optimizes everything before execution, while DataFrame API optimizes lazily.
4️⃣ SQL API is best for batch processing, while DataFrame API is better for debugging & step-by-step transformations.

Both PySpark SQL API and DataFrame API use Catalyst Optimizer, and in the end, SQL queries are converted into DataFrame operations before execution. However, the key difference lies in how and when optimization happens in each approach.

🔍 1. SQL API Optimization (Pre-Optimized Before Execution)

What Happens?

When you write spark.sql("SELECT ... FROM table"), PySpark immediately parses the query.
Catalyst Optimizer applies logical optimizations (e.g., filter pushdown, constant folding).
The optimized query plan is created before execution.
Then, it is translated into DataFrame operations, and lazy execution kicks in.

Example: SQL API Execution Flow

df.createOrReplaceTempView("source_data")

query = """
    SELECT category, SUM(amount) AS total_amount
    FROM source_data
    WHERE status = 'active'
    GROUP BY category
"""
final_df = spark.sql(query)
final_df.show()

👉 Steps in SQL API Execution:

Parsing: SQL query is parsed into an unoptimized logical plan.
Optimization: Catalyst applies logical optimizations before execution.
Conversion: Optimized SQL is converted into a DataFrame execution plan.
Execution: Only when .show() (or another action) is called, execution happens.

✅ Key Insight:

Optimization happens before DataFrame API conversion, so SQL API sends a pre-optimized plan to execution.
The optimizer has a full view of the query upfront, making multi-step optimizations easier.

🔍 2. DataFrame API Optimization (Optimized Lazily During Execution)

What Happens?

When you chain DataFrame transformations (.select(), .filter(), etc.), each transformation adds to the logical execution plan.
No execution happens until an action (.show(), .collect()) is triggered.
Catalyst Optimizer optimizes the entire execution plan at the last moment before execution.

Example: DataFrame API Execution Flow

filtered_df = df.filter(df.status == "active")
aggregated_df = filtered_df.groupBy("category").sum("amount")
aggregated_df.show()

👉 Steps in DataFrame API Execution:

Transformation Building: Each .filter(), .groupBy() adds a step to the logical execution plan.
Lazy Optimization: No optimization happens yet.
Triggering Execution: When .show() is called, the entire plan is optimized just before execution.
Execution: Spark runs the optimized execution plan.

✅ Key Insight:

Optimization happens at the last step before execution.
Spark does not have full query context until execution is triggered, which may limit certain optimizations.

🔥 Core Differences Between SQL API & DataFrame API Optimization

Feature	SQL API (`spark.sql()`)	DataFrame API (`df.select()`, `df.filter()`)
When Optimization Happens	Before execution (on query parsing)	Just before execution (lazy)
Execution Plan Generation	Optimized upfront before DataFrame conversion	Built step-by-step, optimized at the end
Handling of Complex Queries	Full query view allows better optimizations	Step-wise transformations may limit some optimizations
Best Use Case	Multi-step SQL queries, joins, batch processing	Iterative processing, ML pipelines, debugging

🚀 When to Use Which?

✅ Use SQL API When:

You have multi-step transformations that need global query optimization.
You prefer writing complex logic in SQL (better readability for SQL-heavy workloads).
You want Catalyst to optimize the entire query upfront before execution.

✅ Use DataFrame API When:

You need iterative, flexible transformations (easier debugging).
Your workflow involves dynamic logic (e.g., using variables and conditions).
You work with ML pipelines, UDFs, or Python-specific transformations.

🔬 Example: SQL API vs DataFrame API Optimization Difference

Scenario: Filtering, grouping, and aggregation on a dataset.

🔹 SQL API Approach (Optimized Before Execution)

df.createOrReplaceTempView("source_data")

query = """
    SELECT category, SUM(amount) AS total_amount
    FROM source_data
    WHERE status = 'active'
    GROUP BY category
"""
final_df = spark.sql(query)
final_df.explain(True)  # Show execution plan

✅ Optimization Advantage:

Predicate Pushdown: WHERE status = 'active' is applied before aggregation.
Better Join Order (if applicable): Joins get pre-optimized.

🔹 DataFrame API Approach (Optimized Lazily)

filtered_df = df.filter(df.status == "active")
aggregated_df = filtered_df.groupBy("category").agg({"amount": "sum"})
aggregated_df.explain(True)  # Show execution plan

✅ Optimization Happens Later:

Transformations are built step-by-step.
Catalyst does not optimize until .show() is called.

🧐 Key Takeaway

Both SQL API and DataFrame API are optimized by Catalyst, but:

SQL API pre-optimizes the entire query before converting it into DataFrame operations.
DataFrame API builds the execution plan step-by-step and optimizes only when an action is triggered.

🔥 Catalyst Optimizer & Tungsten Execution Engine in PySpark

PySpark uses two main components for optimization and execution:

Catalyst Optimizer → Responsible for query optimization (logical & physical plans).
Tungsten Execution Engine → Handles low-level execution optimizations (CPU, memory, code generation).

Let’s break down both optimizers in detail.

🏗 1. Catalyst Optimizer (Logical & Physical Query Optimization)

What it Does

Catalyst Optimizer is a rule-based and cost-based optimizer in Spark that optimizes queries before execution. It transforms SQL queries and DataFrame operations into the most efficient execution plan.

Catalyst Workflow (4 Steps)

When you run a DataFrame operation or an SQL query, Catalyst goes through 4 phases:

1️⃣ Parse SQL Query / Convert DataFrame to Logical Plan

If using SQL: The SQL string is parsed into an Unresolved Logical Plan.
If using DataFrame API: Spark directly creates an Unresolved Logical Plan.

2️⃣ Analyze: Resolve Column Names & Types

Checks whether tables, columns, and functions exist.
Resolves column data types from the schema.

3️⃣ Optimize: Apply Logical Optimizations (Rule-Based Optimizations)

Pushdown Filters: Move WHERE conditions close to data source.
Constant Folding: Precompute expressions (e.g., 2+3 → 5).
Column Pruning: Remove unused columns.
Predicate Simplification: Convert complex conditions into simpler ones.
Join Reordering: Choose the best join order.

4️⃣ Generate Physical Plan (Execution Plan Selection)

Decides execution strategy (e.g., SortMergeJoin vs BroadcastJoin).
Generates RDD transformations (Resilient Distributed Datasets).
This optimized plan is sent to the Tungsten execution engine.

Example: Catalyst Optimization in Action

🔹 SQL Query

df.createOrReplaceTempView("transactions")
query = "SELECT category, SUM(amount) FROM transactions WHERE status = 'active' GROUP BY category"
optimized_df = spark.sql(query)
optimized_df.explain(True)  # Shows Catalyst Optimized Execution Plan

🔹 DataFrame API

optimized_df = df.filter(df.status == "active").groupBy("category").agg({"amount": "sum"})
optimized_df.explain(True)  # Shows Catalyst Optimized Execution Plan

🛠 What Catalyst Does Here

Moves WHERE status = 'active' before aggregation (Predicate Pushdown).
Keeps only category and amount columns (Column Pruning).
Selects the best join strategy if multiple tables are involved (Join Reordering).

⚡ 2. Tungsten Execution Engine (Physical Execution Optimizations)

What it Does

Once Catalyst generates the optimized execution plan, Tungsten takes over and optimizes at a lower level (CPU & memory management).

Tungsten Optimizations (3 Key Areas)

1️⃣ Memory Management & Binary Processing

Uses off-heap memory (bypasses JVM Garbage Collection).
Avoids unnecessary memory allocations.

2️⃣ Code Generation (Whole-Stage Codegen)

Generates low-level Java bytecode for faster execution.
Converts high-level Spark plans into optimized machine code.

3️⃣ Efficient Data Structures & Algorithms

Uses compressed columnar storage for faster processing.
Vectorized Execution: Processes multiple rows in parallel (SIMD operations).

Example: How Tungsten Optimizes Execution

df.groupBy("category").sum("amount").explain(True)

🛠 What Tungsten Does Here

Uses Whole-Stage Code Generation: Converts .groupBy().sum() into optimized Java bytecode.
Uses Columnar Memory Layout: Reduces memory overhead.
Applies SIMD & Cache-Aware Execution: Processes batches of rows instead of row-by-row.

🧐 Catalyst vs Tungsten: How They Work Together

Feature	Catalyst Optimizer	Tungsten Execution Engine
Optimization Level	Query-level optimization (logical & physical)	Execution-level optimization (CPU & memory)
Key Responsibilities	Optimizing query plans (filter pushdown, column pruning, join reordering)	Managing memory, generating optimized bytecode, using efficient data structures
When It Runs?	Before execution (query transformation)	During execution (hardware-level optimizations)
Goal	Minimize data movement & optimize transformations	Maximize execution speed & minimize CPU/memory overhead

🚀 Final Takeaways

Catalyst Optimizer improves query plans before execution.
Tungsten Execution Engine boosts runtime performance using efficient memory management & code generation.
Together, they make PySpark fast & scalable 🚀.

🚀 PySpark Optimizations, Configurations & DAG Explained

Now that you understand Catalyst Optimizer and Tungsten Execution Engine, let’s explore other key optimizations and configurations to improve PySpark execution. We’ll also dive into DAG (Directed Acyclic Graph) and how Spark uses it for execution.

🔥 1. Optimization Methods & Configurations in PySpark

PySpark optimizations can be categorized into 4 main areas:

Category	Optimization Technique
Query Optimization	Predicate Pushdown, Column Pruning, Join Optimization
Execution Optimization	Caching, Broadcast Joins, Data Skew Handling
Shuffle Optimization	Repartitioning, Coalesce, ReduceByKey
Memory & Performance	Serialization, GC Tuning, Adaptive Query Execution (AQE)

🏗 A. Query Optimization Techniques

These optimizations reduce data movement and minimize processing time.

✅ 1. Predicate Pushdown (Filter Early)

👉 Move filters as close as possible to data source

Catalyst automatically does this, but explicit .filter() improves readability.

# ✅ Best practice: Filter first, then process
df_filtered = df.filter(df.status == "active").select("category", "amount")

✅ 2. Column Pruning (Select Only Required Columns)

👉 Avoid selecting unnecessary columns to reduce data transfer.

# ✅ Best practice: Select only required columns
df = df.select("category", "amount")

✅ 3. Join Optimization (Broadcast Joins for Small Tables)

👉 Use Broadcast Join when one table is small (≤ 10MB).

from pyspark.sql.functions import broadcast
df_large = spark.read.parquet("large_table.parquet")
df_small = spark.read.parquet("small_lookup.parquet")

# ✅ Best practice: Broadcast small table to avoid shuffle
df_result = df_large.join(broadcast(df_small), "common_key")

🔹 Why? Normal joins trigger shuffles, but Broadcast Joins send small tables to all nodes.

⚡ B. Execution Optimizations

These optimizations improve PySpark job execution by reducing processing overhead.

✅ 4. Caching & Persisting

👉 Cache DataFrames that are reused multiple times

df.cache()  # Keeps data in memory for faster access
df.show()

🔹 Use .persist(StorageLevel.MEMORY_AND_DISK) if data is too large to fit in memory.

✅ 5. Handling Data Skew (Salting Technique)

👉 If one key has too much data, Spark creates an imbalance

Add a salt column to distribute data evenly across partitions.

from pyspark.sql.functions import monotonically_increasing_id

df = df.withColumn("salt", (monotonically_increasing_id() % 5)) 
df_skew_fixed = df.repartition("common_key", "salt")  # Distributes load

🔹 Why? This prevents one partition from handling most of the data, avoiding slow execution.

🔄 C. Shuffle Optimizations (Partitioning & Repartitioning)

👉 Shuffling is expensive (network & disk I/O). Optimize with smart partitioning.

✅ 6. Repartition vs Coalesce

Operation	Use Case
`repartition(n)`	Increases partitions (full shuffle, used for balancing)
`coalesce(n)`	Reduces partitions (avoids full shuffle, best for minimizing data movement)

df_repartitioned = df.repartition(10)  # Full shuffle
df_coalesced = df.coalesce(2)  # Merges partitions (faster for output writing)

✅ 7. Use `reduceByKey()` Instead of `groupBy()`

👉 reduceByKey() avoids shuffling intermediate data.

# ❌ groupByKey() causes unnecessary shuffle
df.groupBy("category").agg({"amount": "sum"})

# ✅ Use reduceByKey() for aggregation
rdd = df.rdd.map(lambda x: (x["category"], x["amount"]))
rdd_reduced = rdd.reduceByKey(lambda a, b: a + b)

🔹 Why? reduceByKey() combines values locally before shuffling, reducing data transfer.

🛠 D. Memory & Performance Optimizations

✅ 8. Kryo Serialization (Faster Object Serialization)

👉 Use Kryo instead of Java serialization for better performance.

spark = SparkSession.builder.config("spark.serializer", "org.apache.spark.serializer.KryoSerializer").getOrCreate()

🔹 Why? Kryo is 2-3x faster than default Java serialization.

✅ 9. Adaptive Query Execution (AQE)

👉 Dynamically optimizes joins & partitions at runtime (introduced in Spark 3.0+).

spark.conf.set("spark.sql.adaptive.enabled", True)

🔹 Why? Adjusts join strategies & shuffle partitions dynamically based on data size.

🔄 2. What is DAG (Directed Acyclic Graph) in PySpark?

DAG = Execution Plan for a PySpark Job

When you execute a PySpark job, Spark creates a DAG (Directed Acyclic Graph) that represents all transformations & actions.

How DAG Works

1️⃣ Transforms DataFrames into a DAG

Each .select(), .filter(), .join() adds a node to DAG.
No execution happens yet (lazy evaluation).

2️⃣ Splits DAG into Stages

Stages = Boundaries of Shuffles.
If a shuffle (e.g., .groupBy(), .join()) is needed, Spark creates a new stage.

3️⃣ Converts DAG to RDDs & Executes in Stages

RDD (Resilient Distributed Dataset) execution happens in parallel across worker nodes.
Tasks are scheduled & executed stage-by-stage.

🖥 Example: DAG in PySpark

df_filtered = df.filter(df.status == "active")
df_grouped = df_filtered.groupBy("category").sum("amount")
df_grouped.show()

👉 DAG Breakdown:

Step 1: df.filter(df.status == "active")
- Transformation (no execution yet).
Step 2: groupBy().sum()
- New stage is created (shuffle required).
Step 3: .show()
- Triggers DAG execution (final stage).

🛠 Visualizing DAG

df_grouped.explain(mode="formatted")

🚀 Final Takeaways

Optimization Area	Best Practices
Query Optimization	Pushdown filters, column pruning, broadcast joins
Execution Optimization	Caching, handling skew, adaptive query execution (AQE)
Shuffle Optimization	Reduce partitions with coalesce, prefer reduceByKey over groupBy
Memory & Performance	Kryo serialization, vectorized execution

Understanding DAG

DAG represents the execution flow of a PySpark job.
Splits execution into stages (shuffle boundaries).
Ensures parallel execution & fault tolerance.


---

### **🚀 PySpark Performance Comparison Script**
```python
from pyspark.sql import SparkSession
from pyspark.sql.functions import broadcast

# Initialize Spark Session
spark = SparkSession.builder \
    .appName("PySpark Optimization Comparison") \
    .config("spark.sql.adaptive.enabled", True) \  # Enable AQE (Adaptive Query Execution)
    .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer") \  # Use Kryo Serialization
    .getOrCreate()

# Load Sample Data
df_large = spark.read.parquet("large_dataset.parquet")
df_small = spark.read.parquet("small_lookup.parquet")

# --------------------------------------------------
# 🏆 1. Filter Pushdown Optimization
# --------------------------------------------------

# ❌ Without Filter Pushdown (Inefficient)
df_no_pushdown = df_large.select("*").filter(df_large.status == "active")
df_no_pushdown.explain(mode="formatted")

# ✅ With Filter Pushdown (Optimized)
df_pushdown = df_large.filter("status = 'active'")
df_pushdown.explain(mode="formatted")

# --------------------------------------------------
# 🏆 2. Column Pruning Optimization
# --------------------------------------------------

# ❌ Selecting All Columns (Inefficient)
df_all_columns = df_large.select("*")
df_all_columns.explain(mode="formatted")

# ✅ Selecting Only Required Columns (Optimized)
df_pruned_columns = df_large.select("id", "category", "amount")
df_pruned_columns.explain(mode="formatted")

# --------------------------------------------------
# 🏆 3. Broadcast Join Optimization
# --------------------------------------------------

# ❌ Normal Join (Causes Expensive Shuffle)
df_normal_join = df_large.join(df_small, "common_key")
df_normal_join.explain(mode="formatted")

# ✅ Broadcast Join (Optimized for Small Tables)
df_broadcast_join = df_large.join(broadcast(df_small), "common_key")
df_broadcast_join.explain(mode="formatted")

# --------------------------------------------------
# 🏆 4. Repartition vs Coalesce Optimization
# --------------------------------------------------

# ❌ Using Repartition (Forces Full Shuffle)
df_repartitioned = df_large.repartition(10)
df_repartitioned.explain(mode="formatted")

# ✅ Using Coalesce (Minimizes Data Movement)
df_coalesced = df_large.coalesce(2)
df_coalesced.explain(mode="formatted")

# --------------------------------------------------
# 🏆 5. groupBy() vs reduceByKey() Optimization
# --------------------------------------------------

# ❌ Using groupBy() (Expensive Shuffle)
df_grouped = df_large.groupBy("category").agg({"amount": "sum"})
df_grouped.explain(mode="formatted")

# ✅ Using reduceByKey() (Optimized for Aggregations)
rdd = df_large.rdd.map(lambda x: (x["category"], x["amount"]))
rdd_reduced = rdd.reduceByKey(lambda a, b: a + b)
rdd_reduced_df = rdd_reduced.toDF(["category", "total_amount"])
rdd_reduced_df.explain(mode="formatted")

# --------------------------------------------------
# 🏆 6. Adaptive Query Execution (AQE) Impact
# --------------------------------------------------

# ✅ Enabling AQE allows Spark to dynamically optimize joins & partitions
spark.conf.set("spark.sql.adaptive.enabled", True)
df_aqe_test = df_large.join(df_small, "common_key")
df_aqe_test.explain(mode="formatted")

You can generate a real DAG (Directed Acyclic Graph) visualization using Spark UI. Here’s how you can do it step by step:

🚀 Steps to Generate a DAG in Spark UI
1️⃣ Start Your PySpark Session with Spark UI Enabled
Run the following in your PySpark environment (local or cluster):


from pyspark.sql import SparkSession

# Start Spark session with UI enabled
spark = SparkSession.builder \
    .appName("DAG_Visualization") \
    .config("spark.ui.port", "4040") \  # Enable Spark UI on port 4040
    .getOrCreate()
🔹 By default, Spark UI runs on localhost:4040.
🔹 Open http://localhost:4040 in your browser to view DAGs.

2️⃣ Run a Spark Job to Generate a DAG
Now, execute a simple transformation to create a DAG visualization:

python
Copy
Edit
df_large = spark.read.parquet("large_dataset.parquet")
df_small = spark.read.parquet("small_lookup.parquet")

# Perform transformations
df_filtered = df_large.filter("status = 'active'")
df_joined = df_filtered.join(df_small, "common_key")
df_result = df_joined.groupBy("category").agg({"amount": "sum"})

# Trigger an action (forces DAG execution)
df_result.show()
🔹 The DAG (Directed Acyclic Graph) will appear in Spark UI under the "Jobs" tab.

3️⃣ View DAG in Spark UI
Open http://localhost:4040 in your browser.
Navigate to the "Jobs" section.
Click on your job to see the DAG Visualization.
You can also check Stages → Executors → SQL → Storage tabs to analyze execution details.
4️⃣ Save DAG as an Image (Optional)
If you want to export the DAG, you can take a screenshot, or use:


wget -O dag.png http://localhost:4040/stages/stage/0/dagViz.svg
This saves the DAG as an image.

Discover more from HintsToday

Subscribe to get the latest posts sent to your email.

Pages: 1 2 3 4

Pages ( 1 of 4 ): 1 2 3 4 Next »

PySpark SQL API Programming- How To, Approaches, Optimization

1️⃣ PySpark SQL API Programming (Temp Tables / Views)

2️⃣ Common Table Expressions (CTEs) for Multi-Step Queries

Which Approach is Better?

Optimization in PySpark SQL API programming (using spark.sql()) Vs optimization in PySpark DataFrame API programming

🔹 1. PySpark SQL API Optimization (spark.sql())

How it Works:

Example (SQL API with Temp Views)

🔹 2. PySpark DataFrame API Optimization (df.filter().groupBy())

How it Works:

Example (DataFrame API Approach)

🔥 Key Differences Between SQL API & DataFrame API Optimization

🔹 Which One Should You Use?

🚀 Best Practice: Combine Both

🔹 Step 1: Create a Sample DataFrame

🔹 Step 2: SQL API Optimization (spark.sql())

🔹 SQL API Execution Plan Output (Sample)

🔹 Step 3: DataFrame API Optimization (df.filter().groupBy())

🔹 DataFrame API Execution Plan Output (Sample)

🔹 Step 4: Performance Comparison

🔹 Key Takeaways

🔍 1. SQL API Optimization (Pre-Optimized Before Execution)

What Happens?

Example: SQL API Execution Flow

🔍 2. DataFrame API Optimization (Optimized Lazily During Execution)

What Happens?

Example: DataFrame API Execution Flow

🔥 Core Differences Between SQL API & DataFrame API Optimization

🚀 When to Use Which?

✅ Use SQL API When:

✅ Use DataFrame API When:

🔬 Example: SQL API vs DataFrame API Optimization Difference

Scenario: Filtering, grouping, and aggregation on a dataset.

🔹 SQL API Approach (Optimized Before Execution)

🔹 DataFrame API Approach (Optimized Lazily)

🧐 Key Takeaway

🔥 Catalyst Optimizer & Tungsten Execution Engine in PySpark

🏗 1. Catalyst Optimizer (Logical & Physical Query Optimization)

What it Does

Catalyst Workflow (4 Steps)

Example: Catalyst Optimization in Action

🔹 SQL Query

🔹 DataFrame API

🛠 What Catalyst Does Here

⚡ 2. Tungsten Execution Engine (Physical Execution Optimizations)

What it Does

Tungsten Optimizations (3 Key Areas)

Example: How Tungsten Optimizes Execution

🛠 What Tungsten Does Here

🧐 Catalyst vs Tungsten: How They Work Together

🚀 Final Takeaways

🚀 PySpark Optimizations, Configurations & DAG Explained

🔥 1. Optimization Methods & Configurations in PySpark

🏗 A. Query Optimization Techniques

✅ 1. Predicate Pushdown (Filter Early)

✅ 2. Column Pruning (Select Only Required Columns)

✅ 3. Join Optimization (Broadcast Joins for Small Tables)

⚡ B. Execution Optimizations

✅ 4. Caching & Persisting

✅ 5. Handling Data Skew (Salting Technique)

🔄 C. Shuffle Optimizations (Partitioning & Repartitioning)

✅ 6. Repartition vs Coalesce

✅ 7. Use reduceByKey() Instead of groupBy()

🛠 D. Memory & Performance Optimizations

✅ 8. Kryo Serialization (Faster Object Serialization)

✅ 9. Adaptive Query Execution (AQE)

🔄 2. What is DAG (Directed Acyclic Graph) in PySpark?

DAG = Execution Plan for a PySpark Job

How DAG Works

🖥 Example: DAG in PySpark

🚀 Final Takeaways

Understanding DAG

Share this- Make us Famous:-

Discover more from HintsToday

Jetpack has locked your site's login page.

Optimization in PySpark SQL API programming (using `spark.sql()`) Vs optimization in PySpark DataFrame API programming

🔹 1. PySpark SQL API Optimization (`spark.sql()`)

🔹 2. PySpark DataFrame API Optimization (`df.filter().groupBy()`)

🔹 Step 2: SQL API Optimization (`spark.sql()`)

🔹 Step 3: DataFrame API Optimization (`df.filter().groupBy()`)

✅ 7. Use `reduceByKey()` Instead of `groupBy()`