Adaptive query execution in Apache Spark - Mini Project: Build & Apply

Adaptive Query Execution in Apache Spark

📖 Scenario: You work as a data analyst at a retail company. You have sales data in a Spark DataFrame. You want to optimize your query performance using Adaptive Query Execution (AQE) in Apache Spark.AQE helps Spark adjust query plans dynamically based on runtime statistics, improving speed and resource use.

🎯 Goal: Enable Adaptive Query Execution in Spark, run a simple aggregation query on sales data, and observe the optimized query plan.

📋 What You'll Learn

Create a Spark DataFrame with sales data

Enable Adaptive Query Execution in Spark configuration

Run a group-by aggregation query on the DataFrame

Show the query execution plan to verify AQE is enabled

💡 Why This Matters

🌍 Real World

Adaptive Query Execution helps data engineers and analysts optimize big data queries automatically, saving time and computing resources.

💼 Career

Understanding AQE is important for roles like Data Engineer, Data Scientist, and Big Data Developer working with Apache Spark.

1

Create the sales DataFrame

Create a Spark DataFrame called sales_df with these exact rows: ("Store1", "2024-01-01", 100), ("Store2", "2024-01-01", 150), ("Store1", "2024-01-02", 200), ("Store2", "2024-01-02", 300). Use columns named store, date, and sales.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("AQE Example").getOrCreate()

# Create sales_df DataFrame with given rows and columns
# Your code here

Hint

Use spark.createDataFrame() with a list of tuples and specify the schema as a list of column names.

2

Enable Adaptive Query Execution

Set the Spark configuration spark.sql.adaptive.enabled to true using spark.conf.set.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("AQE Example").getOrCreate()

sales_data = [("Store1", "2024-01-01", 100), ("Store2", "2024-01-01", 150), ("Store1", "2024-01-02", 200), ("Store2", "2024-01-02", 300)]
sales_df = spark.createDataFrame(sales_data, schema=["store", "date", "sales"])

# Enable Adaptive Query Execution
# Your code here

Hint

Use spark.conf.set with the key "spark.sql.adaptive.enabled" and value "true" (string).

3

Run aggregation query with AQE

Create a new DataFrame called agg_df by grouping sales_df by store and summing the sales column using groupBy and sum.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("AQE Example").getOrCreate()

sales_data = [("Store1", "2024-01-01", 100), ("Store2", "2024-01-01", 150), ("Store1", "2024-01-02", 200), ("Store2", "2024-01-02", 300)]
sales_df = spark.createDataFrame(sales_data, schema=["store", "date", "sales"])

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

# Group sales_df by 'store' and sum 'sales' into agg_df
# Your code here

Hint

Use sales_df.groupBy("store").sum("sales") and assign it to agg_df.

4

Show the query execution plan

Print the physical query plan of agg_df using agg_df.explain() to verify Adaptive Query Execution is enabled.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("AQE Example").getOrCreate()

sales_data = [("Store1", "2024-01-01", 100), ("Store2", "2024-01-01", 150), ("Store1", "2024-01-02", 200), ("Store2", "2024-01-02", 300)]
sales_df = spark.createDataFrame(sales_data, schema=["store", "date", "sales"])

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

agg_df = sales_df.groupBy("store").sum("sales")

# Print the physical query plan of agg_df
# Your code here

Hint

Use agg_df.explain() to print the physical plan. Look for AdaptiveSparkPlanExec in the output.

Practice

1.

What is the main purpose of adaptive query execution in Apache Spark?

easy

A. To improve query performance by adjusting the execution plan during runtime

B. To statically compile queries before execution

C. To disable query optimization for faster execution

D. To manually rewrite queries for better speed

Solution

1. Step 1: Understand adaptive query execution concept

   Adaptive query execution allows Spark to change the query plan dynamically while the query runs.

2. Step 2: Identify the main benefit

   This dynamic adjustment improves performance by optimizing joins and data shuffles based on actual data size.

3. Final Answer:

   To improve query performance by adjusting the execution plan during runtime -> Option A

4. Quick Check:

   Adaptive query execution = dynamic plan adjustment [OK]

Hint: Think: adaptive means changing during execution [OK]

Common Mistakes:

• Confusing adaptive execution with static query compilation
• Believing it disables optimization
• Thinking it requires manual query rewriting

2.

Which Spark configuration enables adaptive query execution?

?

easy

A. spark.sql.execution.adaptive.enabled = false

B. spark.execution.adaptive = true

C. spark.adaptive.query.execution = true

D. spark.sql.adaptive.enabled = true

Solution

1. Step 1: Recall the exact configuration key

   The correct key to enable adaptive query execution is spark.sql.adaptive.enabled.

2. Step 2: Confirm the value to enable

   Setting it to true activates adaptive query execution.

3. Final Answer:

   spark.sql.adaptive.enabled = true -> Option D

4. Quick Check:

   Enable adaptive query with spark.sql.adaptive.enabled [OK]

Hint: Remember full key: spark.sql.adaptive.enabled [OK]

Common Mistakes:

• Using incorrect config keys like spark.execution.adaptive
• Setting the value to false instead of true
• Mixing up config names with similar words

3.

Given the following Spark SQL code with adaptive query execution enabled, what will be the effect on the join?

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

val df1 = spark.range(1000)
val df2 = spark.range(10)

val joined = df1.join(df2, df1("id") === df2("id"))
joined.explain(true)

What does adaptive query execution do here?

medium

A. It converts the join to a broadcast join because df2 is small

B. It disables the join optimization and runs a shuffle join

C. It throws an error because adaptive execution cannot handle joins

D. It caches both dataframes before joining

Solution

1. Step 1: Identify adaptive execution behavior on join

   Adaptive query execution detects small tables and converts joins to broadcast joins to reduce shuffle.

2. Step 2: Analyze data sizes in example

   df2 has only 10 rows, which is small enough to broadcast.

3. Final Answer:

   It converts the join to a broadcast join because df2 is small -> Option A

4. Quick Check:

   Small table join = broadcast join [OK]

Hint: Small table in join? Adaptive broadcasts it [OK]

Common Mistakes:

• Assuming adaptive disables optimization
• Thinking adaptive causes errors on joins
• Believing it caches dataframes automatically

4.

Consider this Spark code snippet with adaptive query execution enabled:

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

val df1 = spark.range(1000)
val df2 = spark.range(1000000)

val joined = df1.join(df2, df1("id") === df2("id"))
joined.show()

The job runs very slowly. What is the likely cause?

medium

A. Adaptive query execution is disabled by default

B. Adaptive query execution tries to broadcast df2, which is too large

C. The join condition is invalid causing a runtime error

D. Spark automatically caches df2 causing memory overflow

Solution

1. Step 1: Understand adaptive join behavior with large tables

   Adaptive query execution may try to broadcast a table if it thinks it's small enough.

2. Step 2: Analyze table sizes

   df2 has 1,000,000 rows, which is too large to broadcast efficiently, causing slow execution.

3. Final Answer:

   Adaptive query execution tries to broadcast df2, which is too large -> Option B

4. Quick Check:

   Broadcasting large tables causes slow jobs [OK]

Hint: Large table broadcast causes slow join [OK]

Common Mistakes:

• Assuming adaptive is disabled by default
• Thinking join condition is invalid
• Believing Spark caches large tables automatically

5.

You have a Spark job joining three datasets of unknown sizes. How can adaptive query execution help optimize this multi-join query?

hard

A. It forces all joins to be shuffle joins regardless of size

B. It disables all joins and runs map-only jobs

C. It dynamically chooses broadcast or shuffle joins for each join based on runtime data size

D. It requires manual hints to optimize each join

Solution

1. Step 1: Understand adaptive query execution with multiple joins

   Adaptive query execution evaluates data sizes during runtime for each join.

2. Step 2: Explain optimization choice

   It chooses broadcast joins for small tables and shuffle joins for large tables automatically.

3. Step 3: Apply to multi-join scenario

   For three datasets, it optimizes each join individually based on actual data size.

4. Final Answer:

   It dynamically chooses broadcast or shuffle joins for each join based on runtime data size -> Option C

5. Quick Check:

   Adaptive = dynamic join strategy per join [OK]

Hint: Adaptive picks join type per join at runtime [OK]

Common Mistakes:

• Thinking adaptive disables joins
• Assuming all joins become shuffle joins
• Believing manual hints are always required