Structured Streaming basics in Apache Spark - Mini Project: Build & Apply

Structured Streaming basics

📖 Scenario: You work at a company that receives live data about customer orders. You want to process this data as it arrives to get quick insights.

🎯 Goal: Build a simple Structured Streaming application in Apache Spark that reads streaming data from a folder, counts the number of orders per product, and displays the results.

📋 What You'll Learn

Create a streaming DataFrame reading JSON files from a folder

Define a query to count orders by product using Structured Streaming

Start the streaming query and display the output in the console

💡 Why This Matters

🌍 Real World

Companies use Structured Streaming to process live data like orders, sensor readings, or logs to get real-time insights.

💼 Career

Data engineers and data scientists use Structured Streaming to build pipelines that handle continuous data flows efficiently.

1

Create streaming DataFrame from JSON files

Create a streaming DataFrame called orders_stream that reads JSON files from the folder "/path/to/orders" using spark.readStream and .json().

Apache Spark

# Create a streaming DataFrame called orders_stream reading JSON files from "/path/to/orders"
# Your code here

Hint

Use spark.readStream.json(path) to read streaming JSON data.

2

Define aggregation query to count orders by product

Create a streaming aggregation DataFrame called orders_count that groups orders_stream by the column product and counts the number of orders using .groupBy("product").count().

Apache Spark

orders_stream = spark.readStream.json("/path/to/orders")
# Create orders_count by grouping orders_stream by 'product' and counting
# Your code here

Hint

Use groupBy("product").count() on the streaming DataFrame.

3

Start streaming query to output counts to console

Start a streaming query called query on orders_count that writes the output to the console using .writeStream.format("console").start().

Apache Spark

orders_stream = spark.readStream.json("/path/to/orders")
orders_count = orders_stream.groupBy("product").count()
# Start streaming query called query to write orders_count to console
# Your code here

Hint

Use .writeStream.format("console").start() to start the query.

4

Print query status and stop the query

Print the current status of the streaming query query using print(query.status). Then stop the query using query.stop().

Apache Spark

orders_stream = spark.readStream.json("/path/to/orders")
orders_count = orders_stream.groupBy("product").count()
query = orders_count.writeStream.format("console").start()
# Print the query status and stop the query
# Your code here

Hint

Use print(query.status) to see the query status and query.stop() to stop it.

Practice

1. What is the main purpose of Structured Streaming in Apache Spark?

easy

A. To visualize data using charts and graphs

B. To batch process large static datasets

C. To store data permanently in a database

D. To process live data continuously as it arrives

Solution

1. Step 1: Understand Structured Streaming concept

   Structured Streaming is designed to handle data streams, meaning data that arrives continuously over time.

2. Step 2: Compare with other options

   Batch processing handles static data, storing data is not the main function, and visualization is separate from streaming.

3. Final Answer:

   To process live data continuously as it arrives -> Option D

4. Quick Check:

   Structured Streaming = continuous live data processing [OK]

Hint: Streaming means live, continuous data processing [OK]

Common Mistakes:

• Confusing streaming with batch processing
• Thinking streaming stores data permanently
• Assuming streaming is for visualization

2. Which of the following is the correct way to start reading a streaming DataFrame in Spark?

easy

A. spark.read.format("json").load("path")

B. spark.readStream.format("json").load("path")

C. spark.writeStream.format("json").load("path")

D. spark.write.format("json").load("path")

Solution

1. Step 1: Identify streaming read syntax

   To read streaming data, use readStream with format and load.

2. Step 2: Eliminate non-streaming options

   read is for batch, writeStream is for output, so only readStream with load is correct.

3. Final Answer:

   spark.readStream.format("json").load("path") -> Option B

4. Quick Check:

   Streaming read uses readStream [OK]

Hint: Use readStream to read streaming data [OK]

Common Mistakes:

• Using read instead of readStream for streaming data
• Confusing writeStream with readStream
• Using writeStream with load instead of start

3. Given the code below, what will be the output schema of the streaming DataFrame?

df = spark.readStream.format("csv").option("header", "true").load("/data/clicks")
df.printSchema()

medium

A. Schema with columns inferred as integers only

B. Schema with no columns because streaming can't infer schema

C. Schema with columns inferred from CSV header as strings

D. Error because schema must be specified explicitly

Solution

1. Step 1: Understand schema inference in streaming CSV

   When reading CSV with header option true, Spark infers schema as strings by default for streaming.

2. Step 2: Check if explicit schema is required

   Explicit schema is recommended but not mandatory; Spark infers schema as string types if not provided.

3. Final Answer:

   Schema with columns inferred from CSV header as strings -> Option C

4. Quick Check:

   CSV header + streaming = inferred string schema [OK]

Hint: CSV header enables schema inference as strings [OK]

Common Mistakes:

• Assuming streaming can't infer schema
• Expecting integer types without schema
• Thinking explicit schema is always required

4. Identify the error in the following Structured Streaming code:

streamDF = spark.readStream.format("json").load("/data/events")
query = streamDF.writeStream.format("console").start()
query.awaitTermination(10)

medium

A. Missing option to specify output mode in writeStream

B. awaitTermination does not take any arguments

C. Incorrect format 'console' should be 'Console'

D. start() must be called after awaitTermination()

Solution

1. Step 1: Check writeStream requirements

   In Structured Streaming, writeStream requires outputMode() (e.g., "append") to be specified before format() for sinks like console.

2. Step 2: Verify other parts

   awaitTermination(10) accepts a timeout argument, format 'console' is correct lowercase, and start() must be called before awaitTermination().

3. Final Answer:

   Missing option to specify output mode in writeStream -> Option A

4. Quick Check:

   writeStream requires outputMode [OK]

Hint: writeStream requires outputMode [OK]

Common Mistakes:

• Missing outputMode in writeStream
• Thinking awaitTermination does not take timeout
• Misusing start and awaitTermination order

5. You want to count the number of words in a live stream of text lines from a socket source. Which sequence of operations correctly applies Structured Streaming to achieve this?

hard

A. Read stream from socket, split lines into words, group by word, count, write stream to console

B. Read batch from socket, split lines into words, group by word, count, write batch to file

C. Read stream from socket, count lines directly, write stream to console

D. Read stream from socket, split lines into words, filter empty words, write batch to console

Solution

1. Step 1: Understand streaming word count steps

   Streaming word count requires reading live data, splitting lines into words, grouping by word, and counting occurrences continuously.

2. Step 2: Check options for correctness

   Read stream from socket, split lines into words, group by word, count, write stream to console follows the correct streaming steps with writeStream to console. Read batch from socket, split lines into words, group by word, count, write batch to file uses batch, not streaming. Read stream from socket, count lines directly, write stream to console counts lines, not words. Read stream from socket, split lines into words, filter empty words, write batch to console writes batch, not stream, and misses counting.

3. Final Answer:

   Read stream from socket, split lines into words, group by word, count, write stream to console -> Option A

4. Quick Check:

   Streaming word count = readStream + split + groupBy + count + writeStream [OK]

Hint: For streaming word count, use readStream + groupBy + count + writeStream [OK]

Common Mistakes:

• Using batch read instead of streaming
• Counting lines instead of words
• Writing batch output instead of streaming