Structured Streaming basics in Apache Spark

Introduction

Structured Streaming helps you process live data as it arrives. It makes working with real-time data easy and reliable.

When you want to analyze live sensor data from machines.

When you need to monitor social media feeds in real-time.

When you want to track website clicks as they happen.

When you want to update dashboards with fresh data continuously.

When you want to detect fraud instantly from transaction streams.

Syntax

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("StreamingExample").getOrCreate()

# Read streaming data
streamingDF = spark.readStream.format("source_format").option("option_name", "value").load()

# Process data (example: select columns)
processedDF = streamingDF.select("column1", "column2")

# Write streaming output
query = processedDF.writeStream.format("console").start()

query.awaitTermination()

Use readStream to read live data.

Use writeStream to output results continuously.

Examples

Read CSV files as they arrive in a folder.

Apache Spark

streamingDF = spark.readStream.format("csv").option("header", "true").load("/path/to/folder")

Print streaming data to the console for quick checks.

Apache Spark

query = streamingDF.writeStream.format("console").start()

Store streaming results in memory for SQL queries.

Apache Spark

query = streamingDF.writeStream.format("memory").queryName("tableName").start()

Sample Program

This program reads live text data from a network socket, converts all text to uppercase, and prints it to the console continuously.

Apache Spark

from pyspark.sql import SparkSession
from pyspark.sql.functions import upper

spark = SparkSession.builder.appName("StructuredStreamingExample").getOrCreate()

# Read streaming data from a socket (for example, localhost:9999)
streamingDF = spark.readStream.format("socket").option("host", "localhost").option("port", 9999).load()

# Convert the text column to uppercase
processedDF = streamingDF.select(upper(streamingDF.value).alias("upper_value"))

# Write the results to the console
query = processedDF.writeStream.format("console").start()

query.awaitTermination()

Important Notes

Structured Streaming treats live data as an unending table.

Always call awaitTermination() to keep the stream running.

Use checkpoints to save progress and avoid data loss.

Summary

Structured Streaming lets you process live data easily.

Use readStream to get data and writeStream to output results.

It works like running SQL queries continuously on new data.

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