Kappa architecture (streaming only) in Hadoop - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a streaming data source in Kappa architecture.

Hadoop

stream = spark.readStream.format([1]).load()

Drag options to blanks, or click blank then click option'

Aparquet

Bcsv

Cjson

Dkafka

Attempts:

3 left

💡 Hint

Common Mistakes

Using batch file formats like csv or json instead of streaming sources.

Confusing batch processing with streaming.

In Kappa architecture, streaming data is often read from Kafka, so the format should be 'kafka'.

2fill in blank

medium

Complete the code to write streaming data to a sink in Kappa architecture.

Hadoop

query = stream.writeStream.format([1]).start()

Drag options to blanks, or click blank then click option'

Aparquet

Bmemory

Cconsole

Djdbc

Attempts:

3 left

💡 Hint

Common Mistakes

Using batch sinks like parquet or jdbc which are not suitable for streaming output.

Choosing 'memory' which is for in-memory tables, not direct streaming output.

For testing or debugging, writing streaming output to the console is common in Kappa architecture.

3fill in blank

hard

Fix the error in the code to correctly select the value field from Kafka streaming data.

Hadoop

values = stream.selectExpr("CAST([1] AS STRING)")

Drag options to blanks, or click blank then click option'

Avalue

Btimestamp

Ctopic

Dkey

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting 'key' instead of 'value' which contains the message.

Selecting metadata fields like 'topic' or 'timestamp' instead of the message.

Kafka streaming data stores the actual message in the 'value' field, which needs to be cast to string.

4fill in blank

hard

Fill both blanks to filter streaming data for messages containing the word 'error'.

Hadoop

filtered = stream.filter([1].contains([2]))

Drag options to blanks, or click blank then click option'

Avalue

B'error'

C'warning'

Dkey

Attempts:

3 left

💡 Hint

Common Mistakes

Filtering on 'key' instead of 'value'.

Using the wrong filter string like 'warning'.

We filter the 'value' field for messages containing the string 'error'.

5fill in blank

hard

Fill all three blanks to create a streaming aggregation counting messages by key.

Hadoop

counts = stream.groupBy([1]).count().orderBy([2], [3])

Drag options to blanks, or click blank then click option'

Akey

Bcount

C"desc"

D"asc"

Attempts:

3 left

💡 Hint

Common Mistakes

Ordering by 'key' instead of 'count'.

Using ascending order instead of descending.

Group by 'key', count messages, then order by 'count' descending to see most frequent keys first.

Practice

1.

What is the main idea behind the Kappa architecture in data processing?

easy

A. Store data in batches and process later

B. Process data only once as a continuous stream

C. Use separate systems for batch and streaming data

D. Process data multiple times for accuracy

Solution

1. Step 1: Understand Kappa architecture concept

   Kappa architecture focuses on processing data as a stream only once, avoiding batch processing.

2. Step 2: Compare with other architectures

   Unlike Lambda architecture, Kappa does not separate batch and streaming layers.

3. Final Answer:

   Process data only once as a continuous stream -> Option B

4. Quick Check:

   Kappa = single stream processing [OK]

Hint: Kappa means one stream, one processing path [OK]

Common Mistakes:

• Confusing Kappa with Lambda architecture
• Thinking Kappa uses batch processing
• Assuming multiple processing passes

2.

Which of the following is a correct component used in a typical Kappa architecture setup?

A) Separate batch and speed layers

B) Hadoop MapReduce for batch jobs

C) HDFS for storing batch data

D) Kafka for streaming data

easy

A. Separate batch and speed layers

B. Hadoop MapReduce for batch jobs

C. HDFS for storing batch data

D. Kafka for streaming data

Solution

1. Step 1: Identify streaming components in Kappa

   Kappa architecture uses streaming tools like Kafka to handle real-time data.

2. Step 2: Exclude batch-specific components

   Batch tools like MapReduce or HDFS batch storage are not part of Kappa's streaming-only design.

3. Final Answer:

   Kafka for streaming data -> Option D

4. Quick Check:

   Kappa uses Kafka, not batch tools [OK]

Hint: Kappa = streaming tools like Kafka only [OK]

Common Mistakes:

• Choosing batch tools like MapReduce
• Selecting batch storage like HDFS
• Thinking Kappa has separate batch layers

3.

Consider this simplified streaming code snippet using Kafka and Spark in a Kappa architecture:

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('KappaExample').getOrCreate()

# Read stream from Kafka topic
stream_df = spark.readStream.format('kafka') \
  .option('kafka.bootstrap.servers', 'localhost:9092') \
  .option('subscribe', 'events') \
  .load()

# Select and cast value to string
value_df = stream_df.selectExpr('CAST(value AS STRING)')

# Write stream to console
query = value_df.writeStream.outputMode('append').format('console').start()

query.awaitTermination()

What will this code do?

medium

A. Continuously print new Kafka messages from 'events' topic to console

B. Read all past Kafka messages once and stop

C. Write data back to Kafka topic 'events'

D. Throw an error due to missing schema

Solution

1. Step 1: Understand the streaming read

   The code reads a continuous stream from Kafka topic 'events' using Spark Structured Streaming.

2. Step 2: Analyze output mode and sink

   Output mode 'append' with console sink means new messages print continuously to console.

3. Final Answer:

   Continuously print new Kafka messages from 'events' topic to console -> Option A

4. Quick Check:

   Streaming read + console output = continuous print [OK]

Hint: Streaming read + console output = live print [OK]

Common Mistakes:

• Thinking it reads all past messages once
• Assuming it writes back to Kafka
• Expecting schema error without explicit schema

4.

Identify the error in this Kappa architecture streaming code snippet using Kafka and Spark:

stream_df = spark.readStream.format('kafka') \
  .option('kafka.bootstrap.servers', 'localhost:9092') \
  .option('subscribe', 'events') \
  .load()

value_df = stream_df.selectExpr('CAST(value AS STRING)')

query = value_df.writeStream.outputMode('complete').format('console').start()

query.awaitTermination()

What is the problem?

medium

A. Output mode 'complete' is invalid for streaming without aggregation

B. Missing schema definition causes failure

C. Kafka server address is incorrect

D. No error, code runs fine

Solution

1. Step 1: Check output mode usage

   Output mode 'complete' requires aggregation queries to output full results.

2. Step 2: Analyze query type

   This query only selects and casts values without aggregation, so 'complete' mode is invalid.

3. Final Answer:

   Output mode 'complete' is invalid for streaming without aggregation -> Option A

4. Quick Check:

   'complete' mode needs aggregation [OK]

Hint: 'complete' mode only with aggregation queries [OK]

Common Mistakes:

• Assuming missing schema causes error
• Thinking Kafka address is wrong
• Believing code runs without error

5.

You want to build a real-time dashboard using Kappa architecture that shows the count of events per user from a Kafka stream. Which approach fits best?

A) Use two separate systems: one for batch counts and one for streaming counts, then merge results.

B) Store Kafka data in HDFS, run daily batch jobs to count events per user, then update dashboard.

C) Use Spark Structured Streaming to read Kafka, group by user, count events, and update dashboard continuously.

D) Read Kafka stream once and save raw data only, no processing.

hard

A. Use two separate systems: one for batch counts and one for streaming counts, then merge results.

B. Store Kafka data in HDFS, run daily batch jobs to count events per user, then update dashboard.

C. Use Spark Structured Streaming to read Kafka, group by user, count events, and update dashboard continuously.

D. Read Kafka stream once and save raw data only, no processing.

Solution

1. Step 1: Understand Kappa architecture goal

   Kappa architecture processes data as a stream only, avoiding batch layers.

2. Step 2: Choose streaming aggregation for real-time dashboard

   Using Spark Structured Streaming to group and count events per user fits Kappa's streaming-only design and real-time needs.

3. Step 3: Exclude batch or hybrid approaches

   Batch jobs or separate batch/stream systems contradict Kappa principles.

4. Final Answer:

   Use Spark Structured Streaming to read Kafka, group by user, count events, and update dashboard continuously. -> Option C

5. Quick Check:

   Real-time + streaming aggregation = Use Spark Structured Streaming to read Kafka, group by user, count events, and update dashboard continuously. [OK]

Hint: Real-time dashboard needs streaming aggregation [OK]

Common Mistakes:

• Choosing batch processing for real-time needs
• Using hybrid batch and streaming systems
• Not processing data, just storing raw stream