Kappa architecture (streaming only) in Hadoop - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main idea behind Kappa architecture?

Kappa architecture processes data only as a stream, avoiding separate batch layers. It treats all data as a continuous flow for real-time processing.

intermediate

How does Kappa architecture handle data reprocessing?

It reprocesses data by replaying the entire stream from the beginning, using the same streaming code, instead of having a separate batch process.

beginner

Which component is central in Kappa architecture for storing data streams?

A durable log system like Apache Kafka stores the data stream, allowing replay and fault tolerance.

intermediate

Why might Kappa architecture be simpler than Lambda architecture?

Because it uses only one processing path (streaming), it avoids maintaining separate batch and streaming code, reducing complexity.

beginner

Name a common streaming processing framework used in Kappa architecture.

Apache Flink, Apache Spark Streaming, or Apache Kafka Streams are popular frameworks for processing streams in Kappa architecture.

What type of data processing does Kappa architecture use?

A Only streaming processing

B Only batch processing

C Both batch and streaming processing

D Neither batch nor streaming processing

Kappa architecture uses only streaming processing to handle data.

How does Kappa architecture reprocess data?

A By running a batch job on stored data

B By replaying the data stream from the start

C By ignoring old data

D By manual data correction

It reprocesses data by replaying the entire data stream from the beginning.

Which system is commonly used to store streams in Kappa architecture?

A Relational database

B Distributed file system

C Durable log like Kafka

D In-memory cache

Durable logs like Kafka store streams reliably for replay and fault tolerance.

What is a key benefit of Kappa architecture over Lambda architecture?

A Simpler code maintenance

B Supports batch processing

C Requires more storage

D Uses multiple processing paths

Kappa architecture simplifies maintenance by using a single streaming code path.

Which framework is NOT typically used in Kappa architecture?

A Apache Flink

B Apache Spark Streaming

C Apache Kafka Streams

D Hadoop MapReduce batch jobs

Hadoop MapReduce is batch processing, not streaming, so it is not typical in Kappa architecture.

Explain how Kappa architecture processes and reprocesses data using streaming.

Think about how data flows and how old data is handled.

Compare Kappa architecture with Lambda architecture focusing on complexity and processing layers.

Focus on the number of processing paths and maintenance.

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