Kafka integration with Hadoop - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify the Kafka topic to consume data from.

Hadoop

kafkaParams = {"metadata.broker.list": "localhost:9092"}
topic = "[1]"

rdd = KafkaUtils.createDirectStream(ssc, [topic], kafkaParams)

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

Akafka_topic

Bdata_stream

Chadoop_topic

Dinput_topic

Attempts:

3 left

💡 Hint

Common Mistakes

Using an incorrect or non-existent topic name.

Leaving the topic name empty or misspelled.

The Kafka topic name must be set correctly to consume data. Here, "kafka_topic" is the correct topic name used in the example.

2fill in blank

medium

Complete the code to set the Kafka broker list for Hadoop integration.

Hadoop

kafkaParams = {"metadata.broker.list": "[1]"}

rdd = KafkaUtils.createDirectStream(ssc, ["kafka_topic"], kafkaParams)

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

Alocalhost:9092

Blocalhost:2181

C127.0.0.1:8080

Dkafka:9092

Attempts:

3 left

💡 Hint

Common Mistakes

Using the Zookeeper port (2181) instead of Kafka broker port.

Using incorrect host or port format.

The Kafka broker list specifies the host and port where Kafka brokers listen. The default Kafka port is 9092.

3fill in blank

hard

Fix the error in the code to correctly create a direct Kafka stream in Spark Streaming.

Hadoop

from pyspark.streaming.kafka import KafkaUtils

rdd = KafkaUtils.createDirectStream(ssc, ["kafka_topic"], [1])

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

AkafkaParams()

Bkafka_params

CkafkaParams

DkafkaParams[]

Attempts:

3 left

💡 Hint

Common Mistakes

Calling the dictionary as a function.

Using incorrect variable name casing.

The parameter must be the dictionary variable 'kafkaParams' without parentheses or brackets.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters Kafka messages with value length greater than 5.

Hadoop

filtered_messages = {msg.key: msg.value for msg in messages if len(msg.[1]) [2] 5}

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

Avalue

B>

C<

Dkey

Attempts:

3 left

💡 Hint

Common Mistakes

Filtering by key length instead of value length.

Using '<' instead of '>' causing wrong filtering.

We check the length of the message value and filter those with length greater than 5.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase keys to values where value length is less than 10.

Hadoop

result = {msg.[1].upper(): msg.[2] for msg in messages if len(msg.[3]) < 10}

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

Akey

Bvalue

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up keys and values in comprehension.

Not converting keys to uppercase.

The keys are converted to uppercase, values are mapped directly, and filtering is done on value length less than 10.

Practice

1. What is the main purpose of integrating Kafka with Hadoop in data science?

easy

A. To move streaming data into Hadoop storage for analysis

B. To replace Hadoop's storage with Kafka's storage

C. To run machine learning models inside Kafka

D. To visualize data directly from Kafka without storage

Solution

1. Step 1: Understand Kafka and Hadoop roles

   Kafka streams data in real-time, Hadoop stores large data for analysis.

2. Step 2: Identify integration purpose

   Integration moves streaming data from Kafka into Hadoop for big data processing.

3. Final Answer:

   To move streaming data into Hadoop storage for analysis -> Option A

4. Quick Check:

   Kafka streams + Hadoop stores = Integration purpose [OK]

Hint: Kafka streams data; Hadoop stores it for analysis [OK]

Common Mistakes:

• Thinking Kafka replaces Hadoop storage
• Assuming Kafka runs ML models
• Believing visualization happens directly in Kafka

2. Which of the following is the correct command to consume messages from a Kafka topic named sensor_data?

easy

A. kafka-run-class kafka.Consumer sensor_data

B. kafka-console-producer --topic sensor_data --bootstrap-server localhost:9092

C. kafka-console-consumer --topic sensor_data --bootstrap-server localhost:9092

D. hadoop fs -cat /sensor_data

Solution

1. Step 1: Identify Kafka consumer command

   The command to read messages is kafka-console-consumer with topic and server options.

2. Step 2: Match command to options

   kafka-console-consumer --topic sensor_data --bootstrap-server localhost:9092 uses correct consumer command and parameters for topic sensor_data.

3. Final Answer:

   kafka-console-consumer --topic sensor_data --bootstrap-server localhost:9092 -> Option C

4. Quick Check:

   Consumer command with topic and server [OK]

Hint: Consumer command starts with kafka-console-consumer [OK]

Common Mistakes:

• Using producer command instead of consumer
• Confusing Hadoop fs commands with Kafka
• Using outdated or incorrect Kafka class commands

3. Given the following Flume configuration snippet to move Kafka data to HDFS:

agent.sources = kafkaSource
agent.channels = memoryChannel
agent.sinks = hdfsSink

agent.sources.kafkaSource.type = org.apache.flume.source.kafka.KafkaSource
agent.sources.kafkaSource.kafka.bootstrap.servers = localhost:9092
agent.sources.kafkaSource.kafka.topics = sensor_data

agent.channels.memoryChannel.type = memory

agent.sinks.hdfsSink.type = hdfs
agent.sinks.hdfsSink.hdfs.path = /data/sensor
agent.sinks.hdfsSink.channel = memoryChannel

What will be the HDFS directory where Kafka data is stored?

medium

A. /data/sensor

B. /kafka/sensor_data

C. /user/flume/kafka

D. /tmp/kafka_data

Solution

1. Step 1: Locate HDFS path in sink configuration

   The sink hdfsSink has hdfs.path = /data/sensor.

2. Step 2: Confirm data storage directory

   Kafka data consumed by Flume will be stored in /data/sensor on HDFS.

3. Final Answer:

   /data/sensor -> Option A

4. Quick Check:

   HDFS sink path = /data/sensor [OK]

Hint: Check hdfs.path in sink config for storage location [OK]

Common Mistakes:

• Confusing Kafka topic name with HDFS path
• Assuming default Flume user directory
• Ignoring channel configuration

4. You set up Kafka to HDFS data transfer using Flume, but no data appears in HDFS. Which of the following is the most likely error in the Flume config?

medium

A. The memory channel type is set to file

B. The HDFS path is set correctly

C. The Flume agent name is missing

D. The Kafka topic name in kafka.topics is misspelled

Solution

1. Step 1: Check common config errors

   Misspelling Kafka topic means Flume reads no data from Kafka.

2. Step 2: Evaluate options

   Correct HDFS path and channel type do not cause no data; missing agent name causes startup error, not silent failure.

3. Final Answer:

   The Kafka topic name in kafka.topics is misspelled -> Option D

4. Quick Check:

   Wrong topic name = no data read [OK]

Hint: Check Kafka topic spelling first if no data in HDFS [OK]

Common Mistakes:

• Assuming HDFS path causes no data
• Changing channel type incorrectly
• Ignoring agent name errors

5. You want to build a pipeline that streams sensor data from Kafka into Hadoop HDFS and then runs batch analysis daily. Which combination of tools and steps is best?

hard

A. Use Kafka producer to write directly to HDFS, then run Spark streaming

B. Use KafkaSource in Flume to ingest data into HDFS, then run Hadoop MapReduce daily

C. Use Hadoop fs commands to read Kafka topics, then store in local files

D. Use Kafka consumer to write data to local disk, then upload manually to HDFS

Solution

1. Step 1: Identify streaming ingestion method

   Flume with KafkaSource is designed to stream Kafka data into HDFS efficiently.

2. Step 2: Choose batch analysis tool

   Hadoop MapReduce runs batch jobs on HDFS data daily, fitting the requirement.

3. Final Answer:

   Use KafkaSource in Flume to ingest data into HDFS, then run Hadoop MapReduce daily -> Option B

4. Quick Check:

   Flume + KafkaSource + HDFS + MapReduce = correct pipeline [OK]

Hint: Flume streams Kafka to HDFS; MapReduce runs batch jobs [OK]

Common Mistakes:

• Using Kafka producer to write directly to HDFS (not supported)
• Confusing streaming with batch tools
• Manual upload instead of automated pipeline