Batch vs real-time ingestion in Hadoop - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Batch Ingestion Timing

Which statement best describes when batch ingestion processes data in a Hadoop ecosystem?

A Data is processed continuously as it arrives in small chunks.

B Data is processed immediately upon arrival without any delay.

C Data is processed only when a user requests it in real-time.

D Data is collected over a period and processed all at once at scheduled intervals.

💡 Hint

Think about how batch jobs usually run on a schedule, not instantly.

Batch ingestion collects data over time and processes it in bulk at set times, unlike real-time ingestion which processes data instantly.

❓ Predict Output

intermediate

Output of a Real-time Data Stream Simulation

Given the following pseudo-code simulating real-time ingestion in Hadoop streaming, what will be the output after processing 3 data points?

Hadoop

stream = ['data1', 'data2', 'data3']
processed = []
for item in stream:
    processed.append(item.upper())
print(processed)

A ['DATA1', 'DATA2', 'DATA3']

B ['data1', 'data2', 'data3']

C ['Data1', 'Data2', 'Data3']

D Error: 'upper' method not found

💡 Hint

Check what the upper() method does to strings.

The upper() method converts all characters in a string to uppercase, so each data point becomes uppercase.

❓ data_output

advanced

Data Volume Comparison in Batch vs Real-time

Consider a Hadoop system where batch ingestion processes 10,000 records every hour, and real-time ingestion processes 100 records every minute. How many records does each process handle in 3 hours?

A Batch: 10,000 records; Real-time: 6,000 records

B Batch: 30,000 records; Real-time: 18,000 records

C Batch: 30,000 records; Real-time: 300 records

D Batch: 3,000 records; Real-time: 1,800 records

💡 Hint

Calculate total records by multiplying rate by time for both methods.

Batch: 10,000 records/hour * 3 hours = 30,000 records.
Real-time: 100 records/minute * 60 minutes/hour * 3 hours = 18,000 records.

🔧 Debug

advanced

Identifying Error in Real-time Data Processing Code

What error will the following Hadoop streaming code produce?

data_stream = ['a', 'b', 'c']
result = []
for d in data_stream:
    result.append(d / 2)
print(result)

A TypeError: unsupported operand type(s) for /: 'str' and 'int'

B SyntaxError: invalid syntax

C IndexError: list index out of range

D No error; output will be ['a', 'b', 'c']

💡 Hint

Consider what happens when dividing a string by a number.

You cannot divide a string by an integer, so Python raises a TypeError.

🚀 Application

expert

Choosing Ingestion Method for Time-sensitive Data

You manage a Hadoop system that collects sensor data. The sensors send data every second, and you need to detect anomalies within 5 seconds of data arrival. Which ingestion method is best to meet this requirement?

A Batch ingestion, processing data every hour to reduce system load.

B Batch ingestion, processing data once a day for thorough analysis.

C Real-time ingestion, processing data as it arrives to detect anomalies quickly.

D Neither; manual data checks are better for anomaly detection.

💡 Hint

Think about how quickly you need to respond to data to detect anomalies.

Real-time ingestion processes data immediately, allowing anomaly detection within seconds, which fits the 5-second requirement.

Practice

1. Which statement best describes batch ingestion in Hadoop?

easy

A. It only works with streaming data sources.

B. It processes data instantly as it arrives.

C. It requires real-time user interaction to start.

D. It collects and processes data in groups at scheduled times.

Solution

1. Step 1: Understand batch ingestion concept

   Batch ingestion collects data in chunks or groups rather than continuously.

2. Step 2: Compare with real-time ingestion

   Real-time ingestion processes data immediately, unlike batch which waits for scheduled times.

3. Final Answer:

   It collects and processes data in groups at scheduled times. -> Option D

4. Quick Check:

   Batch ingestion = scheduled group processing [OK]

Hint: Batch = grouped data at set times, not instant [OK]

Common Mistakes:

• Confusing batch with real-time ingestion
• Thinking batch requires instant processing
• Assuming batch needs user interaction

2. Which of the following is the correct syntax to start a real-time ingestion job in Hadoop using Apache Flume?

easy

A. hadoop fs -put data.txt /input

B. flume-ng agent -n agent1 -c conf -f conf/flume.conf

C. hadoop jar batchjob.jar input output

D. flume-ng batch -start agent1

Solution

1. Step 1: Identify real-time ingestion tool syntax

   Apache Flume uses the command flume-ng agent to start an agent for real-time ingestion.

2. Step 2: Check other options for correctness

   Options B and C relate to batch or file operations, and D is not a valid Flume command.

3. Final Answer:

   flume-ng agent -n agent1 -c conf -f conf/flume.conf -> Option B

4. Quick Check:

   Flume agent command = real-time start [OK]

Hint: Flume agent command starts real-time ingestion [OK]

Common Mistakes:

• Using batch job commands for real-time ingestion
• Confusing file upload with ingestion start
• Incorrect Flume command syntax

3. Consider this simplified code snippet for batch ingestion in Hadoop MapReduce:

hadoop jar batchjob.jar input_dir output_dir

What will happen when this command runs?

medium

A. It processes all files in input_dir at once and writes results to output_dir.

B. It continuously processes new files as they arrive in input_dir.

C. It deletes input_dir after processing.

D. It streams data from input_dir to output_dir in real-time.

Solution

1. Step 1: Analyze the batch job command

   The command runs a MapReduce job on all data present in input_dir at the time of execution.

2. Step 2: Understand batch vs real-time behavior

   Batch jobs process data in bulk, not continuously or in real-time.

3. Final Answer:

   It processes all files in input_dir at once and writes results to output_dir. -> Option A

4. Quick Check:

   Batch job = bulk processing [OK]

Hint: Batch job runs once on all current input data [OK]

Common Mistakes:

• Thinking batch jobs run continuously
• Assuming input directory is deleted
• Confusing batch with streaming

4. You wrote this Flume configuration to ingest data in real-time:

agent1.sources = source1
agent1.sinks = sink1
agent1.channels = channel1
agent1.sources.source1.type = exec
agent1.sources.source1.command = tail -F /var/log/syslog
agent1.channels.channel1.type = memory
agent1.sinks.sink1.type = hdfs
agent1.sinks.sink1.hdfs.path = /data/logs/

But no data appears in HDFS. What is the likely error?

medium

A. Flume agent was not started with the correct configuration file.

B. The source command 'tail -F' is incorrect; should be 'tail -f'.

C. The channel is not connected to the sink and source.

D. The sink path '/data/logs/' does not exist in HDFS.

Solution

1. Step 1: Check Flume configuration connections

   Flume requires explicit binding of sources, channels, and sinks. Missing bindings cause no data flow.

2. Step 2: Verify bindings in config

   The config lacks lines like agent1.sources.source1.channels = channel1 and agent1.sinks.sink1.channel = channel1.

3. Final Answer:

   The channel is not connected to the sink and source. -> Option C

4. Quick Check:

   Flume needs source-channel-sink bindings [OK]

Hint: Always bind source, channel, and sink in Flume config [OK]

Common Mistakes:

• Ignoring missing bindings in Flume config
• Assuming wrong tail command causes failure
• Thinking sink path must pre-exist

5. You have a large dataset arriving daily and also need to monitor user activity instantly. Which ingestion strategy should you use in Hadoop for each case?

hard

A. Use batch ingestion for daily data and real-time ingestion for user activity.

B. Use real-time ingestion for both daily data and user activity.

C. Use batch ingestion for user activity and real-time ingestion for daily data.

D. Use batch ingestion only for both cases.

Solution

1. Step 1: Analyze data arrival and processing needs

   Large daily data fits batch ingestion since it processes data in groups periodically.

2. Step 2: Consider immediacy of user activity monitoring

   User activity requires instant processing, so real-time ingestion is suitable.

3. Final Answer:

   Use batch ingestion for daily data and real-time ingestion for user activity. -> Option A

4. Quick Check:

   Batch = large periodic, Real-time = instant [OK]

Hint: Batch for big sets, real-time for instant needs [OK]

Common Mistakes:

• Mixing batch and real-time roles
• Using real-time for large daily data unnecessarily
• Ignoring immediacy of user activity data