Chapter 1: Meet Kafka

Question 1

Batches can contain messages from multiple partitions. T/F

Answer 1

F

Question 2

Batches can contain messages from multiple topics. T/F

Answer 2

F

Question 3

What is the benefit of a larger batch size?

Answer 3

Increased throughput

Question 4

What is the benefit of a smaller batch size?

Answer 4

Decreased latency

Question 5

How does Kafka reduce the bytes in a batch before sending it across the network

Answer 5

Compression

Question 6

What are the 3 most common schema types

Answer 6

JSON, XML, Arvo

Question 7

How do Arvo messages achieve a smaller size than JSON or XML messages?

Answer 7

Separating message payload and schema

Question 8

Are messages guaranteed to be ordered within a topic?

Answer 8

No

Question 9

Are messages guaranteed to be ordered within a partition?

Answer 9

Yes

Question 10

How do partitions increase scalability and redundancy?

Answer 10

Splitting partitions across servers

Question 11

A producer is about to produce a message which has no key, and the producer is not using a custom partitioner. How does the producer decide which partition to use?

Answer 11

The producer will distribute the message evenly across partitions

Question 12

Describe two different methods of ensuring that two messages will be written to the same partition?

Answer 12

Use a custom partitioner or give both messages the same key

Question 13

If I were to produce two messages to a topic, how could I ensure that those messages were consumed in the same order they were produced?

Answer 13

Put the messages in the same partition

Question 14

What is the data type of an offset?

Answer 14

An integer

Question 15

When a consumer restarts, how does it decide which message it should start reading

Answer 15

It reads the offset

Question 16

What are the two places that the offset could be stored?

Answer 16

Zookeeper or Kafka

Question 17

What is the cardinality between consumer groups and partitions?

Answer 17

One-to-many

Question 18

How could one increase the throughput of a consumer group?

Answer 18

Adding more consumers

Question 19

Consumer A owns partition B. How does Kafka ensure that partition B will continue to be processed in the event that consumer A dies?

Answer 19

Kafka re-assigns partition B to another consumer from the group of consumer A

Question 20

True or false? A single broker can handle millions of partitions.

Answer 20

False

Question 21

True or false? A single broker can handle thousands of partitions.

Answer 21

True

Question 22

True or false? A single broker can handle millions of messages per second.

Answer 22

True

Question 23

Within a cluster, how many brokers are responsible for assigning partitions to consumers?

Answer 23

One

Question 24

What is the name of a broker that is responsible for assigning partitions to brokers

Answer 24

Controller

Question 25

Fill in the blank: all producers and consumers of a partition can be connected to a single broker, called the ___

Answer 25

Leader

Question 26

How does Kafka ensure redundancy of messages in a partition?

Answer 26

By replicating the partition in multiple brokers

Question 27

Describe two simple ways that I could use to make a Kafka topic store messages for 1 month?

Answer 27

Change the broker retention setting or topic retention setting to 1 month

Question 28

How could I limit the size of data (in bytes) stored in a Kafka topic to 1 GB, without affecting other topics?

Answer 28

Change the topic retention settings

Question 29

How could I limit a Kafka topic so that only the most recent message is stored

Answer 29

Change the topic to be log compacted

Question 30

Name 3 ways that one could increase the throughput of a topic?

Answer 30

It depends on the bottleneck. Options include:
- Increase batch size
- Increase number of consumers
- Increase number of brokers
- Increase number of partitions
- Increase computing power of servers
- Make consumer processing code more efficient
- Decrease message size

Question 31

A topic is experiencing high latency between the producer and the consumer. What could be done to reduce this latency?

Answer 31

Decrease batch size, decrease message size, address any bottlenecks in the system (e.g not enough brokers)

Question 32

How could I enforce the processing order of two messages?

Answer 32

Put the messages in the same topic and partition

Question 33

A topic was being processed quickly but a spike in message frequency has increased the processing time of messages. The brokers have plenty of computing power to spare so they are not the bottleneck. How could I increase the speed at which messages are processed without altering the code which processes messages?

Answer 33

Increase the number of consumers and/or number of partitions