Apache Kafka is a distributed streaming platform used for building real-time data pipelines and streaming applications.
The Internal Architecture of Kafka
- Producer: Sends records to Kafka topics.
- Consumer: Reads records from Kafka topics.
- Broker: Individual Kafka server, holding data and serving clients.
- ZooKeeper: Manages brokers and maintains metadata.
Understanding Kafka Brokers and Clusters
- Broker: Stores data and serves clients.
- Cluster: A set of Kafka brokers.
Brokers store topic partitions and serve producers and consumers. Multiple brokers form a cluster, managed by ZooKeeper.
How Topics and Partitions Work
- Topic: Logical channel to which records are sent by producers.
- Partition: Physical subdivision of a topic.
A topic is divided into partitions for parallelism and distributed storage.
Replica Management in Kafka
- Leader Replica: Handles reads and writes for a partition.
- Follower Replica: Mirrors the data and can take over if the leader fails.
Replicas ensure data availability and resilience.
Data Flow and Message Routing in Kafka
- Producer API: Sends records to the broker.
- Consumer API: Fetches records from the broker.
Records are produced to topics, stored in partitions, and consumed from partitions.
Load Balancing and Data Distribution in Kafka
- Partitioning: Distributes data across multiple brokers.
Producers use algorithms like round-robin or hash-based partitioning to distribute records.
Understanding Leader and Follower Roles
- Leader: Serves client requests.
- Follower: Mirrors the leader and can become leader if needed.
Leaders are elected by ZooKeeper.
Zookeeper’s Role in Kafka Architecture
- Manages broker metadata.
- Handles broker failure and recovery.
- Manages leader elections.
Extended Practice Questions for CCKAD
- What is a Kafka broker?
- How is a topic different from a partition?
- What role does ZooKeeper play in Kafka?
- Explain the difference between leader and follower replicas.
- How does Kafka handle load balancing?
- Describe the data flow in Kafka.
- What algorithms can be used for partitioning in Kafka?
- How are consumer offsets managed?
- What happens when a broker fails?
- What are the core APIs in Kafka?
- How does Kafka ensure data durability?
- What are consumer groups and how do they work?
- How do producers decide which partition to send a message to?
- What is the role of a Kafka “Producer Record”?
- Describe the process of a leader election in a Kafka cluster.
- How is data ordered in Kafka partitions?
- What is “Log Compaction” in Kafka?
- How are read and write operations optimized in Kafka?
- What is a “Topic Log” in Kafka?
- Explain how Kafka enables fault-tolerance.
- What is the significance of the
acksconfiguration in Kafka producers?
- How can you secure Kafka brokers?
- What is “Exactly Once Semantics” in Kafka?
- How does Kafka handle schema evolution?
- How can you monitor the health and performance of a Kafka cluster?
- A Kafka broker is an individual Kafka server that stores data and serves clients.
- A topic is a logical channel for storing records, whereas a partition is a physical subdivision of a topic.
- ZooKeeper manages the Kafka brokers and maintains the metadata.
- The leader replica handles client requests, while the follower replica mirrors the leader.
- Kafka uses partitioning to distribute data across multiple brokers.
- Producers send records to topics, which are stored in partitions. Consumers read from these partitions.
- Round-robin and hash-based partitioning.
- Consumer offsets are pointers that track the last read position in a partition.
- ZooKeeper detects the failure and initiates a leader re-election for partitions that the failed broker led.
- Producer API, Consumer API, Streams API, and Admin API.
- Through replication.
- Consumer groups allow multiple consumers to share the load of reading messages from topics.
- Producers use partitioning algorithms like round-robin or hash-based partitioning.
- A Producer Record contains the topic and partition information along with the key-value payload.
- ZooKeeper initiates leader election when a broker fails or a new broker is added.
- Data is ordered by the offset, a sequential ID, within each partition.
- Log compaction retains the latest update for each record key within a partition.
- Kafka optimizes read and write operations through sequential disk I/O.
- A Topic Log is the physical storage layer in a broker where messages for a particular topic partition are stored.
- Through data replication and leader elections.
acksconfiguration specifies the number of acknowledgments the producer requires the broker to receive before considering a request complete.
- Through SSL/TLS, SASL, and ACLs.
- “Exactly Once Semantics” ensure that records are neither lost nor seen more than once.
- Kafka handles schema evolution through schema registry services like Confluent’s Schema Registry.
- You can use monitoring tools like JMX, Grafana, or built-in Kafka tools to monitor cluster health and performance.