- Effective Kafka
- Chapter 1: Event Streaming Fundamentals
- Chapter 2: Apache Kafka
- Chapter 3: Architecture and Core concept
- Chapter 6: Desing Consideration
- is a paradigm -> reaction to events
- event -> change in state
- consist of
- emitters (producers)
- consumers (subscribers)
- channels(brokers)
- An emitter of an event is not aware of any of the event's downstream consumers.
- Event notifications are immutable.
- Features:
- Coupling - loosely coupled
- Resilience - less prone to outage (limited to individual component)
- Consistency - maintained by enforcing the single writer principle.
- Event stream -> durable, totally-ordered, unbounded sequence of immutable event records, delivered at least once to its subscriber(s).
- Publish-subscribe
- Log aggregation -> act as a buffer, offering an intermediate, durable database.
- Log shipping -> real-time copying of data which can be replayed later.
- SEDA pipeline -> Staged Event-Driven Architecture
- event flow through a series of processing stages linked by topics.
- CEP - Complex Event Processing
- extracts meaningful information and pattersn in a stream of discrete events.
- Used in algo sotck trading, security thread analysis, real time fraud detection.
- Broker Node: Host a set of append-only log files by partition.
- Zookeeper Nodes: Act as a consistent and highly available configuration repository of cluster metadata.
- It's not an internal component of Kafka, but an open-source project in its own.
- Produces: Client app responsible for appending records to Kafka topics
- Consumers: Client app that read from topics
- Total ordered set - every element has a well-defined ordering relationship with every other element in the set
- Ex - 1-> 2 -> 3 -> 4
- Every element has a predecessor-successor relationship with every other element.
- We can remove any element from the set and reinsert them back and will arrive at the same sequence.
- Unordered set -> {Delhi, Mumbai, KolKata}
- We don't know why Delhi should appear before Mumbai without applying any constraints.
- No pair of elements are comparable.
- Partially ordered set -> Ex - set of natural no. ordered by divisibility, such that a number must appear after its divisor.
- {2, 3, 5, 7, 4, 6, 9, 10, 8}
- 2 appear before 4 and 4 appear before 8 or 12.
- Not every pair of elements needs to be comparable.
- Casual order -> two elements may be bounded by a happened-before relationship.
- each element represents an event and some pairs of events have a happened-before relationship.
- corresponds to some event of interest.
- Attributes:
- Key - optional non-unique key
- represented as an array of bytes.
- Value - payload of a record.
- Headers - a set of free-form key-value pairs that can optionally annotate a record.
- Partition number - A zero-based index of the partition that the record appears in.
- Offset- A 64-bit interger for locating a record withing a partition.
- uniquely identifies it in the partition.
- acts as a priamary key, allowing for fast, O(1) lookups.
- strictly monotonically increasing integer
- Key - optional non-unique key
- Here, key is not a primary key.
- Primary key - record's partition number and its offset.
- Published records are appeneded to a partition.
- Any pair of records in the same partition is bound by a predecessor-successor relationship (assigned by produer application)
- Comprises of one or more partitions and a partition must be a part of exactly one topic
- A consumer is a process or thread that attach to a kafka cluster via a client library.
- A consumer group are effectively a load-balancing mechanism within kafka - distributing partition assignements evenly among the individual consumer instances withing the group.
- A consumer internally maintains an offset that points to the next record in a partition.
Consumer groups also ensures availability.
- By peridically reading records from a topic, the consumer implicitly signals to the cluster that its in a healthy state.
- If the consumer fail to read again within the allowable deadline, it will be deemed faulty nad its partition will be reassigned.
- Persisting the consumer state back to the Kafka cluster is called commiting the offset.
- Consumer will read a record and commit the offset of the last record +1.
- If a new consumer takes over the topic, it will commence processing from the last commit offset
-
- Who owns the topic and who's responsible for it's upkeep?
- In single-producer-multiple-consumers, there may be a number of disaggreeing parties.
- Sol - Rather than feeding consumers directly, intermediate processing stages are employed to conform to consumer group's expectations.
- Consumer takes ownership of the conditioning stage.
- Producer-driven partitioning
- Responsibility of assigning records to partition lies with the producer.