kafka persistent queue

1. Is Kafka persistent?
2. Why Kafka is better than RabbitMQ?
3. How do you persist data in Kafka?
4. What is the difference between Kafka and RabbitMQ?
5. Is Kafka pull or push?
6. Can Kafka replace database?
7. What is the purpose of message queue?
8. Why Kafka is so fast?
9. Is Kafka a message queue?
10. How long does Kafka keep data?
11. How data is stored in Kafka?
12. How reliable is Kafka?

Is Kafka persistent?

As we described, Kafka stores a persistent log which can be re-read and kept indefinitely. Kafka is built as a modern distributed system: it's runs as a cluster, can expand or contract elastically, and replicates data internally for fault-tolerance and high-availability.

Why Kafka is better than RabbitMQ?

Kafka offers much higher performance than message brokers like RabbitMQ. It uses sequential disk I/O to boost performance, making it a suitable option for implementing queues. It can achieve high throughput (millions of messages per second) with limited resources, a necessity for big data use cases.

How do you persist data in Kafka?

Let's illustrate these concepts with an example that persists streaming data in 5 simple steps:

1. Setup stream and database connections.
2. Consume records from a MapR stream using the standard Kafka API.
3. Convert each consumed record to a JSON object.
4. Persist that JSON object in HPE Ezmeral Data Fabric Document Database.

What is the difference between Kafka and RabbitMQ?

RabbitMQ's queues are fastest when they're empty, while Kafka is designed for holding and distributing large volumes of messages. Kafka retains large amounts of data with very little overhead. People that are trying out RabbitMQ are probably not aware of the the feature lazy queues.

Is Kafka pull or push?

With Kafka consumers pull data from brokers. Other systems brokers push data or stream data to consumers. ... Since Kafka is pull-based, it implements aggressive batching of data. Kafka like many pull based systems implements a long poll (SQS, Kafka both do).

Can Kafka replace database?

Kafka as Query Engine and its Limitations

Therefore, Kafka will not replace other databases. It is complementary. The main idea behind Kafka is to continuously process streaming data; with additional options to query stored data. Kafka is good enough as database for some use cases.

What is the purpose of message queue?

Message queues allow different parts of a system to communicate and process operations asynchronously. A message queue provides a lightweight buffer which temporarily stores messages, and endpoints that allow software components to connect to the queue in order to send and receive messages.

Why Kafka is so fast?

Compression & Batching of Data: Kafka batches the data into chunks which helps in reducing the network calls and converting most of the random writes to sequential ones. It's more efficient to compress a batch of data as compared to compressing individual messages.

Is Kafka a message queue?

We can use Kafka as a Message Queue or a Messaging System but as a distributed streaming platform Kafka has several other usages for stream processing or storing data. We can use Apache Kafka as: Messaging System: a highly scalable, fault-tolerant and distributed Publish/Subscribe messaging system.

How long does Kafka keep data?

The Kafka cluster retains all published messages—whether or not they have been consumed—for a configurable period of time. For example if the log retention is set to two days, then for the two days after a message is published it is available for consumption, after which it will be discarded to free up space.

How data is stored in Kafka?

Kafka stores all the messages with the same key into a single partition. Each new message in the partition gets an Id which is one more than the previous Id number. This Id number is also called as the Offset . So, the first message is at 'offset' 0, the second message is at offset 1 and so on.

How reliable is Kafka?

Therefore, Apache-Kafka offers strong durability and fault tolerance guarantees. Note about Leaders: At any time, only one broker can be a leader of a partition and only that leader can receive and serve data for that partition. The remaining brokers will just synchronize the data (in-sync replicas).