Confluent CCDAK Dumps

When you create a new topic, partitions replicas are spreads across racks to maintain availability. Hence, the Rack A, which currently does not hold the topic partition, will be selected for the last replica

KSQL is not ANSI SQL compliant, for now there are no defined standards on streaming SQL languages

One consumer per partition assignment will keep 5 consumers idle.

session.timeout.ms must be decreased to 3 seconds to allow for a faster rebalance, and the heartbeat thread must be quicker, so we also need to decrease heartbeat.interval.ms

Setting a group.id that's consistent across restarts will allow your consumers part of the same group to resume reading from where offsets were last committed for that group

Multiple topics can be passed as a list or regex pattern.

Kafka Streams can only guarantee exactly once processing if you have a Kafka to Kafka topology.

Using Future.get() to wait for a reply from Kafka will limit throughput.

First local cache is checked for the message schema. In case of cache miss, schema is pulled from the schema registry. An exception will be thrown in the Schema Registry does not have the schema (which should never happen if you set it up properly)

2181 - client port, 2888 - peer port, 3888 - leader port

Keys are necessary if you require strong ordering or grouping for messages that share the same key. If you require that messages with the same key are always seen in the correct order, attaching a key to messages will ensure messages with the same key always go to the same partition in a topic. Kafka guarantees order within a partition, but not across partitions in a topic, so alternatively not providing a key - which will result in round-robin distribution across partitions - will not maintain such order.

Two brokers can go down, and one replica will still be able to receive and serve data

There is only one controller in a cluster at all times.

In this case, because the offset 3000 has been committed and all the messages between 0 and 3000 have all been processed, it is okay not to have committed offset 2000. The right answer is to do "nothing", this behaviour is acceptable

Each broker must have a unique broker id and connect to the same zk ensemble and root zNode

With SSL, messages will need to be encrypted and decrypted, by being first loaded into the JVM, so you lose the zero copy optimization. See more information

linger.ms forces the producer to wait before sending messages, hence increasing the chance of creating batches that can be heavily compressed.

SHOW STREAMS and EXPLAIN statements run against the KSQL server that the KSQL client is connected to. They don't communicate directly with Kafka. CREATE STREAM WITH and CREATE TABLE WITH write metadata to the KSQL command topic. Persistent queries based on CREATE STREAM AS SELECT and CREATE TABLE AS SELECT read and write to Kafka topics. Non-persistent queries based on SELECT that are stateless only read from Kafka topics, for example SELECT … FROM foo WHERE …. Non-persistent queries that are stateful read and write to Kafka, for example, COUNT and JOIN. The data in Kafka is deleted automatically when you terminate the query with CTRL-C.

See

Latest means that data retrievals will start from where the offsets currently end

Once the client connects to any broker, it is connected to the entire cluster and in case of leadership changes, the clients automatically do a Metadata Request to an available broker to find out who is the new leader for the topic. Hence the producer will automatically keep on producing to the correct Kafka Broker

The Confluent Schema Registry is your safeguard against incompatible schema changes and will be the component that ensures no breaking schema evolution will be possible. Kafka Brokers do not look at your payload and your payload schema, and therefore will not reject data