Kafka Topology Best Practice

I have 4 machines where a Kafka Cluster is configured with topology that each machine has one zookeeper and two broker.

With this configuration what do you advice for maximum topic&partition for best performance?

Replication Factor 3: using kafka 0.10.XX

Thanks?

Each topic is restricted to 100,000 partitions no matter how many nodes (as of July 2017)

As to the number of topics that depends on how large the smallest RAM is across the machines. This is due to Zookeeper keeping everything in memory for quick access (also it doesnt shard the znodes, just replicates across ZK nodes upon write). This effectively means once you exhaust one machines memory that ZK will fail to add more topics. You will most likely run out of file handles before reaching this limit on the Kafka broker nodes.

To quote the KAFKA docs on their site (6.1 Basic Kafka Operations https://kafka.apache.org/documentation/#basic_ops_add_topic):

Each sharded partition log is placed into its own folder under the Kafka log directory. The name of such folders consists of the topic name, appended by a dash (-) and the partition id. Since a typical folder name can not be over 255 characters long, there will be a limitation on the length of topic names. We assume the number of partitions will not ever be above 100,000. Therefore, topic names cannot be longer than 249 characters. This leaves just enough room in the folder name for a dash and a potentially 5 digit long partition id.

To quote the Zookeeper docs (https://zookeeper.apache.org/doc/trunk/zookeeperOver.html):

The replicated database is an in-memory database containing the entire data tree. Updates are logged to disk for recoverability, and writes are serialized to disk before they are applied to the in-memory database.

Performance:

Depending on your publishing and consumption semantics the topic-partition finity will change. The following are a set of questions you should ask yourself to gain insight into a potential solution (your question is very open ended):

Is the data I am publishing mission critical (i.e. cannot lose it, must be sure I published it, must have exactly once consumption)?
Should I make the producer.send() call as synchronous as possible or continue to use the asynchronous method with batching (do I trade-off publishing guarantees for speed)?
Are the messages I am publishing dependent on one another? Does message A have to be consumed before message B (implies A published before B)?
How do I choose which partition to send my message to? Should I: assign the message to a partition (extra producer logic), let the cluster decide in a round robin fashion, or assign a key which will hash to one of the partitions for the topic (need to come up with an evenly distributed hash to get good load balancing across partitions)
How many topics should you have? How is this connected to the semantics of your data? Will auto-creating topics for many distinct logical data domains be efficient (think of the effect on Zookeeper and administrative pain to delete stale topics)?
Partitions provide parallelism (more consumers possible) and possibly increased positive load balancing effects (if producer publishes correctly). Would you want to assign parts of your problem domain elements to specific partitions (when publishing send data for client A to partition 1)? What side-effects does this have (think refactorability and maintainability)?
Will you want to make more partitions than you need so you can scale up if needed with more brokers/consumers? How realistic is automatic scaling of a KAFKA cluster given your expertise? Will this be done manually? Is manual scaling viable for your problem domain (are you building KAFKA around a fixed system with well known characteristics or are you required to be able to handle severe spikes in messages)?
How will my consumers subscribe to topics? Will they use pre-configured configurations or use a regex to consume many topics? Are the messages between topics dependent or prioritized (need extra logic on consumer to implement priority)?
Should you use different network interfaces for replication between brokers (i.e. port 9092 for producers/consumers and 9093 for replication traffic)?

Good Links:

http://cloudurable.com/ppt/4-kafka-detailed-architecture.pdf https://www.slideshare.net/ToddPalino/putting-kafka-into-overdrive https://www.slideshare.net/JiangjieQin/no-data-loss-pipeline-with-apache-kafka-49753844 https://kafka.apache.org/documentation/