Kafka Partitions vs Mongo Sharding which one is better throughput
My question is how we can compare which will provide better through
  put and run time scalability in case of heavy load as i understand
  both use same mechanism adding new partitions in case of Kafka or
  adding new shards in case of Mongo.


Definitely each and every technology they have their own pros and cons. In case on MongoDB and Kafka, it's depends on your types of data like.

MongoDB does do data distribution and balancing by itself, but it is only for Document data.

Streaming like Kafka or MapR Streams can handle streaming data on HDFS/MapRFS.

I am writing down some of pros and cons of MongoDB as well as Kafka as mention below. May be it will help out to you.

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

Pros


Document-oriented storage
No sql
Ease of use
Fast
High performance
Free
Open source
Flexible
Replication &amp; high availability
Easy to maintain


Cons

Very slowly for connected models that require joins

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

Pros


High-throughput
Distributed
Scalable
High-Performance
Durable
Publish-Subscribe
Simple-to-use
Written in Scala and java. Runs on JVM
Open source
Message broker + Streaming system


Cons


Needs Zookeeper
Non-Java clients are second-class citizens


I would say, it all depends on your use cases.

If you only have one type of data, like Document data, and you do not need to process other data types, then yes, MongoDB alone is the best bet as of now.
However in modern big data environment, different data sources and data types may require different processing engines.

For further your ref here, here and here