In this tutorial we will cover which are the key differences between Apache Camel and Apache Kafka from an architecture point of view.
In general terms, a comparison between Apache Camel and Apache Kafka is (partly) like comparing apples and pears. As a matter of fact, Apache Camel is a complete integration framework, while Apache Kafka is a distributed messaging platform. So, at high level, they don’t exclude each other. Apache Kafka can indeed well fit within the integration layer which can be designed as a Camel Route.
See the following picture as an example:
A more rational comparison would be Apache Camel vs CloverDX as integration platform or Apache Kafka vs Artemis Active MQ as Messaging platform.
To understand better the differences between Apache Kafka and Apache Camel, let’s have a basic overview of both products.
An overview of Camel
Apache Camel is an open source integration framework that targets the integration between different systems. At its core, Camel is a routing engine, or more precisely a routing-engine builder. It allows you to define your own routing rules, decide from which sources to accept messages, and determine how to process and send those messages to other destinations.
Within its Routes, Camel uses a set of Components which are an implementation of Enterprise Integration Patterns (EIPs). EIPs are used to address a fairly extensive list of integration scenario using different integration strategies such as File Transfer, Shared Database and asynchronous Messaging. Each strategy can require to routed, split and aggregate messages. Besides it is also needed to monitor the correct outcome of each strategy applied.
Here is an overview of a Camel Route:
Apache Camel Use Cases
Application Integration: Camel is meant to be used for every scenario where you need to move data between different protocols and applications (such as files, emails, APIs) Thanks to its large set of Components (over 200), you can use Apache Camel when you want to move data back and forth between any one of the apps using most known protocols. All components in Camel work more or less in a similar way. So, once you understand how to use one component, you will find it easier to use others. Besides standard File, FTP, JMS components there are components also for complex apps such as Facebook,Twitter,Salesforce and Workday. You can also write your own custom component.
Pattern-based development: Many frequent use cases for application integration – such as support for transactions, or transformation – would usually be complicated to plan and write in code. But Camel can simplify this process by providing components that can be chained in your Route. Camel provides patterns and functionality for things like:
- Routing data based on its content, using Camel’s content-based routing
- Transforming Data
- Handling errors, transactions and rollbacks
- Caching frequently-accessed data
- Security concerns (encryption and authentication)
These requirements are easy to implement in Camel, because it includes these features as a set of patterns, also known as Enterprise Integration Patterns (EIP). You can pull any of these EIP, and use them in your code, without having to write your own solution every time you need these capabilities.
High-level architecture for many integrations: Once you’ve mastered the basic patterns, you’ll find that it becomes easy to develop common integrations in Camel. This is clearly an advantage: you will have the ability to create many integrations fairly quickly by reusing patterns like templates. This can be really attractive option in larger companies where it helps to pick one approach which is shared and understood by the development team.
Working with data, and in particular with Java objects: Since Camel is a Java framework, it’s especially good at working with Java objects. So if you’re working with a file format that can be de-serialised into a Java object (many file formats can be converted into Java objects, like XML, JSON….) then it will be handled easily by Camel.
An overview of Apache Kafka
Apache Kafka, on the other hand, is pure distributed messaging with a publish-subscribe schema that provides scalability and it can handle enormous load of data. Its main characteristics are as follows:
- Distributed: Cluster-centric design that supports the distribution of the messages over the cluster members, maintaining the semantics. So you can grow the cluster horizontally without downtime.
- Multiclient Easy integration with different clients from different platforms: Java,.NET, PHP, Ruby, Python, etc.
- Persistent: You cannot afford any data lost. Kafka is designed with efficient data structures that provide constant time performance no matter the data size.
- Real time: The messages produced are immediately seen by consumer threads;
- Very high throughput: As we mentioned, all the technologies in the stack are designed to work in commodity hardware. Kafka can handle hundreds of read and write operations per second from a large number of clients.
Here’s a bird’s eye view of Apache Kafka:
As you can see, A Kafka cluster has five main components:
- Topic: A category or feed name in which messages are published by the message producers. Topics are partitioned; each partition is represented by an ordered immutable messages sequence. The cluster has a partitioned log for each topic. Each message in the partition has a unique sequential id called an offset.
- Broker: A Kafka cluster has one or more physical servers in which each one may have one or more server processes running. Each server process is called a broker . The topics live in the broker processes.
- Producer: Publishes data to topics by choosing the appropriate partition in the topic. For load balancing, the messages allocation to the topic partition can be done in a round-robin mode or by defining a custom function.
- Consumer: Applications or processes subscribed to topics and process the feed of published messages.
- ZooKeeper: ZooKeeper is the coordinator between the broker and the consumers. ZooKeeper coordinates the distributed processes through a shared hierarchical name space of data registers; these registers are called znodes
Apache Kafka Use cases
Here are some popular (real examples with real enterprises) use cases:
- Commit logs: Many times systems do not have logs, simply because it’s not possible to handle a large data volume. Kafka can manage huge volumes of logs and also
- Log aggregation: log analysis is a key aspect of every support team. Kafka can physically collect the logs and remove cumbersome details such as file location
- Messaging: Systems are often heterogeneous: Kafka is a perfect solution to integrate different messaging systems thanks to its plugins.
- Stream processing: With Kafka, the information can be collected and further enriched. This is also known as stream processing.
- Record user activity: recording user activity can be a complex task where the data volume is huge. Kafka is a perfect use case for real-time process and monitoring of large data sets.
We have covered in detail the key differences between Apache Camel and Apache Kafka. Continue learning both frameworks on this site!