When a system needs to change, you have two options: Evolving a system takes small changes with more control, instead of placing a totally new system instead of the old. Apache Kafka ™ is a distributed streaming message queue. Start reading data from the snapshot into the right Kafka topic. Apache Flink is a stream processing framework and distributed processing engine for stateful computations over unbounded and bounded data streams. The reason for that is because Docker compose has a naming convention the for containers it creates, it is __. And I already specified the Kafka domain names in KafkaTrigger.yml as cluster_kafka_1 and cluster_kafka_2, in case the Docker compose is run from another location, container naming would change and KafkaTrigger.yml would need to be updated. I could have done it without ThreadPoolExecutor, but the reason for it is that the trigger augment call is on Cassandra’s write path and in that way it impacts Cassandra’s write performances. Cassandra will show the same error even if class is found but there is some problem instantiating it or casting it to theITrigger interface. But since the JAR file and KafkaTrigger.yml need to be copied into the docker container, there are two options: The first option is not an option actually, it is not in the spirit of Docker to do such thing so I will go with the second option. That’s all! In order to satisfy the first item from the Evolution breakdown, I need a way to push each Cassandra change to Kafka with a timestamp. Earlier versions of Cassandra used the following interface: Before I dive into implementation, let’s discuss the interface a bit more. Here at SmartCat, we have developed a tool for such purpose. To make sure everything is in order, I think monitoring of time to propagate the change to the new Cassandra cluster will help and if the number is decent (a few milliseconds), I can proceed to the next step. Start collecting each Cassandra change to temporary Kafka topic. This may cause unforeseeable production issues. To build all that into Docker, I created a Dockerfile with the following content: In the console, just position yourself in the Cassandra directory and run: That will create a Docker image with the name trigger-cassandra. Actually, it is just one simple table, but it should be enough to demonstrate the idea. You should be able to spin a dockerfile for it quickly. I recommend this book to everyone. When each application instance is updated, change the application to write directly to Kafka right topic. In this post, we will break down the pipeline into three sections and discuss each of them in more detail: Cassandra to Kafka … Follow. In this journal blog, we are going to have a look at how to build a data pipeline using Flink and Kafka. Firstly, there is a FILE_PATH constant, which points to /etc/cassandra/triggers/KafkaTrigger.yml and this is where YAML configuration for the trigger class needs to be. The tool is called Berserker, you can give it a try. I tried to break down the evolution process to a few conceptual steps and this is what I came up with: 1. I’ll start with data model. There are a few motivating factors why I’ve chosen to evolve an existing system instead of building one the way I want from scratch. If you haven’t read the previous part of this blog, you can find it here. When observing the diagrams, it seems like a pretty straightforward and trivial thing to do, but there’s more to it, especially when you want to do it with no downtime. Validate and manipulate data. 4. Sending Cassandra mutations to Kafka, on the other hand, feels backwards and for 99% of teams, more work than it's worth. Every node will run in a separate Docker container. That's pretty normal. It also needs to go to a temporary topic since there's data in the database that should be first in an ordered sequence of events… Wait for the temporary Kafka topic to deplete. Each message contains enough information to recreate Cassandra CQL query from it. Also, make sure that you implemented the ITrigger interface from the right Cassandra version (versions of cassandra in the JAR file and of the cassandra node should match). As a result, the new cassandra cluster should be practically a copy/clone of the existing one. Apache Kafka is a highly-available, high-throughput, distributed message broker that handles real-time data feeds. Kafka is straightforward to install, though. Start collecting each Cassandra change to a temporary Kafka topic. There are several important points regarding the implementation that need to be honored and those points are explained on the interface’s javadoc: Besides that, augment method is called exactly once per update and Partition object contains all relevant information about the update. +1 (415) 8710744, Železnička 30, Data Pipeline Recap. Martin explains all concepts from their basic building blocks in a really simple, understandable way. Cassandra introduced a change data capture (CDC) feature in 3.0 to expose its commit logs. Since writing about this in a single post would render quite a huge post, I’ve decided to split it into a few, I’m still not sure how many, but I’ll start and see where it takes me. Apache Cassandra is a distributed and wide-column NoS… You might notice that return type is not void but rather a collection of mutations. 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