Kafka is an event-driven data streaming platform that provides a way to build reliable data pipelines in real-time. Using an event-driven framework like Mesos and Marathon can simplify the operationalization of processes and enable dynamic elasticity for Kafka Streams.
IT teams must carefully consider and set their cluster configurations to ensure the smooth running of the event-driven system. Some critical best practices include:
Use the Sink Connector
Kafka is a distributed event streaming platform that handles large-scale real-time data feeds. Applications post records to topics, and other applications subscribe to those topics, forming the basis of the publish-subscribe messaging architecture. This decouples the writing and reading of data, which enables applications to scale and run independently.
Data is written to Kafka topics based on their names and partitions. A partitioning strategy is determined by the shape of your data and the type of processing you need to do with it. The most common patterns include transforming the data before it’s sent to a sink using KSQL and then writing that transformed data to a Kafka topic with a schema different from the original one (see this tutorial for more details).
Forget data stuck in isolated islands! With Kafka on Kubernetes, you can unlock the power of real-time data integration and effortlessly stream information from any system into your vibrant Kafka hub. It’s like building a data superhighway, connecting the dots between databases, Elasticsearch indexes, Hadoop behemoths, and countless other systems.
When connecting external systems to Kafka, it’s important to remember that it demands low latency for network traffic and fast storage for brokers to access. That’s why deploying the cluster in a way that ensures both of those factors is critical to performance. For example, ensuring that all brokers have their dedicated IP and deploying them on high-performance media such as SSDs can help to improve performance.
Use the Sink Connector with Kafka Streams
Kafka is an event stream processing technology that is distributed and effectively manages real-time data flows. Based on the publish-subscribe communications concept, it keeps track of a partitioned, sorted, and persistent series of records known as topics. Producer applications write documents on the issues, and consumers read from them. The partitioning strategy setup determines the partitions of a topic. To ensure that the data is available in the correct order, it is recommended to use a partitioning strategy that guarantees messages arrive in the correct order within the same partition.
The data stored in Kafka is protected by several features, including authentication, access controls for operations, and encryption using SSL between brokers. The backing filesystems on disk are also encrypted to secure the data layer further. This combination of features provides a high level of security to protect against attackers who try to manipulate the system or database at the filesystem level.
In addition to implementing security at the application layer, the team behind your Kafka cluster should also implement security measures at the system and database levels. In most organizations, managing Kafka infrastructure like brokers and ZooKeeper and maintaining consumer and producer code will be split across different teams or groups. Planning upgrades to your Kafka cluster with these separate teams in mind is essential. Otherwise, a sudden change in configuring the cluster could impact production.
Use the Sink Connector with Kafka Connect
The Sink Connector streams data from Kafka into external systems like databases, file systems, key-value stores, and search indexes. Kafka Connect transforms the data during the transfer process to make it suitable for the target system. These transformations are known as Single Message Transforms (SMTs). Kafka Connect has some built-in SMTs, but plugins can add additional ones.
The SMTs in Kafka Connect perform different adjustments, such as filtering and parsing, to prepare the data for its destination. This allows the connector to use a more minor schema to store the data in the database, improving DB performance.
Once the data is ready for its destination, Kafka streams it into the external data system by using tasks polling Kafka topics to return records. The tasks run in parallel, which enables high throughput and efficient data streaming.
There are a few essential things to remember when designing and configuring a Kafka production environment on Kubernetes. First, it’s necessary to consider the performance requirements of your application. A few best practices include:
- Limiting the number of brokers to the minimum required for your application.
- Maximizing disk space through compaction.
- Using a default partition count that is appropriate for your use case.
Another consideration is implementing security policies that meet your needs. This includes data encryption in flight and ensuring that only the authorized parties read or write about a topic.
Use the Sink Connector with Kafka Sink
Kafka is a critical component in many organizations’ event-driven architectures. Deploying it with Kubernetes and taking advantage of its capabilities, such as streaming, can improve data processing efficiency. However, to get the most value out of a Kafka deployment, teams must fine-tune Kafka parameters related to partitions, logging, and other features.
One of the most common Kafka use cases is pushing records into RDBMS databases. To do this, they can use the Kafka-native JDBC Sink Connector. The connector consumes records from a given Kafka topic and pushes them toward the database via JDBC.
The Sink Connector has a built-in DB driver, which supports SQL for most major RDBMS. It’s possible to further enhance the connector’s performance by using a custom SMT, which transforms each record in the stream into a simple JSON string and adds a UUID field for item identification. This string is pushed into the target DB as a Key/Value table. The DB side – via triggers and stored procedures – can then map the string into whatever relational schema is required.
Another performance best practice is using compact serialization formats such as Avro or Protobuf and compression codecs like LZ4. This will reduce the overall payload size of a message, which can save on network bandwidth. Finally, it’s essential to carefully consider the size of your log segment sizes and partition counts. It’s best to keep these minimal to avoid disk space or CPU limitations,
