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Development-vs-Production.md

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When working with Karafka and Kafka, it's essential to understand the nuances between development (development and test environments) and production. Awareness of these differences ensures a smoother work experience and optimal system performance. Here's a detailed breakdown of some of the crucial considerations to keep in mind:

Avoid Using Karafka's Reload Mode in Production

While Karafka offers a reload mode, which can be very helpful during development, it's crucial not to use this in a production environment. This mode can impact the performance and stability of your system. Always ensure that this mode is disabled before deploying to production.

class KarafkaApp < Karafka::App
  setup do |config|
    # Other settings...

    # Make sure, that persistence is always disabled for non-dev environments as it
    # yields benefits only in dev
    config.consumer_persistence = !Rails.env.development?
  end
end

Pre-create Necessary Topics in the Production Kafka Cluster

Kafka topics act as communication channels for your messages. It would be best to create all the required topics in your production Kafka cluster upfront. Doing so ensures no interruptions or issues when your application starts sending or receiving messages and that your topics have the desired number of partitions. You can use Declarative Topics functionality for that.

class KarafkaApp < Karafka::App
  routes.draw do
    topic :system_events do
      config(
        partitions: 6,
        replication_factor: 3,
        'retention.ms': 86_400_000 # 1 day in ms,
        'cleanup.policy': 'delete',
        'compression.codec': 'gzip'
      )

      consumer EventsConsumer
    end
  end
end

Disable Automatic Topic Creation in Production

When set to true, the allow.auto.create.topics setting enables Kafka to create topics automatically. However, it's recommended not to rely on this feature in a production environment. It's more controlled and predictable to manually set up your topics, ensuring they are configured correctly for your production needs.

class KarafkaApp < Karafka::App
  setup do |config|
    config.kafka = {
      'bootstrap.servers': '127.0.0.1:9092',
      'allow.auto.create.topics': !Rails.env.production?
    }
  end
end

Lock Your Topics List in Development After Stabilization

As you develop and test, you may often modify your list of Kafka topics and their settings. However, once you stabilize your topic list, it's a good idea to lock it. Typos and minor errors can easily be overlooked, leading to potential issues propagating to production.

Be Cautious with the Default Single Partition for Auto-created Topics

Topics that are automatically created because of allow.auto.create.topics are assigned just one partition by default. While this may suffice for development purposes, production environments often require multiple partitions for better performance and scalability. Ensure you configure your topics' appropriate number of partitions before deploying to production.

Consider the Impact of Rolling Deployments on Rebalances

Whenever you do a rolling deployment of N processes, expect N rebalances to occur. Rebalances can impact the performance and stability of your Kafka cluster. However, using the cooperative-sticky rebalance strategy can mitigate some of these issues.

class KarafkaApp < Karafka::App
  setup do |config|
    config.kafka = {
      'bootstrap.servers': '127.0.0.1:9092',
      'partition.assignment.strategy': 'cooperative-sticky'
    }
  end
end

Manual Topic Creation and Consumer Starting Sequence

Creating a topic manually or by sending the first message and then initiating a consumer is recommended. While Karafka does refresh cluster metadata information to detect new topics, this process can sometimes take over five minutes. Ensuring that the topic exists before starting a consumer reduces potential delays.

Adjust Topic Metadata Refresh Interval for Production

In the development environment, the topic.metadata.refresh.interval.ms setting defaults to 5 seconds. This means Karafka quickly discovers any topic created after starting the Karafka service. However, in production, this short interval is not recommended. The default value for a production environment should be 5 minutes to reduce unnecessary overhead.

class KarafkaApp < Karafka::App
  setup do |config|
    config.kafka = {
      'bootstrap.servers': '127.0.0.1:9092',
      'topic.metadata.refresh.interval.ms': 5 * 60 * 1_000
    }
  end
end

Opt for the cooperative-sticky Rebalance Strategy in Production

The cooperative-sticky rebalance strategy set via the partition.assignment.strategy configuration is highly recommended for production environments. It offers better performance and stability compared to other rebalance strategies.

class KarafkaApp < Karafka::App
  setup do |config|
    config.kafka = {
      'bootstrap.servers': '127.0.0.1:9092',
      'partition.assignment.strategy': 'cooperative-sticky'
    }
  end
end

Set the compression.codec for Both Topic/Broker Settings and Karafka

The compression.codec parameter in Kafka's configuration allows you to specify the compression algorithm to be used for messages. Kafka supports multiple compression algorithms like GZIP, LZ4, and Snappy. Karafka also honors the compression settings.

There are several reasons why you should configure compression for your production environments and why it needs to be set on both Kafka and Karafka levels:

  • Network Traffic Volume Reduction: One of the main benefits of compression is to reduce the amount of data transmitted over the network. When producers send compressed data to the broker, and consumers receive it, it reduces the bandwidth utilized. Remember that compression needs to be set for both Kafka topics and Karafka to ensure data is being compressed before it is sent over the wire. Otherwise, the compression will occur only on the broker, and no network traffic savings will occur.

  • Consistency: Keeping the compression setting consistent between producers, consumers, and brokers ensures the data is uniformly compressed throughout its lifecycle. This minimizes issues related to unsupported compression formats or mismatched compression expectations.

  • Performance & Storage: Compressed data is typically smaller, leading to better storage efficiency on the broker side and quicker transmission times.

class KarafkaApp < Karafka::App
  setup do |config|
    config.kafka = {
      'bootstrap.servers': '127.0.0.1:9092',
      'compression.codec': 'gzip'
    }
  end
end

Avoid Rolling Upgrades for partition.assignment.strategy Changes

The partition.assignment.strategy in Kafka determines how topic partitions are allocated amongst the consumers in a consumer group. Adjusting this strategy can influence the distribution of partitions and, thus, the performance and efficiency of your consumers.

When you switch between assignment strategies, be aware that:

  1. Deployment Concerns: Direct strategy shifts using rolling upgrades can result in conflicts. Running consumers with distinct assignment strategies within the same group will trigger an "Inconsistent group protocol" error, "assignors must have the same protocol type" error or similar

  2. Performance Variations: Different strategies can lead to diverse load distributions, influencing the processing efficiency of individual consumers.

  3. Potential for Uneven Distribution: Some strategies might result in specific consumers being assigned a larger share of partitions, leading to uneven work distribution.

  4. Compatibility Concerns: Ensure the chosen strategy is compatible with your Kafka broker version. Some strategies might be exclusive to specific Kafka versions.

To ensure a smooth transition when adjusting the assignment strategy, follow these steps:

  1. Backup Configuration: Initiate the process by backing up your existing Kafka and Karafka configurations. This creates a recovery point in case complications arise.

  2. Test in a Non-Production Environment: Before rolling out changes in a live setting, validate the new strategy in a controlled, non-production environment.

  3. Shutdown Consumers: To sidestep the "Inconsistent group protocol" error and other critical issues, stop all consumers in the consumer group before enacting the change.

  4. Update Strategy & Restart: Modify the partition.assignment.strategy with all consumers offline. Once adjusted, you can bring all consumers back online.

  5. Monitor Behavior: Post-transition, maintain rigorous oversight of the consumer behaviors. Specifically, observe for unexpected rebalances or any imbalances in partition assignments.

To recap, while modifying partition.assignment.strategy in Karafka may promise enhanced consumer efficiency, the transition demands solid planning and execution. With the insights and procedure outlined above, you're equipped to undertake the shift methodically and with minimal disruption.

Messages from the Future / Time Drift Problem

In Kafka, every message (or record) produced to a topic carries metadata, including a timestamp. This timestamp is set by the producer or the broker when the message gets appended to the log. Consumers then use the timestamp to understand the chronological order of messages.

Under regular operations, this system works seamlessly. However, problems arise when there's a time drift between the Kafka cluster nodes and the consumer. Essentially, if the Kafka broker believes it's 2:00 PM, while the consumer thinks it's 1:50 PM, the messages produced in that 10-minute interval by the broker will appear as if they're coming from the "future" when consumed.

The time synchronization issue usually boils down to the Network Time Protocol (NTP). NTP is a protocol used to synchronize the clocks of computers to some time reference, which can be an atomic clock, GPS, or another reliable source.

  • NTP Not Installed: If NTP isn't installed on the machines running Kafka or the consumer application, they rely on their internal clocks. Over time, even minor discrepancies between internal clocks can add up, leading to significant drifts.

  • NTP Malfunctions: Even with NTP installed, there might be cases where it's not working correctly. This can happen for various reasons, like network issues, software bugs, or misconfigurations.

Consequences of Time Drift

When Kafka and the consumer drift apart in time, it doesn’t just result in the odd phenomenon of messages from the future. It can:

  • Impact consumer logic that relies on time-based processing.

  • Affect windowed operations in stream processing applications.

  • Cause retention policies based on time to behave unpredictably.

  • Cause other problems in Karafka Web UI tracking and reporting.

How to Prevent Time Drift

  1. Ensure NTP is Installed: Always ensure that NTP is installed on all machines running Kafka brokers and consumer applications.

  2. Monitor NTP Status: Regularly monitor the NTP status to ensure it's running and is in sync with its time sources.

  3. Configure Alerts: Set alerts for any significant time drift between the servers. This can provide early warnings before time drift becomes a problem.

  4. Synchronize Frequently: Reduce the time between synchronization intervals to ensure that even minor drifts are corrected promptly.

In conclusion, while Kafka is a powerful tool, it's essential to remember the importance of time synchronization to ensure the reliable delivery and consumption of messages. Regularly monitoring and ensuring the correct functioning of NTP can prevent time drift issues, providing a smoother Kafka experience.

Impact on Karafka

Karafka and Karafka Web UI internal operations starting from 2.2.4 are resilient to this issue, as Karafka normalizes the time for internal computation. While this will not crash your operations, please note that time-sensitive metrics may not be accurate.