Consumer lag predictor for Aiven for Apache Kafka®#

The consumer lag predictor for Aiven for Apache Kafka estimates the delay between the time a message is produced and when it’s eventually consumed by a consumer group. This information can be used to improve the performance, scalability, and cost-effectiveness of your Kafka cluster.


Consumer Lag Predictor for Aiven for Apache Kafka® is a limited availability feature. If you’re interested in trying out this feature, contact the sales team at

To use the consumer lag predictor effectively, setting up Prometheus integration with your Aiven for Apache Kafka® service is essential. Prometheus integration enables the extraction of key metrics necessary for lag prediction and monitoring.

Why use consumer lag predictor?#

By periodically analyzing the Kafka cluster, including topic and consumer group offsets, this Consumer Lag Predictor provides insights crucial for scenarios like auto-scaling consumers, ensuring timely message processing.

Use case#

  • Auto-scaling of consumers: Leveraging the time lag metric allows users to optimize message processing by dynamically adapting their consumers.


Using the consumer lag predictor can impact your Apache Kafka cluster’s performance. While it offers valuable insights, it also demands more CPU and memory on each Kafka node. To optimize its use, consider the following:

  • Resource management: Enabling the Consumer Lag Predictor will lead to higher CPU and memory consumption on each Kafka node. Before enabling it, review your current resource usage to ensure smooth performance.

  • Topic selection: It is advisable to be selective when choosing topics for lag calculation. Starting with a few topics and gradually including more can effectively manage system performance impact.


Metrics offer a tangible way to measure and understand consumer lag. These metrics can be viewed and analyzed using monitoring tools like Prometheus. Here are the key metrics provided:

  • kafka_lag_predictor_topic_produced_records: Represents the number of records produced, categorized by topic and partition.

  • kafka_lag_predictor_group_consumed_records: Represents the number of records consumed, sorted by group, topic, and partition.

  • kafka_lag_predictor_group_lag_predicted_seconds: Provides the predicted lag time in seconds, organized by group, topic, and partition.

Next steps#