Constructing serverless occasion streaming functions with Amazon MSK and AWS Lambda

As organizations construct fashionable functions with event-driven architectures (EDA), they usually search options that decrease infrastructure administration overhead whereas maximizing developer productiveness. Amazon Managed Streaming for Apache Kafka (Amazon MSK) and AWS Lambda collectively present a serverless, scalable, and cost-efficient platform for real-time event-driven processing.

On this put up, we describe how one can simplify your event-driven utility structure utilizing AWS Lambda with Amazon MSK. We reveal how you can configure Lambda as a shopper for Kafka subjects, together with a cross-account setup and how you can optimize value and efficiency for these functions.

Why use Lambda with Amazon MSK?

Clients constructing event-driven functions have a number of key priorities with regards to their structure selections. They sometimes search to scale back their operational overhead through the use of Amazon Net Companies (AWS) to deal with the complicated, underlying infrastructure parts so their groups can give attention to core enterprise logic. Moreover, builders choose a streamlined expertise that minimizes the necessity for repetitive boilerplate code, enabling them to be extra productive and give attention to creating worth. Moreover, these prospects need to obtain each scalability and cost-effectiveness with out the burden of managing compute infrastructure instantly. Lambda integration with Amazon MSK successfully addresses these necessities, delivering a complete resolution that mixes the advantages of serverless computing with managed Kafka companies. For instance, an ecommerce firm can use Amazon MSK to gather real-time clickstream information from its web site and course of these occasions utilizing AWS Lambda. With this integration, they’ll set off Lambda features to replace suggestion fashions, ship customized gives, or analyze person conduct immediately—with out provisioning or managing servers. The important thing advantages of utilizing Lambda with Amazon MSK embrace:

1. Simplicity by means of native integration – AWS Lambda gives native integration with Amazon MSK by means of a connector useful resource known as occasion supply mapping. You need to use this integration to instantly affiliate a Kafka matter—whether or not it’s on Amazon MSK or a self-managed Kafka cluster—as an occasion supply for a Lambda perform with out writing customized shopper logic. With only a few configuration steps, occasion supply mapping handles partition project, offset monitoring, and parallelized batch processing underneath the hood. It makes use of the Kafka shopper group protocol to distribute matter partitions throughout a number of concurrent Lambda invocations, helps batch windowing, and permits at-least-once supply semantics. Furthermore, it robotically commits offsets upon profitable perform execution whereas dealing with retries and dead-letter queue (DLQ) routing for failed information, considerably lowering the operational overhead historically related to Kafka shoppers.
2. Auto scaling and throughput controls – When utilizing AWS Lambda with Amazon MSK by means of occasion supply mapping, Lambda robotically scales by assigning a devoted occasion poller per Kafka partition, enabling parallel, partition-based processing. This enables the system to elastically deal with various visitors with out guide intervention. For superior management, provisioned concurrency pre-initializes Lambda execution environments, eliminating chilly begins and delivering constant low-latency efficiency. Moreover, with provisioned occasion supply mapping, you possibly can configure the minimal and most variety of Kafka pollers, offering exact management over throughput and concurrency. That is superb for functions with unpredictable visitors patterns or strict latency necessities.
3. Value-effectiveness – AWS Lambda makes use of a pay-per-use mannequin by which you solely pay for compute time and variety of invocations. When built-in with Amazon MSK, there aren’t any expenses for idle time, making it superb for bursty or low-frequency Kafka workloads. You’ll be able to additional optimize prices by tuning batch dimension and batch window settings. For mission-critical workloads, provisioned concurrency gives constant efficiency with managed pricing.
4. Occasion filtering – AWS Lambda helps occasion filtering for Amazon MSK occasion sources, which suggests you possibly can course of solely the Kafka information that match particular standards. This reduces pointless perform invocations and optimizes your perform prices. You’ll be able to outline as much as 5 filters per occasion supply mapping (with the choice to request a rise to 10). Every filter makes use of a JSON-based sample to specify the circumstances a document should meet to be processed. Filters will be utilized utilizing the AWS Administration Console, AWS Command Line Interface (AWS CLI), or AWS Serverless Software Mannequin (AWS SAM) templates. For extra particulars and examples, check with the AWS Lambda documentation on occasion filtering with Amazon MSK.
5. Dealing with Availability Zone outage in your shopper – Amazon MSK permits excessive availability in your Kafka brokers by distributing them throughout a number of Availability Zones inside a Area. To take care of excessive availability throughout your utility, you equally want a shopper that gives excessive availability. AWS Lambda gives excessive availability and resilience by operating your shopper features throughout a number of Availability Zones in a Area. Which means even when one Availability Zone experiences an outage, your Lambda perform will proceed to function in different wholesome Availability Zones. Whereas Lambda manages safety patching and Availability Zone failure situations, you possibly can focus in your utility logic.
6. Cross-account occasion processing – Cross-account connectivity between AWS Lambda and Amazon MSK permits a Lambda perform in a single AWS account to eat information from an MSK cluster in one other account utilizing MSK multi-VPC non-public connectivity powered by AWS PrivateLink. This setup is especially useful for organizations that centralize Kafka infrastructure whereas sustaining separate accounts for various functions or groups.
7. Assist for JSON, Avro, Protobuf, and Schema Registries – AWS Lambda helps Kafka occasions in JSON, Avro and Protobuf codecs through occasion supply mapping. It integrates with AWS Glue Schema registry, Confluent Cloud Schema registry, and self-managed Confluent Schema registry , enabling native schema validation, filtering, and deserialization with out customized code.

How Lambda processes messages out of your Kafka matter

Lambda makes use of occasion supply mappings to course of information from Amazon MSK by actively polling Kafka subjects by means of occasion pollers that invoke Lambda features with batches of information. These mappings are Lambda managed sources designed for high-throughput, stream-based processing. By default, Lambda detects the OffsetLag for all partitions in your Kafka matter and robotically scales pollers based mostly on visitors. For prime-throughput functions, you possibly can allow provisioned mode to outline minimal and most pollers, and your occasion supply mapping auto scales between the minimal and most outlined values. Within the provisioned mode, every poller can course of as much as 5 MBps and helps concurrent Lambda invocations.

After Lambda processes every batch, it commits the offsets of the messages in that batch. In case your perform returns an error for a message in a batch, Lambda retries the entire batch of messages till processing succeeds or the messages expire. You’ll be able to ship information that fail all retry makes an attempt to an on-failure vacation spot for later processing. To take care of ordered processing inside a partition, Lambda limits the utmost occasion pollers to the variety of partitions within the matter. When establishing Kafka as a Lambda occasion supply, you possibly can specify a shopper group ID to let Lambda be part of an current Kafka shopper group. If different shoppers are lively in that group, Lambda will obtain solely a part of the subject’s messages. If the group exists, Lambda begins from the group’s dedicated offset, ignoring the StartingPosition. The next diagram illustrates this movement.

Walkthrough: Construct a serverless Kafka app with AWS Lambda

Comply with these steps to construct a serverless utility that consumes messages from an MSK cluster utilizing AWS Lambda:

1. Create an Amazon MSK cluster. Use the AWS Administration Console or AWS CLI to create your MSK cluster. When the cluster is up, create your Kafka matter(s). For detailed directions, check with the Amazon MSK documentation.
2. Create a Lambda perform utilizing the AWS Administration Console or the AWS CLI. To be taught extra about making a Lambda perform, check with Create your first Lambda perform. The Lambda perform’s execution position must have the next permissions:
   1. Entry to connect with your MSK cluster
   2. Permissions to handle elastic community interfaces in your VPC
3. To attach Lambda to Amazon MSK as a shopper, arrange occasion supply mapping to hyperlink your MSK matter with the Lambda perform. This enables Lambda to robotically ballot for brand spanking new messages and course of them. Comply with the information on how you can configure occasion supply mapping.

For reference, configuring occasion supply mapping includes three steps:

4. Community setup – Within the default occasion supply mapping mode, you must configure a networking setup utilizing a PrivateLink endpoint or NAT gateway for occasion supply mapping to invoke Lambda features. In provisioned mode, no networking configuration is required (and also you don’t incur the price of networking parts).
5. Occasion supply mapping parameter configuration – This includes setting essential configuration parameters for the occasion supply mapping to have the ability to ballot messages out of your Kafka cluster. This contains the MSK cluster, matter title, shopper group ID, authentication technique, and optionally, schema registry, scaling mode. You’ll be able to configure the scaling mode for provisioned throughput, together with batch dimension, batch window, and occasion filtering in your occasion supply mapping.
6. Entry permissions – This includes configuring required permissions to entry the required AWS sources, and contains configuring permissions for the perform to execute the code, permissions for the occasion supply mapping to entry your MSK cluster, and permissions for Lambda to entry your VPC sources.

The next screenshot exhibits the console setup for configuring Amazon MSK occasion supply mapping, together with the Amazon MSK set off associated fields.

The next screenshot exhibits occasion poller configuration.

The next screenshot exhibits extra settings you should utilize, relying in your use case.

Optimizing AWS Lambda for stream processing with Amazon MSK

When constructing real-time information processing pipelines with Amazon MSK and AWS Lambda, it’s vital to tune your setup for each efficiency and cost-efficiency. Lambda gives highly effective serverless compute capabilities, however to get essentially the most out of it in a streaming context, you must make just a few key optimizations:

1. Allow provisioned concurrency for low-latency processing – For workloads which can be delicate to latency—chilly begins can introduce undesirable delays. By enabling provisioned concurrency, you possibly can pre-warm a specified variety of Lambda situations so that they’re all the time able to deal with visitors instantly. This eliminates chilly begins and gives constant response instances, which is essential for latency-critical use circumstances.
2. Allow provisioned mode for occasion supply mapping for high-throughput processing – For Kafka workloads with stringent throughput necessities, activate the provisioned mode. The optimum configuration of minimal and most occasion pollers in your Kafka occasion supply mapping is dependent upon your utility’s efficiency necessities. Begin with the default minimal occasion pollers to baseline the efficiency profile and alter occasion pollers based mostly on noticed message processing patterns and your utility’s efficiency necessities. For workloads with spiky visitors and strict efficiency wants, improve the minimal occasion pollers to deal with sudden surges. You’ll be able to fine-tune the minimal occasion pollers by evaluating your required throughput, your noticed throughput, which is dependent upon components such because the ingested messages per second and common payload dimension, and utilizing the throughput capability of 1 occasion poller (as much as 5 MB/s) as reference. To take care of ordered processing inside a partition, Lambda caps the utmost occasion pollers on the variety of partitions within the matter.
3. Optimize message batching utilizing dimension and windowing – By integrating Lambda with Amazon MSK, you possibly can management how messages are batched earlier than they’re despatched to your perform. Tuning parameters similar to batch dimension (the variety of information per invocation: 1–10,000 information) and most batching window (how lengthy to attend for a full batch: 0–300 seconds) can considerably impression efficiency. Bigger batches imply fewer invocations, which reduces overhead and improves throughput. Nonetheless, it’s vital to strike a stability—too giant a batch or window may introduce undesirable processing delays. Monitor your stream’s conduct and alter these settings based mostly on throughput necessities and acceptable latency.
4. Apply filters to scale back pointless invocations – Not each document in your Kafka matter may require processing. To keep away from pointless Lambda invocations (and related prices), apply filtering logic instantly when configuring the occasion supply mapping. With Lambda, you possibly can outline filtering (as much as 10 filters) standards in order that solely related information set off your perform. This helps cut back compute time, decrease noise, and optimize your funds, particularly when coping with high-throughput subjects with blended content material. For Amazon MSK, Lambda commits offsets for matched and unmatched messages after efficiently invoking the perform.

Conclusion

By combining Amazon MSK with AWS Lambda, you possibly can seamlessly construct fashionable, serverless event-driven functions. This integration eliminates the necessity to handle shopper teams, compute infrastructure, or scaling logic so groups can give attention to delivering enterprise worth sooner.

Whether or not you’re integrating Kafka into microservices, reworking information pipelines, or constructing reactive functions, Lambda with Amazon MSK is a strong and versatile serverless resolution. For detailed documentation on how you can configure Lambda with Amazon MSK, check with the AWS Lambda Developer Information. For extra serverless studying sources, go to Serverless Land.

Concerning the Authors

Tarun Rai Madan is a Principal Product Supervisor at Amazon Net Companies (AWS). He makes a speciality of serverless applied sciences and leads product technique to assist prospects obtain accelerated enterprise outcomes with event-driven functions, utilizing companies like AWS Lambda, AWS Step Features, Apache Kafka, and Amazon SQS/SNS. Previous to AWS, he was an engineering chief within the semiconductor business, and led improvement of high-performance processors for wi-fi, automotive, and information heart functions.

Masudur Rahaman Sayem is a Streaming Knowledge Architect at AWS with over 25 years of expertise within the IT business. He collaborates with AWS prospects worldwide to architect and implement subtle information streaming options that deal with complicated enterprise challenges. As an professional in distributed computing, Sayem makes a speciality of designing large-scale distributed techniques structure for optimum efficiency and scalability. He has a eager curiosity and fervour for distributed structure, which he applies to designing enterprise-grade options at web scale.