Posts Tagged Amazon Kinesis Data Streams
Streaming Data on AWS: Amazon Kinesis Data Streams or Amazon MSK?
Posted by Gary A. Stafford in Analytics, AWS, Big Data, Serverless on April 23, 2023
Given similar functionality, what differences make one AWS-managed streaming service a better choice over the other?
Data streaming has emerged as a powerful tool in the last few years thanks to its ability to quickly and efficiently process large volumes of data, provide real-time insights, and scale and adapt to meet changing needs. As IoT, social media, and mobile devices continue to generate vast amounts of data, it has become imperative to have platforms that can handle the real-time ingestion, processing, and analysis of this data.
Amazon Kinesis Data Streams and Amazon Managed Streaming for Apache Kafka (Amazon MSK) are two managed streaming services offered by AWS. While both platforms offer similar features, choosing the right service largely depends on your specific use cases and business requirements.
Amazon Kinesis Data Streams
- Simplicity: Kinesis Data Streams is generally considered a less complicated service than Amazon MSK, which requires you to manage more of the underlying infrastructure. This can make setting up and managing your streaming data pipeline easier, especially if you have limited experience with Apache Kafka. Amazon MSK Serverless, which went GA in April 2022, is a cluster type for Amazon MSK that allows you to run Apache Kafka without managing and scaling cluster capacity. Unlike Amazon MSK provisioned, Amazon MSK Serverless greatly reduces the effort required to use Amazon MSK, making ‘Simplicity’ less of a Kinesis differentiator.
- Integration with AWS services: Kinesis Data Streams integrates well with other AWS services, such as AWS Lambda, Amazon S3, and Amazon OpenSearch. This can make building end-to-end data processing pipelines easier using these services.
- Low latency: Kinesis Data Streams is designed to deliver low-latency processing of streaming data, which can be important for applications that require near real-time processing.
- Predictable pricing: Kinesis Data Streams is generally considered to have a more predictable pricing model than Amazon MSK, based on instance sizes and hourly usage. With Kinesis Data Streams, you pay for the data you process, making estimating and managing fees easier (additional fees may apply).
- Compatibility with Apache Kafka: Amazon MSK may be a better choice if you have an existing Apache Kafka deployment or are already familiar with Kafka. Amazon MSK is a fully managed version of Apache Kafka, which you can use with existing Kafka applications and tools.
- Customization: With Amazon MSK, you have more control over the underlying cluster infrastructure, configuration, deployment, and version of Kafka, which means you can customize the cluster to meet your needs. This can be important if you have specialized requirements or want to optimize performance (e.g., high-volume financial trading, real-time gaming).
- Larger ecosystem: Apache Kafka has a large ecosystem of tools and integrations compared to Kinesis Data Streams. This can provide flexibility and choice when building and managing your streaming data pipeline. Some common tools include MirrorMaker, Kafka Connect, LinkedIn’s Cruise Control, kcat (fka kafkacat), Lenses, Confluent Schema Registry, and Appicurio Registry.
- Preference for Open Source: You may prefer the flexibility, transparency, pace of innovation, and interoperability of employing open source software (OSS) over proprietary software and services for your streaming solution.
Ultimately, the choice between Amazon Kinesis Data Streams and Amazon MSK will depend on your specific needs and priorities. Kinesis Data Streams might be better if you prioritize simplicity, integration with other AWS services, and low latency. If you have an existing Kafka deployment, require more customization, or need access to a larger ecosystem of tools and integrations, Amazon MSK might be a better fit. In my opinion, the newer Amazon MSK Serverless option lessens several traditional differentiators between the two services.
Amazon Kinesis Data Streams and Amazon MSK are designed to be scalable streaming services that can handle large volumes of data. However, there are some differences in their scaling capabilities.
Amazon Kinesis Data Streams
- Scalability: Kinesis Data Streams has two capacity modes, on-demand and provisioned. With the on-demand mode, Kinesis Data Streams automatically manages the shards to provide the necessary throughput based on the amount of data you process. This means the service can automatically adjust the number of shards based on the incoming data volume, allowing you to handle increased traffic without manually adjusting the infrastructure.
- Limitations: Per the documentation, there is no upper quota on the number of streams with the provisioned mode you can have in an account. A shard can ingest up to 1 MB of data per second (including partition keys) or 1,000 records per second for writes. The maximum size of the data payload of a record before base64-encoding is up to 1 MB. GetRecords can retrieve up to 10 MB of data per call from a single shard and up to 10,000 records per call. Each call to GetRecords is counted as one read transaction. Each shard can support up to five read transactions per second. Each read transaction can provide up to 10,000 records with an upper quota of 10 MB per transaction. Each shard can support a maximum total data read rate of 2 MB per second via GetRecords. If a call to GetRecords returns 10 MB, subsequent calls made within the next 5 seconds throw an exception.
- Cost: Kinesis Data Streams has two capacity modes — on-demand and provisioned — with different pricing models. With on-demand capacity mode, you pay per GB of data written and read from your data streams. You do not need to specify how much read and write throughput you expect your application to perform. With provisioned capacity mode, you select the number of shards necessary for your application based on its write and read request rate. There are additional fees
PUTPayload Units, enhanced fan-out, extended data retention, and retrieval of long-term retention data.
- Scalability: Amazon MSK is designed to be highly scalable and can handle millions of messages per second. With Amazon MSK provisioned, you can scale your Kafka cluster by adding or removing instances (brokers) and storage as needed. Amazon MSK can automatically rebalance partitions across instances. Alternately, Amazon MSK Serverless automatically provisions and scales capacity while managing the partitions in your topic, so you can stream data without thinking about right-sizing or scaling clusters.
- Flexibility: With Amazon MSK, you have more control over the underlying infrastructure, which means you can customize the deployment to meet your needs. This can be important if you have specialized requirements or want to optimize performance.
- Amazon MSK also offers multiple authentication methods. You can use IAM to authenticate clients and to allow or deny Apache Kafka actions. Alternatively, with Amazon MSK provisioned, you can use TLS or SASL/SCRAM to authenticate clients and Apache Kafka ACLs to allow or deny actions.
- Cost: Scaling up or down with Amazon MSK can impact the cost based on instance sizes and hourly usage. Therefore, adding more instances can increase the overall cost of the service. Pricing models for Amazon MSK and Amazon MSK Serverless vary.
Amazon Kinesis Data Streams and Amazon MSK are highly scalable services. Kinesis Data Streams can scale automatically based on the amount of data you process. At the same time, Amazon MSK allows you to scale your Kafka cluster by adding or removing instances and adding storage as needed. However, adding more shards with Kinesis can lead to a more manual process that can take some time to propagate and impact cost, while scaling up or down with Amazon MSK is based on instance sizes and hourly usage. Ultimately, the choice between the two will depend on your specific use case and requirements.
Throughput can be measured in the maximum MB/s of data and the maximum number of records per second. The maximum throughput of both Amazon Kinesis Data Streams and Amazon MSK are not hard limits. Depending on the service, you can exceed these limits by adding more resources, including shards or brokers. Total maximum system throughput is affected by the maximum throughput of both upstream and downstream producing and consuming components.
Amazon Kinesis Data Streams
The maximum throughput of Kinesis Data Streams depends on the number of shards and the size of the data being processed. Each shard in a Kinesis stream can handle up to 1 MB/s of data input and up to 2 MB/s of data output, or up to 1,000 records per second for writes and up to 10,000 records per second for reads. When a consumer uses enhanced fan-out, it gets its own 2 MB/s allotment of read throughput, allowing multiple consumers to read data from the same stream in parallel without contending for read throughput with other consumers.
The maximum throughput of a Kinesis stream is determined by the number of shards you have multiplied by the maximum throughput per shard. For example, if you have a stream with 10 shards, the maximum throughput of the stream would be 10 MB/s for data input and 20 MB/s for data output, or up to 10,000 records per second for writes and up to 100,000 records per second for reads.
The maximum throughput is not a hard limit, and you can exceed these limits by adding more shards to your stream. However, adding more shards can impact the cost of the service, and you should consider the optimal shard count for your use case to ensure efficient and cost-effective processing of your data.
As discussed in the Amazon MSK best practices documentation, the maximum throughput of Amazon MSK depends on the number of brokers and the instance type of those brokers. Amazon MSK allows you to scale the number of instances in a Kafka cluster up or down based on your needs.
The maximum throughput of an Amazon MSK cluster depends on the number of brokers and the performance characteristics of the instance types you are using. Each broker in an Amazon MSK cluster can handle tens of thousands of messages per second, depending on the instance type and configuration. The actual throughput you can achieve will depend on your specific use case and the message size. The AWS blog post, Best practices for right-sizing your Apache Kafka clusters to optimize performance and cost, is an excellent reference.
The maximum throughput is not a hard limit, and you can exceed these limits by adding more brokers or upgrading to more powerful instances. However, adding more instances or upgrading to more powerful instances can impact the service’s cost. Therefore, consider your use case’s optimal instance count and type to ensure efficient and cost-effective data processing.
Compatibility with multiple producers and consumers is essential when choosing a streaming technology. There are multiple ways to write messages to Amazon Kinesis Data Streams and Amazon MSK.
Amazon Kinesis Data Streams
- AWS SDK: Use the AWS SDK for your preferred programming language.
- Kinesis Producer Library (KPL): KPL is a high-performance library that allows you to write data to Kinesis Data Streams at a high rate. KPL handles all heavy lifting, including batching, retrying failed records, and load balancing across shards.
- Amazon Kinesis Data Firehose: Kinesis Data Firehose is a fully managed service that can ingest and transform streaming data in real-time. It can be used to write data to Kinesis Data Streams, as well as to other AWS services such as S3, Redshift, and Elasticsearch.
- Amazon Kinesis Data Analytics: Kinesis Data Analytics is a fully managed service that allows you to process and analyze streaming data in real-time. It can read data from Kinesis Data Streams, perform real-time analytics and transformations, and write the results to another Kinesis stream or an external data store.
- Kinesis Agent: Kinesis Agent is a standalone Java application that collects and sends data to Kinesis Data Streams. It can monitor log files or other data sources and automatically send data to Kinesis Data Streams as it is generated.
- Third-party libraries and tools: There are many third-party libraries and tools available for writing data to Kinesis Data Streams, including Apache Kafka Connect, Apache Storm, and Fluentd. These tools can integrate Kinesis Data Streams with existing data processing pipelines or build custom streaming applications.
- Kafka command line tools: The Kafka command line tools (e.g.,
kafka-console-producer.sh) can be used to write messages to a Kafka topic in an Amazon MSK cluster. These tools are part of the Kafka distribution and are pre-installed on the Amazon MSK broker nodes.
- Kafka client libraries: You can use Kafka client libraries in your preferred programming language (e.g., Java, Python, C#) to write messages to an Amazon MSK cluster. These libraries provide a more flexible and customizable way to produce messages to Kafka topics.
- AWS SDKs: You can use AWS SDKs (e.g., AWS SDK for Java, AWS SDK for Python) to interact with Amazon MSK and write messages to Kafka topics. These SDKs provide a higher-level abstraction over the Kafka client libraries, making integrating Amazon MSK into your AWS infrastructure easier.
- Third-party libraries and tools: There are many third-party tools and frameworks, including Apache NiFi, Apache Camel, and Apache Beam. They provide Kafka connectors and producers, which can be used to write messages to Kafka topics in Amazon MSK. These tools can simplify the process of writing messages and provide additional features such as data transformation and routing.
You can use AWS Glue Schema Registry with Amazon Kinesis Data Streams and Amazon MSK. AWS Glue Schema Registry is a fully managed service that provides a central schema repository for organizing, validating, and tracking the evolution of your data schemas. It enables you to store, manage, and discover schemas for your data in a single, centralized location.
With AWS Glue Schema Registry, you can define and register schemas for your data in the registry. You can then use these schemas to validate the data being ingested into your streaming applications, ensuring that the data conforms to the expected structure and format.
Both Kinesis Data Streams and Amazon MSK support the use of AWS Glue Schema Registry through the use of Apache Avro schemas. Avro is a compact, fast, binary data format that can improve the performance of your streaming applications. You can configure your streaming applications to use the registry to validate incoming data, ensuring that it conforms to the schema before processing.
Using AWS Glue Schema Registry can help ensure the consistency and quality of your data across your streaming applications and provide a centralized location for managing and tracking schema changes. Amazon MSK is also compatible with popular alternative schema registries, such as Confluent Schema Registry and RedHat’s open-source Apicurio Registry.
According to TechTarget, Stream processing is a data management technique that involves ingesting a continuous data stream to quickly analyze, filter, transform, or enhance the data in real-time. Several leading stream processing tools are available, compatible with Amazon Kinesis Data Streams and Amazon MSK. Each tool with its own strengths and use cases. Some of the more popular tools include:
- Apache Flink: Apache Flink is a distributed stream processing framework that provides fast, scalable, and fault-tolerant data processing for real-time and batch data streams. It supports a variety of data sources and sinks and provides a powerful stream processing API and SQL interface. In addition, Amazon offers its managed version of Apache Flink, Amazon Kinesis Data Analytics (KDA), which is compatible with both Amazon Kinesis Data Streams and Amazon MSK.
- Apache Spark Structured Streaming: Apache Spark Structured Streaming is a stream processing framework that allows developers to build real-time stream processing applications using the familiar Spark API. It provides high-level APIs for processing data streams and supports integration with various data sources and sinks. Apache Spark is compatible with both Amazon Kinesis Data Streams and Amazon MSK. Spark Streaming is available as a managed service on AWS via AWS Glue Studio and Amazon EMR.
- Apache NiFi: Apache NiFi is an open-source data integration and processing tool that provides a web-based UI for building data pipelines. It supports batch and stream processing and offers a variety of processors for data ingestion, transformation, and delivery. Apache NiFi is compatible with both Amazon Kinesis Data Streams and Amazon MSK.
- Amazon Kinesis Data Firehose (KDA): Kinesis Data Firehose is a fully managed service that can ingest and transform streaming data in real time. It can be used to write data to Kinesis Data Streams, as well as to other AWS services such as S3, Redshift, and Elasticsearch. Kinesis Data Firehose is compatible with Amazon Kinesis Data Streams and Amazon MSK.
- Apache Kafka Streams (aka KStream): Apache Kafka Streams is a lightweight stream processing library that allows developers to build scalable and fault-tolerant real-time applications and microservices. KStreams integrates seamlessly with Amazon MSK and provides a high-level DSL for stream processing.
- ksqlDB: ksqlDB is a database for building stream processing applications on top of Apache Kafka. It is distributed, scalable, reliable, and real-time. ksqlDB combines the power of real-time stream processing with the approachable feel of a relational database through a familiar, lightweight SQL syntax. ksqlDB is compatible with Amazon MSK.
Several stream-processing tools are detailed in my recent two-part blog post, Exploring Popular Open-source Stream Processing Technologies.
Amazon Kinesis Data Streams and Amazon Managed Streaming for Apache Kafka (Amazon MSK) are managed streaming services. While they offer similar functionality, some differences might make one a better choice, depending on your use cases and experience. Ensure you understand your streaming requirements and each service’s capabilities before making a final architectural decision.
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