从表面上看，它们有点相似，但是用例将决定哪种工具是合适的。在我看来，如果你可以使用SQS，那么你应该——如果它能做你想做的事情，它会更简单、更便宜，但这里有一个来自AWS FAQ的更好解释，它给出了两个工具的适当用例示例，以帮助你决定:

常见问题的

摘自AWS文档:

We recommend Amazon Kinesis Streams for use cases with requirements that are similar to the following: Routing related records to the same record processor (as in streaming MapReduce). For example, counting and aggregation are simpler when all records for a given key are routed to the same record processor. Ordering of records. For example, you want to transfer log data from the application host to the processing/archival host while maintaining the order of log statements. Ability for multiple applications to consume the same stream concurrently. For example, you have one application that updates a real-time dashboard and another that archives data to Amazon Redshift. You want both applications to consume data from the same stream concurrently and independently. Ability to consume records in the same order a few hours later. For example, you have a billing application and an audit application that runs a few hours behind the billing application. Because Amazon Kinesis Streams stores data for up to 7 days, you can run the audit application up to 7 days behind the billing application. We recommend Amazon SQS for use cases with requirements that are similar to the following: Messaging semantics (such as message-level ack/fail) and visibility timeout. For example, you have a queue of work items and want to track the successful completion of each item independently. Amazon SQS tracks the ack/fail, so the application does not have to maintain a persistent checkpoint/cursor. Amazon SQS will delete acked messages and redeliver failed messages after a configured visibility timeout. Individual message delay. For example, you have a job queue and need to schedule individual jobs with a delay. With Amazon SQS, you can configure individual messages to have a delay of up to 15 minutes. Dynamically increasing concurrency/throughput at read time. For example, you have a work queue and want to add more readers until the backlog is cleared. With Amazon Kinesis Streams, you can scale up to a sufficient number of shards (note, however, that you'll need to provision enough shards ahead of time). Leveraging Amazon SQS’s ability to scale transparently. For example, you buffer requests and the load changes as a result of occasional load spikes or the natural growth of your business. Because each buffered request can be processed independently, Amazon SQS can scale transparently to handle the load without any provisioning instructions from you.

Kinesis支持多消费者功能，这意味着相同的数据记录可以在同一时间或24小时内不同的时间在不同的消费者处处理，SQS中的类似行为可以通过写入多个队列实现，消费者可以从多个队列中读取。然而，再次写入多个队列将在系统中增加几秒(几毫秒)的延迟。

其次，Kinesis提供了路由功能，可以使用分区键将数据记录选择性地路由到不同的分片，这些分区键可以由特定的EC2实例处理，并可以启用微批处理计算{计数和聚合}。

使用任何AWS软件都很容易，但使用SQS是最简单的。使用Kinesis，需要提前提供足够的碎片，动态增加碎片数量以管理尖峰负载，并减少以节省管理所需的成本。这是运动的疼痛，SQS不需要这样的事情。SQS是无限可伸缩的。

Kinesis用例

日志和事件数据收集 实时分析 移动数据采集 “物联网”数据馈送

SQS用例

应用程序集成 解耦microservices 将任务分配给多个工作节点 将实时用户请求与密集的后台工作分离 批处理消息以供将来处理

请记住，这个答案在2015年6月是正确的

在研究了这个问题一段时间后，我心里有同样的问题，我发现SQS(带SNS)是大多数用例的首选，除非消息的顺序对您很重要(SQS不保证消息的FIFO)。

Kinesis有2个主要优势:

您可以从多个应用程序读取相同的消息 如果需要的话，您可以重新阅读邮件。

这两个优点都可以通过使用SNS作为SQS的扇出来实现。这意味着消息的生产者只向SNS发送一条消息，然后SNS将消息分散到多个SQSs，每个使用者应用程序一个SQSs。通过这种方式，您可以拥有尽可能多的消费者，而无需考虑分片容量。

此外，我们还增加了一个订阅SNS的SQS，可以保存14天的消息。在正常情况下，没有人从这个SQS中读取数据，但如果出现让我们想要倒带数据的错误，我们可以轻松地从这个SQS中读取所有消息，并将它们重新发送到SNS。而Kinesis仅提供7天的留存。

总之，SNS+SQSs更简单，提供了大部分功能。在我看来，你需要一个非常有力的案例来选择Kinesis。

Kinesis解决了流数据典型的映射缩减场景中的映射部分问题。而SQS并不确定这一点。如果你有需要在一个键上聚合的流数据，kinesis可以确保该键的所有数据都到一个特定的分片，并且该分片可以在单个主机上使用，这使得在键上的聚合比SQS更容易