What approach could improve query performance for a streaming application tied to Amazon Kinesis Data Streams?

Increasing the number of shards in Kinesis can significantly enhance query performance for a streaming application. Each shard in a Kinesis Data Stream can handle a certain amount of read and write throughput, so by increasing the number of shards, you increase the overall capacity to read from the stream concurrently. This is especially useful for applications with high throughput requirements, allowing them to handle more data and to parallelize the processing.

Running queries on a distributed cluster can also improve query performance, as it allows for parallel processing and can manage larger datasets more efficiently. However, this approach is typically more relevant for data stored in databases or data lakes rather than directly tied to the stream.

Merging smaller files in Amazon S3 into larger ones can optimize query performance for analytics frameworks that read from S3, as larger files reduce the overhead of managing numerous objects. This is especially relevant when leveraging services like Amazon Athena or Amazon Redshift Spectrum, but it does not directly apply to query performance improvements within the Kinesis streaming context, as Kinesis handles data differently.

Scaling up the memory and CPU resources of the streaming application can enhance the performance of specific processing tasks but may not address the concurrent read limitations of Kinesis Data Streams directly.

Thus, increasing the number of shards allows