Troubleshoot Amazon Kinesis Data Streams producers
The following topics offer solutions to common issues with Amazon Kinesis Data Streams producers:
My producer application is writing at a slower rate than expected
The most common reasons for write throughput being slower than expected are:
Service limits exceeded
To find out if service limits are being exceeded, check to see if your producer is throwing throughput exceptions from the service, and validate what API operations are being throttled. Keep in mind that there are different limits based on the call, see Quotas and limits. For example, in addition to the shard-level limits for writes and reads that are most commonly known, there are the following stream-level limits:
The operations CreateStream, DeleteStream,
ListStreams, GetShardIterator, and
MergeShards are limited to 5 calls per second. The
DescribeStream operation is limited to 10 calls per second. The
DescribeStreamSummary operation is limited to 20 calls per
second.
If these calls aren't the issue, make sure you've selected a partition key that allows you to distribute put operations evenly across all shards, and that you don't have a particular partition key that's bumping into the service limits when the rest are not. This requires that you measure peak throughput and take into account the number of shards in your stream. For more information about managing streams, see Create and manage Kinesis data streams.
Tip
Remember to round up to the nearest kilobyte for throughput throttling
calculations when using the single-record operation PutRecord, while
the multi-record operation PutRecords rounds on the cumulative sum of
the records in each call. For example, a PutRecords request with
600 records that are 1.1 KB in size will not get throttled.
I want to optimize my producer
Before you begin optimizing your producer, complete the following key tasks. First, identify your desired peak throughput in terms of record size and records per second. Next, rule out stream capacity as the limiting factor (Service limits exceeded). If you've ruled out stream capacity, use the following troubleshooting tips and optimization guidelines for the two common types of producers.
Large Producer
A large producer is usually running from an on-premises server or Amazon EC2 instance. Customers who need higher throughput from a large producer typically care about per-record latency. Strategies for dealing with latency include the following: If the customer can micro-batch/buffer records, use the Kinesis Producer Library (which has advanced aggregation logic), the multi-record operation PutRecords, or aggregate records into a larger file before using the single-record operation PutRecord. If you are unable to batch/buffer, use multiple threads to write to the Kinesis Data Streams service at the same time. The Amazon SDK for Java and other SDKs include async clients that can do this with very little code.
Small Producer
A small producer is usually a mobile app, IoT device, or web client. If it’s a
mobile app, we recommend using the PutRecords operation or the Kinesis
Recorder in the Amazon Mobile SDKs. For more information, see Amazon Mobile SDK for Android Getting Started Guide and
Amazon Mobile SDK for iOS Getting Started Guide. Mobile apps must handle intermittent connections inherently and
need some sort of batch put, such as PutRecords. If you are unable to
batch for some reason, see the Large Producer information above. If your producer is
a browser, the amount of data being generated is typically very small. However, you
are putting the put operations on the critical path of the
application, which we don’t recommend.
Misuse of flushSync() operations
Using flushSync() incorrectly can significantly impact write
performance. The flushSync() operation is designed for shutdown
scenarios to make sure that all buffered records are sent before the
KPL application terminates. If you implemented this operation
after every write operation, it can add substantial extra latency, around 500ms per
write. Make sure that you have implemented flushSync() only for the
application shutdown to avoid unnecessary extra delay in write performance.
I receive an unauthorized KMS master key permission error
This error occurs when a producer application writes to an encrypted stream without permissions on the KMS master key. To assign permissions to an application to access a KMS key, see Using Key Policies in Amazon KMS and Using IAM Policies with Amazon KMS.