How to Fix Kinesis Stream Throttling by Increasing Shards
Network Topology
Refer to the exhibit. A data engineer is using a Kinesis Data Stream with 2 shards. The producer uses a partition key that is the user ID (a UUID). The consumer is falling behind. Which change would improve throughput?
Quick Answer
The answer is to increase the number of shards, as this directly boosts the stream’s ingestion capacity and resolves throttling. Each shard provides a fixed 1 MB/s write and 2 MB/s read throughput, so adding shards scales the total available throughput linearly, allowing the consumer to catch up without being bottlenecked. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Kinesis shard-level limits and the distinction between scaling throughput versus adjusting data retention or delivery methods. A common trap is confusing Firehose as a throughput fix—it only adds latency—or thinking a poor partition key is the issue when a UUID already ensures even distribution. Remember the memory tip: “More shards, more cards” to play with throughput, meaning shards are the only lever for scaling Kinesis performance.
⚠ Common exam trap
Watch out — candidates often think changing the partition key to a constant value would simplify processing, but it actually destroys parallelism and reduces throughput to a single shard, making the lag worse.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the number of shards
The consumer is falling behind because the total throughput of the stream (1 MB/s or 1,000 records/s per shard for writes, and 2 MB/s per shard for reads) is insufficient for the incoming data volume. Increasing the number of shards scales both the write and read capacity linearly, allowing the consumer to process records faster and catch up. Changing the partition key or retention period does not increase throughput, and switching to Firehose changes the delivery model but does not inherently solve the consumer lag.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to Kinesis Data Firehose
Why it's wrong here
Firehose would add buffering latency, not improve consumer throughput.
- ✓
Increase the number of shards
Why this is correct
More shards increase the read capacity for consumers.
- ✗
Increase the retention period
Why it's wrong here
Retention period does not affect ingestion or consumption throughput.
- ✗
Change the partition key to a constant value
Why it's wrong here
A constant partition key would send all data to one shard, reducing throughput.
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Same concept, more angles
3 more ways this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data engineering team is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data is then consumed by an AWS Lambda function that transforms and loads it into Amazon S3. Recently, the team noticed that the Lambda function is failing with throttling errors (HTTP 429) from the Kinesis API. Which configuration change should the team make to resolve this issue?
medium- A.Disable retries on the Lambda function and configure a dead-letter queue for failed records.
- B.Replace Kinesis Data Streams with Amazon DynamoDB Streams for ingestion.
- ✓ C.Reduce the batch size and increase the number of shards in the Kinesis stream to increase parallelism.
- D.Increase the batch size in the Lambda event source mapping to reduce the number of invocations.
Why C: Reducing the batch size and increasing the number of shards directly addresses the HTTP 429 throttling errors from the Kinesis API. Each shard supports up to 5 read transactions per second and a maximum read rate of 2 MB/s; by increasing shards, you increase the available read throughput, and reducing the batch size lowers the number of records per invocation, preventing the Lambda function from exceeding the per-shard read limits.
Variation 2. Refer to the exhibit. A data engineer is using a Kinesis Data Stream with one shard. The application writes 2000 records per second, each 1 KB. The put record calls are frequently throttled. What is the most likely cause?
medium- ✓ A.The stream has only one shard, which limits writes to 1000 records per second
- B.The retention period of 24 hours is too short
- C.The stream uses KMS encryption, causing additional latency
- D.Enhanced monitoring is not enabled, causing performance issues
Why A: A Kinesis Data Stream shard has a write throughput limit of 1,000 records per second (or 1 MB per second). Since the application is writing 2,000 records per second (each 1 KB) to a single shard, it exceeds the shard's record-per-second quota, causing the PutRecord calls to be throttled. The solution is to increase the number of shards to at least two to distribute the load.
Variation 3. Refer to the exhibit. A data engineer is troubleshooting a Kinesis Data Streams consumer that is falling behind. The stream has 2 shards and is receiving data at a rate of 2 MB/s. The consumer is an AWS Lambda function with a batch size of 100 records. What should the engineer do to improve consumer throughput?
medium- A.Decrease the Lambda batch size to 10 records
- B.Increase the retention period of the stream to 168 hours
- ✓ C.Increase the number of shards in the stream to 4
- D.Increase the memory allocation of the Lambda function
Why C: Increasing the number of shards to 4 doubles the stream's total ingestion capacity to 4 MB/s, which directly increases the number of concurrent Lambda invocations and thus consumer throughput. With 2 shards, each shard can support up to 1 MB/s input and 2 MB/s output, so the current 2 MB/s load is at the shard-level output limit, causing the consumer to fall behind.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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