Resolving Kinesis Data Streams Throttling from Hot Shards

DEA-C01 By islever Editorial Last updated Oct 15, 2026

Orchestrate data pipelines.

Short answer

A Use a random partition key to distribute the ingested records.

Question

A data engineer uses Amazon Kinesis Data Streams to ingest and process records that contain user behavior data from an application every day. The data engineer notices that the data stream is experiencing throttling because hot shards receive much more data than other shards in the data stream. How should the data engineer resolve the throttling issue?

Use a random partition key to distribute the ingested records. Correct Answer
Increase the number of shards in the data stream. Distribute the records across the shards.
Limit the number of records that are sent each second by the producer to match the capacity of the stream.
Decrease the size of the records that the producer sends to match the capacity of the stream.

Key Takeaways

The core concept tested is Kinesis shard distribution mechanics, with the common trap being the assumption that increasing capacity (adding shards) fixes uneven data skew rather than addressing the root cause of the hot shard.

This question addresses how to resolve throttling caused by hot shards in Amazon Kinesis Data Streams. The correct approach involves modifying the partition key strategy to ensure even data distribution across all shards.

Common mistake: Many candidates choose Option B (increase shards) because it seems like a logical way to handle high load, but adding shards does not solve the problem if the partition key still directs traffic to specific shards, leaving them 'hot' and throttled.

Expert Analysis

Why the Answer Is Correct

Option A is correct because 'hot shards' are caused by an uneven distribution of data, typically due to a partition key that has low cardinality or predictable patterns (e.g., sequential IDs). By using a random partition key (or appending a random suffix), the data engineer ensures that records are distributed uniformly across all available shards. This prevents any single shard from becoming a bottleneck, thereby eliminating the throttling issue at its source.

Why the Other Options Are Wrong

Option B is incorrect because simply increasing the number of shards increases overall throughput capacity, but if the partition key logic remains unchanged, the new shards will also receive disproportionate loads. The 'hot' shards remain hot, just with potentially higher absolute limits, but the distribution skew persists. Option C is incorrect because limiting the producer rate is a workaround that sacrifices ingestion speed and doesn't fix the inefficiency of the stream design. Option D is incorrect because decreasing record size helps with bandwidth but does not address the fundamental issue of uneven sharding caused by the partition key.

Community Comment Notes

The community discussion highlights the distinction between capacity and distribution. As Faye15599 noted, "A is the best solution because the issue of hot shards is typically caused by an uneven distribution... Using a random partition key ensures that records are distributed more evenly." JekChong correctly identified that increasing shards doesn't solve the desproporción (disproportion) if the key causes concentration, reinforcing that partition key strategy is the primary lever for balancing load.

Exam Strategy

When dealing with Kinesis throttling, always distinguish between insufficient capacity (need more shards) and uneven distribution (need better partition keys). If the problem is specifically 'hot shards,' focus on the partition key's randomness and cardinality before considering scaling up.

Frequently Asked Questions

Why isn't increasing shards the right answer for hot shards?

Increasing shards adds capacity but doesn't fix the underlying skew. If the partition key is still deterministic, new shards will also suffer from uneven loads.

What is a hot shard in Kinesis?

A hot shard is one that receives significantly more data than others, hitting its throughput limit and causing throttling while other shards remain underutilized.

More DEA-C01 FAQ →

Community Votes

71%

29%

71% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 3 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 3

Faye15599 👍 3 Answered A

A is the best solution because the issue of hot shards is typically caused by an uneven distribution of records across shards due to poorly chosen partition keys. Using a random partition key ensures that records are distributed more evenly across all shards, reducing the likelihood of any single shard becoming "hot" and experiencing throttling. B is incorrect because while increasing the number of shards can help handle more data, it does not resolve the root cause of hot shards, which is uneven distribution due to poor partition key selection. Without addressing the partition key issue, adding shards may still result in some shards being overloaded.
JekChong 👍 2 Answered B

Amazon Kinesis Data Streams uses shards to distribute data, and each shard has a fixed throughput limit. If certain shards receive significantly more data than others (hot shards), they will experience throttling. To resolve this issue: Increase the number of shards – This increases the overall capacity of the stream. Distribute records more evenly across shards – This can be done by modifying the partition key strategy so that data is spread more evenly.
italiancloud2025 👍 2 Answered A

A: Sí, usar una clave de partición aleatoria distribuirá uniformemente los registros entre los shards, reduciendo cuellos de botella en shards "calientes". B: No, aumentar shards no soluciona la desproporción si la clave sigue causando concentración.

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