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To improve DynamoDB performance, first identify whether the problem is latency, throttling, throughput, or cost. Then trace it to the request pattern, key distribution, capacity mode, or an explicit throughput limit. A table can have enough total capacity and still throttle when activity concentrates on particular key ranges; adding capacity alone will not fix every bottleneck.
What does “DynamoDB performance” mean for your application?
Separate the symptom you are trying to fix. Latency is how long an operation takes; throughput is how much work the system handles; throttling means requests are being limited; and cost is what the workload consumes financially. These can be related, but one does not automatically explain another.
- Measure the operation users experience. Include the application-side work around the DynamoDB request rather than treating a database service description as an end-to-end guarantee.
- Look beyond averages. A low average latency can conceal slower requests in the tail of the distribution.
- Correlate outcomes with service metrics. Examine the request results and the relevant Amazon CloudWatch metrics for the same period before changing schema or capacity.
AWS describes DynamoDB as a managed NoSQL database with single-digit-millisecond performance, but that is not a latency target or guarantee for an individual application’s complete operation. The reviewed AWS guidance does not establish a workload-independent end-to-end latency target.
How do I improve DynamoDB performance?
Start with the access patterns the application actually needs. List its reads and writes, including how each request identifies the items it needs, then check whether the table’s keys and secondary indexes support those requests without concentrating activity on a small subset of partition keys.
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Distribute activity across keys
AWS recommends designing for uniform activity across partition keys in both the table and its secondary indexes. Its current DynamoDB Developer Guide gives designed maxima of 3,000 read units per second and 1,000 write units per second per partition. These are per-partition documentation figures, not whole-table throughput guarantees or an application benchmark.
Capacity units describe particular item sizes and consistency settings: one read unit represents one strongly consistent read per second, or two eventually consistent reads per second, for an item up to 4 KB. One write unit represents one write per second for an item up to 1 KB. Larger items consume more capacity, so request frequency alone does not tell you how much capacity a workload needs.
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Match the schema to the requests
For each important operation, ask whether the application knows the partition key and whether its request can retrieve the intended items directly. If common requests depend on searching broadly and filtering afterward, the data model may not provide an efficient access path. A secondary index can support a concrete alternate-key access pattern, but it adds capacity and operational considerations; do not add one without identifying the requests it serves.
Should I use on-demand or provisioned capacity?
Choose a capacity mode based on observed traffic, billing preferences, and the team’s ability to monitor and adjust capacity—not on an assumption that one mode is inherently faster. AWS requires a table to use either on-demand or provisioned capacity mode.
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| Consideration | On-demand | Provisioned |
|---|---|---|
| Traffic fit | Designed for variable or unpredictable request patterns. | Can fit steadier, predictable traffic when allocated capacity is kept aligned with consumption. |
| Billing basis | Charges per request. | Charges for allocated capacity. |
| Operational focus | Review request volume, burst behavior, and any configured on-demand maximum. | Monitor consumption against provisioned capacity and right-size as traffic changes. |
| Useful decision inputs | Peaks, idle periods, unpredictability, and tolerance for variable request-based charges. | Traffic stability, capacity utilization, monitoring practices, and preference for capacity planning. |
AWS suggests reviewing fine-grained usage metrics—for example, over a 14-day period—before changing provisioned capacity. That is an example of a review window, not a universal minimum or tuning rule. The guide also says on-demand may cost less when average provisioned-capacity utilization is below 35%; it notes that workloads above that figure can still favor on-demand depending on peaks and low-activity periods. Treat this as conditional guidance, not a price comparison for your account.
Is a Scan slowing down the workload?
A Scan examines every item in a table or index and applies its filter afterward. A filter expression does not make the read selective at scan time. As the table or index grows, the amount of data examined can grow with it, consuming capacity that other requests may need.
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| Request type | What it does | When it fits |
|---|---|---|
| Scan | Examines all items in a table or index, then filters the results. | When the workload genuinely needs to examine the collection rather than target known keys; manage page size to limit the impact of each request. |
| Query | Retrieves items through a modeled key access pattern. | When the application can identify the relevant partition-key value and needs the associated items. |
| GetItem | Retrieves an item by its known key. | When the application already knows the key of one item. |
| BatchGetItem | Requests items by known keys in a batch. | When the application needs multiple specifically identified items. |
If a Scan is unavoidable, control its page size so each request has a more manageable effect on shared capacity. For recurring application paths, consider whether an index or a different key design can support a targeted Query instead.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why is my DynamoDB table throttling?
Throttling is a symptom, not a single diagnosis. AWS documents 16 distinct throttling reasons across four categories. Use the exception’s reason together with the matching CloudWatch metrics to determine which limit is involved before changing capacity.
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| Reason category | What to inspect | Likely direction |
|---|---|---|
| Key-range throughput | Whether activity is concentrated on particular keys or key ranges. | Revisit key distribution and hot access patterns; more total table capacity may not resolve concentrated demand. |
| Provisioned throughput | Table or secondary-index consumption relative to allocated units. | Check whether provisioned capacity matches the workload, then right-size if the evidence supports it. |
| Account limits | The applicable quota in the AWS Region. | Investigate the regional account quota rather than treating the issue as a table-level capacity shortage. |
| On-demand maximum throughput | The configured on-demand maximum acting as a cost-control limit. | Review whether the configured maximum matches the workload and intended spending boundary. |
Retries can help with transient bursts, but they do not correct persistent throttling caused by a hot key range, insufficient provisioned capacity, a quota, or a configured maximum. Match the remedy to the reason reported rather than increasing capacity indiscriminately.
When should I use DynamoDB Accelerator?
DynamoDB Accelerator (DAX) is an in-memory acceleration option to evaluate for a workload whose read patterns and consistency requirements make it a plausible fit. It is not a universal fix for slow requests, uneven key distribution, an inefficient Scan, or a throttling limit.
Compare direct DynamoDB requests with a DAX-backed design using the application’s actual read mix, consistency needs, and measured user-facing operations. Consider the additional operational and capacity implications alongside any measured benefit. The reviewed AWS guidance does not establish a universal percentage improvement or guarantee that DAX will reduce a particular application’s latency.
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