Evaluate your provisioned capacity for right-sized provisioning
This section provides an overview of how to evaluate if you have right-sized provisioning on your Amazon Keyspaces tables. As your workload evolves, you should modify your operational procedures appropriately, especially when your Amazon Keyspaces table is configured in provisioned mode and you have the risk to over-provision or under-provision your tables.
The procedures described in this section require statistical information that should be captured from the Amazon Keyspaces tables that are supporting your production application. To understand your application behavior, you should define a period of time that is significant enough to capture the data seasonality of your application. For example, if your application shows weekly patterns, using a three week period should give you enough room for analysing application throughput needs.
If you don’t know where to start, use at least one month’s worth of data usage for the calculations below.
While evaluating capacity, for Amazon Keyspaces tables you can configure Read Capacity Units (RCUs) and Write Capacity Units (WCU) independently.
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How to retrieve consumption metrics from your Amazon Keyspaces tables
To evaluate the table capacity, monitor the following CloudWatch metrics and select the appropriate dimension to retrieve table information:
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You can do this either through the Amazon CLI or the Amazon Web Services Management Console.
How to identify under-provisioned Amazon Keyspaces tables
For most workloads, a table is considered under-provisioned when it constantly consumes more than 80% of its provisioned capacity.
Burst capacity is an Amazon Keyspaces feature that allow customers to temporarily consume more RCUs/WCUs than originally provisioned (more than the per-second provisioned throughput that was defined for the table). The burst capacity was created to absorb sudden increases in traffic due to special events or usage spikes. This burst capacity limited, for more information, see Use burst capacity effectively in Amazon Keyspaces. As soon as the unused RCUs and WCUs are depleted, you can experience low capacity throughput error events if you try to consume more capacity than provisioned. When your application traffic is getting close to the 80% utilization rate, your risk of experiencing low capacity throughput error events is significantly higher.
The 80% utilization rate rule varies from the seasonality of your data and your traffic growth. Consider the following scenarios:
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If your traffic has been stable at ~90% utilization rate for the last 12 months, your table has just the right capacity
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If your application traffic is growing at a rate of 8% monthly in less than 3 months, you will arrive at 100%
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If your application traffic is growing at a rate of 5% in a little more than 4 months, you will still arrive at 100%
The results from the queries above provide a picture of your utilization rate. Use them as a guide to further evaluate other metrics that can help you choose to increase your table capacity as required (for example: a monthly or weekly growth rate). Work with your operations team to define what is a good percentage for your workload and your tables.
There are special scenarios where the data is skewed when you analyse it on a daily or weekly basis. For example, with seasonal applications that have spikes in usage during working hours (but then drop to almost zero outside of working hours), you could benefit from scheduling application auto-scaling, where you specify the hours of the day (and the days of the week) to increase the provisioned capacity, as well as when to reduce it. Instead of aiming for higher capacity so you can cover the busy hours, you can also benefit from Amazon Keyspaces table auto-scaling configurations if your seasonality is less pronounced.
How to identify over-provisioned Amazon Keyspaces tables
The query results obtained from the scripts above provide the data points required to perform some initial analysis. If your data set presents values lower than 20% utilization for several intervals, your table might be over-provisioned. To further define if you need to reduce the number of WCUs and RCUS, you should revisit the other readings in the intervals.
When your table contains several low usage intervals, you can benefit from using Application Auto Scaling policies, either by scheduling Application Auto Scaling or by just configuring the default Application Auto Scaling policies for the table that are based on utilization.
If you have a workload with a low utilization to high throttle ratio (Max(ThrottleEvents)/Min(ThrottleEvents) in the interval), this could happen when you have a very spiky workload where traffic increases significantly on specific days (or times of day), but is otherwise consistently low. In these scenarios, it might be beneficial to use scheduled Application Auto Scaling.