Frequently Asked Questions

Amazon DynamoDB Auto Scaling: Concepts & Benefits

What is auto scaling in Amazon DynamoDB and how does it work?

Auto scaling in Amazon DynamoDB is a mechanism that automatically adjusts the provisioned read and write capacity of a table or global secondary index based on actual traffic patterns. This means DynamoDB can increase its provisioned throughput to handle spikes in traffic and decrease it when demand drops, helping to avoid both performance issues and unnecessary costs. The process involves monitoring consumed capacity with Amazon CloudWatch, triggering alarms via Amazon SNS, and updating provisioned throughput through the DynamoDB API. Note: There are defined limits on how frequently capacity can be decreased each day.

What are the main benefits of using auto scaling with DynamoDB?

Key benefits of DynamoDB auto scaling include improved fault tolerance (automatically replacing unhealthy servers), high availability (scaling capacity to meet demand and prevent downtime), and commercial savings (pay-as-you-go pricing by only paying for resources consumed). Note: Cost savings depend on actual usage patterns and correct configuration of scaling policies.

How does DynamoDB auto scaling help manage costs?

DynamoDB auto scaling dynamically adjusts provisioned throughput to match actual usage, so you avoid over-provisioning (which increases costs) and under-provisioning (which can cause performance issues). This pay-as-you-go model means you only pay for the resources you use. Note: If scaling policies are not set appropriately, you may still incur unnecessary costs or experience throttling.

What are the limits of auto scaling in DynamoDB?

DynamoDB auto scaling allows you to decrease read and write capacity up to 4 times per day. If no decrease has occurred in the past four hours, an additional decrease is allowed, making the maximum number of decreases in a day nine (4 in the first 4 hours, then 1 per subsequent 4-hour window). Note: These limits may affect how quickly you can reduce costs during periods of low demand.

What components are involved in DynamoDB auto scaling?

The main components involved are Amazon CloudWatch (monitors consumed capacity and triggers alarms), Amazon SNS (sends notifications), and the DynamoDB API (applies changes to provisioned throughput). Scaling policies are defined for each table or index, specifying target utilization and thresholds. Note: Proper configuration of these components is required for effective auto scaling.

N2W Backup & Recovery: Features, Use Cases, and Integrations

What products and services does N2W offer?

N2W provides a cloud-native backup, recovery, and disaster recovery solution for Amazon Web Services (AWS) and Microsoft Azure. Key features include automated backup and recovery, near-instant disaster recovery, immutable backups, cost optimization (up to 92% savings on long-term backup costs), compliance and security tools, multi-cloud management, and granular restore capabilities. Note: N2W is not a replacement for DynamoDB's native auto scaling but complements AWS and Azure backup and recovery needs. Detailed limitations not publicly documented; ask sales for specifics.

What integrations does N2W support?

N2W integrates with RESTful API for custom automation, CLI for advanced management, third-party monitoring tools like Datadog, Splunk, and Bocada, and various data management tools. These integrations enable enhanced automation, observability, and compliance tracking. Note: Integration capabilities may require additional configuration; see the API documentation for details.

What security and compliance certifications does N2W have?

N2W is independently certified for ISO/IEC 27001:2022 and is SOC compliant by inheritance (leveraging AWS and Azure compliance). It also supports HIPAA, GDPR, FedRAMP, ITAR, and CJIS frameworks. For a copy of the ISO certificate, contact customer.success@n2ws.com. Note: For full compliance details, see the N2W Trust Center.

How easy is it to implement N2W and how long does it take?

N2W can be implemented in as little as two weeks, with support from dedicated Customer Success Managers, onboarding calls, and detailed documentation. Customers can deploy via AWS Marketplace AMI or CloudFormation templates, and access resources like video tutorials and user guides. A 30-day free trial is available without a credit card. Note: Implementation time may vary based on environment complexity.

Use Cases, Customer Success & Industry Fit

Who can benefit from using N2W?

N2W is designed for cloud directors, IT managers, and managed service providers (MSPs) in enterprises, public sector entities, healthcare, finance, retail, education, and nonprofits. It is especially valuable for organizations with petabyte-scale data, strict compliance needs, or multi-cloud environments. Note: Best fit for AWS and Azure users; organizations using other clouds may need alternative solutions.

What problems does N2W solve for its customers?

N2W addresses high disaster recovery costs, downtime and data loss, ransomware threats, manual backup processes, compliance challenges, complexity in multi-cloud environments, scalability for large data volumes, and long-term backup costs. Features like automated backup, immutable backups, and intelligent storage tiering help solve these issues. Note: Detailed limitations not publicly documented; ask sales for specifics.

What feedback have customers given about N2W's ease of use?

Customers have praised N2W for its simplicity and user-friendly features. For example, Shane H. (MSP) noted, "It's very simple to use and we are an MSP for multiple companies. Support is great and quick to respond." Julian Ware (City of Oakland) said, "You’re just clicking and going. And, to me, that’s what the modern world of backup is." Note: Some advanced features may require additional configuration; see documentation for details.

What industries are represented in N2W's case studies?

N2W's case studies include enterprises (Johnson & Johnson, Dyson, HP, Western Union), retail & e-commerce (Skechers, Dressbarn), public sector (City of Oakland, Bahrain Ministry), education (St. John's University), transportation & logistics (Deutsche Bahn), nonprofits (Best Friends Animal Society, Goodwill), healthcare & pharmaceuticals (Philips, Merck), finance & insurance, and IT software companies. See case studies for details. Note: Not all industries may be represented in every feature set.

Can you share specific customer success stories using N2W?

Yes. For example, Skechers standardized backup and recovery across a multi-cloud IT estate, improving data protection and reducing costs (Skechers case study). St. John's University eliminated legacy tape storage and achieved rapid recovery from accidental deletions (St. John's case study). DB Systel automated backup for thousands of routes and servers (DB Systel case study). Note: Outcomes may vary by organization and use case.

Competition & Comparison

How does N2W compare to AWS Backup?

N2W offers immutable backups (not available in AWS Backup), cross-cloud recovery for AWS and Azure (AWS Backup is AWS-only), granular file/folder-level restore (AWS Backup does not support this), custom disaster recovery retention policies, and multi-tenancy for MSPs. N2W also provides a RESTful API for automation, while AWS Backup requires Lambda scripting. AWS Backup provides preconfigured report templates, but N2W offers customizable compliance reports and integrations with Datadog and Splunk. Note: AWS Backup may be preferred for organizations fully standardized on AWS with simpler backup needs.

Auto Scaling: With Amazon DynamoDB, It’s As Easy As 1-2-3

Discover the advantages of using auto-scaling with Amazon DynamoDB, including components and steps involved in setting up auto-scaling with AWS DynamoDB.
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Better fault tolerance, high availability, and reduced cost are the Holy Grail for any developer and a top priority for any business wanting to succeed in today’s highly dynamic marketplace. Thanks to the advent of cloud computing, auto scaling can deliver those advantages and more. Auto scaling is the mechanism by which the computational resources allocated to an application scale up or down, at any given point in time, depending on the application’s load. Traditionally, scaling has been a manual and costly proposition. With the advent of cloud computing, however, that has now changed. Today, you can design auto-scalable applications that sustain increasing user requests without any downtime.

Before cloud computing, the idea of increasing resources on even a single server on the fly was laughable, leading to frequent application performance issues and crashes, and in turn causing potential business and financial losses. In Diagram 1, consider how auto scaling can be used to sustain peak load for an application. On weekends when user requests are quite low, the application would require just one server. On work days, two servers would be required for normal operation, while four would be needed at the end of each month.

Auto Scaling in DynamoDB

With the Amazon DynamoDB cloud database service, you must set a provisioned read and write capacity for a table or global secondary index (whether existing or new) at the database level. Here’s the problem with that approach. If you provision something too low, your application will have performance issues. It may even crash. If you provision something too high, your bill will skyrocket.

How can auto-scaling in DynamoDB address this issue and further enhance your cloud elasticity? The answer is simple. DynamoDB dynamically adjusts provisioned throughput capacity on your behalf, in response to actual traffic patterns under monitoring. As a result, a table or global secondary index can increase its provisioned read and write capacity to handle sudden increases in traffic, without throttling. Conversely, when the workload decreases, application auto-scaling decreases the throughput, so that you don’t pay for unused provisioned capacity.

Scaling Policy

By specifying scaling policies, auto-scaling can augment resources when application demand increases or decreases. Policies are defined for every table, global secondary index, or both, essentially wherever you want to dynamically scale up or down the read and/or write capacity as needed to accommodate the system load. Scaling policy also tracks target utilization, with tracking algorithms, at any given point in time and adjusts provisioned throughput in response to actual traffic.

Here’s how it works:

Target utilization is calculated as the ratio of real-time consumed capacity, to provisioned capacity, as defined by the administrator. Let’s say at one point in time, the consumed capacity is 700 units for read activities, meaning that target utilization is 700/1000 = 70%. However, as shown in Diagram 2, we have already defined the target utilization as 50%. Consequently, to bring the target utilization to 50%, the maximum provisioned capacity must be increased to 1400 units.

A diagram of setting auto-scaling parameters

Diagram 2. Entering parameters

Components and Steps Involved in Auto Scaling

To better understand how auto-scaling works, here’s a look at the various components and steps involved in auto-scaling a single AWS DynamoDB table (Diagram 3).

  • Step 1. A set of users accesses a DynamoDB table for which an auto-scaling policy has been configured.
  • Step 2. The consumed capacity is monitored by Amazon CloudWatch, a tool that collects and tracks metrics, and triggers an alarm when a specified threshold is breached.
  • Step 3. If the consumed capacity is greater than the configured target utilization (for a considerable period of time), then Amazon CloudWatch triggers an alarm received by an individual through the Amazon simple notification service (SNS) messaging service from Amazon Web Services (AWS).
  • Steps 4 and 5. Amazon CloudWatch directs the auto scaling policy to issue an update table command to change the provisioned throughput.
  • Step 6. The DynamoDB application program interface (API) consumes the request to update the table with new values of provisioned throughput capacity.
A diagram of the auto-scaling process

Diagram 3. Auto-scaling process

While these steps highlight just one cycle of auto-scaling resources, DynamoDB can actually scale up resources multiple times a day in accordance with the defined auto-scaling policy. However, there is a defined limit to how many times a day the read and write capacity units can be decreased—up to 4 times a day. Additionally, if no decrease has occurred in the past four hours, an additional dial down is allowed, effectively bringing the maximum number of decreases in a day to nine times (4 decreases in the first 4 hours, and 1 decrease for each of the subsequent 4-hour windows in a day).

Benefits of Auto Scaling

Auto-scaling has a number of key benefits, including:

  • Better Fault Tolerance. Auto-scaling keeps a tab on server health. When an issue is detected, it can bring down the faulty server and replace it with a healthy one on the fly.
  • High Availability. When the number of user requests is high, provisioned capacity auto scales to accommodate the increased load, maintaining the application performance and eliminating any availability issues.
  • Commercial Savings. The dynamic scale up and down functionality that comes with auto-scaling has a pay-as-you-go model. Because you pay for only the resources you consume, the cost savings can be substantial.

Summary

Without a doubt, auto-scaling can effectively eliminate the guesswork involved in provisioning adequate capacity when creating new tables. It also reduces the operational burden of continuously monitoring consumed throughput and having to adjust provisioned capacity manually. Moreover, auto-scaling helps ensure application availability and reduces the cost associated with unused provisioned capacity. Such capabilities make this function, now available in Amazon DynamoDB, one that is sure to benefit both developers and businesses alike. Try N2WS Backup & Recovery Free.

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