Amazon DynamoDB Standard-IA vs DynamoDB Standard

RecentlyAmazon DynamoDB Standard Infrequent Access (DynamoDB Standard-IA) table class, which AWS claims helps you to reduce your DynamoDB costs by up to 60% for tables that store data that is infrequently accessed.

In this article let me give you the price dynamics of DynamoDB Standard-IA



What DynamoDB costs break down

  1. Data storage
  2. Write traffic
  3. Read traffic
  4. Other Costs
    1. On-demand backup
    2. Continuous (PITR) backup
    3. Table restore
    4. Change data capture for Kinesis Data Streams
    5. Data export to Amazon S3
    6. DynamoDB Accelerator (DAX)
    7. DynamoDB Streams
Let's just focus on data storage costs, which is a recurring cost, that grows as your storage grows.

DynamoDB storage:        20 Terrabytes

DynamoDB Standard     
Cost per 1 GB-month:      $0.25 
Cost per 20 Terrabytes:    20000 * $0.25 = $5000 per month


DynamoDB Standard-IA
Cost per 1 GB-month:      $0.10 
Cost per 20 Terrabytes:    20000 * $0.10= $2000 per month






*In US-East

The cost slop is steep for DynamoDB Standard storage. If you do not use the TTL function of DyanmoDB you and if your data size grows steadily, you end up paying a steady storage cost.

If you are going with DynamoDB Standard-IA, you should be aware of the difference between Writes & Read. Writes and reads are 25% more expensive than DyanamoDB Standard storage.


DynamoDB Standard

On-Demand Throughput Type

PriceWrite Request Units (WRU): $1.25 per million write request units

Read Request Units (RRU): $0.25 per million read request units

DynamoDB Standard-IA

PriceWrite Request Units (WRU): $1.56 per million write request units
Read Request Units (RRU): $0.31 per million read request unitsDynamoDB Standard-IA







Conclusion: Have a prediction of data usage and storage prediction before choosing DynamoDB Standard-IA







Comments

Post a Comment

Popular posts from this blog

AWS S3 Bucket limitations

Image
Software systems always have some limits, reasons for limitations can varies. Hard limits boils down to limitations of underlying hardware resources. Soft limits can be to avoid noisy neighbor problems, that's where rate-limiting, service quotas comes into place. Let's explore on limitations of AWS S3 bucket   Number of S3 buckets in a AWS Account By default, you can create up to 100 buckets in each of your AWS accounts. If you need additional buckets, you can increase your account bucket limit to a maximum of 1,000 buckets by submitting a service limit increase. There is no difference in performance whether you use many buckets or just a few. TL;DR; 1000 buckets per account Cannot change S3 Bucket name or Region When you create a bucket, you choose its name and the AWS Region to create it in. After you create a bucket, you can't change its name or Region. Reuse S3 bucket name You can reuse S3 bucket name, but.. when you delete the bucket and the name becomes available for
Read more

Gradient descent algorithm in Machine learning

Image
Machine Learning and Deep Learning commonly uses Gradient descent (GD) to find a local minimum/maximum of a given function. Gradient descent minimises a cost/loss function (e.g. in a linear regression). In mathematical terms, Gradient descent is a way to minimize an objective function J(θ) parameterized by a model’s parameters θ ∈ Rd by updating the parameters in the opposite direction of the gradient of the objective function ∇θJ(θ) w.r.t. to the parameters. The learning rate η determines the size of the steps we take to reach a (local) minimum. In other words, we follow the direction of the slope of the surface created by the objective function downhill until we reach a valley. There are three variants of gradient descent, which differ in how much data we use to compute the gradient of the objective function. Choice of variant depends on accuracy and time taken to compute parameters. Batch Gradient descent A batch gradient descent, computes the gradient of the cost function w.r.t. to
Read more