Amazon DynamoDB Pros and Cons

Product from AWS. You will know automatically it's best in class. Granular and scalable Review collected by and hosted on G2.com.

1. Dense documentation. You will spend a lot of time reading up on the docs. Until JSON was supported, the API was really hard to use

2. Server side logic not really well formed. ie, need to first read and retrieve the item back to client side to do logic manipulation. And then write back to server side.

3. Expensive to conduct scan / batch operations. Need to spend a lot of time reading the docs and plan what is possible and what is not Review collected by and hosted on G2.com.

Fast and reliable service, allows you to grow indefinitely Review collected by and hosted on G2.com.

Very-very expensive on a large scale !!! Review collected by and hosted on G2.com.

DynamoDB scales without limit, requires no maintenance, no backups, no dedicated server instances in that it is provided as an AWS service. Regardless of the size of the data set stored and the number of accessing clients, access times are constant and extremely low and can be configured dynamically at run-time. This makes it possible to use DynamoDB where SQL solutions inevitably will fall down when load increases. With DynamoDB, it's easy to authenticate millions of users simultaneously without any degradation. Review collected by and hosted on G2.com.

There's really very little to dislike. The only thing might be that counting the number of items in the data set is an expensive operation. However, this can be overcome in a number of ways. There are also very few ORM handlers available, something I've tried to remedy with the ocean-dynamo Ruby gem (https://rubygems.org/gems/ocean-dynamo). Review collected by and hosted on G2.com.

I worked on the backend for mobile games with millions of users and used DynamoDB as the primary persistent data store.

The best feature for us was the ease of scaling. We could literally change a number on a control panel as our capacity needs increase and Amazon would rapidly and automatically handle the scaling behind the scenes.

Documentation is clear and comprehensive. We had no trouble adapting our more traditional SQL DB models to Dynamo's. Review collected by and hosted on G2.com.

There is a learning curve involved since you will likely need to remodel your domain objects unless you are already using a similar NoSQL DB. Review collected by and hosted on G2.com.

It is a hosted service, which greatly increases your ability to abstract away the Data Layer, allowing you to focus on Application and Business Logic.

I also like its ability to let you scale throughput dynamically with your application needs.

Dynamo is also very fast. Review collected by and hosted on G2.com.

There are some shortcomings to Dynamo.

For example, Dynamo lacks TTL support, which removes certain use cases, like expiring tokens, or rate limiting. Dynamo also lacks a sleek query language like Cassandra's CQL. This makes performing Ad-Hoc queries much harder.

Also for larger workloads, it becomes expensive provisioning a certain TPS. Review collected by and hosted on G2.com.

The simplicity of the API and data model really appeals to me, it kind of just does its thing well and doesn't try to be the perfect database for everything. After learning how it works, which you can do in the docs as well as the original dynamo whitepaper which is very good, it becomes pretty clear where the tradeoffs are.

Once you get a system built on top of dynamo, the big upside is you actually get pretty much unlimited scalability just by turning some knobs as you grow.

Because it's so simple and forces you to work within its framework, it just feels a lot more solid and predictable than other marketing-heavy datastores (MongoDB being the one I've been most burned by, though I haven't used it in years now so maybe it's better) that promise scalability but also try to do everything and so offer a lot of features for the single node case without making it very clear which features you need to avoid using if you want to run in sharded mode.

And then aside from data model, that fact that it's hosted is amazing. I set up a Cassandra cluster a few years ago and it took weeks just getting the JVM running stably with the right GC generation sizes, setting up monitoring, etc. As of a year and a half or so ago, DynamoDB now has basically all the features that Cassandra does (the notable new additions being set and dictionary types that you can use for some kinds of CRDT data types).

It also works well with Amazon Elastic Map Reduce - you can run a huge Hadoop job, say, and output the results to s3, and then turn up the writes/sec very high temporarily on DynamoDB to write the results to dynamo quickly, where they can be used in your app. Review collected by and hosted on G2.com.

The downside to dynamodb is that you have to structure your data as simple key-value lookups, and this makes some things that are simple in SQL databases, like adding an index to query a new field, a little harder in dynamo. There also are no joins, so you have to do any joining you need either in memory or by duplicating data and denormalizing.

Again, it's not the perfect database for everything. Particularly if you have a system that you expect will always be well within a single server's capacity, and you want to be able to do OLTP and OLAP workloads in the same place, using a SQL database probably makes more sense. Review collected by and hosted on G2.com.

Cost effective and scalable solution that provides a lot of flexibility. The build in monitoring support is really powerful and I enjoy having such strong security in a database solution. It was really easy to learn and the learning curve was very low. Review collected by and hosted on G2.com.

1MB Limit on Queries which can be problematic for larger datasets. 64KB Row Size limit which is sometimes too little. The database seems to round up the size of read operations. Joins are not a possibility in the current solution and would be a much better addition in the future. The backup process is more difficult in comparison to some of the other solutions that are out there. Review collected by and hosted on G2.com.

Its nature of providing database as a service. We can increase and decrease the size of it any time without any problem. Review collected by and hosted on G2.com.

It does not have any standard Object relational mapping library for JavaScript like mongoose. There are one-two i found on internet but they does not have community support like mongoose.js. Some of them are https://github.com/ryanfitz/vogels and https://github.com/automategreen/dynamoose. Review collected by and hosted on G2.com.

DynamoDB allows you to build your software applications without having to worry about scaling your database. My company has dozens of Amazon DynamoDB tables, some of which only perform a few reads/writes per day, and others get thousands of reads and writes per second. We can build our application and allow Amazon to scale out the infrastructure without much database administration or server management. Review collected by and hosted on G2.com.

It's very important to choose the right Partition Key and Sort Key for each of your database tables. (Previously these were called Hash Key and Range Key.) In particular, your Partition Key should be uniformly distributed across your dataset: your usage for each Partition Key should be roughly equal. This means that there should be an equal number of database rows, and your reads/writes should be uniform as well. We chose a non-uniform partition key, which caused us to run into thousands of "ProvisionedThroughputExceededException" errors. Ultimately we had to migrate our data into another table which had a uniform Partition Key. This took a few weeks, but since then our table has been perfectly scalable. Review collected by and hosted on G2.com.

As a backing store in relevant cases, if you architect right and implement carefully, DynamoDB lets you think about scaling workloads in an altogether different way. Compared to running your own mongo clusters, ar complex MySQL or PostgreSQL replicated systems, Dynamo lets you worry about your data models and access patterns instead. Review collected by and hosted on G2.com.

It takes a bit to adapt to the feature set, and of course this is definitely a case of vendor lock-in. Review collected by and hosted on G2.com.