DynamoDB vs Google Datastore - The Ultimate Comparison

Amazon DynamoDB and Google Datastore: An Overview

Amazon DynamoDB is a fully managed serverless, key-value NoSQL database service that delivers high performance and scalability for applications. In addition, DynamoDB offers many features, such as multi-region replication, in-memory caching, and built-in security.

Google Datastore is a highly scalable NoSQL database that automatically handles sharding and replication to provide a highly available database that can automatically scale to cater to the application's load.

Shared Attributes for DynamoDB and Google Datastore

DynamoDB & Google Datastore share many attributes making it a tough and fierce competition. However, some of the common areas between these databases include:

1. Both DynamoDB & Google Datastore are fully managed, thus not requiring users to perform any maintenance of the actual database service.
2. Both DynamoDB & Google Datastore are NoSQL database services.
3. Both DynamoDB & Google Datastore are entirely cloud-based when running production workloads.
4. DynamoDB & Google Datastore use sharding as their partitioning method when storing data on different nodes.
5. DynamoDB & Google Datastore comply with ACID (Atomicity, Consistency, Isolation, and Durability) principles, although the implementation details may vary.

Performance of Amazon DynamoDB vs. Google Datastore

Amazon DynamoDB

DynamoDB is known for its ability to process a staggering amount of requests while providing the same level of performance to the applications. It can handle up to 10 trillion requests per day and an excess of 20 million requests per second, ensuring that it provides far superior performance to any other NoSQL database service. In addition, its internal hash function provides an O(1) time complexity request routing, thus, enabling it to perform the same at low to high workloads.

It can also provide single-digit millisecond latency while processing these staggering volumes of requests. If this wasn't enough, AWS provides an in-memory caching solution DAX (DynamoDB Accelerator) that reduces the latency to microseconds. DAX is particularly useful for read-heavy workloads, providing a significant performance boost without requiring changes to the application logic.

Google Datastore

Google Datastore also provides scalability to cater to application load increases. Theoretically, the database service can handle up to 740,000 operations per second; however, to reach these capacities, the database requires a steady growth of maximum operations starting from 500 operations per second to have sufficient time to prepare for the increased workload. This gradual scaling approach ensures that the underlying infrastructure can handle the increased load without compromising performance.

Availability and Durability

DynamoDB service level agreements provide its users with at least 99.99% availability while automatically replicating its data across multiple availability zones and regions to mitigate a single point of failure. Additionally, users can utilize the DynamoDB Global Tables further to increase the uptime to at least 99.999%.

Google Datastore allows its users to select between two modes for its replication, ensuring the availability of its database service. Users can choose between Multi-Region and Regional replication methods; the Regional replication method provides a monthly uptime of at least 99.9%, while the Multi-Region SLA ensures a monthly uptime of at least 99.95%.

Security

Since their respective IAM service governs access to both database services, security between them has its differences.

DynamoDB and Google Datastore provide server-side encryption, thus ensuring that it encrypts all data at rest. However, DynamoDB takes it a step further by implementing encryption for the data in transit with its encryption client capable of using custom keys from AWS CloudHSM or AWS KMS. Additionally, DynamoDB supports fine-grained access control, allowing users to define permissions at the item level.

Comparing Other Features of DynamoDB and Google Datastore

Backups

DynamoDB facilitates multiple backup options to cater to most backup needs of users. It offers an on-demand backup and restores function that allows users to manually take snapshots of the DynamoDB table at any specific time (typically using AWS Backup). It also provides Point-In-Time recovery, enabling users to restore the DynamoDB table to a particular point in time (within the last 35 days) without managing snapshots.

Additionally, AWS Backup services allow users to back up and restore data across multiple AWS services with added benefits such as improved control over cross-region backups, scheduling backups, etc.

Google Datastore also contains its backup functionality, enabling users to back up data to prevent accidental or intentional data deletion. These backups require a storage bucket with the appropriate permissions to store the data backups created. However, Google Datastore's backup options are less flexible compared to DynamoDB's, and users may need to implement additional solutions for comprehensive backup strategies.

Scalability

DynamoDB brings far superior scalability enabling it to handle more than 20 million requests per second at single-digit millisecond latency. It also allows provisioned and On-demand scaling methods that will help scale depending on the increase in traffic. The auto-scaling feature in DynamoDB adjusts the throughput capacity based on the actual usage, ensuring cost-efficiency.

Google Datastore uses a distributed architecture to enable automatic scaling capabilities. However, it can theoretically only process up to 740,000 operations per second with higher latency than DynamoDB. The automatic scaling in Google Datastore is designed to handle varying workloads, but it may not match the extreme scalability offered by DynamoDB.

When and Where to Pick Which Database Service

Both of these database services bring their own set of strengths. Therefore, choosing the correct database service for the specific use case is imperative.

Use Cases for DynamoDB

DynamoDB is far from a traditional database service since it offers superior speeds while handling staggering requests. Users may choose to use DynamoDB where applications require speed over other factors; some of the more widespread use cases include:

1. Application logging, where users require to store logs in a fast yet easy-to-read mechanism.
2. Ease of Analytics data collection due to the virtually unrestricted constraints of the DynamoDB auto-scaling feature.
3. Combining DynamoDB with Amazon DynamoDB Accelerator (DAX) enables the application to benefit from faster response times, thus serving as a cache without making any changes on the application or database levels.
4. Real-time bidding platforms where low latency and high throughput are critical.
5. E-commerce applications that require rapid scaling during peak shopping periods.

Use Cases for Google Datastore

Google Datastore also allows users to utilize the managed NoSQL database service in various applications.

Users can utilize the database service's diverse data types and multiple access methods to the best effect when designing their applications. Some common use cases include:

1. Mobile and web applications that require a flexible and scalable backend.
2. Applications that benefit from Google's ecosystem, such as those integrated with other Google Cloud services.
3. Content management systems where the schema-less nature of Datastore provides flexibility.
4. Applications that require strong consistency and transactional support.
5. IoT applications that need to handle large volumes of data from various sources.

Pricing

Amazon DynamoDB

DynamoDB provides pricing based on multiple criteria, such as read and write capacity units and the actual data consumed by the data. For example, DynamoDB counts 4KB as one read unit and 1KB as one write unit; these terms are essential when determining the actual cost and pricing for utilizing the database service.

DynamoDB splits its pricing tiers into two modes:

1. On-Demand: The cost and payment depend on the traffic and do not require a specified throughput, as the database service automatically scales up and down depending on the utilization.
2. Provisioned: The user specifies the required read and writes per second and has the added feature of enabling auto-scaling.

The free tier provided by DynamoDB also provides 12-month access with limitations that allow users to use the database service without additional cost. However, charges will occur once users exceed these limits.

Google Datastore

Like DynamoDB, Google Datastore follows similar pricing criteria such as entity reads, writes, deletes, and the actual data stored.

Users will see a difference in pricing depending on the region they select. There will also be an additional charge for any egress between regions; however, all ingress traffic is provided free of charge.

Google Datastore also provides its users with a free tier, and to remove the limits of the free tier, the users must enable billing.

Conclusion

This article discusses the similarities and differences between DynamoDB and Google Datastore database services. It will help compare and identify the significant differences between these managed NoSQL database services. Both services offer robust solutions for different use cases, and the choice between them should be based on specific application requirements, performance needs, and integration preferences.

Thank you for reading!