AWS DynamoDB vs Google Cloud Datastore: Top Differences
Let’s deeply understand the critical aspects of AWS DynamoDB vs Google Cloud Datastore:
1. Data Model
DynamoDB
It employs a key-value data model. Each item is uniquely identified by a primary key, which can be a simple string or a hash/range combination. Additionally, secondary indexes can be created on non-key attributes for faster retrieval. However, these secondary indexes come with limitations. They can only be created on a single attribute or a combination of two attributes from the same item. Complex queries involving multiple attributes or joins can be challenging and impact performance.
Cloud Datastore
Cloud Datastore Leverages a document data model. Documents are stored in JSON format, allowing for flexible schema design. This facilitates representation of complex relationships between points of data since documents may contain nested structures where necessary. Another aspect supported by Cloud Datastore is entities which allow organizing related documents into logical groups.
2. Consistency
DynamoDB
It offers tunable consistency options. You can choose between:
- Eventually Consistent Reads (default): Faster but may return outdated data, suitable for scenarios where the most recent data isn’t critical.
- Strongly Consistent Reads: Slower but guaranteed to return the latest data, ideal for applications requiring real-time data accuracy.
Cloud Datastore
It provides strong consistency by default. All reads are guaranteed to return the latest data, ensuring consistency across all replicas. This consistency model offers high data integrity but can impact performance for high-traffic applications encountering frequent writes. Cloud Datastore also offers an option for eventual consistency reads, but it’s rarely used due to its limitations compared to strongly consistent reads.
3. Scalability
DynamoDB
DynamoDB is generally highly scalable. Its storage capacity and throughput automatically scale based on application needs. You can provision read capacity units (RCUs) and write capacity units (WCUs). The number of read operations per second your table can handle is represented by RCUs while WCUs represent the number of write operations per second respectively. To avoid under- or over-provisioning, you need to monitor the system carefully since provisioning provides predictable performance.
Cloud Datastore
It automatically scales storage and throughput based on usage. Unlike DynamoDB, there is no manual configuration needed for scaling units in Cloud Datastore. In order to be cost-effective when handling unpredictable workloads, pricing model for operations used in cloud datastore is pay-per-use type. However, this lack of fine-grained control over scaling can make it challenging to achieve the same level of predictable performance as DynamoDB.
4. Pricing
DynamoDB
It employs a pay-per-use pricing model based on:
- Consumed read and write capacity units (RCUs and WCUs)
- Storage used
This model can be cost-effective for applications with predictable workloads but can become expensive for applications experiencing bursts of traffic or requiring high write volumes.
Cloud Datastore
It offers a tiered pricing structure. You pay for:
- Storage used
- Operations performed on the data (reads, writes, deletes)
Cloud Datastore provides a free tier with limited storage and operations, suitable for small projects or initial development phases. Paid tiers offer increased storage and operation quotas with a pay-as-you-go structure. This can be attractive for budget-conscious projects in the initial stages or applications with unpredictable workloads.
5. Security
DynamoDB
Dynamo DB provides robust security features, including:
- Encryption at rest and in transit: Data is encrypted both when stored on disk and during transmission between your application and DynamoDB.
- IAM (Identity and Access Management): You determine who accesses the tables of your DynamoDB and what actions they can do with them.
- Granular permissions for data operations: This allows fine-grained permissions to be established for users or groups to perform read, write or delete operations on specific tables or attributes.
Cloud Datastore
Cloud Datastore includes a user authentication as well as authorization through Google Cloud IAM along with encryption at rest during it’s transit. Like Dynamo DB, Cloud Datastore enables one to define roles for different users and groups to control access to data and functions.
6. Query Capabilities
DynamoDB
DynamoDB Supports querying data using:
- Primary Key: Fastest and most efficient way to retrieve data in DynamoDB.
- Secondary Indexes: Can be created on non-key attributes to enable faster retrieval based on those attributes. However, these indexes have limitations on complexity and can impact performance for intricate queries.
- Expression-based queries: Allow for more complex filtering and sorting of data, but can be challenging to write and optimize for performance.
Cloud Datastore
Cloud Datastore offers a more robust query language called GQL (Google Cloud Query Language). GQL allows for querying documents based on various criteria, including:
- Filtering data based on property values and comparisons
- Performing logical operations (AND, OR, NOT)
- Sorting results based on specific properties
- Projecting only specific fields from documents
- Text search capabilities for searching within textual data stored in documents
While Cloud Datastore offers a more powerful query language, it’s essential to consider the trade-off. Complex GQL queries can be computationally expensive and impact performance. For simple queries, DynamoDB’s primary key and secondary indexes might be sufficient and more performant.
7. Integrations
DynamoDB
DynamoDB integrates seamlessly with other AWS services like:
- Lambda: Lambda is a service which provides an ability of running code within response to events in Dynamo DB such as putting, deleting, updating etc., .
- S3 (Simple Storage Service): S3 can be used for storing large files or backups of your DynamoDB data.
- Kinesis: Kinesis is stream based service that captures data streams from Dynamo DB to process it in real-time.
Cloud Datastore
Cloud Datastore works hand-in-glove with other GCP services like:
- App Engine: App Engine is a platform as a service environment that allows developers create web applications right from the cloud.
- Cloud Functions: Cloud Functions is a serverless compute service like AWS Lambda that can be triggered by events in Cloud Datastore.
- BigQuery: BigQuery is a data warehouse for big data analytics. For more analysis, Cloud Datastore can easily export its data to BigQuery.
AWS DynamoDB vs Google Cloud Datastore: Top Differences
In the ever-evolving landscape of application development, choosing the right database system is very crucial. Modern applications prefer NoSQL databases because they are flexible and scalable. These include AWS DynamoDB and Google Cloud Datastore as major contenders.
But which one suits your project most? This article explains the fundamental differences between DynamoDB and Cloud Datastore so that you can be able to make a good choice.