Google Cloud Datastore is a NoSQL document database built for automatic scaling, high performance, and ease of application development. Datastore features include:
- Atomic transactions. Datastore can execute a set of operations where either all succeed, or none occur.
- High availability of reads and writes. Datastore runs in Google data centers, which use redundancy to minimize impact from points of failure.
- Massive scalability with high performance. Datastore uses a distributed architecture to automatically manage scaling. Datastore uses a mix of indexes and query constraints so your queries scale with the size of your result set, not the size of your data set.
- Flexible storage and querying of data. Datastore maps naturally to object-oriented and scripting languages, and is exposed to applications through multiple clients. It also provides a SQL-like query language.
- Balance of strong and eventual consistency. Datastore ensures that entity lookups and ancestor queries always receive strongly consistent data. All other queries are eventually consistent. The consistency models allow your application to deliver a great user experience while handling large amounts of data and users.
- Encryption at rest. Datastore automatically encrypts all data before it is written to disk and automatically decrypts the data when read by an authorized user. For more information, see Server-Side Encryption.
- Fully managed with no planned downtime. Google handles the administration of the Datastore service so you can focus on your application. Your application can still use Datastore when the service receives a planned upgrade.
Comparison with traditional databases
While the Datastore interface has many of the same features as traditional databases, as a NoSQL database it differs from them in the way it describes relationships between data objects. Here’s a high-level comparison of Datastore and relational database concepts:
|Category of object||Kind||Table|
|Individual data for an object||Property||Field|
|Unique ID for an object||Key||Primary key|
Unlike rows in a relational database table, Datastore entities of the same kind can have different properties, and different entities can have properties with the same name but different value types. These unique characteristics imply a different way of designing and managing data to take advantage of the ability to scale automatically. In particular, Datastore differs from a traditional relational database in the following important ways:
- Datastore is designed to automatically scale to very large data sets, allowing applications to maintain high performance as they receive more traffic:
- Datastore writes scale by automatically distributing data as necessary.
- Datastore reads scale because the only queries supported are those whose performance scales with the size of the result set (as opposed to the data set). This means that a query whose result set contains 100 entities performs the same whether it searches over a hundred entities or a million. This property is the key reason some types of queries are not supported.
- Because all queries are served by previously built indexes, the types of queries that can be executed are more restrictive than those allowed on a relational database with SQL. In particular, Datastore does not include support for join operations, inequality filtering on multiple properties, or filtering on data based on results of a subquery.
- Unlike traditional relational databases which enforce a schema, Datastore doesn't require entities of the same kind to have a consistent property set (although you can choose to enforce such a requirement in your own application code).
What it's good for
Cloud Datastore is ideal for applications that rely on highly available structured data at scale. You can use Cloud Datastore to store and query all of the following types of data:
- Product catalogs that provide real-time inventory and product details for a retailer.
- User profiles that deliver a customized experience based on the user’s past activities and preferences.
- Transactions based on ACID properties, for example, transferring funds from one bank account to another.
Other storage options
Datastore is not ideal for every use case. For example, Datastore is not a relational database, and it is not an effective storage solution for analytic data.
Here are some common scenarios where you should probably consider an alternative to Datastore:
- If you need a relational database with full SQL support for an online transaction processing (OLTP) system, consider Google Cloud SQL.
- If you don’t require support for ACID transactions or if your data is not highly structured, consider Google Bigtable.
- If you need interactive querying in an online analytical processing (OLAP) system, consider Google BigQuery.
- If you need to store large immutable blobs, such as large images or movies, consider Google Cloud Storage.
Connecting to Cloud Datastore with App Engine
You can access Cloud Datastore using the following APIs:
- Objectify: The Google-recommended open-source Java API which provides a higher-level APIs for Datastore with ORM-like features.
- Java Datastore API: The low-level Datastore API built into the App Engine SDK provides direct access to all of Datastore's features and is described throughout the Java App Engine Datastore documentation.