Cloud Firestore vs Google Cloud Datastore

Overview

Google Cloud Datastore

Stacks290

Followers357

Votes12

Cloud Firestore

Stacks751

Followers900

Votes112

Cloud Firestore vs Google Cloud Datastore: What are the differences?

Cloud Firestore and Google Cloud Datastore are both NoSQL document databases offered by Google. They have similar features and functionality, but there are key differences between them that make each suitable for different use cases.

Data hierarchy: Cloud Firestore provides a more structured approach to organizing data by using collections and documents. It allows for nested data structures within documents, providing more flexibility in data modeling. On the other hand, Google Cloud Datastore uses a flat data model where entities have properties, but there are no nested subcollections or documents.
Transactions: Cloud Firestore offers atomic transactions that allow multiple document updates to be treated as a single atomic operation. This ensures data consistency and integrity. In contrast, Google Cloud Datastore only supports single-entity transactions, meaning that if you need to update multiple entities atomically, you would have to implement it manually.
Scalability: Cloud Firestore scales automatically to handle high read and write loads. It can support larger collections and is better suited for applications that require real-time updates and high scalability. Google Cloud Datastore also scales well, but it has some limitations on the number of entities you can read or write per second.
Queries: Cloud Firestore enables more powerful and flexible querying with compound queries, range comparison, and array-contains queries. It allows you to perform complex queries with less code. Google Cloud Datastore, on the other hand, has a simpler querying model. It only allows querying by a single property value.
Pricing: Cloud Firestore has a different pricing model compared to Google Cloud Datastore. Firestore’s pricing is based on the number of documents read, written, and deleted, as well as the amount of data stored. Google Cloud Datastore pricing is based on the number of entities read, written, and accessed.
Data synchronization: Cloud Firestore has built-in support for real-time data synchronization through its native SDKs, allowing you to easily build collaborative and real-time applications. Google Cloud Datastore does not have this built-in functionality and would require additional implementation for real-time data synchronization.

In summary, Cloud Firestore offers a more structured approach to data organization, supports atomic transactions, scales better, provides more powerful querying capabilities, has a different pricing model, and offers built-in support for real-time data synchronization. Google Cloud Datastore, on the other hand, has a simpler data model, supports single-entity transactions, and has a different pricing model.

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Detailed Comparison

Google Cloud Datastore	Cloud Firestore
Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.	Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.
Schemaless access, with SQL-like querying;Managed database;Autoscale with your users;ACID transactions;Built-in redundancy;Local development tools	Documents and collections with powerful querying;iOS, Android, and Web SDKs with offline data access;Real-time data synchronization;Automatic, multi-region data replication with strong consistency;Node, Python, Go, and Java server SDKs
Statistics
Stacks 290	Stacks 751
Followers 357	Followers 900
Votes 12	Votes 112
Pros & Cons
Pros 7 High scalability 2 Serverless 2 Ability to query any property 1 Pay for what you use	Pros 15 Cloud Storage 15 Easy to use 12 Easy setup 12 Realtime Database 9 Super fast Cons 8 Doesn't support FullTextSearch natively
Integrations
No integrations available	Golang Node.js Java Python Firebase Cloud Functions for Firebase Google Cloud Functions

What are some alternatives to Google Cloud Datastore, Cloud Firestore?

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

Azure Cosmos DB

Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

Cloudant

Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own.

Google Cloud Bigtable

Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.

CloudBoost

CloudBoost.io is a database service for the “next web” - that not only does data-storage, but also search, real-time and a whole lot more which enables developers to build much richer apps with 50% less time saving them a ton of cost and helping them go to market much faster.

Firebase Realtime Database

It is a cloud-hosted NoSQL database that lets you store and sync data between your users in realtime. Data is synced across all clients in realtime, and remains available when your app goes offline.

restdb.io

RestDB is a NoSql document oriented database cloud service. Data is accessed as JSON objects via HTTPS. This gives great flexibility, easy system integration and future compatibility.

Amazon DocumentDB

Amazon DocumentDB is a non-relational database service designed from the ground-up to give you the performance, scalability, and availability you need when operating mission-critical MongoDB workloads at scale. In Amazon DocumentDB, the storage and compute are decoupled, allowing each to scale independently, and you can increase the read capacity to millions of requests per second by adding up to 15 low latency read replicas in minutes, regardless of the size of your data.

Amazon SimpleDB

Developers simply store and query data items via web services requests and Amazon SimpleDB does the rest. Behind the scenes, Amazon SimpleDB creates and manages multiple geographically distributed replicas of your data automatically to enable high availability and data durability. Amazon SimpleDB provides a simple web services interface to create and store multiple data sets, query your data easily, and return the results. Your data is automatically indexed, making it easy to quickly find the information that you need. There is no need to pre-define a schema or change a schema if new data is added later. And scale-out is as simple as creating new domains, rather than building out new servers.

Datomic Cloud

A transactional database with a flexible data model, elastic scaling, and rich queries. Datomic is designed from the ground up to run on AWS. Datomic leverages AWS technology, including DynamoDB, S3, EFS, and CloudFormation to provide a fully integrated solution.

Related Comparisons

Cloud Firestore vs Google Cloud Datastore: What are the differences?

Data hierarchy: Cloud Firestore provides a more structured approach to organizing data by using collections and documents. It allows for nested data structures within documents, providing more flexibility in data modeling. On the other hand, Google Cloud Datastore uses a flat data model where entities have properties, but there are no nested subcollections or documents.
Transactions: Cloud Firestore offers atomic transactions that allow multiple document updates to be treated as a single atomic operation. This ensures data consistency and integrity. In contrast, Google Cloud Datastore only supports single-entity transactions, meaning that if you need to update multiple entities atomically, you would have to implement it manually.
Scalability: Cloud Firestore scales automatically to handle high read and write loads. It can support larger collections and is better suited for applications that require real-time updates and high scalability. Google Cloud Datastore also scales well, but it has some limitations on the number of entities you can read or write per second.
Queries: Cloud Firestore enables more powerful and flexible querying with compound queries, range comparison, and array-contains queries. It allows you to perform complex queries with less code. Google Cloud Datastore, on the other hand, has a simpler querying model. It only allows querying by a single property value.
Pricing: Cloud Firestore has a different pricing model compared to Google Cloud Datastore. Firestore’s pricing is based on the number of documents read, written, and deleted, as well as the amount of data stored. Google Cloud Datastore pricing is based on the number of entities read, written, and accessed.
Data synchronization: Cloud Firestore has built-in support for real-time data synchronization through its native SDKs, allowing you to easily build collaborative and real-time applications. Google Cloud Datastore does not have this built-in functionality and would require additional implementation for real-time data synchronization.

Cloud Firestore vs Google Cloud Datastore

Overview