Google Cloud Datastore vs MongoDB: What are the differences?
What is Google Cloud Datastore? A Fully Managed NoSQL Data Storage Service. 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.
What is MongoDB? The database for giant ideas. MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
Google Cloud Datastore and MongoDB are primarily classified as "NoSQL Database as a Service" and "Databases" tools respectively.
"High scalability" is the primary reason why developers consider Google Cloud Datastore over the competitors, whereas "Document-oriented storage" was stated as the key factor in picking MongoDB.
MongoDB is an open source tool with 16.3K GitHub stars and 4.1K GitHub forks. Here's a link to MongoDB's open source repository on GitHub.
According to the StackShare community, MongoDB has a broader approval, being mentioned in 2189 company stacks & 2218 developers stacks; compared to Google Cloud Datastore, which is listed in 46 company stacks and 16 developer stacks.
What is Google Cloud Datastore?
What is MongoDB?
Want advice about which of these to choose?Ask the StackShare community!
Sign up to add, upvote and see more prosMake informed product decisions
What are the cons of using Google Cloud Datastore?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with Google Cloud Datastore?
Sign up to get full access to all the tool integrationsMake informed product decisions
I started using PostgreSQL because I started a job at a company that was already using it as well as MongoDB. The main difference between the two from my perspective is that postgres columns are a chore to add/remove/modify whereas you can throw whatever you want into a mongo collection. And personally I prefer the query language for postgres over that of mongo, but they both have their merits. Maybe someday I'll be a DBA and have more insight to share but for now there's my 2 cents.
Used MongoDB as primary database. It holds trip data of NYC taxis for the year 2013. It is a huge dataset and it's primary feature is geo coordinates with pickup and drop off locations. Also used MongoDB's map reduce to process this large dataset for aggregation. This aggregated result was then used to show visualizations.
MongoDB fills our more traditional database needs. We knew we wanted Trello to be blisteringly fast. One of the coolest and most performance-obsessed teams we know is our next-door neighbor and sister company StackExchange. Talking to their dev lead David at lunch one day, I learned that even though they use SQL Server for data storage, they actually primarily store a lot of their data in a denormalized format for performance, and normalize only when they need to.
Nearly all of our backend storage is on MongoDB. This has also worked out pretty well. It's enabled us to scale up faster/easier than if we had rolled our own solution on top of PostgreSQL (which we were using previously). There have been a few roadbumps along the way, but the team at 10gen has been a big help with thing.
We are testing out MongoDB at the moment. Currently we are only using a small EC2 setup for a delayed job queue backed by
agenda. If it works out well we might look to see where it could become a primary document storage engine for us.
Used for proofs of concept and personal projects with a document data model, especially with need for strong geographic queries. Often not chosen in long term apps due to chance data model can end up relational as needs develop.
When creating proofs of concept or small personal projects that are hosted primarily in GCP, with non-relational data models, this is the NoSQL managed database I usually pair them with.
This is our primary database, though most of our actual data is stored in static storage. This database houses the metadata necessary for indexing and finding static data.
worked with a client that used datastore as their backend database. helped plan out their schema and architecture. loved the speed and simplicity.