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  1. Stackups
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  4. Databases
  5. Couchbase vs Google Cloud Datastore

Couchbase vs Google Cloud Datastore

OverviewDecisionsComparisonAlternatives

Overview

Couchbase
Couchbase
Stacks505
Followers606
Votes110
Google Cloud Datastore
Google Cloud Datastore
Stacks290
Followers357
Votes12

Couchbase vs Google Cloud Datastore: What are the differences?

Developers describe Couchbase as "Document-Oriented NoSQL Database". Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands. On the other hand, Google Cloud Datastore is detailed as "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.

Couchbase belongs to "Databases" category of the tech stack, while Google Cloud Datastore can be primarily classified under "NoSQL Database as a Service".

Some of the features offered by Couchbase are:

  • JSON document database
  • N1QL (SQL-like query language)
  • Secondary Indexing

On the other hand, Google Cloud Datastore provides the following key features:

  • Schemaless access, with SQL-like querying
  • Managed database
  • Autoscale with your users

"Flexible data model, easy scalability, extremely fast" is the top reason why over 13 developers like Couchbase, while over 4 developers mention "High scalability" as the leading cause for choosing Google Cloud Datastore.

According to the StackShare community, Couchbase has a broader approval, being mentioned in 45 company stacks & 21 developers stacks; compared to Google Cloud Datastore, which is listed in 46 company stacks and 16 developer stacks.

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Advice on Couchbase, Google Cloud Datastore

Gabriel
Gabriel

CEO at Naologic

Jan 2, 2020

DecidedonCouchDBCouchDBCouchbaseCouchbaseMemcachedMemcached

We implemented our first large scale EPR application from naologic.com using CouchDB .

Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.

It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.

592k views592k
Comments
Mike
Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k
Comments

Detailed Comparison

Couchbase
Couchbase
Google Cloud Datastore
Google Cloud Datastore

Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.

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.

JSON document database; N1QL (SQL-like query language); Secondary Indexing; Full-Text Indexing; Eventing/Triggers; Real-Time Analytics; Mobile Synchronization for offline support; Autonomous Operator for Kubernetes and OpenShift
Schemaless access, with SQL-like querying;Managed database;Autoscale with your users;ACID transactions;Built-in redundancy;Local development tools
Statistics
Stacks
505
Stacks
290
Followers
606
Followers
357
Votes
110
Votes
12
Pros & Cons
Pros
  • 18
    High performance
  • 18
    Flexible data model, easy scalability, extremely fast
  • 9
    Mobile app support
  • 7
    You can query it with Ansi-92 SQL
  • 6
    All nodes can be read/write
Cons
  • 3
    Terrible query language
Pros
  • 7
    High scalability
  • 2
    Serverless
  • 2
    Ability to query any property
  • 1
    Pay for what you use
Integrations
Hadoop
Hadoop
Kafka
Kafka
Elasticsearch
Elasticsearch
Kubernetes
Kubernetes
Apache Spark
Apache Spark
No integrations available

What are some alternatives to Couchbase, Google Cloud Datastore?

MongoDB

MongoDB

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.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

Amazon DynamoDB

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.

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