Couchbase vs Google Cloud Spanner

Overview

Couchbase

Stacks505

Followers606

Votes110

Google Cloud Spanner

Stacks57

Followers117

Votes3

GitHub Stars2.0K

Forks1.1K

Couchbase vs Google Cloud Spanner: What are the differences?

Introduction: 
When considering database solutions for your project, it is important to weigh the differences between Couchbase and Google Cloud Spanner. Here are the key distinctions to keep in mind.

1. **Data Model**:
   Couchbase is a NoSQL distributed document database whereas Google Cloud Spanner is a horizontally scalable, strongly consistent relational database service. Couchbase stores data in JSON format, allowing for flexible schemas, while Google Cloud Spanner adheres to a traditional relational model with strong consistency guarantees.

2. **Consistency**:
   Couchbase offers eventual consistency by default with the option to configure stronger consistency levels, while Google Cloud Spanner provides strong consistency across regions with synchronous replication. This difference in consistency models can impact the latency and availability of data updates in both databases.

3. **Scale and Distribution**:
   Couchbase allows for easy horizontal scaling with its shared-nothing architecture, making it suitable for high availability and large workloads. On the other hand, Google Cloud Spanner is designed for global distribution and guaranteed uptime with its automatic sharding and replication capabilities, making it ideal for mission-critical applications requiring low latency worldwide.

4. **Query Language**:
   Couchbase uses N1QL (SQL for JSON) as its query language, which allows for querying JSON data with SQL-like syntax. Google Cloud Spanner supports standard SQL queries with additional distributed SQL features for scalable and efficient data retrieval. This difference in query languages can impact the ease of development and migration for database users.

5. **Consistency Models**:
   Couchbase offers different consistency levels such as eventual consistency and strong consistency, while Google Cloud Spanner ensures strong consistency across regions with synchronous replication. The choice between these two databases should consider the consistency requirements of your application, as it can significantly impact performance and data integrity.

6. **Pricing Model**:
   Couchbase is typically priced based on the number of nodes in the cluster and the desired features, while Google Cloud Spanner follows a pay-as-you-go pricing model based on resources consumed and storage usage. Understanding the pricing structure of each database can help in determining the cost-effectiveness of using either Couchbase or Google Cloud Spanner for your project.

In Summary, understanding the key differences in data model, consistency, scale, query language, consistency models, and pricing can help in making an informed decision between Couchbase and Google Cloud Spanner for database solutions.

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CLI (Node.js)

Manual

Advice on Couchbase, Google Cloud Spanner

Gabriel

CEO at Naologic

Jan 2, 2020

Decidedon

CouchDB

Couchbase

Memcached

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

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	Google Cloud Spanner
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.	It is a globally distributed database service that gives developers a production-ready storage solution. It provides key features such as global transactions, strongly consistent reads, and automatic multi-site replication and failover.
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	Global transactions; Strongly consistent reads; Automatic multi-site replication; Failover.
Statistics
GitHub Stars -	GitHub Stars 2.0K
GitHub Forks -	GitHub Forks 1.1K
Stacks 505	Stacks 57
Followers 606	Followers 117
Votes 110	Votes 3
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 1 Scalable 1 Horizontal scaling 1 Strongly consistent
Integrations
Hadoop Kafka Elasticsearch Kubernetes Apache Spark	MySQL PostgreSQL MongoDB SQLite

What are some alternatives to Couchbase, Google Cloud Spanner?

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

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

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

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 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

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 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.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Couchbase vs Google Cloud Spanner: What are the differences?

Introduction: 
When considering database solutions for your project, it is important to weigh the differences between Couchbase and Google Cloud Spanner. Here are the key distinctions to keep in mind.

1. **Data Model**:
   Couchbase is a NoSQL distributed document database whereas Google Cloud Spanner is a horizontally scalable, strongly consistent relational database service. Couchbase stores data in JSON format, allowing for flexible schemas, while Google Cloud Spanner adheres to a traditional relational model with strong consistency guarantees.

2. **Consistency**:
   Couchbase offers eventual consistency by default with the option to configure stronger consistency levels, while Google Cloud Spanner provides strong consistency across regions with synchronous replication. This difference in consistency models can impact the latency and availability of data updates in both databases.

3. **Scale and Distribution**:
   Couchbase allows for easy horizontal scaling with its shared-nothing architecture, making it suitable for high availability and large workloads. On the other hand, Google Cloud Spanner is designed for global distribution and guaranteed uptime with its automatic sharding and replication capabilities, making it ideal for mission-critical applications requiring low latency worldwide.

4. **Query Language**:
   Couchbase uses N1QL (SQL for JSON) as its query language, which allows for querying JSON data with SQL-like syntax. Google Cloud Spanner supports standard SQL queries with additional distributed SQL features for scalable and efficient data retrieval. This difference in query languages can impact the ease of development and migration for database users.

5. **Consistency Models**:
   Couchbase offers different consistency levels such as eventual consistency and strong consistency, while Google Cloud Spanner ensures strong consistency across regions with synchronous replication. The choice between these two databases should consider the consistency requirements of your application, as it can significantly impact performance and data integrity.

6. **Pricing Model**:
   Couchbase is typically priced based on the number of nodes in the cluster and the desired features, while Google Cloud Spanner follows a pay-as-you-go pricing model based on resources consumed and storage usage. Understanding the pricing structure of each database can help in determining the cost-effectiveness of using either Couchbase or Google Cloud Spanner for your project.

Couchbase vs Google Cloud Spanner

Overview