DBMS > BoltDB vs. Google Cloud Bigtable vs. Google Cloud Spanner vs. IBM Cloudant vs. Snowflake

System Properties Comparison BoltDB vs. Google Cloud Bigtable vs. Google Cloud Spanner vs. IBM Cloudant vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Database as a Service offering based on Apache CouchDB

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Key-value store

Key-value store
Wide column store

Relational DBMS

Document store

Relational DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

github.com/boltdb/bolt

cloud.google.com/bigtable

cloud.google.com/spanner

www.ibm.com/products/cloudant

www.snowflake.com

Technical documentation

cloud.google.com/bigtable/docs

cloud.google.com/spanner/docs

cloud.ibm.com/docs/Cloudant

docs.snowflake.net/manuals/index.html

Developer

Google

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Snowflake Computing Inc.

Initial release

2013

2015

2017

2010

2014

License

Commercial or Open Source

Open Source

MIT License

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Erlang

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

Data scheme

schema-free

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

yes

APIs and other access methods

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
Python

Go
Java
JavaScript (Node.js)
Python

C#
Java
JavaScript
Objective-C
PHP
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Internal replication in Colossus, and regional replication between two clusters in different zones

Multi-source replication with 3 replicas for regional instances.

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

yes

Atomic single-row operations

ACID

Strict serializable isolation

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights for users can be defined per database

Users with fine-grained authorization concept, user roles and pluggable authentication

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