DBMS > Amazon DynamoDB vs. FoundationDB vs. Google Cloud Bigtable vs. Solr

System Properties Comparison Amazon DynamoDB vs. FoundationDB vs. Google Cloud Bigtable vs. Solr

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Editorial information provided by DB-Engines

Name

Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the ranking.

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Ordered key-value store. Core features are complimented by layers.

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 widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Document store
Key-value store

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Key-value store
Wide column store

Search engine

Secondary database models

Spatial DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	1.03
Rank	#190	Overall
	#31	Document stores
	#28	Key-value stores
	#89	Relational DBMS

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

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/dynamodb

github.com/apple/foundationdb

cloud.google.com/bigtable

solr.apache.org

Technical documentation

docs.aws.amazon.com/dynamodb

apple.github.io/foundationdb

cloud.google.com/bigtable/docs

solr.apache.org/resources.html

Developer

Amazon

FoundationDB

Google

Apache Software Foundation

Initial release

2012

2013

2015

2006

Current release

6.2.28, November 2020

9.6.0, April 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2

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

C++

Java

Server operating systems

hosted

Linux
OS X
Windows

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

some layers support schemas

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

some layers support typing

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

supported in specific SQL layer only

Solr Parallel SQL Interface

APIs and other access methods

RESTful HTTP API

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

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

in SQL-layer only

Java plugins

Triggers

yes

by integration with AWS Lambda

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Linearizable consistency

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

Eventual Consistency

Foreign keys

Referential integrity

in SQL-layer only

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

Atomic single-row operations

optimistic locking

Concurrency

Support for concurrent manipulation of data

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.

yes

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

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

yes

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