DBMS > Amazon DynamoDB vs. Axibase vs. EJDB vs. eXtremeDB vs. Google Cloud Datastore

System Properties Comparison Amazon DynamoDB vs. Axibase vs. EJDB vs. eXtremeDB vs. Google Cloud Datastore

Editorial information provided by DB-Engines

Name

Description

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

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Natively in-memory DBMS with options for persistency, high-availability and clustering

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Primary database model

Document store
Key-value store

Time Series DBMS

Document store

Relational DBMS
Time Series DBMS

Document store

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

Score	70.06
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.21
Rank	#308	Overall
	#25	Time Series DBMS

Score	0.13
Rank	#331	Overall
	#46	Document stores

Score	0.79
Rank	#212	Overall
	#100	Relational DBMS
	#18	Time Series DBMS

Score	4.13
Rank	#71	Overall
	#12	Document stores

Website

aws.amazon.com/dynamodb

axibase.com/docs/atsd/finance

github.com/Softmotions/ejdb

www.mcobject.com

cloud.google.com/datastore

Technical documentation

docs.aws.amazon.com/dynamodb

github.com/Softmotions/ejdb/blob/master/README.md

www.mcobject.com/docs/extremedb.htm

cloud.google.com/datastore/docs

Developer

Amazon

Axibase Corporation

Softmotions

McObject

Google

Initial release

2012

2013

2012

2001

2008

Current release

15585

8.2, 2021

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

Open Source

GPLv2

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

Java

C and C++

Server operating systems

hosted

Linux

server-less

AIX
HP-UX
Linux
macOS
Solaris
Windows

hosted

Data scheme

schema-free

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes

string, integer, double, bool, date, object_id

yes

yes, details here

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.

support of XML interfaces available

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

with the option: eXtremeSQL

SQL-like query language (GQL)

APIs and other access methods

RESTful HTTP API

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

in-process shared library

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Go
Java
PHP
Python
R
Ruby

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

.Net
C
C#
C++
Java
Lua
Python
Scala

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

using Google App Engine

Triggers

yes

by integration with AWS Lambda

yes

by defining events

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning / sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

Multi-source replication using Paxos

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

via ReferenceProperties or Ancestor paths

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

Serializable Isolation within Transactions, Read Committed outside of Transactions

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

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)

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