DBMS > Amazon Redshift vs. Axibase vs. Google Cloud Datastore vs. Oracle Berkeley DB vs. Warp 10

System Properties Comparison Amazon Redshift vs. Axibase vs. Google Cloud Datastore vs. Oracle Berkeley DB vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

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

Widely used in-process key-value store

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

aws.amazon.com/redshift

axibase.com/docs/atsd/finance

cloud.google.com/datastore

www.oracle.com/database/technologies/related/berkeleydb.html

www.warp10.io

Technical documentation

docs.aws.amazon.com/redshift

cloud.google.com/datastore/docs

docs.oracle.com/cd/E17076_05/html/index.html

www.warp10.io/content/02_Getting_started

Developer

Amazon (based on PostgreSQL)

Axibase Corporation

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

SenX

Initial release

2012

2013

2008

1994

2015

Current release

15585

18.1.40, May 2020

License

Commercial or Open Source

commercial

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

commercial

Open Source

commercial license available

Open Source

Apache License 2.0

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, Java, C++ (depending on the Berkeley DB edition)

Java

Server operating systems

hosted

Linux

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

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

yes, details here

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

only with the Berkeley DB XML edition

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

SQL-like query language (GQL)

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JDBC
ODBC

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

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

HTTP API
Jupyter
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
PHP
Python
R
Ruby

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

user defined functions

in Python

yes

using Google App Engine

yes

WarpScript

Triggers

yes

Callbacks using the Google Apps Engine

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication using Paxos

Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

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.

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

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

fine grained access rights according to SQL-standard

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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