DBMS > Axibase vs. Google Cloud Datastore vs. MonetDB vs. RocksDB vs. STSdb

System Properties Comparison Axibase vs. Google Cloud Datastore vs. MonetDB vs. RocksDB vs. STSdb

Editorial information provided by DB-Engines

Name

Description

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

A relational database management system that stores data in columns

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

axibase.com/docs/atsd/finance

cloud.google.com/datastore

www.monetdb.org

rocksdb.org

github.com/STSSoft/STSdb4

Technical documentation

cloud.google.com/datastore/docs

www.monetdb.org/Documentation

github.com/facebook/rocksdb/wiki

Developer

Axibase Corporation

Google

MonetDB BV

Facebook, Inc.

STS Soft SC

Initial release

2013

2008

2004

2013

2011

Current release

15585

Dec2023 (11.49), December 2023

8.11.4, April 2024

4.0.8, September 2015

License

Commercial or Open Source

commercial

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

commercial

Open Source

Mozilla Public License 2.0

Open Source

BSD

Open Source

GPLv2, commercial license available

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++

Server operating systems

Linux

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Windows

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

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

yes, details here

yes

primitive types and user defined types (classes)

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (GQL)

yes

SQL 2003 with some extensions

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

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

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

C++ API
Java API

.NET Client API

Supported programming languages

Go
Java
PHP
Python
R
Ruby

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

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C
C++
Go
Java
Perl
Python
Ruby

C#
Java

Server-side scripts

Stored procedures

yes

using Google App Engine

yes, in SQL, C, R

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding via remote tables

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication using Paxos

none

Source-replica replication available in experimental status

yes

none

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 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

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

yes

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

fine grained access rights according to SQL-standard

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