DBMS > AgensGraph vs. Cassandra vs. Google Cloud Datastore vs. Hawkular Metrics vs. STSdb

System Properties Comparison AgensGraph vs. Cassandra vs. Google Cloud Datastore vs. Hawkular Metrics vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Graph DBMS
Relational DBMS

Secondary database models

Vector DBMS

starting with release V5

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

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

Website

bitnine.net/agensgraph

cassandra.apache.org

cloud.google.com/datastore

www.hawkular.org

github.com/STSSoft/STSdb4

Technical documentation

bitnine.net/documentation

cassandra.apache.org/doc/latest

cloud.google.com/datastore/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

Developer

Bitnine Global Inc.

Apache Software Foundation

Apache top level project, originally developped by Facebook

Google

Community supported by Red Hat

STS Soft SC

Initial release

2016

2008

2014

2011

Current release

2.1, December 2018

4.1.3, July 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache version 2

commercial

Open Source

Apache 2.0

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.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

Server operating systems

Linux
OS X
Windows

BSD
Linux
OS X
Windows

hosted

Linux
OS X
Windows

Windows

Data scheme

depending on used data model

schema-free

yes

Typing

predefined data types such as float or date

yes

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

restricted

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

yes

SQL-like SELECT, DML and DDL statements (CQL)

SQL-like query language (GQL)

APIs and other access methods

Cypher Query Language
JDBC

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

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

HTTP REST

.NET Client API

Supported programming languages

C
Java
JavaScript
Python

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

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

Go
Java
Python
Ruby

C#
Java

Server-side scripts

Stored procedures

yes

using Google App Engine

Triggers

yes

Callbacks using the Google Apps Engine

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

no "single point of failure"

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Representation of geographical distribution of servers is possible

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

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

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

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.

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users can be defined per object

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

More information provided by the system vendor

Specific characteristics

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