DBMS > atoti vs. Google Cloud Datastore vs. HarperDB vs. Hawkular Metrics vs. STSdb

System Properties Comparison atoti vs. Google Cloud Datastore vs. HarperDB vs. Hawkular Metrics vs. STSdb

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

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

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

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

Website

atoti.io

cloud.google.com/datastore

www.harperdb.io

www.hawkular.org

github.com/STSSoft/STSdb4

Technical documentation

docs.atoti.io

cloud.google.com/datastore/docs

docs.harperdb.io/docs

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

Developer

ActiveViam

Google

HarperDB

Community supported by Red Hat

STS Soft SC

Initial release

2008

2017

2014

2011

Current release

3.1, August 2021

4.0.8, September 2015

License

Commercial or Open Source

commercial

free versions available

commercial

free community edition available

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.

Implementation language

Java

Node.js

Java

Server operating systems

hosted

Linux
OS X

Linux
OS X
Windows

Windows

Data scheme

schema-free

dynamic schema

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

yes

JSON data types

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

Multidimensional Expressions (MDX)

SQL-like query language (GQL)

SQL-like data manipulation statements

APIs and other access methods

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

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HTTP REST

.NET Client API

Supported programming languages

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

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

Go
Java
Python
Ruby

C#
Java

Server-side scripts

Stored procedures

Python

using Google App Engine

Custom Functions

since release 3.1

Triggers

Callbacks using the Google Apps Engine

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

A table resides as a whole on one (or more) nodes in a cluster

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

the nodes on which a table resides can be defined

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 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

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

Serializable Isolation within Transactions, Read Committed outside of Transactions

Atomic execution of specific operations

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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)

Access rights for users and roles

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