DBMS > Google Cloud Datastore vs. GridGain vs. Hawkular Metrics vs. RocksDB vs. STSdb

System Properties Comparison Google Cloud Datastore vs. GridGain vs. Hawkular Metrics vs. RocksDB vs. STSdb

Editorial information provided by DB-Engines

Name

Description

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

GridGain is an in-memory computing platform, built on Apache Ignite

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

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

Document store

Key-value store
Relational DBMS

Time Series DBMS

Key-value store

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

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

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

Website

cloud.google.com/datastore

www.gridgain.com

www.hawkular.org

rocksdb.org

github.com/STSSoft/STSdb4

Technical documentation

cloud.google.com/datastore/docs

www.gridgain.com/docs/index.html

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

github.com/facebook/rocksdb/wiki

Developer

Google

GridGain Systems, Inc.

Community supported by Red Hat

Facebook, Inc.

STS Soft SC

Initial release

2008

2007

2014

2013

2011

Current release

GridGain 8.5.1

9.2.1, May 2024

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache 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++, .Net

Java

C++

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux

Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

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

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

HTTP REST

C++ API
Java API

.NET Client API

Supported programming languages

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

C#
C++
Java
PHP
Python
Ruby
Scala

Go
Java
Python
Ruby

C
C++
Go
Java
Perl
Python
Ruby

C#
Java

Server-side scripts

Stored procedures

using Google App Engine

yes (compute grid and cache interceptors can be used instead)

Triggers

Callbacks using the Google Apps Engine

yes (cache interceptors and events)

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes (replicated cache)

selectable replication factor

based on Cassandra

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes (compute grid and hadoop accelerator)

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

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)

Security Hooks for custom implementations

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