DBMS > GridGain vs. HBase vs. Hypertable vs. Newts vs. Snowflake

System Properties Comparison GridGain vs. HBase vs. Hypertable vs. Newts vs. Snowflake

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

An open source BigTable implementation based on distributed file systems such as Hadoop

Time Series DBMS based on Cassandra

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Key-value store
Relational DBMS

Wide column store

Time Series DBMS

Relational DBMS

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

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.gridgain.com

hbase.apache.org

opennms.github.io/newts

www.snowflake.com

Technical documentation

www.gridgain.com/docs/index.html

hbase.apache.org/book.html

github.com/OpenNMS/newts/wiki

docs.snowflake.net/manuals/index.html

Developer

GridGain Systems, Inc.

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Hypertable Inc.

OpenNMS Group

Snowflake Computing Inc.

Initial release

2007

2008

2009

2014

Current release

GridGain 8.5.1

2.3.4, January 2021

0.9.8.11, March 2016

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

GNU version 3. Commercial license available

Open Source

Apache 2.0

commercial

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++

Java

Server operating systems

Linux
OS X
Solaris
Windows

Linux
Unix
Windows

using Cygwin

Linux
OS X
Windows

an inofficial Windows port is available

Linux
OS X
Windows

hosted

Data scheme

yes

schema-free, schema definition possible

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

Secondary indexes

yes

restricted

only exact value or prefix value scans

SQL

Support of SQL

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

yes

APIs and other access methods

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

Java API
RESTful HTTP API
Thrift

C++ API
Thrift

HTTP REST
Java API

CLI Client
JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++
Java
Perl
PHP
Python
Ruby

Java

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

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

yes

Coprocessors in Java

user defined functions

Triggers

yes (cache interceptors and events)

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Multi-source replication
Source-replica replication

selectable replication factor on file system level

selectable replication factor

based on Cassandra

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

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

Security Hooks for custom implementations

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Users with fine-grained authorization concept, user roles and pluggable authentication

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