DBMS > GridGain vs. Newts vs. Riak TS vs. Snowflake vs. SurrealDB

System Properties Comparison GridGain vs. Newts vs. Riak TS vs. Snowflake vs. SurrealDB

Editorial information provided by DB-Engines

Name

Description

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

Time Series DBMS based on Cassandra

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

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

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Document store
Graph 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	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.86
Rank	#203	Overall
	#34	Document stores
	#18	Graph DBMS

Website

www.gridgain.com

opennms.github.io/newts

www.snowflake.com

surrealdb.com

Technical documentation

www.gridgain.com/docs/index.html

github.com/OpenNMS/newts/wiki

www.tiot.jp/riak-docs/riak/ts/latest

docs.snowflake.net/manuals/index.html

surrealdb.com/docs

Developer

GridGain Systems, Inc.

OpenNMS Group

Open Source, formerly Basho Technologies

Snowflake Computing Inc.

SurrealDB Ltd

Initial release

2007

2014

2015

2014

2022

Current release

GridGain 8.5.1

3.0.0, September 2022

v1.5.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

commercial

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

Java

Erlang

Rust

Server operating systems

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux
OS X

hosted

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

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

yes, limited

yes

SQL-like query language

APIs and other access methods

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

HTTP REST
Java API

HTTP API
Native Erlang Interface

CLI Client
JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Java

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

JavaScript (Node.js)
Python

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

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

Erlang

user defined functions

Triggers

yes (cache interceptors and events)

yes

pre-commit hooks and post-commit hooks

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

selectable replication factor

based on Cassandra

selectable replication factor

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

yes

Transaction concepts

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

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

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

yes, based on authentication and database rules

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