DBMS > Dragonfly vs. Google Cloud Datastore vs. InterSystems IRIS vs. Kinetica vs. Snowflake

System Properties Comparison Dragonfly vs. Google Cloud Datastore vs. InterSystems IRIS vs. Kinetica vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

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

A containerised multi-model DBMS, interoperability and analytics data platform with wide capabilities for vertical and horizontal scalability

Fully vectorized database across both GPUs and CPUs

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

Primary database model

Key-value store

Document store

Document store
Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.41
Rank	#266	Overall
	#38	Key-value stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	4.05
Rank	#83	Overall
	#14	Document stores
	#10	Key-value stores
	#1	Object oriented DBMS
	#45	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

github.com/dragonflydb/dragonfly
www.dragonflydb.io

cloud.google.com/datastore

www.intersystems.com/products/intersystems-iris

www.kinetica.com

www.snowflake.com

Technical documentation

www.dragonflydb.io/docs

cloud.google.com/datastore/docs

docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls

docs.kinetica.com

docs.snowflake.net/manuals/index.html

Developer

DragonflyDB team and community contributors

Google

InterSystems

Kinetica

Snowflake Computing Inc.

Initial release

2023

2008

2018

2012

2014

Current release

1.0, March 2023

2023.3, June 2023

7.1, August 2021

License

Commercial or Open Source

Open Source

BSL 1.1

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

C++

C, C++

Server operating systems

Linux

hosted

AIX
Linux
macOS
Ubuntu
Windows

Linux

hosted

Data scheme

scheme-free

schema-free

depending on used data model

yes

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

Typing

predefined data types such as float or date

strings, hashes, lists, sets, sorted sets, bit arrays

yes, details here

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

SQL

Support of SQL

SQL-like query language (GQL)

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

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

JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

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

.Net
C++
Java
JavaScript (Node.js)
Python

C++
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Lua

using Google App Engine

yes

user defined functions

Triggers

publish/subscribe channels provide some trigger functionality

Callbacks using the Google Apps Engine

yes

triggers when inserted values for one or more columns fall within a specified range

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication using Paxos

Source-replica replication

yes

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

Eventual Consistency

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

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

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

GPU vRAM or System RAM

User concepts

Access control

Password-based authentication

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

yes

Access rights for users and roles on table level

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

More information provided by the system vendor

Specific characteristics

InterSystems IRIS is a complete cloud-first data platform which includes a multi-model...
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