DBMS > Dragonfly vs. Google Cloud Datastore vs. Newts vs. Snowflake vs. YDB

System Properties Comparison Dragonfly vs. Google Cloud Datastore vs. Newts vs. Snowflake vs. YDB

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

Time Series DBMS based on Cassandra

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

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Document store
Relational DBMS

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

Score	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.33
Rank	#287	Overall
	#43	Document stores
	#132	Relational DBMS

Website

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

cloud.google.com/datastore

opennms.github.io/newts

www.snowflake.com

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

www.dragonflydb.io/docs

cloud.google.com/datastore/docs

github.com/OpenNMS/newts/wiki

docs.snowflake.net/manuals/index.html

ydb.tech/en/docs

Developer

DragonflyDB team and community contributors

Google

OpenNMS Group

Snowflake Computing Inc.

Yandex

Initial release

2023

2008

2014

2019

Current release

1.0, March 2023

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0; 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

C++

Java

Server operating systems

Linux

hosted

Linux
OS X
Windows

hosted

Linux

Data scheme

scheme-free

schema-free

yes

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

Flexible Schema (defined schema, partial schema, schema free)

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 query language (YQL)

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

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

HTTP REST
Java API

CLI Client
JDBC
ODBC

RESTful HTTP API (DynamoDB compatible)

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

Java

JavaScript (Node.js)
Python

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

Lua

using Google App Engine

user defined functions

Triggers

publish/subscribe channels provide some trigger functionality

Callbacks using the Google Apps Engine

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

yes

Active-passive shard replication

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.

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

User concepts

Access control

Password-based authentication

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

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

Access rights defined for Yandex Cloud users

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