DBMS > Dragonfly vs. Rockset vs. Snowflake vs. TimescaleDB vs. XTDB

System Properties Comparison Dragonfly vs. Rockset vs. Snowflake vs. TimescaleDB vs. XTDB

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

A scalable, reliable search and analytics service in the cloud, built on RocksDB

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

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Secondary database models

Relational DBMS
Search engine

Relational DBMS

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

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

Score	0.79
Rank	#211	Overall
	#35	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

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

rockset.com

www.snowflake.com

www.timescale.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

www.dragonflydb.io/docs

docs.rockset.com

docs.snowflake.net/manuals/index.html

docs.timescale.com

www.xtdb.com/docs

Developer

DragonflyDB team and community contributors

Rockset

Snowflake Computing Inc.

Timescale

Juxt Ltd.

Initial release

2023

2019

2014

2017

2019

Current release

1.0, March 2023

2.15.0, May 2024

1.19, September 2021

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

Apache 2.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Clojure

Server operating systems

Linux

hosted

Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

scheme-free

schema-free

yes

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

yes

schema-free

Typing

predefined data types such as float or date

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

dynamic typing

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

yes, extensible-data-notation format

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.

ingestion from XML files supported

yes

Secondary indexes

all fields are automatically indexed

yes

SQL

Support of SQL

Read-only SQL queries, including JOINs

yes

full PostgreSQL SQL syntax

limited SQL, making use of Apache Calcite

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

HTTP REST

CLI Client
JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP REST
JDBC

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

Go
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Clojure
Java

Server-side scripts

Stored procedures

Lua

user defined functions

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

publish/subscribe channels provide some trigger functionality

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

yes

yes, across time and space (hash partitioning) attributes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Source-replica replication with hot standby and reads on replicas

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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 and organizations can be defined via Rockset console

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

fine grained access rights according to SQL-standard

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