DBMS > Dragonfly vs. KairosDB vs. OpenQM vs. Spark SQL

System Properties Comparison Dragonfly vs. KairosDB vs. OpenQM vs. Spark SQL

Please select another system to include it in the comparison.

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

Distributed Time Series DBMS based on Cassandra or H2

QpenQM is a high-performance, self-tuning, multi-value DBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

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

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

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

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

github.com/kairosdb/kairosdb

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

spark.apache.org/sql

Technical documentation

www.dragonflydb.io/docs

kairosdb.github.io

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

DragonflyDB team and community contributors

Rocket Software, originally Martin Phillips

Apache Software Foundation

Initial release

2023

2013

1993

2014

Current release

1.0, March 2023

1.2.2, November 2018

3.4-12

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

BSL 1.1

Open Source

Apache 2.0

Open Source

GPLv2, extended commercial license available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

Scala

Server operating systems

Linux

Linux
OS X
Windows

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux
OS X
Windows

Data scheme

scheme-free

schema-free

yes

with some exceptions

yes

Typing

predefined data types such as float or date

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

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 DML and DDL statements

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

Graphite protocol
HTTP REST
Telnet API

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

Java
JavaScript

Node.js
PHP
Python

.Net
Basic
C
Java
Objective C
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

Lua

yes

Triggers

publish/subscribe channels provide some trigger functionality

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

based on Cassandra

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

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

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

simple password-based access control

Access rights can be defined down to the item level

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

DragonflyDB Announces $21m in New Funding and General Availability
21 March 2023, Business Wire

DragonflyDB reels in $21M for its speedy in-memory database
21 March 2023, SiliconANGLE News

Intel Linux Kernel Optimizations Show Huge Benefit For High Core Count Servers
29 March 2023, Phoronix

New Kubernetes Operator for Dragonfly In-Memory Datastore Now Available for Simplified Operations and Increased ...
18 April 2023, Business Wire

SFU Computing Science researchers receive 2022 ACM SIGMOD Research Highlight Award.
24 February 2023, Simon Fraser University News

provided by Google News

Real-Time Performance and Health Monitoring Using Netdata
2 September 2019, CNX Software

Expo: Real Time A/B Testing and Monitoring with Spark Streaming and Kafka at Walmart Labs
24 May 2019, InfoQ.com

provided by Google News

Use Amazon Athena with Spark SQL for your open-source transactional table formats | Amazon Web Services
24 January 2024, AWS Blog

What is Apache Spark? The big data platform that crushed Hadoop
3 April 2024, InfoWorld

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024
1 April 2024, Simplilearn

Feature Engineering for Time-Series Using PySpark on Databricks
8 May 2024, Towards Data Science

18 Top Big Data Tools and Technologies to Know About in 2024
24 January 2024, TechTarget

provided by Google News

Share this page