DBMS > Dragonfly vs. IBM Db2 Event Store vs. searchxml vs. ToroDB

System Properties Comparison Dragonfly vs. IBM Db2 Event Store vs. searchxml vs. ToroDB

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Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

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 Event Store optimized for Internet of Things use cases

DBMS for structured and unstructured content wrapped with an application server

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Key-value store

Event Store
Time Series DBMS

Native XML DBMS
Search engine

Document store

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

Score	0.44
Rank	#255	Overall
	#38	Key-value stores

Score	0.18
Rank	#315	Overall
	#2	Event Stores
	#26	Time Series DBMS

Score	0.00
Rank	#385	Overall
	#7	Native XML DBMS
	#24	Search engines

Website

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

www.ibm.com/products/db2-event-store

www.searchxml.net/category/products

github.com/torodb/server

Technical documentation

www.dragonflydb.io/docs

www.ibm.com/docs/en/db2-event-store

www.searchxml.net/support/handouts

Developer

DragonflyDB team and community contributors

IBM

informationpartners gmbh

8Kdata

Initial release

2023

2017

2015

2016

Current release

1.0, March 2023

2.0

1.0

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

free developer edition available

commercial

Open Source

AGPL-V3

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++

C and C++

C++

Java

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Windows

All OS with a Java 7 VM

Data scheme

scheme-free

yes

schema-free

Typing

predefined data types such as float or date

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

yes

string, integer, double, boolean, date, object_id

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

yes

through the embedded Spark runtime

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

RESTful HTTP API
WebDAV
XQuery
XSLT

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

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

Lua

yes

on the application server

Triggers

publish/subscribe channels provide some trigger functionality

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Active-active shard replication

yes

sychronisation to multiple collections

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
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

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

fine grained access rights according to SQL-standard

Domain, group and role-based access control at the document level and for application services

Access rights for users and roles

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