DBMS > DolphinDB vs. Memcached vs. Realm vs. TigerGraph

System Properties Comparison DolphinDB vs. Memcached vs. Realm vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Description

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

In-memory key-value store, originally intended for caching

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Time Series DBMS
Vector DBMS

Key-value store

Document store

Graph DBMS

Secondary database models

Relational DBMS

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

Score	2.72
Rank	#100	Overall
	#8	Time Series DBMS
	#5	Vector DBMS

Score	16.84
Rank	#32	Overall
	#4	Key-value stores

Score	7.18
Rank	#52	Overall
	#8	Document stores

Score	1.44
Rank	#152	Overall
	#14	Graph DBMS

Website

Technical documentation

docs.dolphindb.cn/en/help200/index.html

github.com/memcached/memcached/wiki

realm.io/docs

docs.tigergraph.com

Developer

DolphinDB, Inc

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Realm, acquired by MongoDB in May 2019

Initial release

2018

2003

2014

2017

Current release

v2.00.4, January 2022

1.6.29, June 2024

License

Commercial or Open Source

commercial

free community version available

Open Source

BSD license

Open Source

commercial

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++

Server operating systems

Linux
Windows

FreeBSD
Linux
OS X
Unix
Windows

Android
Backend: server-less
iOS
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (GSQL)

APIs and other access methods

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

Proprietary protocol

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

.Net
Java

with Android only
Objective-C
React Native
Swift

C++
Java

Server-side scripts

Stored procedures

yes

runs within the applications so server-side scripts are unnecessary

yes

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Repcached, a Memcached patch, provides this functionallity

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

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

In-Memory realm

User concepts

Access control

Administrators, Users, Groups

yes

using SASL (Simple Authentication and Security Layer) protocol

yes

Role-based access control

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More resources

DB-Engines blog posts

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3 October 2012, Matthias Gelbmann, Paul Andlinger

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2 March 2016, Paul Andlinger

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