DBMS > Apache IoTDB vs. DolphinDB vs. Graph Engine vs. Riak KV vs. Sqrrl

System Properties Comparison Apache IoTDB vs. DolphinDB vs. Graph Engine vs. Riak KV vs. Sqrrl

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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.

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Distributed, fault tolerant key-value store

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Time Series DBMS

Graph DBMS
Key-value store

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Relational DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

iotdb.apache.org

www.dolphindb.com

www.graphengine.io

sqrrl.com

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

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

www.graphengine.io/docs/manual

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Apache Software Foundation

DolphinDB, Inc

Microsoft

OpenSource, formerly Basho Technologies

Amazon

originally Sqrrl Data, Inc.

Initial release

2018

2010

2009

2012

Current release

1.1.0, April 2023

v2.00.4, January 2022

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community version available

Open Source

MIT License

Open Source

Apache version 2, commercial enterprise edition

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

Java

C++

.NET and C

Erlang

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Windows

.NET

Linux
OS X

Linux

Data scheme

yes

schema-free

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

restricted

yes

SQL

Support of SQL

SQL-like query language

APIs and other access methods

JDBC
Native API

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

RESTful HTTP API

HTTP API
Native Erlang Interface

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

C#
C++
F#
Visual Basic

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

yes

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

horizontal partitioning

Sharding

no "single point of failure"

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

links between data sets can be stored

Transaction concepts

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

yes

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent 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

yes

Administrators, Users, Groups

yes, using Riak Security

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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