DBMS > Apache IoTDB vs. Graph Engine vs. HugeGraph vs. Microsoft Azure Data Explorer vs. Transwarp ArgoDB

System Properties Comparison Apache IoTDB vs. Graph Engine vs. HugeGraph vs. Microsoft Azure Data Explorer vs. Transwarp ArgoDB

Editorial information provided by DB-Engines

Name

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

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

A fast-speed and highly-scalable Graph DBMS

Fully managed big data interactive analytics platform

Distributed Analytical Database to replace Hadoop+MPP hybrid architecture

Primary database model

Time Series DBMS

Graph DBMS
Key-value store

Graph DBMS

Relational DBMS

column oriented

Relational DBMS

Secondary database models

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

Search engine

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	0.07
Rank	#350	Overall
	#153	Relational DBMS

Website

iotdb.apache.org

www.graphengine.io

github.com/hugegraph
hugegraph.apache.org

azure.microsoft.com/services/data-explorer

www.transwarp.cn/en/product/argodb

Technical documentation

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

www.graphengine.io/docs/manual

hugegraph.apache.org/docs

docs.microsoft.com/en-us/azure/data-explorer

docs.transwarp.cn/#/documents-support/docs?category=ARGODB

Developer

Apache Software Foundation

Microsoft

Baidu

Microsoft

Transwarp

Initial release

2018

2010

2018

2019

Current release

1.1.0, April 2023

0.9

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

MIT License

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

.NET and C

Java

Server operating systems

All OS with a Java VM (>= 1.8)

.NET

Linux
macOS
Unix

hosted

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

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

also supports composite index and range index

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

Kusto Query Language (KQL), SQL subset

yes

APIs and other access methods

JDBC
Native API

RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
C++
F#
Visual Basic

Groovy
Java
Python

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

Yes, possible languages: KQL, Python, R

Triggers

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

Partitioning methods

Methods for storing different data on different nodes

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

horizontal partitioning

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Implicit feature of the cloud service

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

depending on used storage backend, e.g. Cassandra and HBase

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

via hugegraph-spark

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

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

Users, roles and permissions

Azure Active Directory Authentication

More information provided by the system vendor

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