DBMS > Apache IoTDB vs. Coveo vs. Machbase Neo vs. TigerGraph

System Properties Comparison Apache IoTDB vs. Coveo vs. Machbase Neo vs. TigerGraph

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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

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

TimeSeries DBMS for AIoT and BigData

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

Primary database model

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	2.14
Rank	#116	Overall
	#11	Search engines

Score	0.07
Rank	#348	Overall
	#34	Time Series DBMS

Score	1.44
Rank	#152	Overall
	#14	Graph DBMS

Website

Technical documentation

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

docs.coveo.com

machbase.com/dbms

docs.tigergraph.com

Developer

Apache Software Foundation

Coveo

Machbase

Initial release

2018

2012

2013

2017

Current release

1.1.0, April 2023

V8.0, August 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free test version available

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

C++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

Linux
macOS
Windows

Linux

Data scheme

yes

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

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
Native API

RESTful HTTP API

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

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

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
Java
JavaScript
Python

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

C++
Java

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

yes

Sharding

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

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

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

volatile and lookup table

User concepts

Access control

yes

granular access controls, API key management, content filters

simple password-based access control

Role-based access control

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