DBMS > Apache IoTDB vs. Couchbase vs. GeoMesa vs. OrientDB vs. SAP Adaptive Server

System Properties Comparison Apache IoTDB vs. Couchbase vs. GeoMesa vs. OrientDB vs. SAP Adaptive Server

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 document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Multi-model DBMS (Document, Graph, Key/Value)

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

Primary database model

Time Series DBMS

Document store

Spatial DBMS

Document store
Graph DBMS
Key-value store

Relational DBMS

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	36.31
Rank	#25	Overall
	#17	Relational DBMS

Website

www.sap.com/products/technology-platform/sybase-ase.html

Technical documentation

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

docs.couchbase.com

www.geomesa.org/documentation/stable/user/index.html

www.orientdb.com/docs/last/index.html

help.sap.com/docs/SAP_ASE

Developer

Apache Software Foundation

Couchbase, Inc.

CCRi and others

OrientDB LTD; CallidusCloud; SAP

SAP, Sybase

Initial release

2018

2011

2014

2010

1987

Current release

1.1.0, April 2023

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

4.0.5, February 2024

3.2.29, March 2024

16.0

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

Apache License 2.0

Open Source

Apache version 2

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, C++, Go and Erlang

Scala

Java

C and C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X
Windows

All OS with a Java JDK (>= JDK 6)

AIX
HP-UX
Linux
Solaris
Unix
Windows

Data scheme

yes

schema-free

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

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++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

SQL-like query language, no joins

yes

APIs and other access methods

JDBC
Native API

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C
C++
Cobol
Java
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

yes

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

Java, Javascript

Java and Transact-SQL

Triggers

yes

via the TAP protocol

Hooks

yes

Partitioning methods

Methods for storing different data on different nodes

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

Automatic Sharding

depending on storage layer

Sharding

horizontal partitioning

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

Multi-source replication

including cross data center replication
Source-replica replication

depending on storage layer

Multi-source replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

depending on storage layer

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

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

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Ephemeral buckets

depending on storage layer

User concepts

Access control

yes

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

yes

depending on the DBMS used for storage

Access rights for users and roles; record level security configurable

fine grained access rights according to SQL-standard

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