DBMS > Apache IoTDB vs. LeanXcale vs. RavenDB vs. SAP SQL Anywhere vs. SQLite

System Properties Comparison Apache IoTDB vs. LeanXcale vs. RavenDB vs. SAP SQL Anywhere vs. SQLite

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 highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Open Source Operational and Transactional Enterprise NoSQL Document Database

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Widely used embeddable, in-process RDBMS

Primary database model

Time Series DBMS

Key-value store
Relational DBMS

Document store

Relational DBMS

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

iotdb.apache.org

www.leanxcale.com

ravendb.net

www.sap.com/products/technology-platform/sql-anywhere.html

www.sqlite.org

Technical documentation

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

ravendb.net/docs

help.sap.com/docs/SAP_SQL_Anywhere

www.sqlite.org/docs.html

Developer

Apache Software Foundation

LeanXcale

Hibernating Rhinos

SAP

formerly Sybase

Dwayne Richard Hipp

Initial release

2018

2015

2010

1992

2000

Current release

1.1.0, April 2023

5.4, July 2022

17, July 2015

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

AGPL version 3, commercial license available

commercial

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
macOS
Raspberry Pi
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

server-less

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

SQL

Support of SQL

SQL-like query language

yes

through Apache Derby

SQL-like query language (RQL)

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
Native API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

ADO.NET
HTTP API
JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
Java
Scala

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

yes, in C/C++, Java, .Net or Perl

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

none

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

Source-replica replication

Database mirroring

none

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

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

User concepts

Access control

yes

Authorization levels configured per client per database

fine grained access rights according to SQL-standard

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