DBMS > Apache IoTDB vs. Lovefield vs. Solr vs. TimesTen vs. VoltDB

System Properties Comparison Apache IoTDB vs. Lovefield vs. Solr vs. TimesTen vs. VoltDB

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

Embeddable relational database for web apps written in pure JavaScript

A widely used distributed, scalable search engine based on Apache Lucene

In-Memory RDBMS compatible to Oracle

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Secondary database models

Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

iotdb.apache.org

google.github.io/lovefield

solr.apache.org

www.oracle.com/database/technologies/related/timesten.html

www.voltdb.com

Technical documentation

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

github.com/google/lovefield/blob/master/docs/spec_index.md

solr.apache.org/resources.html

docs.oracle.com/database/timesten-18.1

docs.voltdb.com

Developer

Apache Software Foundation

Google

Apache Software Foundation

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

VoltDB Inc.

Initial release

2018

2014

2006

1998

2010

Current release

1.1.0, April 2023

2.1.12, February 2017

9.6.1, May 2024

11 Release 2 (11.2.2.8.0)

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache Version 2

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

JavaScript

Java

Java, C++

Server operating systems

All OS with a Java VM (>= 1.8)

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
OS X

for development

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

Solr Parallel SQL Interface

yes

only a subset of SQL 99

APIs and other access methods

JDBC
Native API

Java API
RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

JavaScript

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C
C++
Java
PL/SQL

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

Java plugins

PL/SQL

Java

Triggers

yes

Using read-only observers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

none

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

none

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

optimistic locking

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

by means of logfiles and checkpoints

yes

Snapshots and command logging

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

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