DBMS > IRONdb vs. Spark SQL vs. SQLite vs. Tibero vs. VictoriaMetrics

System Properties Comparison IRONdb vs. Spark SQL vs. SQLite vs. Tibero vs. VictoriaMetrics

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Spark SQL is a component on top of 'Spark Core' for structured data processing

Widely used embeddable, in-process RDBMS

A secure RDBMS, designed for easy portability from Oracle

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

Primary database model

Time Series DBMS

Relational DBMS

Time Series DBMS

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

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Score	1.32
Rank	#162	Overall
	#14	Time Series DBMS

Website

www.circonus.com/solutions/time-series-database/

spark.apache.org/sql

www.sqlite.org

us.tmaxsoft.com/products/tibero

victoriametrics.com

Technical documentation

docs.circonus.com/irondb/category/getting-started

spark.apache.org/docs/latest/sql-programming-guide.html

www.sqlite.org/docs.html

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

Developer

Circonus LLC.

Apache Software Foundation

Dwayne Richard Hipp

TmaxSoft

VictoriaMetrics

Initial release

2017

2014

2000

2003

2018

Current release

V0.10.20, January 2018

3.5.0 ( 2.13), September 2023

3.45.3 (15 April 2024), April 2024

6, April 2015

v1.91, May 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Public Domain

commercial

Open Source

Apache Version 2.0

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

C and C++

Scala

C and Assembler

Server operating systems

Linux

Linux
OS X
Windows

server-less

AIX
HP-UX
Linux
Solaris
Windows

FreeBSD
Linux
macOS
OpenBSD

Data scheme

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

not rigid because of 'dynamic typing' concept.

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

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

yes

APIs and other access methods

HTTP API

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Java
Python
R
Scala

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

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes, in Lua

Persistent Stored Procedure (PSM)

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

yes, utilizing Spark Core

none

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

none

Multi-source replication
Source-replica replication

Synchronous replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

planned for next version

User concepts

Access control

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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