DBMS > IRONdb vs. LevelDB vs. Splice Machine vs. TempoIQ

System Properties Comparison IRONdb vs. LevelDB vs. Splice Machine vs. TempoIQ

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Editorial information provided by DB-Engines

Name

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

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

Description

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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

Primary database model

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

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

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

github.com/google/leveldb

splicemachine.com

tempoiq.com (offline)

Technical documentation

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

github.com/google/leveldb/blob/main/doc/index.md

splicemachine.com/how-it-works

Developer

Circonus LLC.

Google

Splice Machine

TempoIQ

Initial release

2017

2011

2014

2012

Current release

V0.10.20, January 2018

1.23, February 2021

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

AGPL 3.0, commercial license 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

C and C++

C++

Java

Server operating systems

Linux

Illumos
Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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 (Circonus Analytics Query Language: CAQL)

yes

APIs and other access methods

HTTP API

JDBC
Native Spark Datasource
ODBC

HTTP API

Supported programming languages

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

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C#
Java
JavaScript

Node.js
Python
Ruby

Server-side scripts

Stored procedures

yes, in Lua

yes

Java

Triggers

yes

Realtime Alerts

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Immediate 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

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

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

Access rights for users, groups and roles according to SQL-standard

simple authentication-based access control

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