DBMS > Geode vs. IRONdb vs. Spark SQL

System Properties Comparison Geode vs. IRONdb vs. Spark SQL

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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.

Description

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

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

Primary database model

Key-value store

Time Series DBMS

Relational DBMS

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

Score	1.99
Rank	#133	Overall
	#23	Key-value stores

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

geode.apache.org

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

spark.apache.org/sql

Technical documentation

geode.apache.org/docs

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

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

Developer

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Circonus LLC.

Apache Software Foundation

Initial release

2002

2017

2014

Current release

1.1, February 2017

V0.10.20, January 2018

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

Open Source

Apache 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

Java

C and C++

Scala

Server operating systems

All OS with a Java VM

the JDK (8 or later) is also required

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

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

SQL

Support of SQL

SQL-like query language (OQL)

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

SQL-like DML and DDL statements

APIs and other access methods

Java Client API
Memcached protocol
RESTful HTTP API

HTTP API

JDBC
ODBC

Supported programming languages

.Net
All JVM based languages
C++
Groovy
Java
Scala

.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

Server-side scripts

Stored procedures

user defined functions

yes, in Lua

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

configurable replication factor, datacenter aware

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

Transaction concepts

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

yes, on a single node

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

User concepts

Access control

Access rights per client and object definable

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