DBMS > Apache Pinot vs. HugeGraph vs. IRONdb vs. Oracle Coherence

System Properties Comparison Apache Pinot vs. HugeGraph vs. IRONdb vs. Oracle Coherence

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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

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

A fast-speed and highly-scalable Graph DBMS

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

Oracles in-memory data grid solution

Primary database model

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

Score	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Website

pinot.apache.org

github.com/hugegraph
hugegraph.apache.org

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

www.oracle.com/java/coherence

Technical documentation

docs.pinot.apache.org

hugegraph.apache.org/docs

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

docs.oracle.com/en/middleware/standalone/coherence

Developer

Apache Software Foundation and contributors

Baidu

Circonus LLC.

Oracle

Initial release

2015

2018

2017

2007

Current release

1.0.0, September 2023

0.9

V0.10.20, January 2018

14.1, August 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

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++

Java

Server operating systems

All OS with a Java JDK11 or higher

Linux
macOS
Unix

Linux

All OS with a Java VM

Data scheme

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

also supports composite index and range index

SQL

Support of SQL

SQL-like query language

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

APIs and other access methods

JDBC

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP API

JCache
JPA
RESTful HTTP API

Supported programming languages

Go
Java
Python

Groovy
Java
Python

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

.Net
C++
Java

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes, in Lua

Triggers

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes

depending on used storage backend, e.g. Cassandra and HBase

Automatic, metric affinity per node

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

configurable replication factor, datacenter aware

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

In-memory capabilities

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

yes

User concepts

Access control

Users, roles and permissions

authentification to access the cache via certificates or http basic authentication

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