DBMS > Hive vs. HugeGraph vs. Infobright vs. LeanXcale

System Properties Comparison Hive vs. HugeGraph vs. Infobright vs. LeanXcale

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Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A fast-speed and highly-scalable Graph DBMS

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Key-value store
Relational DBMS

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

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Website

hive.apache.org

github.com/hugegraph
hugegraph.apache.org

ignitetech.com/softwarelibrary/infobrightdb

www.leanxcale.com

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

hugegraph.apache.org/docs

Developer

Apache Software Foundation

initially developed by Facebook

Baidu

Ignite Technologies Inc.; formerly InfoBright Inc.

LeanXcale

Initial release

2012

2018

2005

2015

Current release

3.1.3, April 2022

0.9

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

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

Server operating systems

All OS with a Java VM

Linux
macOS
Unix

Linux
Windows

Data scheme

yes

Typing

predefined data types such as float or date

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

Knowledge Grid Technology used instead

SQL

Support of SQL

SQL-like DML and DDL statements

yes

through Apache Derby

APIs and other access methods

JDBC
ODBC
Thrift

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Supported programming languages

C++
Java
PHP
Python

Groovy
Java
Python

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
Java
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

asynchronous Gremlin script jobs

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

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 for users, groups and roles

Users, roles and permissions

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

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More resources

DB-Engines blog posts

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