DBMS > Apache Impala vs. Hazelcast vs. JanusGraph vs. NuoDB vs. SQLite

System Properties Comparison Apache Impala vs. Hazelcast vs. JanusGraph vs. NuoDB vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A widely adopted in-memory data grid

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

impala.apache.org

hazelcast.com

janusgraph.org

www.3ds.com/nuodb-distributed-sql-database

www.sqlite.org

Technical documentation

impala.apache.org/impala-docs.html

hazelcast.org/imdg/docs

docs.janusgraph.org

doc.nuodb.com

www.sqlite.org/docs.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Hazelcast

Linux Foundation; originally developed as Titan by Aurelius

Dassault Systèmes

originally NuoDB, Inc.

Dwayne Richard Hipp

Initial release

2013

2008

2017

2013

2000

Current release

4.1.0, June 2022

5.3.6, November 2023

0.6.3, February 2023

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

commercial

limited edition free

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++

Server operating systems

Linux

All OS with a Java VM

Linux
OS X
Unix
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

server-less

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Clojure
Java
Python

.Net
C
C++
Go
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Event Listeners, Executor Services

yes

Java, SQL

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

data is dynamically stored/cached on the nodes where it is read/written

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Replicated Map

yes

Managed transparently by NuoDB

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

ACID

tunable commit protocol

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

via file-system locks

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

In-memory capabilities

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

yes

Temporary table

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Role-based access control

User authentification and security via Rexster Graph Server

Standard SQL roles/ privileges, Administrative Users

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