DBMS > Apache Impala vs. Dragonfly vs. NuoDB vs. Prometheus vs. SQLite

System Properties Comparison Apache Impala vs. Dragonfly vs. NuoDB vs. Prometheus vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

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

Open-source Time Series DBMS and monitoring system

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	0.94
Rank	#197	Overall
	#92	Relational DBMS

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

impala.apache.org

github.com/dragonflydb/dragonfly
www.dragonflydb.io

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

prometheus.io

www.sqlite.org

Technical documentation

impala.apache.org/impala-docs.html

www.dragonflydb.io/docs

doc.nuodb.com

prometheus.io/docs

www.sqlite.org/docs.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

DragonflyDB team and community contributors

Dassault Systèmes

originally NuoDB, Inc.

Dwayne Richard Hipp

Initial release

2013

2023

2013

2015

2000

Current release

4.1.0, June 2022

1.0, March 2023

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSL 1.1

commercial

limited edition free

Open Source

Apache 2.0

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++

Server operating systems

Linux

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux
Windows

server-less

Data scheme

yes

scheme-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit arrays

yes

Numeric data only

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.

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC

Proprietary protocol

RESP - REdis Serialization Protocol

ADO.NET
JDBC
ODBC

RESTful HTTP/JSON API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

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

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
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

Lua

Java, SQL

Triggers

publish/subscribe channels provide some trigger functionality

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

yes

Managed transparently by NuoDB

yes

by Federation

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

tunable commit protocol

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

yes

MVCC

yes

via file-system locks

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

Temporary table

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Password-based authentication

Standard SQL roles/ privileges, Administrative Users

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