DBMS > AlaSQL vs. Apache Phoenix vs. Dragonfly vs. InfinityDB

System Properties Comparison AlaSQL vs. Apache Phoenix vs. Dragonfly vs. InfinityDB

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Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

A scale-out RDBMS with evolutionary schema built on Apache HBase

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

A Java embedded Key-Value Store which extends the Java Map interface

Primary database model

Document store
Relational DBMS

Relational DBMS

Key-value store

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

Score	0.42
Rank	#264	Overall
	#42	Document stores
	#122	Relational DBMS

Score	1.90
Rank	#125	Overall
	#59	Relational DBMS

Score	0.44
Rank	#255	Overall
	#38	Key-value stores

Score	0.00
Rank	#383	Overall
	#59	Key-value stores

Website

alasql.org

phoenix.apache.org

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

boilerbay.com

Technical documentation

github.com/AlaSQL/alasql

phoenix.apache.org

www.dragonflydb.io/docs

boilerbay.com/infinitydb/manual

Developer

Andrey Gershun & Mathias R. Wulff

Apache Software Foundation

DragonflyDB team and community contributors

Boiler Bay Inc.

Initial release

2014

2023

2002

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

1.0, March 2023

4.0

License

Commercial or Open Source

Open Source

MIT-License

Open Source

Apache Version 2.0

Open Source

BSL 1.1

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

JavaScript

Java

C++

Java

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

Linux
Unix
Windows

Linux

All OS with a Java VM

Data scheme

schema-free

yes

late-bound, schema-on-read capabilities

scheme-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

Typing

predefined data types such as float or date

yes

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

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

yes

APIs and other access methods

JavaScript API

JDBC

Proprietary protocol

RESP - REdis Serialization Protocol

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

Supported programming languages

JavaScript

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

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

Java

Server-side scripts

Stored procedures

user defined functions

Lua

Triggers

yes

publish/subscribe channels provide some trigger functionality

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency

Eventual Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Foreign keys

Referential integrity

yes

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

yes

only for local storage and DOM-storage

ACID

Atomic execution of command blocks and scripts

ACID

Optimistic locking for transactions; no isolation for bulk loads

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Password-based authentication

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