DBMS > Apache Phoenix vs. Hazelcast vs. LMDB vs. Spark SQL

System Properties Comparison Apache Phoenix vs. Hazelcast vs. LMDB vs. Spark SQL

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Name

Description

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

A widely adopted in-memory data grid

A high performant, light-weight, embedded key-value database library

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	3.01
Rank	#103	Overall
	#52	Relational DBMS

Score	10.08
Rank	#48	Overall
	#6	Key-value stores

Score	2.44
Rank	#117	Overall
	#22	Key-value stores

Score	22.86
Rank	#35	Overall
	#21	Relational DBMS

Website

phoenix.apache.org

hazelcast.com

www.symas.com/symas-embedded-database-lmdb

spark.apache.org/sql

Technical documentation

phoenix.apache.org

hazelcast.org/imdg/docs

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Apache Software Foundation

Hazelcast

Symas

Apache Software Foundation

Initial release

2014

2008

2011

2014

Current release

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

5.1, March 2022

0.9.29, March 2021

3.2.0, October 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

Server operating systems

Linux
Unix
Windows

All OS with a Java VM

Linux
OS X
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

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.

yes

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC

Supported programming languages

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

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

yes

Event Listeners, Executor Services

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Replicated Map

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

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

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

Role-based access control

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