DBMS > Apache Phoenix vs. Lovefield vs. OpenTenBase vs. Spark SQL

System Properties Comparison Apache Phoenix vs. Lovefield vs. OpenTenBase vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

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

Embeddable relational database for web apps written in pure JavaScript

An enterprise-level distributed HTAP open source database based on PostgreSQL

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

Primary database model

Relational DBMS

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.08
Rank	#370	Overall
	#156	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

phoenix.apache.org

google.github.io/lovefield

github.com/OpenTenBase/OpenTenBase
www.opentenbase.org

spark.apache.org/sql

Technical documentation

phoenix.apache.org

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.opentenbase.org
docs.opentenbase.org/en

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

Developer

Apache Software Foundation

Google

OpenAtom Foundation, previously Tencent

Apache Software Foundation

Initial release

2014

Current release

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

2.1.12, February 2017

2.5, January 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

BSD-3

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

JavaScript

Scala

Server operating systems

Linux
Unix
Windows

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

tested with Chrome, Firefox, IE, Safari

Linux

Linux
OS X
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

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

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC

JDBC
ODBC

Supported programming languages

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

JavaScript

C
Go
Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

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

Access rights for users, groups and roles according to SQL-standard

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More resources

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11 August 2021, Krishna Maheshwari (sponsor)

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