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

System Properties Comparison Apache Phoenix vs. LMDB vs. RocksDB 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

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Primary database model

Relational DBMS

Key-value store

Relational DBMS

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

Score	2.02
Rank	#130	Overall
	#63	Relational DBMS

Score	2.33
Rank	#118	Overall
	#21	Key-value stores

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

phoenix.apache.org

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

rocksdb.org

spark.apache.org/sql

Technical documentation

phoenix.apache.org

www.lmdb.tech/doc

github.com/facebook/rocksdb/wiki

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

Developer

Apache Software Foundation

Symas

Facebook, Inc.

Apache Software Foundation

Initial release

2014

2011

2013

2014

Current release

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

0.9.32, January 2024

8.11.4, April 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

Java

C++

Scala

Server operating systems

Linux
Unix
Windows

Linux

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC

C++ API
Java API

JDBC
ODBC

Supported programming languages

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

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

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

Transaction concepts

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

ACID

yes

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

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