DBMS > Apache Phoenix vs. IRONdb vs. LMDB vs. Microsoft SQL Server vs. Spark SQL

System Properties Comparison Apache Phoenix vs. IRONdb vs. LMDB vs. Microsoft SQL Server vs. Spark SQL

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

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

Microsofts flagship relational DBMS

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

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	1.99
Rank	#125	Overall
	#21	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

phoenix.apache.org

www.circonus.com/solutions/time-series-database/

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

www.microsoft.com/en-us/sql-server

spark.apache.org/sql

Technical documentation

phoenix.apache.org

docs.circonus.com/irondb/category/getting-started

www.lmdb.tech/doc

learn.microsoft.com/en-US/sql/sql-server

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

Developer

Apache Software Foundation

Circonus LLC.

Symas

Microsoft

Apache Software Foundation

Initial release

2014

2017

2011

1989

2014

Current release

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

V0.10.20, January 2018

0.9.32, January 2024

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

commercial

restricted free version is available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C and C++

C++

Scala

Server operating systems

Linux
Unix
Windows

Linux

Linux
Unix
Windows

Linux
Windows

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

text, numeric, histograms

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC

HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
ODBC

Supported programming languages

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

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
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++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

yes, in Lua

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

configurable replication factor, datacenter aware

none

yes, but depending on the SQL-Server Edition

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 per node, eventual consistency across nodes

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

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

fine grained access rights according to SQL-standard

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