DBMS > Apache Impala vs. ArcadeDB vs. Microsoft SQL Server vs. RocksDB

System Properties Comparison Apache Impala vs. ArcadeDB vs. Microsoft SQL Server vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

Microsofts flagship relational DBMS

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

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Relational DBMS

Key-value store

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.02
Rank	#366	Overall
	#50	Document stores
	#38	Graph DBMS
	#53	Key-value stores
	#36	Time Series DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

impala.apache.org

arcadedb.com

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

rocksdb.org

Technical documentation

impala.apache.org/impala-docs.html

docs.arcadedb.com

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

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Arcade Data

Microsoft

Facebook, Inc.

Initial release

2013

2021

1989

2013

Current release

4.1.0, June 2022

September 2021

SQL Server 2022, November 2022

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

BSD

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Java

C++

Server operating systems

Linux

All OS with a Java VM

Linux
Windows

Linux

Data scheme

yes

schema-free

yes

schema-free

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language, no joins

yes

APIs and other access methods

JDBC
ODBC

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

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

C++ API
Java API

Supported programming languages

All languages supporting JDBC/ODBC

Java

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

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

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

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

yes, but depending on the SQL-Server Edition

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

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 rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

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