DBMS > Apache Impala vs. JSqlDb vs. KeyDB vs. Microsoft Azure Table Storage vs. Microsoft SQL Server

System Properties Comparison Apache Impala vs. JSqlDb vs. KeyDB vs. Microsoft Azure Table Storage vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

JSqlDB seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

JavaScript Query Language Database, stores JavaScript objects and primitives

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

A Wide Column Store for rapid development using massive semi-structured datasets

Microsofts flagship relational DBMS

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Key-value store

Wide column store

Relational DBMS

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

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

Score	14.03
Rank	#40	Overall
	#24	Relational DBMS

Score	0.80
Rank	#219	Overall
	#31	Key-value stores

Score	4.92
Rank	#73	Overall
	#6	Wide column stores

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Website

impala.apache.org

jsqldb.org (offline)

github.com/Snapchat/KeyDB
keydb.dev

azure.microsoft.com/en-us/services/storage/tables

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

Technical documentation

impala.apache.org/impala-docs.html

docs.keydb.dev

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Konrad von Backstrom

EQ Alpha Technology Ltd.

Microsoft

Initial release

2013

2018

2019

2012

1989

Current release

4.1.0, June 2022

0.8, December 2018

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSD-3

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Server operating systems

Linux

Linux
macOS
Windows

Linux

hosted

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

Proprietary protocol

RESP - REdis Serialization Protoco

RESTful HTTP API

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

Supported programming languages

All languages supporting JDBC/ODBC

JavaScript

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

functions in JavaScript

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

none

Sharding

Implicit feature of the cloud service

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

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Multi-source replication
Source-replica replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes, but depending on the SQL-Server Edition

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

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Optimistic locking, atomic execution of commands blocks and scripts

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using RocksDB

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

simple password-based access control and ACL

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

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