DBMS > AlaSQL vs. Apache Impala vs. HarperDB vs. Microsoft SQL Server vs. TerarkDB

System Properties Comparison AlaSQL vs. Apache Impala vs. HarperDB vs. Microsoft SQL Server vs. TerarkDB

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Analytic DBMS for Hadoop

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Microsofts flagship relational DBMS

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Document store
Relational DBMS

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

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Trend Chart

Score	0.51
Rank	#256	Overall
	#40	Document stores
	#118	Relational DBMS

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Website

alasql.org

impala.apache.org

www.harperdb.io

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

github.com/bytedance/terarkdb

Technical documentation

github.com/AlaSQL/alasql

impala.apache.org/impala-docs.html

docs.harperdb.io/docs

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

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Andrey Gershun & Mathias R. Wulff

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

HarperDB

Microsoft

ByteDance, originally Terark

Initial release

2014

2013

2017

1989

2016

Current release

4.1.0, June 2022

3.1, August 2021

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

MIT-License

Open Source

Apache Version 2

commercial

free community edition available

commercial

restricted free version is available

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

JavaScript

C++

Node.js

C++

Server operating systems

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

Linux

Linux
OS X

Linux
Windows

Data scheme

schema-free

yes

dynamic schema

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

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

Close to SQL99, but no user access control, stored procedures and host language bindings.

SQL-like DML and DDL statements

SQL-like data manipulation statements

yes

APIs and other access methods

JavaScript API

JDBC
ODBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

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

C++ API
Java API

Supported programming languages

JavaScript

All languages supporting JDBC/ODBC

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

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

C++
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Custom Functions

since release 3.1

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

A table resides as a whole on one (or more) nodes in a cluster

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

yes

the nodes on which a table resides can be defined

yes, but depending on the SQL-Server Edition

none

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

none

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

only for local storage and DOM-storage

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

yes, using LMDB

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

Access rights for users and roles

fine grained access rights according to SQL-standard

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