DBMS > AlaSQL vs. Google BigQuery vs. H2 vs. KeyDB vs. Postgres-XL

System Properties Comparison AlaSQL vs. Google BigQuery vs. H2 vs. KeyDB vs. Postgres-XL

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Large scale data warehouse service with append-only tables

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Document store
Relational DBMS

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	0.70
Rank	#229	Overall
	#32	Key-value stores

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Website

alasql.org

cloud.google.com/bigquery

www.h2database.com

github.com/Snapchat/KeyDB
keydb.dev

www.postgres-xl.org

Technical documentation

github.com/AlaSQL/alasql

cloud.google.com/bigquery/docs

www.h2database.com/html/main.html

docs.keydb.dev

www.postgres-xl.org/documentation

Developer

Andrey Gershun & Mathias R. Wulff

Google

Thomas Mueller

EQ Alpha Technology Ltd.

Initial release

2014

2010

2005

2019

2014

since 2012, originally named StormDB

Current release

2.2.220, July 2023

10 R1, October 2018

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

BSD-3

Open Source

Mozilla public license

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

JavaScript

Java

C++

Server operating systems

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

hosted

All OS with a Java VM

Linux

Linux
macOS

Data scheme

schema-free

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

XML type, but no XML query functionality

Secondary indexes

yes

by using the Redis Search module

yes

SQL

Support of SQL

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

yes

distributed, parallel query execution

APIs and other access methods

JavaScript API

RESTful HTTP/JSON API

JDBC
ODBC

Proprietary protocol

RESP - REdis Serialization Protoco

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

JavaScript

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java

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++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java Stored Procedures and User-Defined Functions

Lua

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

With clustering: 2 database servers on different computers operate on identical copies of a database

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate 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

yes

only for local storage and DOM-storage

Since BigQuery is designed for querying data

ACID

Optimistic locking, atomic execution of commands blocks and scripts

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

simple password-based access control and ACL

fine grained access rights according to SQL-standard

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