DBMS > AlaSQL vs. Databend vs. Datomic vs. GBase

System Properties Comparison AlaSQL vs. Databend vs. Datomic vs. GBase

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Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Primary database model

Document store
Relational DBMS

Relational DBMS

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

Score	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Website

alasql.org

github.com/datafuselabs/databend
www.databend.com

www.datomic.com

www.gbase.cn

Technical documentation

github.com/AlaSQL/alasql

docs.databend.com

docs.datomic.com

Developer

Andrey Gershun & Mathias R. Wulff

Databend Labs

Cognitect

General Data Technology Co., Ltd.

Initial release

2014

2021

2012

2004

Current release

1.0.59, April 2023

1.0.6735, June 2023

GBase 8a, GBase 8s, GBase 8c

License

Commercial or Open Source

Open Source

MIT-License

Open Source

Apache Version 2.0

commercial

limited edition free

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

JavaScript

Rust

Java, Clojure

C, Java, Python

Server operating systems

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

hosted
Linux
macOS

All OS with a Java VM

Linux

Data scheme

schema-free

yes

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

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

yes

Standard with numerous extensions

APIs and other access methods

JavaScript API

CLI Client
JDBC
RESTful HTTP API

RESTful HTTP API

ADO.NET
C API
JDBC
ODBC

Supported programming languages

JavaScript

Go
Java
JavaScript (Node.js)
Python
Rust

Clojure
Java

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

Triggers

yes

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

horizontal partitioning (by range, list and hash) and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

yes

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

User concepts

Access control

Users with fine-grained authorization concept, user roles

yes

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