DBMS > AlaSQL vs. EsgynDB vs. SurrealDB vs. TimescaleDB

System Properties Comparison AlaSQL vs. EsgynDB vs. SurrealDB vs. TimescaleDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A fully ACID transactional, developer-friendly, multi-model DBMS

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Document store
Relational DBMS

Relational DBMS

Document store
Graph DBMS

Time Series DBMS

Secondary database models

Relational 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	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	1.02
Rank	#190	Overall
	#33	Document stores
	#18	Graph DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

Technical documentation

github.com/AlaSQL/alasql

surrealdb.com/docs

docs.timescale.com

Developer

Andrey Gershun & Mathias R. Wulff

Esgyn

SurrealDB Ltd

Timescale

Initial release

2014

2015

2022

2017

Current release

v1.5.0, May 2024

2.15.0, May 2024

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

Apache 2.0

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

C++, Java

Rust

Server operating systems

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

Linux

Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

JavaScript API

ADO.NET
JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

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

Supported programming languages

JavaScript

All languages supporting JDBC/ODBC/ADO.Net

Deno
Go
JavaScript (Node.js)
Rust

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication between multi datacenters

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

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

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Create a Marvel Database with SQL and Javascript, the easy way
2 July 2019, Towards Data Science

Multi faceted data exploration in the browser using Leaflet and amCharts
3 May 2020, Towards Data Science

provided by Google News

SD Times Open-Source Project of the Week: SurrealDB
10 May 2024, SDTimes.com

Meet Tobie Morgan Hitchcock, CEO & Co-Founder Of SurrealDB
25 April 2024, TechRound

Cloud, privacy and AI: Trends defining the future of data and databases
27 September 2023, Sifted

SurrealDB raises $6M for its database-as-a-service offering
4 January 2023, TechCrunch

Introducing SurrealDB: A Quantum Leap in Database Technology
11 September 2023, TechRound

provided by Google News

TimescaleDB Is a Vector Database Now, Too
25 September 2023, Datanami

Timescale Acquires PopSQL to Bring a Modern, Collaborative SQL GUI to PostgreSQL Developers
4 April 2024, PR Newswire

Power IoT and time-series workloads with TimescaleDB for Azure Database for PostgreSQL
18 March 2019, Microsoft

Timescale Valuation Rockets to Over $1B with $110M Round, Marking the Explosive Rise of Time-Series Data
22 February 2022, Business Wire

TimescaleDB goes distributed; implements ‘Chunking’ over ‘Sharding’ for scaling-out
22 August 2019, Packt Hub

provided by Google News

Share this page