DBMS > Alibaba Cloud Table Store vs. EsgynDB vs. Spark SQL

System Properties Comparison Alibaba Cloud Table Store vs. EsgynDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Wide column store

Relational DBMS

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

Score	0.39
Rank	#258	Overall
	#11	Wide column stores

Score	0.21
Rank	#288	Overall
	#134	Relational DBMS

Score	22.86
Rank	#35	Overall
	#21	Relational DBMS

Website

www.alibabacloud.com/product/table-store

www.esgyn.cn

spark.apache.org/sql

Technical documentation

www.alibabacloud.com/help/product/27278.htm

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Alibaba

Esgyn

Apache Software Foundation

Initial release

2016

2015

2014

Current release

3.2.0, October 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Java

Scala

Server operating systems

hosted

Linux

Linux
OS X
Windows

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

HTTP API

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Java
Python

All languages supporting JDBC/ODBC/ADO.Net

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-source replication between multi datacenters

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

User concepts

Access control

Access rights based on subaccounts and tokens

fine grained access rights according to SQL-standard

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