DBMS > EventStoreDB vs. Lovefield vs. SAP HANA vs. Spark SQL

System Properties Comparison EventStoreDB vs. Lovefield vs. SAP HANA vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Embeddable relational database for web apps written in pure JavaScript

In-memory, column based data store. Available as appliance or cloud service

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

Primary database model

Event Store

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.19
Rank	#173	Overall
	#1	Event Stores

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.eventstore.com

google.github.io/lovefield

www.sap.com/products/hana.html

spark.apache.org/sql

Technical documentation

developers.eventstore.com

github.com/google/lovefield/blob/master/docs/spec_index.md

help.sap.com/hana

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

Developer

Event Store Limited

Google

SAP

Apache Software Foundation

Initial release

2012

2014

2010

2014

Current release

21.2, February 2021

2.1.12, February 2017

2.0 SPS07 (April 4, 2023), April 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

JavaScript

Scala

Server operating systems

Linux
Windows

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

tested with Chrome, Firefox, IE, Safari

Appliance or cloud-service

Linux
OS X
Windows

Data scheme

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

SQL-like query language

via JavaScript builder pattern

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

Supported programming languages

JavaScript

Java
Python
R
Scala

Server-side scripts

Stored procedures

SQLScript, R

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

yes

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