DBMS > HEAVY.AI vs. IBM Db2 Event Store vs. JSqlDb vs. Lovefield vs. XTDB

System Properties Comparison HEAVY.AI vs. IBM Db2 Event Store vs. JSqlDb vs. Lovefield vs. XTDB

Editorial information provided by DB-Engines

Name

JSqlDB seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Distributed Event Store optimized for Internet of Things use cases

JavaScript Query Language Database, stores JavaScript objects and primitives

Embeddable relational database for web apps written in pure JavaScript

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Document store
Object oriented DBMS

Relational DBMS

Document store

Secondary database models

Spatial DBMS

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

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

github.com/heavyai/heavydb
www.heavy.ai

www.ibm.com/products/db2-event-store

jsqldb.org (offline)

google.github.io/lovefield

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.heavy.ai

www.ibm.com/docs/en/db2-event-store

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

www.xtdb.com/docs

Developer

HEAVY.AI, Inc.

IBM

Konrad von Backstrom

Google

Juxt Ltd.

Initial release

2016

2017

2018

2014

2019

Current release

5.10, January 2022

2.0

0.8, December 2018

2.1.12, February 2017

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

free developer edition available

Open Source

Apache 2.0

Open Source

MIT License

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

C++ and CUDA

C and C++

JavaScript

Clojure

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux
macOS
Windows

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

tested with Chrome, Firefox, IE, Safari

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

through the embedded Spark runtime

SQL-like query language

via JavaScript builder pattern

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC
Thrift
Vega

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HTTP REST
JDBC

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

JavaScript

Clojure
Java

Server-side scripts

Stored procedures

yes

functions in JavaScript

Triggers

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Active-active shard replication

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

using RocksDB

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

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

fine grained access rights according to SQL-standard

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