DBMS > HEAVY.AI vs. Lovefield vs. OpenTSDB vs. SAP HANA

System Properties Comparison HEAVY.AI vs. Lovefield vs. OpenTSDB vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

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

Embeddable relational database for web apps written in pure JavaScript

Scalable Time Series DBMS based on HBase

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

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	0.32
Rank	#290	Overall
	#132	Relational DBMS

Score	1.73
Rank	#147	Overall
	#12	Time Series DBMS

Score	45.84
Rank	#22	Overall
	#16	Relational DBMS

Website

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

google.github.io/lovefield

opentsdb.net

www.sap.com/products/hana.html

Technical documentation

docs.heavy.ai

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

opentsdb.net/docs/build/html/index.html

help.sap.com/hana

Developer

HEAVY.AI, Inc.

Google

currently maintained by Yahoo and other contributors

SAP

Initial release

2016

2014

2011

2010

Current release

5.10, January 2022

2.1.12, February 2017

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

Open Source

LGPL

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++ and CUDA

JavaScript

Java

Server operating systems

Linux

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

tested with Chrome, Firefox, IE, Safari

Linux
Windows

Appliance or cloud-service

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numeric data for metrics, strings for tags

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 query language

via JavaScript builder pattern

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

HTTP API
Telnet API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

Erlang
Go
Java
Python
R
Ruby

Server-side scripts

Stored procedures

SQLScript, R

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Sharding

based on HBase

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

selectable replication factor

based on HBase

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

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

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

fine grained access rights according to SQL-standard

yes

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