DBMS > HEAVY.AI vs. LevelDB vs. TempoIQ vs. Tkrzw vs. ToroDB

System Properties Comparison HEAVY.AI vs. LevelDB vs. TempoIQ vs. Tkrzw vs. ToroDB

Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

ToroDB 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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Secondary database models

Spatial DBMS

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

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	2.75
Rank	#107	Overall
	#19	Key-value stores

Score	0.09
Rank	#354	Overall
	#51	Key-value stores

Website

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

github.com/google/leveldb

tempoiq.com (offline)

dbmx.net/tkrzw

github.com/torodb/server

Technical documentation

docs.heavy.ai

github.com/google/leveldb/blob/main/doc/index.md

Developer

HEAVY.AI, Inc.

Google

TempoIQ

Mikio Hirabayashi

8Kdata

Initial release

2016

2011

2012

2020

2016

Current release

5.10, January 2022

1.23, February 2021

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

BSD

commercial

Open Source

Apache Version 2.0

Open Source

AGPL-V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++ and CUDA

C++

Java

Server operating systems

Linux

Illumos
Linux
OS X
Windows

Linux
macOS

All OS with a Java 7 VM

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

SQL

Support of SQL

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C#
Java
JavaScript

Node.js
Python
Ruby

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

Triggers

yes

Realtime Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

yes

using specific database classes

User concepts

Access control

fine grained access rights according to SQL-standard

simple authentication-based access control

Access rights for users and roles

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