DBMS > HarperDB vs. HEAVY.AI vs. SpatiaLite vs. Tarantool

System Properties Comparison HarperDB vs. HEAVY.AI vs. SpatiaLite vs. Tarantool

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Editorial information provided by DB-Engines

Name

Description

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

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

Spatial extension of SQLite

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

Primary database model

Document store

Relational DBMS

Spatial DBMS

Document store
Key-value store
Relational DBMS

Secondary database models

Spatial DBMS

Relational DBMS

Spatial DBMS

with Tarantool/GIS extension

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

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Score	1.72
Rank	#144	Overall
	#25	Document stores
	#25	Key-value stores
	#66	Relational DBMS

Website

www.harperdb.io

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

www.gaia-gis.it/fossil/libspatialite/index

www.tarantool.io

Technical documentation

docs.harperdb.io/docs

docs.heavy.ai

www.gaia-gis.it/gaia-sins/spatialite_topics.html

www.tarantool.io/en/doc

Developer

HarperDB

HEAVY.AI, Inc.

Alessandro Furieri

Initial release

2017

2016

2008

Current release

3.1, August 2021

5.10, January 2022

5.0.0, August 2020

2.10.0, May 2022

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2; enterprise edition available

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

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

Node.js

C++ and CUDA

C++

C and C++

Server operating systems

Linux
OS X

Linux

server-less

BSD
Linux
macOS

Data scheme

dynamic schema

yes

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

Typing

predefined data types such as float or date

yes

JSON data types

yes

string, double, decimal, uuid, integer, blob, boolean, datetime

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 data manipulation statements

yes

Full-featured ANSI SQL support

APIs and other access methods

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JDBC
ODBC
Thrift
Vega

Open binary protocol

Supported programming languages

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

All languages supporting JDBC/ODBC/Thrift
Python

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

Lua, C and SQL stored procedures

Triggers

yes

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

A table resides as a whole on one (or more) nodes in a cluster

Sharding

Round robin

none

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

Multi-source replication

none

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes, using LMDB

yes

yes, write ahead logging

In-memory capabilities

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

yes

yes, full featured in-memory storage engine with persistence

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

show all

Recent citations in the news

Startups of the Year 2023: Meet HarperDB - A Database and Application Development Platform
22 June 2023, hackernoon.com

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29 November 2022, AWS Blog

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17 January 2023, Markets Insider

HarperDB: An underdog SQL / NoSQL database
7 February 2018, ZDNet

HarperDB is More Than Just a Database: Here's Why
21 August 2021, hackernoon.com

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19 April 2023, Business Wire

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14 April 2023, InfoWorld

HEAVY.AI Partners with Bain, Maxar, and Nvidia to Provide Digital Twins for Telecom Networks
16 February 2023, Datanami

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11 October 2023, insideBIGDATA

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18 May 2018, Newswire

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