DBMS > Heroic vs. jBASE vs. Kinetica vs. LevelDB vs. Tarantool

System Properties Comparison Heroic vs. jBASE vs. Kinetica vs. LevelDB vs. Tarantool

Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A robust multi-value DBMS comprising development tools and middleware

Fully vectorized database across both GPUs and CPUs

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

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

Primary database model

Document store
Key-value store
Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

with Tarantool/GIS extension

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

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

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

Website

github.com/spotify/heroic

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

www.kinetica.com

github.com/google/leveldb

www.tarantool.io

Technical documentation

spotify.github.io/heroic

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

docs.kinetica.com

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

www.tarantool.io/en/doc

Developer

Spotify

Rocket Software (formerly Zumasys)

Kinetica

Google

Initial release

2014

1991

2012

2011

2008

Current release

5.7

7.1, August 2021

1.23, February 2021

2.10.0, May 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

BSD

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

Java

C, C++

C++

C and C++

Server operating systems

AIX
Linux
Windows

Linux

Illumos
Linux
OS X
Windows

BSD
Linux
macOS

Data scheme

schema-free

yes

schema-free

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

optional

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.

yes

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

Embedded SQL for jBASE in BASIC

SQL-like DML and DDL statements

Full-featured ANSI SQL support

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

JDBC
ODBC
RESTful HTTP API

Open binary protocol

Supported programming languages

.Net
Basic
Jabbascript
Java

C++
Java
JavaScript (Node.js)
Python

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

Server-side scripts

Stored procedures

yes

user defined functions

Lua, C and SQL stored procedures

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Source-replica 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

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

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

with automatic compression on writes

yes, write ahead logging

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes, full featured in-memory storage engine with persistence

User concepts

Access control

Access rights can be defined down to the item level

Access rights for users and roles on table level

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

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

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