DBMS > 4D vs. Hazelcast vs. Hypertable vs. SQLite vs. Tarantool

System Properties Comparison 4D vs. Hazelcast vs. Hypertable vs. SQLite vs. Tarantool

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Application development environment with integrated database management system

A widely adopted in-memory data grid

An open source BigTable implementation based on distributed file systems such as Hadoop

Widely used embeddable, in-process RDBMS

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

Document store

JSON support with IMDG 3.12

Spatial DBMS

with Tarantool/GIS extension

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

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

Website

Technical documentation

developer.4d.com

hazelcast.org/imdg/docs

www.sqlite.org/docs.html

www.tarantool.io/en/doc

Developer

4D, Inc

Hazelcast

Hypertable Inc.

Dwayne Richard Hipp

Initial release

1984

2008

2009

2000

2008

Current release

v20, April 2023

5.3.6, November 2023

0.9.8.11, March 2016

3.46.0 (23 May 2024), May 2024

2.10.0, May 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

GNU version 3. Commercial license available

Open Source

Public Domain

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 and C++

Server operating systems

OS X
Windows

All OS with a Java VM

Linux
OS X
Windows

an inofficial Windows port is available

server-less

BSD
Linux
macOS

Data scheme

yes

schema-free

yes

dynamic column types

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

not rigid because of 'dynamic typing' concept.

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

the object must implement a serialization strategy

Secondary indexes

yes

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language

yes

SQL-92 is not fully supported

Full-featured ANSI SQL support

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JCache
JPA
Memcached protocol
RESTful HTTP API

C++ API
Thrift

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Open binary protocol

Supported programming languages

4D proprietary IDE
PHP

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C++
Java
Perl
PHP
Python
Ruby

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

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

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

Lua, C and SQL stored procedures

Triggers

yes

Events

yes

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

Partitioning methods

Methods for storing different data on different nodes

none

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

Multi-source replication

yes

Replicated Map

selectable replication factor on file system level

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

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

one or two-phase-commit; repeatable reads; read commited

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

via file-system locks

yes, cooperative multitasking

Durability

Support for making data persistent

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

Users and groups

Role-based access control

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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