DBMS > BigObject vs. Riak KV vs. Tarantool vs. Yaacomo

System Properties Comparison BigObject vs. Riak KV vs. Tarantool vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Analytic DBMS for real-time computations and queries

Distributed, fault tolerant key-value store

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Document store
Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	1.67
Rank	#143	Overall
	#25	Document stores
	#25	Key-value stores
	#65	Relational DBMS

Website

bigobject.io

www.tarantool.io

yaacomo.com

Technical documentation

docs.bigobject.io

www.tiot.jp/riak-docs/riak/kv/latest

www.tarantool.io/en/doc

Developer

BigObject, Inc.

OpenSource, formerly Basho Technologies

Q2WEB GmbH

Initial release

2015

2009

2008

2009

Current release

3.2.0, December 2022

2.10.0, May 2022

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache version 2, commercial enterprise edition

Open Source

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

commercial

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

Erlang

C and C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
OS X

BSD
Linux
macOS

Android
Linux
Windows

Data scheme

yes

schema-free

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

yes

Typing

predefined data types such as float or date

yes

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

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

restricted

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Full-featured ANSI SQL support

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

HTTP API
Native Erlang Interface

Open binary protocol

JDBC
ODBC

Supported programming languages

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

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

Server-side scripts

Stored procedures

Lua

Erlang

Lua, C and SQL stored procedures

Triggers

yes

pre-commit hooks and post-commit hooks

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

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

no "single point of failure"

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

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual 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

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

links between data sets can be stored

yes

Transaction concepts

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

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

yes

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

yes

User concepts

Access control

yes, using Riak Security

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

fine grained access rights according to SQL-standard

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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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