DBMS > Lovefield vs. Splice Machine vs. Tarantool vs. Tkrzw vs. Yaacomo

System Properties Comparison Lovefield vs. Splice Machine vs. Tarantool vs. Tkrzw vs. Yaacomo

Editorial information provided by DB-Engines

Name

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

Description

Embeddable relational database for web apps written in pure JavaScript

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

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

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

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

Primary database model

Relational DBMS

Document store
Key-value store
Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

Score	0.32
Rank	#290	Overall
	#132	Relational DBMS

Score	0.54
Rank	#255	Overall
	#116	Relational DBMS

Score	1.67
Rank	#150	Overall
	#25	Document stores
	#25	Key-value stores
	#69	Relational DBMS

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

Website

google.github.io/lovefield

splicemachine.com

www.tarantool.io

dbmx.net/tkrzw

yaacomo.com

Technical documentation

github.com/google/lovefield/blob/master/docs/spec_index.md

splicemachine.com/how-it-works

www.tarantool.io/en/doc

Developer

Google

Splice Machine

Mikio Hirabayashi

Q2WEB GmbH

Initial release

2014

2008

2020

2009

Current release

2.1.12, February 2017

3.1, March 2021

2.10.0, May 2022

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

AGPL 3.0, commercial license available

Open Source

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

Open Source

Apache Version 2.0

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

JavaScript

Java

C and C++

C++

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Solaris
Windows

BSD
Linux
macOS

Linux
macOS

Android
Linux
Windows

Data scheme

yes

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

schema-free

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

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

yes

Full-featured ANSI SQL support

yes

APIs and other access methods

JDBC
Native Spark Datasource
ODBC

Open binary protocol

JDBC
ODBC

Supported programming languages

JavaScript

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

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

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

Java

Lua, C and SQL stored procedures

Triggers

Using read-only observers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

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

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

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

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

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

Immediate Consistency

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes, by using IndexedDB or the cloud service Firebase Realtime Database

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

using MemoryDB

yes

yes, full featured in-memory storage engine with persistence

yes

using specific database classes

yes

User concepts

Access control

Access rights for users, groups and roles 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

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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