DBMS > atoti vs. KeyDB vs. Lovefield vs. Splunk vs. Tkrzw

System Properties Comparison atoti vs. KeyDB vs. Lovefield vs. Splunk vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Embeddable relational database for web apps written in pure JavaScript

Analytics Platform for Big Data

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

Primary database model

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

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	0.70
Rank	#229	Overall
	#32	Key-value stores

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	89.10
Rank	#14	Overall
	#2	Search engines

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

atoti.io

github.com/Snapchat/KeyDB
keydb.dev

google.github.io/lovefield

www.splunk.com

dbmx.net/tkrzw

Technical documentation

docs.atoti.io

docs.keydb.dev

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

docs.splunk.com/Documentation/Splunk

Developer

ActiveViam

EQ Alpha Technology Ltd.

Google

Splunk Inc.

Mikio Hirabayashi

Initial release

2019

2014

2003

2020

Current release

2.1.12, February 2017

0.9.3, August 2020

License

Commercial or Open Source

commercial

free versions available

Open Source

BSD-3

Open Source

Apache 2.0

commercial

Limited free edition and free developer edition available

Open Source

Apache Version 2.0

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++

JavaScript

C++

Server operating systems

Linux

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

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Solaris
Windows

Linux
macOS

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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.

yes

Secondary indexes

yes

by using the Redis Search module

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

SQL-like query language

via JavaScript builder pattern

Splunk Search Processing Language for search commands

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protoco

HTTP REST

Supported programming languages

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

JavaScript

C#
Java
JavaScript
PHP
Python
Ruby

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

Python

Lua

yes

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong eventual consistency with CRDTs

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Optimistic locking, atomic execution of commands blocks and scripts

ACID

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

yes

In-memory capabilities

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

yes

using MemoryDB

yes

using specific database classes

User concepts

Access control

simple password-based access control and ACL

Access rights for users and roles

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