DBMS > HarperDB vs. KeyDB vs. Manticore Search vs. Riak KV

System Properties Comparison HarperDB vs. KeyDB vs. Manticore Search vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

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

Multi-storage database for search, including full-text search.

Distributed, fault tolerant key-value store

Primary database model

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

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	0.60
Rank	#244	Overall
	#38	Document stores

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

Score	0.29
Rank	#302	Overall
	#21	Search engines

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

Website

www.harperdb.io

github.com/Snapchat/KeyDB
keydb.dev

manticoresearch.com

Technical documentation

docs.harperdb.io/docs

docs.keydb.dev

manual.manticoresearch.com

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

Developer

HarperDB

EQ Alpha Technology Ltd.

Manticore Software

OpenSource, formerly Basho Technologies

Initial release

2017

2019

2017

2009

Current release

3.1, August 2021

6.0, February 2023

3.2.0, December 2022

License

Commercial or Open Source

commercial

free community edition available

Open Source

BSD-3

Open Source

GPL version 2

Open Source

Apache version 2, commercial enterprise edition

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

Node.js

C++

Erlang

Server operating systems

Linux
OS X

Linux

FreeBSD
Linux
macOS
Windows

Linux
OS X

Data scheme

dynamic schema

schema-free

Fixed schema

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

partial

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

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

Secondary indexes

yes

by using the Redis Search module

yes

full-text index on all search fields

restricted

SQL

Support of SQL

SQL-like data manipulation statements

SQL-like query language

APIs and other access methods

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

Proprietary protocol

RESP - REdis Serialization Protoco

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

HTTP API
Native Erlang Interface

Supported programming languages

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

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

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

Lua

user defined functions

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

A table resides as a whole on one (or more) nodes in a cluster

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

Multi-source replication
Source-replica replication

Synchronous replication based on Galera library

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

Transaction concepts

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

Atomic execution of specific operations

Optimistic locking, atomic execution of commands blocks and scripts

yes

isolated transactions for atomic changes and binary logging for safe writes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes, using LMDB

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

The original contents of fields are not stored in the Manticore index.

yes

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users and roles

simple password-based access control and ACL

yes, using Riak Security

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