DBMS > etcd vs. HarperDB vs. Hive vs. Percona Server for MongoDB vs. RocksDB

System Properties Comparison etcd vs. HarperDB vs. Hive vs. Percona Server for MongoDB vs. RocksDB

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.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

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Trend Chart

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

etcd.io
github.com/etcd-io/etcd

www.harperdb.io

hive.apache.org

www.percona.com/mongodb/software/percona-server-for-mongodb

rocksdb.org

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

docs.harperdb.io/docs

cwiki.apache.org/confluence/display/Hive/Home

docs.percona.com/percona-distribution-for-mongodb

github.com/facebook/rocksdb/wiki

Developer

HarperDB

Apache Software Foundation

initially developed by Facebook

Percona

Facebook, Inc.

Initial release

2017

2012

2015

2013

Current release

3.4, August 2019

3.1, August 2021

3.1.3, April 2022

3.4.10-2.10, November 2017

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community edition available

Open Source

Apache Version 2

Open Source

GPL Version 2

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Node.js

Java

C++

Server operating systems

FreeBSD
Linux
Windows

experimental

Linux
OS X

All OS with a Java VM

Linux

Data scheme

schema-free

dynamic schema

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

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 data manipulation statements

SQL-like DML and DDL statements

APIs and other access methods

gRPC
JSON over HTTP

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JDBC
ODBC
Thrift

proprietary protocol using JSON

C++ API
Java API

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

.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++
Java
PHP
Python

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

yes

user defined functions and integration of map-reduce

JavaScript

Triggers

yes, watching key changes

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

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

yes

the nodes on which a table resides can be defined

selectable replication factor

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

yes

In-memory capabilities

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

yes

via In-Memory Engine

yes

User concepts

Access control

Access rights for users and roles

Access rights for users, groups and roles

Access rights for users and roles

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