DBMS > Hive vs. LevelDB vs. ReductStore vs. RocksDB

System Properties Comparison Hive vs. LevelDB vs. ReductStore vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

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

Primary database model

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

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

hive.apache.org

github.com/google/leveldb

github.com/reductstore
www.reduct.store

rocksdb.org

Technical documentation

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

github.com/google/leveldb/blob/main/doc/index.md

www.reduct.store/docs

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

initially developed by Facebook

Google

ReductStore LLC

Facebook, Inc.

Initial release

2012

2011

2023

2013

Current release

3.1.3, April 2022

1.23, February 2021

1.9, March 2024

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSD

Open Source

Business Source License 1.1

Open Source

BSD

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

Java

C++

C++, Rust

C++

Server operating systems

All OS with a Java VM

Illumos
Linux
OS X
Windows

Docker
Linux
macOS
Windows

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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 DML and DDL statements

APIs and other access methods

JDBC
ODBC
Thrift

HTTP API

C++ API
Java API

Supported programming languages

C++
Java
PHP
Python

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C++
JavaScript (Node.js)
Python
Rust

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

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, groups and roles

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