DBMS > BoltDB vs. Citus vs. Heroic vs. RocksDB vs. Warp 10

System Properties Comparison BoltDB vs. Citus vs. Heroic vs. RocksDB vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Secondary database models

Document store

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

Score	0.76
Rank	#224	Overall
	#32	Key-value stores

Score	2.15
Rank	#123	Overall
	#60	Relational DBMS

Score	0.57
Rank	#250	Overall
	#21	Time Series DBMS

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Score	0.08
Rank	#358	Overall
	#35	Time Series DBMS

Website

github.com/boltdb/bolt

www.citusdata.com

github.com/spotify/heroic

rocksdb.org

www.warp10.io

Technical documentation

docs.citusdata.com

spotify.github.io/heroic

github.com/facebook/rocksdb/wiki

www.warp10.io/content/02_Getting_started

Developer

Spotify

Facebook, Inc.

SenX

Initial release

2013

2010

2014

2013

2015

Current release

8.1, December 2018

8.11.4, April 2024

License

Commercial or Open Source

Open Source

MIT License

Open Source

AGPL, commercial license also available

Open Source

Apache 2.0

Open Source

BSD

Open Source

Apache License 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++

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Windows

Data scheme

schema-free

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.

yes

specific XML type available, but no XML query functionality

Secondary indexes

yes

via Elasticsearch

SQL

Support of SQL

yes

standard, with numerous extensions

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HQL (Heroic Query Language, a JSON-based language)
HTTP API

C++ API
Java API

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

other methods possible by using 3rd party extensions

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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