DBMS > Blueflood vs. Datastax Enterprise vs. ObjectBox vs. RocksDB

System Properties Comparison Blueflood vs. Datastax Enterprise vs. ObjectBox vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

Extremely fast embedded database for small devices, IoT and Mobile

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

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

Time Series DBMS

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

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	5.93
Rank	#56	Overall
	#4	Wide column stores

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

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

Website

blueflood.io

www.datastax.com/products/datastax-enterprise

objectbox.io

rocksdb.org

Technical documentation

github.com/rax-maas/blueflood/wiki

docs.datastax.com

docs.objectbox.io

github.com/facebook/rocksdb/wiki

Developer

Rackspace

DataStax

ObjectBox Limited

Facebook, Inc.

Initial release

2013

2011

2017

2013

Current release

6.8, April 2020

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache License 2.0

Open Source

BSD

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.

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Implementation language

Java

C and C++

C++

Server operating systems

Linux
OS X

Android
iOS
Linux
macOS
Windows

Linux

Data scheme

predefined 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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements (CQL); Spark SQL

APIs and other access methods

HTTP REST

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

Proprietary native API

C++ API
Java API

Supported programming languages

C
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

no "single point of failure"

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

configurable replication factor, datacenter aware, advanced replication for edge computing

online/offline synchronization between client and server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomicity and isolation are supported for single operations

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

Access rights for users can be defined per object

yes

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
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Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
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Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
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Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
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Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
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Licensing and pricing models

Annual subscription
» more

News

The on-device Vector Database for Android and Java
29 May 2024

Vector search: making sense of search queries
29 May 2024

Python on-device Vector and Object Database for Local AI
28 May 2024

Evolution of search: traditional vs vector search
23 May 2024

On-device Vector Database for Dart/Flutter
21 May 2024

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.