DBMS > Datastax Enterprise vs. GridDB vs. RocksDB

System Properties Comparison Datastax Enterprise vs. GridDB vs. RocksDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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.

Scalable in-memory time series database optimized for IoT and Big Data

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

Primary database model

Wide column store

Time Series DBMS

Key-value store

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

Key-value store
Relational DBMS

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

Score	6.31
Rank	#57	Overall
	#4	Wide column stores

Score	2.02
Rank	#132	Overall
	#11	Time Series DBMS

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

Website

www.datastax.com/products/datastax-enterprise

griddb.net

rocksdb.org

Technical documentation

docs.datastax.com

docs.griddb.net

github.com/facebook/rocksdb/wiki

Developer

DataStax

Toshiba Corporation

Facebook, Inc.

Initial release

2011

2013

Current release

6.8, April 2020

5.1, August 2022

8.11.4, April 2024

License

Commercial or Open Source

commercial

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

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.

Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing.

Implementation language

Java

C++

Server operating systems

Linux
OS X

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

SQL92, SQL-like TQL (Toshiba Query Language)

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

C++ API
Java API

Supported programming languages

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

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

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

no "single point of failure"

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Immediate consistency within container, eventual consistency across containers

Foreign keys

Referential integrity

Transaction concepts

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

Atomicity and isolation are supported for single operations

ACID at container level

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

Access rights for users can be defined per database

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
» more

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
» more

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
» more

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
» more

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
» more

GitHub trending repository
» more

Licensing and pricing models

Annual subscription
» more

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.