DBMS > Amazon Neptune vs. Blueflood vs. CrateDB vs. DolphinDB vs. Ehcache

System Properties Comparison Amazon Neptune vs. Blueflood vs. CrateDB vs. DolphinDB vs. Ehcache

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Scalable TimeSeries DBMS based on Cassandra

Distributed Database based on Lucene

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

A widely adopted Java cache with tiered storage options

Primary database model

Graph DBMS
RDF store

Time Series DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Time Series DBMS
Vector DBMS

Key-value store

Secondary database models

Relational DBMS

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

Score	2.20
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	0.06
Rank	#352	Overall
	#36	Time Series DBMS

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	2.72
Rank	#100	Overall
	#8	Time Series DBMS
	#5	Vector DBMS

Score	4.79
Rank	#66	Overall
	#8	Key-value stores

Website

aws.amazon.com/neptune

Technical documentation

aws.amazon.com/neptune/developer-resources

github.com/rax-maas/blueflood/wiki

cratedb.com/docs

docs.dolphindb.cn/en/help200/index.html

www.ehcache.org/documentation

Developer

Amazon

Rackspace

Crate

DolphinDB, Inc

Terracotta Inc, owned by Software AG

Initial release

2017

2013

2018

2009

Current release

5.8.1, August 2024

v2.00.4, January 2022

3.10.0, March 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

commercial

free community version available

Open Source

Apache Version 2; commercial licenses available

Cloud-based only

Only available as a cloud service

yes

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

hosted

Linux
OS X

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux
Windows

All OS with a Java VM

Data scheme

schema-free

predefined scheme

Flexible Schema (defined schema, partial schema, 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

yes, but no triggers and constraints, and PostgreSQL compatibility

SQL-like query language

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

HTTP REST

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

JCache

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

Java

Server-side scripts

Stored procedures

user defined functions (Javascript)

yes

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

horizontal partitioning

Sharding

by using Terracotta Server

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

selectable replication factor

based on Cassandra

Configurable replication on table/partition-level

yes

by using Terracotta Server

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

unique row identifiers can be used for implementing an optimistic concurrency control strategy

yes

supports JTA and can work as an XA resource

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

using a tiered cache-storage approach

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 and roles can be defined via the AWS Identity and Access Management (IAM)

rights management via user accounts

Administrators, Users, Groups

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