DBMS > Amazon Aurora vs. Apache Impala vs. DolphinDB vs. RavenDB vs. RocksDB

System Properties Comparison Amazon Aurora vs. Apache Impala vs. DolphinDB vs. RavenDB vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Analytic DBMS for Hadoop

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.

Open Source Operational and Transactional Enterprise NoSQL Document Database

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

Primary database model

Secondary database models

Document store

Relational DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	7.91
Rank	#50	Overall
	#32	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

aws.amazon.com/rds/aurora

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

impala.apache.org/impala-docs.html

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

ravendb.net/docs

github.com/facebook/rocksdb/wiki

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

DolphinDB, Inc

Hibernating Rhinos

Facebook, Inc.

Initial release

2015

2013

2018

2010

2013

Current release

4.1.0, June 2022

v2.00.4, January 2022

5.4, July 2022

8.11.4, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

free community version available

Open Source

AGPL version 3, commercial license available

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Server operating systems

hosted

Linux

Linux
Windows

Linux
macOS
Raspberry Pi
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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language

SQL-like query language (RQL)

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC

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

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

C++ API
Java API

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

All languages supporting JDBC/ODBC

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

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

horizontal partitioning

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

yes

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID, Cluster-wide transaction available

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Administrators, Users, Groups

Authorization levels configured per client per database

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