DBMS > Apache Impala vs. Blueflood vs. Dragonfly vs. EsgynDB

System Properties Comparison Apache Impala vs. Blueflood vs. Dragonfly vs. EsgynDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Scalable TimeSeries DBMS based on Cassandra

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Primary database model

Secondary database models

Document store

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	0.06
Rank	#352	Overall
	#36	Time Series DBMS

Score	0.44
Rank	#255	Overall
	#38	Key-value stores

Score	0.15
Rank	#325	Overall
	#144	Relational DBMS

Website

impala.apache.org

blueflood.io

github.com/dragonflydb/dragonfly
www.dragonflydb.io

www.esgyn.cn

Technical documentation

impala.apache.org/impala-docs.html

github.com/rax-maas/blueflood/wiki

www.dragonflydb.io/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Rackspace

DragonflyDB team and community contributors

Esgyn

Initial release

2013

2023

2015

Current release

4.1.0, June 2022

1.0, March 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

BSL 1.1

commercial

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

C++

Java

C++

C++, Java

Server operating systems

Linux

Linux
OS X

Linux

Data scheme

yes

predefined scheme

scheme-free

yes

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit arrays

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

yes

APIs and other access methods

JDBC
ODBC

HTTP REST

Proprietary protocol

RESP - REdis Serialization Protocol

ADO.NET
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Lua

Java Stored Procedures

Triggers

publish/subscribe channels provide some trigger functionality

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Source-replica replication

Multi-source replication between multi datacenters

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

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, groups and roles

based on Apache Sentry and Kerberos

Password-based authentication

fine grained access rights according to SQL-standard

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More resources

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29 July 2021, iProgrammer

Apache Impala becomes Top-Level Project
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Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
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