DBMS > Apache Impala vs. Dragonfly vs. Microsoft Azure Table Storage vs. Snowflake vs. XTDB

System Properties Comparison Apache Impala vs. Dragonfly vs. Microsoft Azure Table Storage vs. Snowflake vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

A Wide Column Store for rapid development using massive semi-structured datasets

Cloud-based data warehousing service for structured and semi-structured data

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

impala.apache.org

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

azure.microsoft.com/en-us/services/storage/tables

www.snowflake.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

impala.apache.org/impala-docs.html

www.dragonflydb.io/docs

docs.snowflake.net/manuals/index.html

www.xtdb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

DragonflyDB team and community contributors

Microsoft

Snowflake Computing Inc.

Juxt Ltd.

Initial release

2013

2023

2012

2014

2019

Current release

4.1.0, June 2022

1.0, March 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSL 1.1

commercial

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Clojure

Server operating systems

Linux

hosted

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

scheme-free

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

schema-free

Typing

predefined data types such as float or date

yes

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

yes

yes, extensible-data-notation format

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

SQL-like DML and DDL statements

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC

Proprietary protocol

RESP - REdis Serialization Protocol

RESTful HTTP API

CLI Client
JDBC
ODBC

HTTP REST
JDBC

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

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

JavaScript (Node.js)
Python

Clojure
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Lua

user defined functions

Triggers

publish/subscribe channels provide some trigger functionality

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

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

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

Access rights based on private key authentication or shared access signatures

Users with fine-grained authorization concept, user roles and pluggable authentication

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