DBMS > Apache Impala vs. Microsoft Azure Table Storage vs. RethinkDB vs. XTDB vs. YTsaurus

System Properties Comparison Apache Impala vs. Microsoft Azure Table Storage vs. RethinkDB vs. XTDB vs. YTsaurus

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

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

YTsaurus is an open source platform for distributed storage and processing.

Primary database model

Relational DBMS

Wide column store

Document store

Document store
Key-value store

Secondary database models

Document store

Spatial DBMS

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

Score	10.22
Rank	#40	Overall
	#24	Relational DBMS

Score	3.09
Rank	#82	Overall
	#6	Wide column stores

Score	2.52
Rank	#105	Overall
	#20	Document stores

Score	0.15
Rank	#327	Overall
	#46	Document stores

Score	0.16
Rank	#320	Overall
	#44	Document stores
	#47	Key-value stores

Website

impala.apache.org

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

rethinkdb.com

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

ytsaurus.tech

Technical documentation

impala.apache.org/impala-docs.html

rethinkdb.com/docs

www.xtdb.com/docs

ytsaurus.tech/docs/en

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Microsoft

The Linux Foundation

since July 2017

Juxt Ltd.

Yandex

Initial release

2013

2012

2009

2019

2023

Current release

4.1.0, June 2022

2.4.1, August 2020

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache Version 2

Open Source

MIT License

Open Source

Apache License, Version 2.0

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++

Clojure

C++

Server operating systems

Linux

hosted

Linux
OS X
Windows

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

Ubuntu

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

yes, extensible-data-notation format

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

limited SQL, making use of Apache Calcite

YQL, an SQL-based language, is supported

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

HTTP REST
JDBC

Supported programming languages

All languages supporting JDBC/ODBC

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

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Clojure
Java

C++
Go
Java
JavaScript
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

range based

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

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

Source-replica replication

yes, each node contains all data

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

optimistic locking

Atomic single-document operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

Durability

Support for making data persistent

yes

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

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights based on private key authentication or shared access signatures

yes

users and table-level permissions

Access Control Lists

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