DBMS > Amazon Redshift vs. Apache Impala vs. Infobright vs. SurrealDB

System Properties Comparison Amazon Redshift vs. Apache Impala vs. Infobright vs. SurrealDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Relational DBMS

Document store
Graph DBMS

Secondary database models

Document store

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	14.03
Rank	#40	Overall
	#24	Relational DBMS

Score	1.00
Rank	#193	Overall
	#90	Relational DBMS

Score	0.91
Rank	#204	Overall
	#33	Document stores
	#18	Graph DBMS

Website

aws.amazon.com/redshift

impala.apache.org

ignitetech.com/softwarelibrary/infobrightdb

surrealdb.com

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

surrealdb.com/docs

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Ignite Technologies Inc.; formerly InfoBright Inc.

SurrealDB Ltd

Initial release

2012

2013

2005

2022

Current release

4.1.0, June 2022

v1.1.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

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++

Rust

Server operating systems

hosted

Linux

Linux
Windows

Linux
macOS
Windows

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.

Secondary indexes

restricted

yes

Knowledge Grid Technology used instead

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Source-replica replication

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

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

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

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

yes, based on authentication and database rules

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