DBMS > Amazon Redshift vs. Apache Impala vs. HarperDB vs. ScyllaDB

System Properties Comparison Amazon Redshift vs. Apache Impala vs. HarperDB vs. ScyllaDB

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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

HarperDB takes the "stack" out of "tech stack" by combining an ultra-fast document-style data store, in-memory cache, real-time message broker, and your application components into a single distributed technology.

Cassandra and DynamoDB compatible wide column store

Primary database model

Relational DBMS

Document store

Wide column store

Secondary database models

Document store

Key-value store

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	0.55
Rank	#243	Overall
	#38	Document stores

Score	4.15
Rank	#69	Overall
	#5	Wide column stores

Website

aws.amazon.com/redshift

impala.apache.org

www.harperdb.io

www.scylladb.com

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

docs.harperdb.io/docs

docs.scylladb.com

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

HarperDB

ScyllaDB

Initial release

2012

2013

2017

2015

Current release

4.1.0, June 2022

3.1, August 2021

ScyllaDB Open Source 6.0.2, July 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

free community edition available

Open Source

Open Source (AGPL), commercial license available

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++

Node.js

C++

Server operating systems

hosted

Linux

Linux
OS X

Linux

Data scheme

yes

dynamic schema

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

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

cluster global secondary indices

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

SQL-like data manipulation statements

SQL-like DML and DDL statements (CQL)

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

Custom Functions

since release 3.1

yes, Lua

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

the nodes on which a table resides can be defined

selectable replication factor

Representation of geographical distribution of servers is possible

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

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

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

Atomic execution of specific operations

Atomicity and isolation are supported for single operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

yes

In-memory capabilities

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

yes

in-memory tables

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

Access rights for users and roles

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
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Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
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Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
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Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
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Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
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Licensing and pricing models

ScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
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