DBMS > Amazon Redshift vs. Databricks vs. GBase vs. QuestDB vs. Sphinx

System Properties Comparison Amazon Redshift vs. Databricks vs. GBase vs. QuestDB vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

A high performance open source SQL database for time series data

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

Document store
Relational DBMS

Relational DBMS

Time Series DBMS

Search engine

Secondary database models

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	2.70
Rank	#105	Overall
	#8	Time Series DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

docs.databricks.com

questdb.io/docs

sphinxsearch.com/docs

Developer

Amazon (based on PostgreSQL)

Databricks

General Data Technology Co., Ltd.

QuestDB Technology Inc

Sphinx Technologies Inc.

Initial release

2012

2013

2004

2014

2001

Current release

GBase 8a, GBase 8s, GBase 8c

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, Java, Python

Java (Zero-GC), C++, Rust

C++

Server operating systems

hosted

Linux

Linux
macOS
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free via InfluxDB Line Protocol

yes

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.

yes

Secondary indexes

restricted

yes

full-text index on all search fields

SQL

Support of SQL

yes

does not fully support an SQL-standard

with Databricks SQL

Standard with numerous extensions

SQL with time-series extensions

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

ADO.NET
C API
JDBC
ODBC

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

horizontal partitioning (by timestamps)

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication with eventual consistency

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

ACID for single-table writes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

yes

through memory mapped files

User concepts

Access control

fine grained access rights according to SQL-standard

yes

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
» more

Relational model with native time series support Column-based storage and time partitioned...
» more

Competitive advantages

High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more

Typical application scenarios

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more

Licensing and pricing models

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

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