DBMS > Amazon Redshift vs. Blueflood vs. GreptimeDB vs. SingleStore

System Properties Comparison Amazon Redshift vs. Blueflood vs. GreptimeDB vs. SingleStore

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Scalable TimeSeries DBMS based on Cassandra

An open source Time Series DBMS built for increased scalability, high performance and efficiency

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	0.12
Rank	#351	Overall
	#34	Time Series DBMS

Score	5.38
Rank	#62	Overall
	#35	Relational DBMS

Website

aws.amazon.com/redshift

blueflood.io

greptime.com

www.singlestore.com

Technical documentation

docs.aws.amazon.com/redshift

github.com/rax-maas/blueflood/wiki

docs.greptime.com

docs.singlestore.com

Developer

Amazon (based on PostgreSQL)

Rackspace

Greptime Inc.

SingleStore Inc.

Initial release

2012

2013

2022

2013

Current release

8.5, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

free developer edition 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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

Java

Rust

C++, Go

Server operating systems

hosted

Linux
OS X

Android
Docker
FreeBSD
Linux
macOS
Windows

Linux

64 bit version required

Data scheme

yes

predefined scheme

schema-free, schema definition possible

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

but no triggers and foreign keys

APIs and other access methods

JDBC
ODBC

HTTP REST

gRPC
HTTP API
JDBC

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C++
Erlang
Go
Java
JavaScript

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Python

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

Source-replica replication

stores two copies of each physical data partition on two separate nodes

MapReduce

Offers an API for user-defined Map/Reduce methods

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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

Simple rights management via user accounts

Fine grained access control via users, groups and roles

More information provided by the system vendor

Specific characteristics

GreptimeDB is a SQL & Python-enabled timeseries database system built from scratch...
» more

SingleStore offers a fully-managed , distributed, highly-scalable SQL database designed...
» more

Competitive advantages

- Inherits advantages of Rust, such as excellent performance, memory safe, resource...
» more

SingleStore’s competitive advantages include: Easy and Simplified Architecture with...
» more

Typical application scenarios

For IoT industries, GreptimeDB can seamless integrate with message queues and other...
» more

Driving Fast Analytics: SingleStore delivers the fastest and most scalable reporting...
» more

Key customers

Greptime's clients span multiple sectors including IoT, connected vehicles, and energy...
» more

IEX Cloud : Improves Financial Data Distribution Speed 15x with Singlestore DB Comcast,...
» more

Market metrics

GreptimeDB has garnered global recognition by topping GitHub trends following its...
» more

Customers in various industries worldwide including US and International Industry...
» more

Licensing and pricing models

GreptimeDB: open source, distributed, cloud-native TSDB; supports Hybrid Time-series...
» more

F ree Tier and Enterprise Edition
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.