DBMS > Amazon Redshift vs. Databend vs. LevelDB vs. NSDb vs. SiriDB

System Properties Comparison Amazon Redshift vs. Databend vs. LevelDB vs. NSDb vs. SiriDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Open Source Time Series DBMS

Primary database model

Relational DBMS

Key-value store

Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

aws.amazon.com/redshift

github.com/datafuselabs/databend
www.databend.com

github.com/google/leveldb

nsdb.io

siridb.com

Technical documentation

docs.aws.amazon.com/redshift

docs.databend.com

github.com/google/leveldb/blob/main/doc/index.md

nsdb.io/Architecture

docs.siridb.com

Developer

Amazon (based on PostgreSQL)

Databend Labs

Google

Cesbit

Initial release

2012

2021

2011

2017

Current release

1.0.59, April 2023

1.23, February 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

BSD

Open Source

Apache Version 2.0

Open Source

MIT License

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

Rust

C++

Java, Scala

Server operating systems

hosted

hosted
Linux
macOS

Illumos
Linux
OS X
Windows

Linux
macOS

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

yes

Numeric data

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

all fields are automatically indexed

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC

CLI Client
JDBC
RESTful HTTP API

gRPC
HTTP REST
WebSocket

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
JavaScript (Node.js)
Python
Rust

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Java
Scala

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

Using Apache Lucene

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

Users with fine-grained authorization concept, user roles

simple rights management via user accounts

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