DBMS > Databend vs. NSDb vs. ObjectBox vs. Oracle vs. Spark SQL

System Properties Comparison Databend vs. NSDb vs. ObjectBox vs. Oracle vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

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

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

Extremely fast embedded database for small devices, IoT and Mobile

Widely used RDBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Secondary database models

Time Series DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

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

nsdb.io

objectbox.io

www.oracle.com/database

spark.apache.org/sql

Technical documentation

docs.databend.com

nsdb.io/Architecture

docs.objectbox.io

docs.oracle.com/en/database

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Databend Labs

ObjectBox Limited

Oracle

Apache Software Foundation

Initial release

2021

2017

1980

2014

Current release

1.0.59, April 2023

23c, September 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

Apache License 2.0

commercial

restricted free version is available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Rust

Java, Scala

C and C++

Scala

Server operating systems

hosted
Linux
macOS

Linux
macOS

Android
iOS
Linux
macOS
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux
OS X
Windows

Data scheme

yes

Schemaless in JSON and XML columns

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

yes

SQL-like query language

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

gRPC
HTTP REST
WebSocket

Proprietary native API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

Java
Scala

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Java
Python
R
Scala

Server-side scripts

Stored procedures

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding, horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

online/offline synchronization between client and server

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

In-memory capabilities

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

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

Users with fine-grained authorization concept, user roles

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

News

The on-device Vector Database for Android and Java
29 May 2024

Vector search: making sense of search queries
29 May 2024

Python on-device Vector and Object Database for Local AI
28 May 2024

Evolution of search: traditional vs vector search
23 May 2024

On-device Vector Database for Dart/Flutter
21 May 2024

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.