DBMS > Oracle NoSQL vs. QuestDB vs. Riak KV vs. SurrealDB

System Properties Comparison Oracle NoSQL vs. QuestDB vs. Riak KV vs. SurrealDB

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Editorial information provided by DB-Engines

Name

Description

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

A high performance open source SQL database for time series data

Distributed, fault tolerant key-value store

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Document store
Key-value store
Relational DBMS

Time Series DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Document store
Graph DBMS

Secondary database models

Relational DBMS

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

Score	3.07
Rank	#86	Overall
	#15	Document stores
	#11	Key-value stores
	#47	Relational DBMS

Score	2.81
Rank	#98	Overall
	#7	Time Series DBMS

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Score	1.11
Rank	#174	Overall
	#30	Document stores
	#17	Graph DBMS

Website

www.oracle.com/database/nosql/technologies/nosql

questdb.io

surrealdb.com

Technical documentation

docs.oracle.com/en/database/other-databases/nosql-database/index.html

questdb.io/docs

www.tiot.jp/riak-docs/riak/kv/latest

surrealdb.com/docs

Developer

Oracle

QuestDB Technology Inc

OpenSource, formerly Basho Technologies

SurrealDB Ltd

Initial release

2011

2014

2009

2022

Current release

24.1, May 2024

3.2.0, December 2022

v1.5.0, May 2024

License

Commercial or Open Source

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

Apache 2.0

Open Source

Apache version 2, commercial enterprise edition

Open Source

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

Java

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

Erlang

Rust

Server operating systems

Linux
Solaris SPARC/x86

Linux
macOS
Windows

Linux
OS X

Linux
macOS
Windows

Data scheme

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

schema-free via InfluxDB Line Protocol

schema-free

Typing

predefined data types such as float or date

optional

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

yes

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

SQL with time-series extensions

SQL-like query language

APIs and other access methods

RESTful HTTP API

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

HTTP API
Native Erlang Interface

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

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

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

over HTTP

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by timestamps)

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Source-replica replication with eventual consistency

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

Transaction concepts

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

configurable

ACID within a storage node (=shard)

ACID for single-table writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

off heap cache

yes

through memory mapped files

User concepts

Access control

Access rights for users and roles

yes, using Riak Security

yes, based on authentication and database rules

More information provided by the system vendor

News

Combine Java and Rust Code Coverage in a Polyglot Project
10 September 2024

Weather data visualization and forecasting with QuestDB, Kafka and Grafana
4 September 2024

Building a new vector based storage model
22 August 2024

Calibrating VWAP executions with QuestDB and Grafana
16 August 2024

Write Time: a call for community writers
13 August 2024

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