DBMS > Databend vs. Google Cloud Datastore vs. OpenQM vs. Postgres-XL

System Properties Comparison Databend vs. Google Cloud Datastore vs. OpenQM vs. Postgres-XL

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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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

QpenQM is a high-performance, self-tuning, multi-value DBMS

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

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

cloud.google.com/datastore

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

www.postgres-xl.org

Technical documentation

docs.databend.com

cloud.google.com/datastore/docs

www.postgres-xl.org/documentation

Developer

Databend Labs

Google

Rocket Software, originally Martin Phillips

Initial release

2021

2008

1993

2014

since 2012, originally named StormDB

Current release

1.0.59, April 2023

3.4-12

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPLv2, extended commercial license available

Open Source

Mozilla public 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

Server operating systems

hosted
Linux
macOS

hosted

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux
macOS

Data scheme

yes

schema-free

yes

with some exceptions

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language (GQL)

yes

distributed, parallel query execution

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
Basic
C
Java
Objective C
PHP
Python

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

using Google App Engine

yes

user defined functions

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

yes

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

MVCC

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.

User concepts

Access control

Users with fine-grained authorization concept, user roles

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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