DBMS > Google Cloud Datastore vs. RethinkDB vs. Transwarp Hippo vs. XTDB

System Properties Comparison Google Cloud Datastore vs. RethinkDB vs. Transwarp Hippo vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

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

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store

Vector DBMS

Document store

Secondary database models

Spatial DBMS

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

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	2.58
Rank	#105	Overall
	#20	Document stores

Score	0.00
Rank	#385	Overall
	#16	Vector DBMS

Score	0.13
Rank	#330	Overall
	#45	Document stores

Website

cloud.google.com/datastore

rethinkdb.com

www.transwarp.cn/en/subproduct/hippo

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

cloud.google.com/datastore/docs

rethinkdb.com/docs

www.xtdb.com/docs

Developer

Google

The Linux Foundation

since July 2017

Juxt Ltd.

Initial release

2008

2009

2023

2019

Current release

2.4.1, August 2020

1.0, May 2023

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

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

C++

Clojure

Server operating systems

hosted

Linux
OS X
Windows

Linux
macOS

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

Typing

predefined data types such as float or date

yes, details here

yes

string, binary, float, bool, date, geometry

Vector, Numeric and String

yes, extensible-data-notation format

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

SQL

Support of SQL

SQL-like query language (GQL)

limited SQL, making use of Apache Calcite

APIs and other access methods

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

RESTful HTTP API

HTTP REST
JDBC

Supported programming languages

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

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

C++
Java
Python

Clojure
Java

Server-side scripts

Stored procedures

using Google App Engine

Triggers

Callbacks using the Google Apps Engine

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

range based

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Atomic single-document operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

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

yes

users and table-level permissions

Role based access control and fine grained access rights

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More resources

DB-Engines blog posts

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