DBMS > Google Cloud Datastore vs. Graph Engine vs. Snowflake vs. Typesense vs. XTDB

System Properties Comparison Google Cloud Datastore vs. Graph Engine vs. Snowflake vs. Typesense vs. XTDB

Editorial information provided by DB-Engines

Name

Description

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Cloud-based data warehousing service for structured and semi-structured data

A typo-tolerant, in-memory search engine optimized for instant search-as-you-type experiences and developer productivity

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

Primary database model

Document store

Graph DBMS
Key-value store

Relational DBMS

Search engine

Document store

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.79
Rank	#210	Overall
	#14	Search engines

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

cloud.google.com/datastore

www.graphengine.io

www.snowflake.com

typesense.org

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

Technical documentation

cloud.google.com/datastore/docs

www.graphengine.io/docs/manual

docs.snowflake.net/manuals/index.html

typesense.org/docs

www.xtdb.com/docs

Developer

Google

Microsoft

Snowflake Computing Inc.

Juxt Ltd.

Initial release

2008

2010

2014

2015

2019

Current release

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

MIT License

commercial

Open Source

GPL V3

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

.NET and C

C++

Clojure

Server operating systems

hosted

.NET

hosted

Linux

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

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

schema-free

pre-defined schema optional

schema-free

Typing

predefined data types such as float or date

yes, details here

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

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

RESTful HTTP API

CLI Client
JDBC
ODBC

RESTful HTTP API

HTTP REST
JDBC

Supported programming languages

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

C#
C++
F#
Visual Basic

JavaScript (Node.js)
Python

.Net

community maintained
Clojure

community maintained
Dart

community maintained
Go

community maintained
Java

community maintained
JavaScript
Perl

community maintained
PHP
Python
Ruby
Rust

community maintained
Swift

community maintained

Clojure
Java

Server-side scripts

Stored procedures

using Google App Engine

yes

user defined functions

Triggers

Callbacks using the Google Apps Engine

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

Multi-source replication using RAFT

yes, each node contains all data

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 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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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)

Users with fine-grained authorization concept, user roles and pluggable authentication

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