DBMS > Amazon DocumentDB vs. EsgynDB vs. Google Cloud Datastore vs. TerarkDB vs. XTDB

System Properties Comparison Amazon DocumentDB vs. EsgynDB vs. Google Cloud Datastore vs. TerarkDB vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

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

Primary database model

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

aws.amazon.com/documentdb

www.esgyn.cn

cloud.google.com/datastore

github.com/bytedance/terarkdb

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

Technical documentation

aws.amazon.com/documentdb/resources

cloud.google.com/datastore/docs

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

www.xtdb.com/docs

Developer

Esgyn

Google

ByteDance, originally Terark

Juxt Ltd.

Initial release

2019

2015

2008

2016

2019

Current release

1.19, September 2021

License

Commercial or Open Source

commercial

restricted open source version available

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++, Java

C++

Clojure

Server operating systems

hosted

Linux

hosted

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

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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

yes

SQL-like query language (GQL)

limited SQL, making use of Apache Calcite

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

ADO.NET
JDBC
ODBC

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

C++ API
Java API

HTTP REST
JDBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

All languages supporting JDBC/ODBC/ADO.Net

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

C++
Java

Clojure
Java

Server-side scripts

Stored procedures

Java Stored Procedures

using Google App Engine

Triggers

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Multi-source replication between multi datacenters

Multi-source replication using Paxos

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

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.

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

Atomic single-document operations

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

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 and roles

fine grained access rights according to SQL-standard

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

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