DBMS > Apache Kylin vs. EJDB vs. Google Cloud Datastore vs. Snowflake vs. XTDB

System Properties Comparison Apache Kylin vs. EJDB vs. Google Cloud Datastore vs. Snowflake vs. XTDB

Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

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

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	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

kylin.apache.org

github.com/Softmotions/ejdb

cloud.google.com/datastore

www.snowflake.com

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

Technical documentation

kylin.apache.org/docs

github.com/Softmotions/ejdb/blob/master/README.md

cloud.google.com/datastore/docs

docs.snowflake.net/manuals/index.html

www.xtdb.com/docs

Developer

Apache Software Foundation, originally contributed from eBay Inc

Softmotions

Google

Snowflake Computing Inc.

Juxt Ltd.

Initial release

2015

2012

2008

2014

2019

Current release

3.1.0, July 2020

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPLv2

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

Java

Clojure

Server operating systems

Linux

server-less

hosted

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

Data scheme

yes

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

ANSI SQL for queries (using Apache Calcite)

SQL-like query language (GQL)

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

in-process shared library

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

CLI Client
JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

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

JavaScript (Node.js)
Python

Clojure
Java

Server-side scripts

Stored procedures

using Google App Engine

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

none

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

yes

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

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

typically not needed, however similar functionality with collection joins possible

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

Read/Write Locking

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.

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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