DBMS > IBM Db2 Event Store vs. LeanXcale vs. Snowflake vs. Tkrzw

System Properties Comparison IBM Db2 Event Store vs. LeanXcale vs. Snowflake vs. Tkrzw

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Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

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

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Event Store
Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

Key-value store

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

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

www.ibm.com/products/db2-event-store

www.leanxcale.com

www.snowflake.com

dbmx.net/tkrzw

Technical documentation

www.ibm.com/docs/en/db2-event-store

docs.snowflake.net/manuals/index.html

Developer

IBM

LeanXcale

Snowflake Computing Inc.

Mikio Hirabayashi

Initial release

2017

2015

2014

2020

Current release

2.0

0.9.3, August 2020

License

Commercial or Open Source

commercial

free developer edition available

commercial

Open Source

Apache Version 2.0

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 and C++

C++

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

hosted

Linux
macOS

Data scheme

yes

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

schema-free

Typing

predefined data types such as float or date

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

Secondary indexes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

through Apache Derby

yes

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

CLI Client
JDBC
ODBC

Supported programming languages

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C
Java
Scala

JavaScript (Node.js)
Python

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

No - written data is immutable

yes

Durability

Support for making data persistent

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

In-memory capabilities

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

yes

using specific database classes

User concepts

Access control

fine grained access rights according to SQL-standard

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

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