DBMS > EXASOL vs. Hazelcast vs. Kdb vs. Snowflake vs. Solr

System Properties Comparison EXASOL vs. Hazelcast vs. Kdb vs. Snowflake vs. Solr

Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

A widely adopted in-memory data grid

High performance Time Series DBMS

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

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS

Key-value store

Time Series DBMS
Vector DBMS

Relational DBMS

Search engine

Secondary database models

Document store

JSON support with IMDG 3.12

Relational DBMS

Spatial DBMS

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

Score	2.25
Rank	#120	Overall
	#57	Relational DBMS

Score	6.87
Rank	#55	Overall
	#6	Key-value stores

Score	7.70
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Score	44.28
Rank	#24	Overall
	#3	Search engines

Website

Technical documentation

www.exasol.com/resources

hazelcast.org/imdg/docs

code.kx.com

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

Developer

Exasol

Hazelcast

Kx Systems, a division of First Derivatives plc

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2000

2008

2000

kdb was released 2000, kdb+ in 2003

2014

2006

Current release

5.3.6, November 2023

3.6, May 2018

9.5.0, February 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

free 32-bit version

commercial

Open Source

Apache Version 2

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

Server operating systems

All OS with a Java VM

Linux
OS X
Solaris
Windows

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

yes

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

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

table attribute 'grouped'

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

SQL-like query language (q)

yes

Solr Parallel SQL Interface

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

JCache
JPA
Memcached protocol
RESTful HTTP API

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

Java
Lua
Python
R

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

JavaScript (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

yes

Event Listeners, Executor Services

user defined functions

Java plugins

Triggers

yes

Events

yes

with views

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

similar paradigm used for internal processing

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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 according to SQL-standard

Role-based access control

rights management via user accounts

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

yes

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
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