DBMS > Apache Kylin vs. Oracle Berkeley DB vs. Qdrant vs. Snowflake vs. XTDB

System Properties Comparison Apache Kylin vs. Oracle Berkeley DB vs. Qdrant 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

Widely used in-process key-value store

A high-performance vector database with neural network or semantic-based matching

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

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Vector DBMS

Relational DBMS

Document store

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	1.28
Rank	#167	Overall
	#6	Vector DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

kylin.apache.org

www.oracle.com/database/technologies/related/berkeleydb.html

github.com/qdrant/qdrant
qdrant.tech

www.snowflake.com

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

Technical documentation

kylin.apache.org/docs

docs.oracle.com/cd/E17076_05/html/index.html

qdrant.tech/documentation

docs.snowflake.net/manuals/index.html

www.xtdb.com/docs

Developer

Apache Software Foundation, originally contributed from eBay Inc

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Qdrant

Snowflake Computing Inc.

Juxt Ltd.

Initial release

2015

1994

2021

2014

2019

Current release

3.1.0, July 2020

18.1.40, May 2020

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial license available

Open Source

Apache Version 2.0

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

C, Java, C++ (depending on the Berkeley DB edition)

Rust

Clojure

Server operating systems

Linux

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Docker
Linux
macOS
Windows

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

Numbers, Strings, Geo, Boolean

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

only with the Berkeley DB XML edition

yes

Secondary indexes

yes

Keywords, numberic ranges, geo, full-text

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

yes

SQL interfaced based on SQLite is available

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

CLI Client
JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

JavaScript (Node.js)
Python

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

only for the SQL API

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

Source-replica replication

Collection-level replication

yes

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency, tunable 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

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

Key-based authentication

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

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