DBMS > Databend vs. Manticore Search vs. Oracle Berkeley DB vs. Snowflake

System Properties Comparison Databend vs. Manticore Search vs. Oracle Berkeley DB vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Multi-storage database for search, including full-text search.

Widely used in-process key-value store

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

Primary database model

Relational DBMS

Search engine

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

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

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

github.com/datafuselabs/databend
www.databend.com

manticoresearch.com

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

www.snowflake.com

Technical documentation

docs.databend.com

manual.manticoresearch.com

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

docs.snowflake.net/manuals/index.html

Developer

Databend Labs

Manticore Software

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Snowflake Computing Inc.

Initial release

2021

2017

1994

2014

Current release

1.0.59, April 2023

6.0, February 2023

18.1.40, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPL version 2

Open Source

commercial license available

commercial

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

Rust

C++

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

Server operating systems

hosted
Linux
macOS

FreeBSD
Linux
macOS
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

hosted

Data scheme

yes

Fixed schema

schema-free

yes

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

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

only with the Berkeley DB XML edition

yes

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL-like query language

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

CLI Client
JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

.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

JavaScript (Node.js)
Python

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

Partitioning is done manually, search queries against distributed index is supported

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Synchronous replication based on Galera library

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

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

Users with fine-grained authorization concept, user roles

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

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