DBMS > Alibaba Cloud TSDB vs. Oracle Berkeley DB vs. searchxml vs. Weaviate

System Properties Comparison Alibaba Cloud TSDB vs. Oracle Berkeley DB vs. searchxml vs. Weaviate

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

A stable, reliable, and cost-effective online high-performance time series database service

Widely used in-process key-value store

DBMS for structured and unstructured content wrapped with an application server

An AI-native realtime vector database engine that integrates scalable machine learning models.

Primary database model

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Native XML DBMS
Search engine

Vector DBMS

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

Score	0.24
Rank	#306	Overall
	#26	Time Series DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Score	1.73
Rank	#143	Overall
	#5	Vector DBMS

Website

www.alibabacloud.com/product/hitsdb

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

www.searchxml.net/category/products

github.com/weaviate/weaviate
weaviate.io

Technical documentation

www.alibabacloud.com/help/en/time-series-database/latest/what-is-tsdb

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

www.searchxml.net/support/handouts

weaviate.io/developers/weaviate

Developer

Alibaba

Oracle

originally developed by Sleepycat, which was acquired by Oracle

informationpartners gmbh

Weaviate B.V.

Initial release

1994

2015

2019

Current release

18.1.40, May 2020

1.0

1.19, May 2023

License

Commercial or Open Source

commercial

Open Source

commercial license available

commercial

Open Source

commercial license available with Weaviate Enterprise

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

C++

Server operating systems

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Windows

Data scheme

schema-free

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

Numeric data and Strings

yes

string, int, float, geo point, date, cross reference, fuzzy references

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

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

GraphQL is used as query language

APIs and other access methods

HTTP REST

RESTful HTTP API
WebDAV
XQuery
XSLT

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

Java

.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

C++

most other programming languages supported via APIs

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

yes

on the application server

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

sychronisation to multiple collections

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

multiple readers, single writer

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 managed on TSDB instance level

Domain, group and role-based access control at the document level and for application services

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
» more

Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service...
» more

Typical application scenarios

As a database supporting the development of generative AI and semantic search applications...
» more

Key customers

All companies that have data.
» more

Market metrics

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
» more

Licensing and pricing models

Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Weaviate, an ANN Database with CRUD support
2 February 2021, Etienne Dilocker, SeMI Technologies (sponsor)

show all

Recent citations in the news

Margo Seltzer Named ACM Athena Lecturer for Technical and Mentoring Contributions
26 April 2023, HPCwire

ACM recognizes far-reaching technical achievements with special awards
26 May 2021, EurekAlert

Margo I. Seltzer | Berkman Klein Center
18 August 2020, Berkman Klein Center

Database Trends Report: SQL Beats NoSQL, MySQL Most Popular -- ADTmag
5 March 2019, ADT Magazine

How to store financial market data for backtesting
26 January 2019, Towards Data Science

provided by Google News

Build enterprise-ready generative AI solutions with Cohere foundation models in Amazon Bedrock and Weaviate vector ...
24 January 2024, AWS Blog

Weaviate Partners with Snowflake to Bring Secure GenAI to Snowpark Container Services
8 February 2024, Datanami

Foley Represents Cortical Ventures in $50M Series B Round for Weaviate
17 December 2023, Foley & Lardner LLP

Getting Started with Weaviate: A Beginner's Guide to Search with Vector Databases
18 July 2023, Towards Data Science

Weaviate Raises $50 Million Series B Funding to Meet Soaring Demand for AI Native Vector Database Technology ...
21 April 2023, PR Newswire

provided by Google News

Share this page