The document discusses Apache CouchDB, a NoSQL database management system. It begins with an overview of NoSQL databases and their characteristics like being non-relational, distributed, and horizontally scalable. It then provides details on CouchDB, describing it as a document-oriented database using JSON documents and JavaScript for queries. The document outlines CouchDB's features like schema-free design, ACID compliance, replication, RESTful API, and MapReduce functions. It concludes with examples of CouchDB use cases and steps to set up a sample project using a CouchDB instance with sample employee data and views/shows to query the data.
ACID ORC, Iceberg, and Delta Lake—An Overview of Table Formats for Large Scal...Databricks
The reality of most large scale data deployments includes storage decoupled from computation, pipelines operating directly on files and metadata services with no locking mechanisms or transaction tracking. For this reason attempts at achieving transactional behavior, snapshot isolation, safe schema evolution or performant support for CRUD operations has always been marred with tradeoffs.
This talk will focus on technical aspects, practical capabilities and the potential future of three table formats that have emerged in recent years as solutions to the issues mentioned above – ACID ORC (in Hive 3.x), Iceberg and Delta Lake. To provide a richer context, a comparison between traditional databases and big data tools as well as an overview of the reasons for the current state of affairs will be included.
After the talk, the audience is expected to have a clear understanding of the current development trends in large scale table formats, on the conceptual and practical level. This should allow the attendees to make better informed assessments about which approaches to data warehousing, metadata management and data pipelining they should adapt in their organizations.
MongoDB World 2019: The Journey of Migration from Oracle to MongoDB at RakutenMongoDB
Find out more about our journey of migrating to MongoDB after using Oracle for our hotel search database for over ten years.
- How did we solve the synchronization problem with the Master Database?
- How to get fast search results (even with massive write operations)?
- How other issues were solved
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
This presentation is related to nosql database and nosql database types information. this presentationa also contains discussion about, how mongodb works and mongodb security and mongodb sharding information.
The presentation begins with an overview of the growth of non-structured data and the benefits NoSQL products provide. It then provides an evaluation of the more popular NoSQL products on the market including MongoDB, Cassandra, Neo4J, and Redis. With NoSQL architectures becoming an increasingly appealing database management option for many organizations, this presentation will help you effectively evaluate the most popular NoSQL offerings and determine which one best meets your business needs.
MongoDB is the most famous and loved NoSQL database. It has many features that are easy to handle when compared to conventional RDBMS. These slides contain the basics of MongoDB.
ACID ORC, Iceberg, and Delta Lake—An Overview of Table Formats for Large Scal...Databricks
The reality of most large scale data deployments includes storage decoupled from computation, pipelines operating directly on files and metadata services with no locking mechanisms or transaction tracking. For this reason attempts at achieving transactional behavior, snapshot isolation, safe schema evolution or performant support for CRUD operations has always been marred with tradeoffs.
This talk will focus on technical aspects, practical capabilities and the potential future of three table formats that have emerged in recent years as solutions to the issues mentioned above – ACID ORC (in Hive 3.x), Iceberg and Delta Lake. To provide a richer context, a comparison between traditional databases and big data tools as well as an overview of the reasons for the current state of affairs will be included.
After the talk, the audience is expected to have a clear understanding of the current development trends in large scale table formats, on the conceptual and practical level. This should allow the attendees to make better informed assessments about which approaches to data warehousing, metadata management and data pipelining they should adapt in their organizations.
MongoDB World 2019: The Journey of Migration from Oracle to MongoDB at RakutenMongoDB
Find out more about our journey of migrating to MongoDB after using Oracle for our hotel search database for over ten years.
- How did we solve the synchronization problem with the Master Database?
- How to get fast search results (even with massive write operations)?
- How other issues were solved
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
This presentation is related to nosql database and nosql database types information. this presentationa also contains discussion about, how mongodb works and mongodb security and mongodb sharding information.
The presentation begins with an overview of the growth of non-structured data and the benefits NoSQL products provide. It then provides an evaluation of the more popular NoSQL products on the market including MongoDB, Cassandra, Neo4J, and Redis. With NoSQL architectures becoming an increasingly appealing database management option for many organizations, this presentation will help you effectively evaluate the most popular NoSQL offerings and determine which one best meets your business needs.
MongoDB is the most famous and loved NoSQL database. It has many features that are easy to handle when compared to conventional RDBMS. These slides contain the basics of MongoDB.
CouchDB has several features that help it stand out from the other databases in this rapidly growing field. Incremental map/reduce, peer to peer replication, mobile device synchronization, a realtime update feed, and the ability to host an application in the database itself (also known as a Couchapp) are just a few. See how companies such as the BBC, Radical Dynamic, Signal, and Incandescent Software are using CouchDB to solve their real world challenges.
Experiences using CouchDB inside Microsoft's Azure teamBrian Benz
Co-presented with Will Perry (@willpe). Real-world experiences using CouchDB inside Microsoft, and also how to get started with CouchDB on Microsoft Azure.
For our next ArcReady, we will explore a topic on everyone’s mind: Cloud computing. Several industry companies have announced cloud computing services . In October 2008 at the Professional Developers Conference, Microsoft announced the next phase of our Software + Services vision: the Azure Services Platform. The Azure Services Platforms provides a wide range of internet services that can be consumed from both on premises environments or the internet.
Session 1: Cloud Services
In our first session we will explore the current state of cloud services. We will then look at how applications should be architected for the cloud and explore a reference application deployed on Windows Azure. We will also look at the services that can be built for on premise application, using .NET Services. We will also address some of the concerns that enterprises have about cloud services, such as regulatory and compliance issues.
Session 2: The Azure Platform
In our second session we will take a slightly different look at cloud based services by exploring Live Mesh and Live Services. Live Mesh is a data synchronization client that has a rich API to build applications on. Live services are a collection of APIs that can be used to create rich applications for your customers. Live Services are based on internet standard protocols and data formats.
I felt necessity of creating this brief slideshow, so as to help PHP Developer interns and communicating the intricacies of development with my clients easier. I thought the more deeply clients understood what really went into translating their ideas to web applications under the hood, the better it could translate to
exchange of design issues,
appreciation of development process intricacies, resulting delivery time & cost issues.
So I quickly put together information that I found on internet & have tried to make an attempt. Hope this helps other developers too... Your comments & critique are welcome in terms of improving & simplifying this slide show.
MongoDB Evenings DC: Get MEAN and Lean with Docker and KubernetesMongoDB
Get MEAN and Lean with Docker and Kubernetes
Vadim Polyakov, Director of Enterprise Application Architecture, Inovalon
MongoDB Evenings DC
April 12, 2016 at 1776
2. First Off, What is NoSQL?
Class of database management systems that do not follow all of the rules of a traditional relational
DBMS.
Systems typically used in very large databases which are prone to performance problems caused
by SQL and the relational model of databases (Techopedia)
Term is misleading and actually means “Not Only SQL” because some systems may support SQL-
like query languages
Main points: non-relational, distributed, open-source and horizontally scalable
Additional points: Other characteristics that often apply include schema free, easy-replication
support, simple API, eventual consistency, and a huge amount of data
Triggered by the needs of Web 2.0 companies such as Facebook, Google and Amazon.com who
deal with gigantic volumes of data
Designed to support requirements of modern Web, Mobile, and Internet of Things applications and
built to overcome the scale, performance, data model, and data distribution limitations of relational
databases
3. First Off, What is NoSQL?
Motivations are that they have simplicity of design, simpler and faster scaling to clusters of machines (which is a problem for relational
databases), and finer control over availability (Wikipedia)
Types:
Document
CouchDB
MongoDB
Columnar
Cassandra
DynamoDB
Key-Value
Redis
MemcacheDB
Graph
Allegro
Neo4J
4. RDBMS vs. NoSQL
Scale up vs. scale out
clustering servers as needed as opposed to adding memory or
CPU to a central machine, or data center
Structured vs semi-structured and unstructured
Rows/columns vs. JSON vs. images, video, text blobs
Atomic transaction vs. eventual consistency
Atomic
All transactions must happen to the database or none will
Eventual
Cannot guarantee a flow of similar transactions are done at exactly
the same time (though difference is usually milliseconds)
5. What Is CouchDB and Why Use It?
Project created in April 2005 by Damien Katz, a former developer at IBM
Defined as a “storage system for a large scale object database”
Meant to serve web applications
Written in Erlang Programming Language
Schema-free document model
Don’t have to model your data up front as in a relational database
Uses JSON to store data
Query language is JavaScript using MapReduce
ACID Semantics
Implements a form of Multi-Version Concurrency Control, meaning CouchDB can handle a high volume of concurrent readers and writers without conflict
Documents are updated with revisions
Map/Reduce Views and Indexes
Stored data is structured using views
Views are created by using a JavaScript function as the map half of the map/reduce operation.
Function takes a document as a parameter and returns a single value
CouchDB indexes views and keeps them updated as documents are added, removed or altered
6. What Is CouchDB and Why Use It?
Distributed Architecture with Replication
Bi-directional replication and off-line operation
Multiple replicas of a CouchDB instance have their own copies of the same data, modify it, and sync the changes at a later time
REST HTTP API
All database items have a unique URI that can be modified using HTTP methods such as POST, GET, PUT, and DELETE
Eventual Consistency
Guarantees eventual consistency
Perfect for self-contained documents such as an invoice
In a relational DB you would have each invoice stored in a table as a row that refers to other rows in other tables - one row for
seller info, one for buyer, one row for each item billed, and more rows still to describe item details, manufacturer details and so
forth
In CouchDB all the data in the invoice would mimic a real world invoice by having all the data contained within one document
7. Use Cases of CouchDB
Replication and synchronization capabilities make it ideal for using it in mobile devices
where network connection is not guaranteed but the application must keep working
offline
Well suited for applications with accumulating, occasionally changing data, on which
pre-defined queries are to be run and where versioning is important
Enterprises that use it
Amadeus IT Group, for some of their backend systems
Ubuntu used it for their synchronization service “Ubuntu One” from 2009-2011
The BBC, for its dynamic content platforms
Credit Suisse, for internal use at commodities department for their marketplace framework
Sophos, for some of their backend systems
8. Steps of the Project
Scenario: As an employer you have access to a database with potential IT
employees to hire for your company. The database contains documents for each
individual.
Documents contain similar descriptive data but not always exactly the same attributes.
Stay tuned for more.
Ideally a front end application would be written on top of the database, known in
CouchDB lingo as a CouchApp. But for the purpose of the Database Applications class
we will just look at the backend database
9. Steps of the Project
Use video and text sources to become acquainted with CouchDB
Download CouchDB software on to local machine which will act as our DBMS
Use cURL utility tool on command line in conjunction with HTTP API as opposed to the built-
in “Futon” GUI that comes with CouchDB (API allows for interaction with the hosting or
remote server, databases, documents, as well as replication)
1. Create empty IT employee database
10. Steps of the Project
2. Use Mockaroo to generate employee objects in JSON format and bulk import JSON
files as individual documents in to IT employee database (using HTTP API)
11. Steps of the Project
Employee documents contain similar properties but are not always uniform, thus
the beauty of a schema-less but realistic and comparatively real-world design
VS.
12. Steps of the Project
Documents can also include and store attachments such as PDF’s, images, videos
and sound recordings
What a document looks like in
Futon
(These are not real people and all pictures are taken from
Google)
13. Steps of the Project
3. Create special design document which will contain “Views” and “Shows”
Design document is also JSON formatted
There is a “views” key which contains named objects as its value. These values act as the names of the different “views”, which is simply
CouchDB speak for queries. The “view” objects contain map and optional reduce properties that act as keys whose values are implemented
JavaScript functions acting on the database documents. Views are for:
Filtering the documents in your database to find those relevant to a particular process.
Extracting data from your documents and presenting it in a specific order.
Building efficient indexes to find documents by any value or structure that resides in them.
Use these indexes to represent relationships among documents.
Finally, with views you can make all sorts of calculations on the data in your documents. For example, a view can answer the question
of what your company’s spending was in the last week, month, or year.
The “shows” functionality is structured the same as the “views” within the design document
Show functions are also written in JavaScript and are used to render the database JSON documents in to HTML web pages for a
client
The point is that it relieves some work of the developer from having to create browser-side JavaScript for different platforms
The intention is to use a framework such as Ruby on Rails or Django to make HTTP requests to couchDB and render the dynamic
content to the client browser
Popular uses of show functions also include outputting CSV files, PNG images, and XML needed for compatibility with a particular
interface
14. Steps of the Project
The design document in Futon
15. Steps of the Project
4. Test and implement code
5. Run “views” and “shows” queries through HTTP API
6. Make any necessary alterations through HTTP API using cURL utility
17. Demo
Queries through the API
Views
Get the names of the employees
http://127.0.0.1:5984/it_employee_database/_design/example/_view/getName
Get the employees and their skills
http://127.0.0.1:5984/it_employee_database/_design/example/_view/getSkills
Search the database and return all employee candidates who have a criminal record
http://127.0.0.1:5984/it_employee_database/_design/example/_view/getCriminals
AND…lets look at an example of a mug shot of ‘Henry Armstrong’!
http://127.0.0.1:5984/it_employee_database/10d44adbfcd104e10ea4561d28113fd9/mugshot.jpg
Say we want to find someone with a certain set of skills to create a full stack developer or a lead developer
http://127.0.0.1:5984/it_employee_database/_design/example/_view/getFullStackDev
Shows
Usually meant to be ran on individual documents, this is a static HTML rendition of the JSON document for employee candidate ‘Gloria Young’
http://127.0.0.1:5984/it_employee_database/_design/example/_show/summary/10d44adbfcd104e10ea4561d28081664
Atomic:
Transactions to the database are guaranteed to write to disc. This sometimes can cause issues if there is a lot of traffic to a database
Say for example someone wants to modify a table and update a row. The database will lock everyone else out of reading that row until it is updated
Eventual Consistency
An idea of loose coupling. A client stores transactions to some sort of queue and eventually the queue writes the transaction to the server
MVCC Eventual Consistency
Versioning example
Consider a set of requests wanting to access a document. While the first request reads the document, a second request changes the document. This creates a whole new version of the document that couchDB appends to the database without having to wait for the read request to finish. When a third request wants to read the same document, couchDB will point it to the new version that has just been written.