An introduction to cloud computing with Amazon Web Services and MongoDB
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This document provides an introduction to cloud computing using Amazon Web Services (AWS) and MongoDB. It defines cloud computing and describes the various service models including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). It outlines some of the key AWS computing, storage, database, and other services like EC2, S3, DynamoDB, and ElastiCache. It also introduces MongoDB as a scalable and natural document-oriented NoSQL database and compares some of its features to SQL databases. Finally, it provides two examples of using AWS and MongoDB for DNA sequencing and genome analysis.
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An introduction to cloud computing with Amazon Web Services and MongoDB
- 1. An introduction to cloud computing with Amazon Web Services and MongoDB Samuel Demharter DTC, 10 March 2016
- 2. Cloud Computing “Everybody's in it and nobody's in it. It's like a cloud that everybody has given a little puff of mist to, and then the cloud does all the heavy thinking for everybody. I don't mean there's really a cloud. I just mean it's something like that.” The Sirens of Titan, Kurt Vonnegut, 1959
- 3. Definition • Gartner Group: “A style of computing in which massively scalable scalable and elastic IT-enabled capabilities are delivered as a service using Internet technologies.”
- 4. Cloud Computing Service Models Software As A Service (SAAS) Platform As A Service (PAAS) Infrastructure As A Service (IAAS)
- 5. Amazon Web Services • Development started in 2002 • In 2006, Amazon launched its Elastic Compute cloud (EC2) and S3 storage service • Amazon EC2/S3 was the first widely accessible cloud computing infrastructure service
- 6. Amazon Web Services (AWS) AWS Computing EC2 MapReduce Storage S3 EBS Databases SimpleDB DynamoDB Others Others
- 7. AWS Computing • Elastic Compute Cloud (EC2) – Access to individual instances as you would with any other machine – Customisable configuration – Auto Scaling • Amazon Elastic MapReduce – Process vast amounts of data – Utilise Hadoop framework
- 8. AWS Storage • Simple Storage Service (S3) – Scalable cloud storage – HTTP access – Object store not a file system – Cheap • Elastic Block Storage (EBS) – Local storage – For use with EC2 instances – Take snapshot backups – Fast
- 9. AWS Databases • Amazon SimpleDB (noSQL) – Ease of administration • Amazon DynamoDB (noSQL) – Scalability & durability • Amazon Relational Database Service (SQL) – Efficient indexing & querying • Amazone ElastiCache – Fast data access
- 14. huMONGOus – scalable – natural
- 15. What is a database? A database is a collection of information that is organized so that it can easily be accessed, managed, and updated.
- 16. Why use a database? • Reusability : You need a single, public, interface for your data storage that all parts of your application can use. • Availability : You need be sure that your application will always be able to read and write data. • Durability : You need to be sure that your data will stick around. • Scalability : You need your data storage to be able to grow with your application.
- 17. Typical SQL and noSQL databases SQL Oracle MySQL Microsoft SQL NoSQL Key-Value Column Document Graph-based SQL – Structured Query Language NoSQL – Not Only SQL MongoDB CouchDB Riak
- 19. MongoDB • Distributed • Document-oriented • Schema-less storage solution • Uses JSON-style documents • Supports Python, PHP, Java, Ruby, C++, etc. • Replica sets for failovers and speeding up reads • Sharding for high performance
- 20. SQL vs MongoDB (noSQL) SQL MongoDB (noSQL) Requires structured data/ well- designed schema semi-structured, unstructured & polymorphic data Table based Document based Database atomicity Document atomicity/ eventual consistency Rules enforced by database Rules enforced by user Scale-up Scale-out (suitable for distributed computing) Flexible & fast
- 24. Table - Who is the account holder for account ID 3?
- 25. Document - Who is the account holder for account ID 3?
- 26. Redundancy and Data Availability - Replication
- 27. Scaling out - Sharding • A means for partitioning data across servers for high performance
- 30. Usage Example 1: DNA Sequencing • Real-time DNA sequencing • Raw Data PC • Basecalling AWS • Basecalled Data PC
- 31. Usage Example 1: DNA Sequencing • Use AWS EC2 computing and S3 storage • Spot market – auction of unused EC2 instances • Pay-Per-Use an important economical factor for Nanopore • Use a combination of MongoDB and SQL
- 32. Usage Example 2: Genome Analysis Genetic Variant Calling Peter White et al., Ohio State University in collaboration with Genome Next https://youtu.be/upAtK_SOtsY
- 33. Resources • AWS Tutorials - https://qwiklabs.com • MapReduce - http://hadoop.apache.org/docs/r1.2.1/mapr ed_tutorial.html • AWS for Research - https://aws.amazon.com/grants/ • MongoDB - http://university.mongodb.com/
- 37. Definitions • Instance: A copy of an Amazon Machine Image running as a virtual server in the AWS cloud • Instance type: A specification that defines the memory, CPU, storage capacity, and hourly cost for an instance. • Amazon Machine Image: AMIs are like a template of a computer's root drive.
- 38. • Pixar accidentally wipes out nearly every file of "Toy Story 2" about 10 months into production. Fortunately, supervising technical director Galyn Susman had just become a new mom and had an entire copy of the movie on her home computer so that she could work from home. Woody and Buzz live to see another day, and movie.
Notes de l'éditeur
- In 2006, Amazon launched its Elastic Compute cloud (EC2) as a commercial web service that allows small companies and individuals to rent computers on which to run their own computer applications. Other key factors that have enabled cloud computing to evolve include the maturing of virtualisation technology, the development of universal high-speed bandwidth, and universal software interoperability standards
- a collection of cloud computing services e.g. Amazon markets AWS as a service to provide large computing capacity more quickly and more cheaply than a client company building an actual physical server farm.[3]
- Hadoop is a framework for distributing data and processing across resizable cluster of EC2 instances
- EMR: A web service that makes it easy to process large amounts of data efficiently. Amazon EMR uses Hadoop processing combined with several AWS products to do such tasks as web indexing, data mining, log file analysis, machine learning, scientific simulation, and data warehousing.
- Open source Popular with start-ups
- Simple application that stores data in file Want to read data later Another programme wants to read data What if not same language? Multiple programmes at same time use data? Overloaded. Scale up or scale out? Scale up – improve hardware – eventually runs out Scale out – distribute data – manage data across multiple hosts
- noSQL termed in 2009
- uses JSON-style documents to represent, query and modify data Similar to CouchBase and CouchDB MongoDB success is largely due to having easy-to-use, familiar tools.
- MongoDB uses memory mapped file for its storage engine (data is structured per record)
- A shard is a replica set that contains a subset of the data for the sharded cluster. Together, the cluster’s shards hold the entire data set for the cluster.
- A virtual machine is a software computer that, like a physical computer, runs an operating system and applications. The virtual machine is comprised of a set of specification and configuration files and is backed by the physical resources of a host. Some instance types are designed for standard applications, whereas others are designed for CPU-intensive, memory-intensive applications, and so on. AMI contains the operating system and can also include software and layers of your application, such as database servers, middleware, web servers, and so on.