Introduction 2 to aws and storage options

Jan 7, 20170 likes497 views

The document provides an overview of Amazon Web Services (AWS) storage options. It discusses Elastic Block Store (EBS) which provides block-level storage volumes for EC2 instances; instance store volumes which are temporary storage attached to an EC2 host; Amazon Simple Storage Service (S3) which provides scalable object storage; and Amazon Glacier which provides low-cost archival storage. For each service, it outlines their key features and characteristics as well as ideal and non-ideal usage patterns. The document concludes with a brief recap of the main differences between EBS, S3, and Glacier storage services.

Introduction to AWS 
Storage Options
Szilveszter Molnar 
@moszinet

Recap
• Infrastructure as a Service -
IAAS
• No reason to do it by yourself
• Elastic Computing
• Regions an Availability Zones
• Elastic Compute Cloud - EC2

Compute
• EC2 (On-Demand, Reserved, Spot)
• Autoscaling
• ELB
• AWS Lambda
• Container Service
• VM Import/Export

Storage & Content Delivery
• Amazon S3
• Glacier
• Elastic Block Store - EBS
• Elastic File System
• CloudFront
• Import/Export Snowball

Database
• Relational Database Service - RDS
• MySQL, MS SQL, Oracle, Postgres, etc ...
• Aurora
• AWS Database Migration Service
• DynamoDB
• Redshift
• ElastiCache

Networking
• Amazon VPC
• AWS Direct Connect
• Amazon Route53

Developer Tools
• AWS CodeCommit
• AWS CodeDeploy
• AWS CodePipeline

...
• Management Tools
• Security & Identity
• Analytics
• Internet of Things
• Mobile Services
• Application Services
• Enterprise Applications

Introduction 2 to aws and storage options

Storage Options
• EBS
• Instance Store Volumes
• S3
• Glacier

Elastic Block Storage
• Block level storage
• Use with EC2 instances
• Types
• Standard - ~100 IOPS, "best effort"
• Provisioned - max 2000 IOPS / volume
• stripe volumes for more (RAID 0)

Elastic Block Storage
• Highly available and reliable
• Replicated across multiple servers in an AZ
• Snapshots
• Incremental
• Stored in S3
• Annual Durability w/ less then 20 GB modiﬁed data
99.5%-99.9%

Elastic Block Storage
• Ideal Usage Patterns
• relatively frequent data changes
• databases with consistent read & writes
• raw block-level storage

Elastic Block Storage
• Anti-Patterns
• Temporary storage (use local instance
volumes, SQS or ElastiCache)
• Highly-durable storage (use S3, Glacier)
• Static data (use S3)

Instance Store Volumes
• Temporary
• SSD
• up to 120,000 IOPS
• attached to the physical server
• NOT Durable: lost if the instance is stopped/fails

Simple Storage Service
• Simple key-value storage
• 1 Byte - 5 TB object size
• Buckets
• Highly scalable, reliable and low-latency
• Durability: 99.999999999 %
• Availability: 99.99 %

Simple Storage Service
• Versioning
• Lifetime control
• Multi-Factor Auth Delete
• Reduced Redundancy - 99.99% durability
• 400x more durable than 1 typical HDD

Simple Storage Service
• Ideal Usage Patterns
• Static web content and media
• Static website hosting
• Archival of critical data

Simple Storage Service
• Anti-Patterns
• File System
• Structured data with query
• Rapidly changing data
• Backup and archival storage

Glacier
• 0.01$ / GB
• Store "archives"
• Retrieve archives by initiating a retrieval job
• Archives organised in vaults
• Durability 99.999999999 %
• Retrieve jobs complete in 3-5 hours

Glacier
• Ideal Usage Patterns
• Offsite archival
• Magnetic tape replacement
• Anti-patterns
• Rapidly changing data
• Real time access

Recap
• EBS
• "Hard disk"
• S3
• Buckets, highly durable 11 9's, fast access
• Glacier
• Archives, highly durable 11 9's, slow access

• AWS Overview White Paper
• AWS Storage Options Whitepaper
• qwiklab.com (free until end of March)

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What Do Contribution Guidelines Say About Software Testing? (MSR 2025)Andre Hora

Software testing plays a crucial role in the contribution process of open-source projects. For example, contributions introducing new features are expected to include tests, and contributions with tests are more likely to be accepted. Although most real-world projects require contributors to write tests, the specific testing practices communicated to contributors remain unclear. In this paper, we present an empirical study to understand better how software testing is approached in contribution guidelines. We analyze the guidelines of 200 Python and JavaScript open-source software projects. We find that 78% of the projects include some form of test documentation for contributors. Test documentation is located in multiple sources, including CONTRIBUTING files (58%), external documentation (24%), and README files (8%). Furthermore, test documentation commonly explains how to run tests (83.5%), but less often provides guidance on how to write tests (37%). It frequently covers unit tests (71%), but rarely addresses integration (20.5%) and end-to-end tests (15.5%). Other key testing aspects are also less frequently discussed: test coverage (25.5%) and mocking (9.5%). We conclude by discussing implications and future research.

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Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)Andre Hora

Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.