AWS Glacier – Certification

AWS Glacier

  • Amazon Glacier is a storage service optimized for archival, infrequently used data, or “cold data.”
  • Glacier is an extremely low-cost storage service that provides durable storage with security features for data archiving and backup.
  • Glacier is designed to provide average annual durability of 99.999999999% for an archive.
  • Glacier redundantly stores data in multiple facilities and on multiple devices within each facility.
  • To increase durability, Glacier synchronously stores the data across multiple facilities before returning SUCCESS on uploading archives.
  • Glacier performs regular, systematic data integrity checks and is built to be automatically self-healing.
  • Glacier enables customers to offload the administrative burdens of operating and scaling storage to AWS, without having to worry about capacity planning, hardware provisioning, data replication, hardware failure detection and recovery, or time-consuming hardware migrations.
  • Glacier is a great storage choice when low storage cost is paramount, with data rarely retrieved, and retrieval latency of several hours is acceptable.
  • S3 should be used if applications requires fast, frequent real time access to the data
  • Glacier can store virtually any kind of data in any format.
  • All data is encrypted on the server side with Glacier handling key management and key protection. It uses AES-256, one of the strongest block ciphers available
  • Glacier allows interaction through AWS Management Console, Command Line Interface CLI and SDKs or REST based APIs.
    • management console can only be used to create and delete vaults.
    • rest of the operations to upload, download data, create jobs for retrieval need CLI, SDK or REST based APIs
  • Use cases include
    • Digital media archives
    • Data that must be retained for regulatory compliance
    • Financial and healthcare records
    • Raw genomic sequence data
    • Long-term database backups

Amazon Glacier Data Model

  • Amazon Glacier data model core concepts include vaults and archives and also includes job and notification-configuration resources
    • Vault
      • A vault is a container for storing archives
      • Each vault resource has a unique address, which comprises of the region the vault was created and the unique vault name within the region and account for e.g. https://glacier.us-west-2.amazonaws.com/111122223333/vaults/examplevault
      • Vault allows storage of unlimited number of archives
      • Glacier supports various vault operations which are region specific
      • An AWS account can create up to 1,000 vaults per region.
    • Archive
      • An archive can be any data such as a photo, video, or document and is a base unit of storage in Glacier.
      • Each archive has a unique ID and an optional description, which can only be specified during the upload of an archive.
      • Glacier assigns the archive an ID, which is unique in the AWS region in which it is stored.
      • Archive can be uploaded in a single request. While for large archives, Glacier provides a multipart upload API that enables uploading an archive in parts.
    • Jobs
      • A Job is required to retrieve an Archive and vault inventory list
      • Data retrieval requests are asynchronous operations, are queued and most jobs take about four hours to complete.
      • A job is first initiated and then the output of the job is downloaded after the job is completes
      • Vault inventory jobs needs the vault name
      • Data retrieval jobs needs both the vault name and the archive id, with an optional description
      • A vault can have multiple jobs in progress at any point in time and can be identified by Job ID, assigned when is it created for tracking
      • Glacier maintains job information such as job type, description, creation date, completion date, and job status and can be queried
      • After the job completes, the job output can be downloaded in full or partially by specifying a byte range.
    • Notification Configuration
      • As the jobs are asynchronous, Glacier supports notification mechanism to a SNS topic when job completes
      • SNS topic for notification can either be specified with each individual job request or with the vault
      • Glacier stores the notification configuration as a JSON document

Glacier Supported Operations

Vault Operations

  • Glacier provides operations to create and delete vaults.
  • A vault can be deleted only if there are no archives in the vault as of the last computed inventory and there have been no writes to the vault since the last inventory (as the inventory is prepared periodically)
  • Vault Inventory
    • Vault inventory helps retrieve list of archives in a vault with information such as archive ID, creation date, and size for each archive
    • Inventory for each vault is prepared periodically, every 24 hours
    • Vault inventory is updated approximately once a day, starting on the day the first archive is uploaded to the vault.
    • When a vault inventory job is, Glacier returns the last inventory it generated, which is a point-in-time snapshot and not real-time data.
  • Vault Metadata or Description can also be obtained for a specific vault or for all vaults in a region, which provides information such as
    • creation date,
    • number of archives in the vault,
    • total size in bytes used by all the archives in the vault,
    • and the date the vault inventory was generated
  • Glacier also provides operations to set, retrieve, and delete a notification configuration on the vault. Notifications can be used to identify vault events.

Archive Operations

  • Glacier provides operations to upload, download and delete archives.

Uploading an Archive

  • An archive can be uploaded in a single operation (1 byte to up to 4 GB in size ) or in parts referred as Multipart upload (40 TB)
  • Multipart Upload helps to
    • improve the upload experience for larger archives.
    • upload archives in parts, independently, parallely and in any order
    • faster recovery by needing to upload only the part that failed upload and not the entire archive.
    • upload archives without even knowing the size
    • upload archives from 1 byte to about 40,000 GB (10,000 parts * 4 GB) in size
  • To upload existing data to Glacier, consider using the AWS Import/Export service, which accelerates moving large amounts of data into and out of AWS using portable storage devices for transport. AWS transfers the data directly onto and off of storage devices using Amazon’s high-speed internal network, bypassing the Internet.
  • Glacier returns a response that includes an archive ID which is unique in the region in which the archive is stored
  • Glacier does not support any additional metadata information apart from an optional description. Any additional metadata information required should be maintained at client side

Downloading an Archive

  • Downloading an archive is an asynchronous operation and is the 2 step process
    • Initiate an archive retrieval job
      • When a Job is initiated, a job ID is returned as a part of the response
      • Job is executed asynchronously and the output can be downloaded after the job completes
      • Job can be initiated to download the entire archive or a portion of the archive
    • After the job completes, download the bytes
      • Archive can downloaded as all the bytes or specific byte range to download only a portion of the output
      • Downloading the archive in chunks helps in the event of the download failure, as only that part needs to be downloaded
      • Job completion status can be checked by
        • Check status explicitly (Not Recommended)
          • periodically poll the describe job operation request to obtain job information
        • Completion notification
          • An SNS topic can be specified, when the job is initiated or with the vault, to be used to notify job completion
About Range Retrievals
  • Amazon Glacier allows retrieving an archive either in whole (default) or a range, or portion
  • Range retrievals need a range to be provided that is megabyte aligned
  • Glacier returns checksum in the response which can be used to verify if any errors in download by comparing with checksum computed on the client side
  • Specifying a range of bytes can be helpful when:
    • Control bandwidth costs
      • Glacier allows retrieval of up to 5 percent of the average monthly storage (pro-rated daily) for free each month
      • Scheduling range retrievals can help in two ways.
        • meet the monthly free allowance of 5 percent by spreading out the data requested
        • if the amount of data retrieved doesn’t meet the free allowance percentage, scheduling range retrievals enables reduction of peak retrieval rate, which determines the retrieval fees.
    • Manage your data downloads
      • Glacier allows retrieved data to be downloaded for 24 hours after the retrieval request completes
      • Only portions of the archive can be retrieved so that the schedule of downloads can be managed within the given download window.
    • Retrieve a targeted part of a large archive
      • Retrieving an archive in range can be useful if an archive is uploaded as an aggregate of multiple individual files, and only few files need to be retrieved

Deleting an Archive

  • An archive can be deleted from the vault only one at a time
  • This operation is idempotent. Deleting an already-deleted archive does not result in an error
  • AWS applies pro-rated charge for items that are deleted prior to 90 days, as it is meant for long term storage

Updating an Archive

  • An existing archive cannot be updated and must be deleted and re-uploaded, which would be assigned a new archive id

AWS Certification Exam Practice Questions

  • Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
  • AWS services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
  • AWS exam questions are not updated to keep up the pace with AWS updates, so even if the underlying feature has changed the question might not be updated
  • Open to further feedback, discussion and correction.
  1. What is Amazon Glacier?
    1. You mean Amazon “Iceberg”: it’s a low-cost storage service.
    2. A security tool that allows to “freeze” an EBS volume and perform computer forensics on it.
    3. A low-cost storage service that provides secure and durable storage for data archiving and backup
    4. It’s a security tool that allows to “freeze” an EC2 instance and perform computer forensics on it.
  2. Amazon Glacier is designed for: (Choose 2 answers)
    1. Active database storage
    2. Infrequently accessed data
    3. Data archives
    4. Frequently accessed data
    5. Cached session data
  3. An organization is generating digital policy files which are required by the admins for verification. Once the files are verified they may not be required in the future unless there is some compliance issue. If the organization wants to save them in a cost effective way, which is the best possible solution?
    1. AWS RRS
    2. AWS S3
    3. AWS RDS
    4. AWS Glacier
  4. A user has moved an object to Glacier using the life cycle rules. The user requests to restore the archive after 6 months. When the restore request is completed the user accesses that archive. Which of the below mentioned statements is not true in this condition?
    1. The archive will be available as an object for the duration specified by the user during the restoration request
    2. The restored object’s storage class will be RRS (After the object is restored the storage class still remains GLACIER. Read more)
    3. The user can modify the restoration period only by issuing a new restore request with the updated period
    4. The user needs to pay storage for both RRS (restored) and Glacier (Archive) Rates
  5. To meet regulatory requirements, a pharmaceuticals company needs to archive data after a drug trial test is concluded. Each drug trial test may generate up to several thousands of files, with compressed file sizes ranging from 1 byte to 100MB. Once archived, data rarely needs to be restored, and on the rare occasion when restoration is needed, the company has 24 hours to restore specific files that match certain metadata. Searches must be possible by numeric file ID, drug name, participant names, date ranges, and other metadata. Which is the most cost-effective architectural approach that can meet the requirements?
    1. Store individual files in Amazon Glacier, using the file ID as the archive name. When restoring data, query the Amazon Glacier vault for files matching the search criteria. (Individual files are expensive and does not allow searching by participant names etc)
    2. Store individual files in Amazon S3, and store search metadata in an Amazon Relational Database Service (RDS) multi-AZ database. Create a lifecycle rule to move the data to Amazon Glacier after a certain number of days. When restoring data, query the Amazon RDS database for files matching the search criteria, and move the files matching the search criteria back to S3 Standard class. (As the data is not needed can be stored to Glacier directly and the data need not be moved back to S3 standard)
    3. Store individual files in Amazon Glacier, and store the search metadata in an Amazon RDS multi-AZ database. When restoring data, query the Amazon RDS database for files matching the search criteria, and retrieve the archive name that matches the file ID returned from the database query. (Individual files and Multi-AZ is expensive)
    4. First, compress and then concatenate all files for a completed drug trial test into a single Amazon Glacier archive. Store the associated byte ranges for the compressed files along with other search metadata in an Amazon RDS database with regular snapshotting. When restoring data, query the database for files that match the search criteria, and create restored files from the retrieved byte ranges.
    5. Store individual compressed files and search metadata in Amazon Simple Storage Service (S3). Create a lifecycle rule to move the data to Amazon Glacier, after a certain number of days. When restoring data, query the Amazon S3 bucket for files matching the search criteria, and retrieve the file to S3 reduced redundancy in order to move it back to S3 Standard class. (Once the data is moved from S3 to Glacier the metadata is lost, as Glacier does not have metadata and must be maintained externally)
  6. A user is uploading archives to Glacier. The user is trying to understand key Glacier resources. Which of the below mentioned options is not a Glacier resource?
    1. Notification configuration
    2. Archive ID
    3. Job
    4. Archive

References

AWS Storage Options – S3 & Glacier

Amazon S3

  • highly-scalable, reliable, and low-latency data storage infrastructure at very low costs.
  • provides a simple web services interface that can be used to store and retrieve any amount of data, at any time, from within Amazon EC2 or from anywhere on the web.
  • allows you to write, read, and delete objects containing from 1 byte to 5 terabytes of data each.
  • number of objects you can store in an Amazon S3 bucket is virtually unlimited.
  • highly secure, supporting encryption at rest, and providing multiple mechanisms to provide fine-grained control of access to Amazon S3 resources.
  • highly scalable, allowing concurrent read or write access to Amazon S3 data by many separate clients or application threads.
  • provides data lifecycle management capabilities, allowing users to define rules to automatically archive Amazon S3 data to Amazon Glacier, or to delete data at end of life.

Ideal Use Cases

  • Storage & Distribution of static web content and media
    • frequently used to host static websites and provides a highly-available and highly-scalable solution for websites with only static content, including HTML files, images, videos, and client-side scripts such as JavaScript
    • works well for fast growing websites hosting data intensive, user-generated content, such as video and photo sharing sites as no storage provisioning is required
    • content can either be directly served from Amazon S3 since each object in Amazon S3 has a unique HTTP URL address
    • can also act as an Origin store for the Content Delivery Network (CDN) such as Amazon CloudFront
    • it works particularly well for hosting web content with extremely spiky bandwidth demands because of S3’s elasticity
  • Data Store for Large Objects
    • can be paired with RDS or NoSQL database and used to store large objects for e.g. file or objects, while the associated metadata for e.g. name, tags, comments etc. can be stored in RDS or NoSQL database where it can be indexed and queried providing faster access to relevant data
  • Data store for computation and large-scale analytics
    • commonly used as a data store for computation and large-scale analytics, such as analyzing financial transactions, clickstream analytics, and media transcoding.
    • data can be accessed from multiple computing nodes concurrently without being constrained by a single connection because of its horizontal scalability
  • Backup and Archival of critical data
    • used as a highly durable, scalable, and secure solution for backup and archival of critical data, and to provide disaster recovery solutions for business continuity.
    • stores objects redundantly on multiple devices across multiple facilities, it provides the highly-durable storage infrastructure needed for these scenarios.
    • it’s versioning capability is available to protect critical data from inadvertent deletion

Anti-Patterns

Amazon S3 has following Anti-Patterns where it is not an optimal solution

  • Dynamic website hosting
    • While Amazon S3 is ideal for hosting static websites, dynamic websites requiring server side interaction, scripting or database interaction cannot be hosted and should rather be hosted on Amazon EC2
  • Backup and archival storage
    • Data requiring long term archival storage with infrequent read access can be stored more cost effectively in Amazon Glacier
  • Structured Data Query
    • Amazon S3 doesn’t offer query capabilities, so to read an object the object name and key must be known. Instead pair up S3 with RDS or Dynamo DB to store, index and query metadata about Amazon S3 objects
  • Rapidly Changing Data
    • Data that needs to updated frequently might be better served by a storage solution with lower read/write latencies, such as Amazon EBS volumes, RDS or Dynamo DB.
  • File System
    • Amazon S3 uses a flat namespace and isn’t meant to serve as a standalone, POSIX-compliant file system. However, by using delimiters (commonly either the ‘/’ or ‘’ character) you are able construct your keys to emulate the hierarchical folder structure of file system within a given bucket.

Performance

  • Access to Amazon S3 from within Amazon EC2 in the same region is fast.
  • Amazon S3 is designed so that server-side latencies are insignificant relative to Internet latencies.
  • Amazon S3 is also built to scale storage, requests, and users to support a virtually unlimited number of web-scale applications.
  • If Amazon S3 is accessed using multiple threads, multiple applications, or multiple clients concurrently, total Amazon S3 aggregate throughput will typically scale to rates that far exceed what any single server can generate or consume.

Durability & Availability

  • Amazon S3 storage provides provides the highest level of data durability and availability, by automatically and synchronously storing your data across both multiple devices and multiple facilities within the selected geographical region
  • Error correction is built-in, and there are no single points of failure. Amazon S3 is designed to sustain the concurrent loss of data in two facilities, making it very well-suited to serve as the primary data storage for mission-critical data.
  • Amazon S3 is designed for 99.999999999% (11 nines) durability per object and 99.99% availability over a one-year period.
  • Amazon S3 data can be protected from unintended deletions or overwrites using Versioning.
  • Versioning can be enabled with MFA (Multi Factor Authentication) Delete on the bucket, which would require two forms of authentication to delete an object
  • For Non Critical and Reproducible data for e.g. thumbnails, transcoded media etc., S3 Reduced Redundancy Storage (RRS) can be used, which provides a lower level of durability at a lower storage cost
  • RRS is designed to provide 99.99% durability per object over a given year. While RRS is less durable than standard Amazon S3, it is still designed to provide 400 times more durability than a typical disk drive

Cost Model

  • With Amazon S3, you pay only for what you use and there is no minimum fee.
  • Amazon S3 has three pricing components: storage (per GB per month), data transfer in or out (per GB per month), and requests (per n thousand requests per month).

Scalability & Elasticity

  • Amazon S3 has been designed to offer a very high level of scalability and elasticity automatically
  • Amazon S3 supports a virtually unlimited number of files in any bucket
  • Amazon S3 bucket can store a virtually unlimited number of bytes
  • Amazon S3 allows you to store any number of objects (files) in a single bucket, and Amazon S3 will automatically manage scaling and distributing redundant copies of your information to other servers in other locations in the same region, all using Amazon’s high-performance infrastructure.

Interfaces

  • Amazon S3 provides standards-based REST and SOAP web services APIs for both management and data operations.
  • Amazon S3 provides easier to use higher level toolkit or SDK in different languages (Java, .NET, PHP, and Ruby) that wraps the underlying APIs
  • Amazon S3 Command Line Interface (CLI) provides a set of high-level, Linux-like Amazon S3 file commands for common operations, such as ls, cp, mv, sync, etc. They also provide the ability to perform recursive uploads and downloads using a single folder-level Amazon S3 command, and supports parallel transfers.
  • AWS Management Console provides the ability to easily create and manage Amazon S3 buckets, upload and download objects, and browse the contents of your Amazon S3 buckets using a simple web-based user interface
  • All interfaces provide the ability to store Amazon S3 objects (files) in uniquely-named buckets (top-level folders), with each object identified by an unique Object key within that bucket.

Glacier

  • extremely low-cost storage service that provides highly secure, durable, and flexible storage for data backup and archival
  • can reliably store their data for as little as $0.01 per gigabyte per month.
  • to offload the administrative burdens of operating and scaling storage to AWS such as capacity planning, hardware provisioning, data replication, hardware failure detection and repair, or time consuming hardware migrations
  • Data is stored in Amazon Glacier as Archives where an archive can represent a single file or multiple files combined into a single archive
  • Archives are stored in Vaults for which the access can be controlled through IAM
  • Retrieving archives from Vaults require initiation of a job and can take anywhere around 3-5 hours
  • Amazon Glacier integrates seamlessly with Amazon S3 by using S3 data lifecycle management policies to move data from S3 to Glacier
  • AWS Import/Export can also be used to accelerate moving large amounts of data into Amazon Glacier using portable storage devices for transport

Ideal Usage Patterns

  • Amazon Glacier is ideally suited for long term archival solution for infrequently accessed data with archiving offsite enterprise information, media assets, research and scientific data, digital preservation and magnetic tape replacement

Anti-Patterns

Amazon Glacier has following Anti-Patterns where it is not an optimal solution

  • Rapidly changing data
    • Data that must be updated very frequently might be better served by a storage solution with lower read/write latencies such as Amazon EBS or a Database
  • Real time access
    • Data stored in Glacier can not be accessed at real time and requires an initiation of a job for object retrieval with retrieval times ranging from 3-5 hours. If immediate access is needed, Amazon S3 is a better choice.

Performance

  • Amazon Glacier is a low-cost storage service designed to store data that is infrequently accessed and long lived.
  • Amazon Glacier jobs typically complete in 3 to 5 hours

Durability and Availability

  • Amazon Glacier redundantly stores data in multiple facilities and on multiple devices within each facility
  • Amazon Glacier is designed to provide average annual durability of 99.999999999% (11 nines) for an archive
  • Amazon Glacier synchronously stores your data across multiple facilities before returning SUCCESS on uploading archives.
  • Amazon Glacier also performs regular, systematic data integrity checks and is built to be automatically self-healing.

Cost Model

  • Amazon Glacier has three pricing components: storage (per GB per month), data transfer out (per GB per month), and requests (per thousand UPLOAD and RETRIEVAL requests per month).
  • Amazon Glacier is designed with the expectation that retrievals are infrequent and unusual, and data will be stored for extended periods of time and allows you to retrieve up to 5% of your average monthly storage (pro-rated daily) for free each month. Any additional amount of data retrieved is charged per GB
  • Amazon Glacier also charges a pro-rated charge (per GB) for items deleted prior to 90 days

Scalability & Elasticity

  • A single archive is limited to 40 TBs, but there is no limit to the total amount of data you can store in the service.
  • Amazon Glacier scales to meet your growing and often unpredictable storage requirements whether you’re storing petabytes or gigabytes, Amazon Glacier automatically scales your storage up or down as needed.

Interfaces

  • Amazon Glacier provides a native, standards-based REST web services interface, as well as Java and .NET SDKs.
  • AWS Management Console or the Amazon Glacier APIs can be used to create vaults to organize the archives in Amazon Glacier.
  • Amazon Glacier APIs can be used to upload and retrieve archives, monitor the status of your jobs and also configure your vault to send you a notification via Amazon Simple Notification Service (Amazon SNS) when your jobs complete.
  • Amazon Glacier can be used as a storage class in Amazon S3 by using object lifecycle management to provide automatic, policy-driven archiving from Amazon S3 to Amazon Glacier.
  • Amazon S3 api provides a RESTORE operation and the retrieval process takes the same 3-5 hours
  • On retrieval, a copy of the retrieved object is placed in Amazon S3 RRS storage for a specified retention period; the original archived object remains stored in Amazon Glacier and you are charged for both the storage.
  • When using Amazon Glacier as a storage class in Amazon S3, use the Amazon S3 APIs, and when using “native” Amazon Glacier, you use the Amazon Glacier APIs
  • Objects archived to Amazon Glacier via Amazon S3 can only be listed and retrieved via the Amazon S3 APIs or the AWS Management Console—they are not visible as archives in an Amazon Glacier vault.

AWS Certification Exam Practice Questions

  • Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
  • AWS services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
  • AWS exam questions are not updated to keep up the pace with AWS updates, so even if the underlying feature has changed the question might not be updated
  • Open to further feedback, discussion and correction.
  1. You want to pass queue messages that are 1GB each. How should you achieve this?
    1. Use Kinesis as a buffer stream for message bodies. Store the checkpoint id for the placement in the Kinesis Stream in SQS.
    2. Use the Amazon SQS Extended Client Library for Java and Amazon S3 as a storage mechanism for message bodies. (Amazon SQS messages with Amazon S3 can be useful for storing and retrieving messages with a message size of up to 2 GB. To manage Amazon SQS messages with Amazon S3, use the Amazon SQS Extended Client Library for Java. Refer link)
    3. Use SQS’s support for message partitioning and multi-part uploads on Amazon S3.
    4. Use AWS EFS as a shared pool storage medium. Store filesystem pointers to the files on disk in the SQS message bodies.
  2. Company ABCD has recently launched an online commerce site for bicycles on AWS. They have a “Product” DynamoDB table that stores details for each bicycle, such as, manufacturer, color, price, quantity and size to display in the online store. Due to customer demand, they want to include an image for each bicycle along with the existing details. Which approach below provides the least impact to provisioned throughput on the “Product” table?
    1. Serialize the image and store it in multiple DynamoDB tables
    2. Create an “Images” DynamoDB table to store the Image with a foreign key constraint to the “Product” table
    3. Add an image data type to the “Product” table to store the images in binary format
    4. Store the images in Amazon S3 and add an S3 URL pointer to the “Product” table item for each image

References

AWS S3 Storage Classes – Certification

AWS S3 Storage Classes Overview

  • Amazon S3 storage classes are designed to sustain the concurrent loss of data in one or two facilities
  • S3 storage classes allows lifecycle management for automatic migration of objects for cost savings
  • S3 storage classes support SSL encryption of data in transit and data encryption at rest
  • S3 also regularly verifies the integrity of your data using checksums and provides auto healing capability

AWS S3 Storage Classes Comparision

Standard

  • Storage class is ideal for performance-sensitive use cases and frequently accessed data and is designed to sustain the loss of data in a two facilities
  • STANDARD is the default storage class, if none specified during upload
  • Low latency and high throughput performance
  • Designed for durability of 99.999999999% of objects
  • Designed for 99.99% availability over a given year
  • Backed with the Amazon S3 Service Level Agreement for availability.

Standard IA

  • S3 STANDARD_IA (Infrequent Access) storage class is optimized for long-lived and less frequently accessed data for e.g. backups and older data where access is limited, but the use case still demands high performance
  • STANDARD_IA is designed to sustain the loss of data in a two facilities
  • STANDARD_IA objects are available for real-time access.
  • STANDARD_IA storage class is suitable for larger objects greater than 128 KB (smaller objects are charged for 128KB only) kept for at least 30 days.
  • Same low latency and high throughput performance of Standard
  • Designed for durability of 99.999999999% of objects
  • Designed for 99.9% availability over a given year
  • Backed with the Amazon S3 Service Level Agreement for availability

Reduced Redundancy Storage – RRS

  • Reduced Redundancy Storage (RRS) storage class is designed for noncritical, reproducible data stored at lower levels of redundancy than the STANDARD storage class, which reduces storage costs
  • Designed for durability of 99.99% of objects
  • Designed for 99.99% availability over a given year
  • Lower level of redundancy results in less durability and availability
  • RRS stores objects on multiple devices across multiple facilities, providing 400 times the durability of a typical disk drive,
  • RRS does not replicate objects as many times as S3 standard storage and is designed to sustain the loss of data in a single facility.
  • If an RRS object is lost, S3 returns a 405 error on requests made to that object
  • S3 can send an event notification, configured on the bucket, to alert a user or start a workflow when it detects that an RRS object is lost which can be used to replace the lost object

Glacier

  • GLACIER storage class is suitable for archiving data where data access is infrequent and retrieval time of several (3-5) hours  is acceptable.
  • GLACIER storage class uses the very low-cost Amazon Glacier storage service, but the objects in this storage class are still managed through S3
  • Designed for durability of 99.999999999% of objects
  • GLACIER cannot be specified as the storage class at the object creation time but has to be transitioned fromSTANDARD, RRS, or STANDARD_IA to GLACIER storage class using lifecycle management.
  • For accessing GLACIER objects,
    • object must be restored which can taken anywhere between 3-5 hours
    • objects are only available for the time period (number of days) specified during the restoration request
    • object’s storage class remains GLACIER
    • charges are levied for both the archive (GLACIER rate) and the copy restored temporarily (RRS rate)
  • Vault Lock feature enforces compliance via a lockable policy

AWS Certification Exam Practice Questions

  • Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
  • AWS services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
  • AWS exam questions are not updated to keep up the pace with AWS updates, so even if the underlying feature has changed the question might not be updated
  • Open to further feedback, discussion and correction.
  1. What does RRS stand for when talking about S3?
    1. Redundancy Removal System
    2. Relational Rights Storage
    3. Regional Rights Standard
    4. Reduced Redundancy Storage
  2. What is the durability of S3 RRS?
    1. 99.99%
    2. 99.95%
    3. 99.995%
    4. 99.999999999%
  3. What is the Reduced Redundancy option in Amazon S3?
    1. Less redundancy for a lower cost
    2. It doesn’t exist in Amazon S3, but in Amazon EBS.
    3. It allows you to destroy any copy of your files outside a specific jurisdiction.
    4. It doesn’t exist at all
  4. An application is generating a log file every 5 minutes. The log file is not critical but may be required only for verification in case of some major issue. The file should be accessible over the internet whenever required. Which of the below mentioned options is a best possible storage solution for it?
    1. AWS S3
    2. AWS Glacier
    3. AWS RDS
    4. AWS S3 RRS
  5. A user has moved an object to Glacier using the life cycle rules. The user requests to restore the archive after 6 months. When the restore request is completed the user accesses that archive. Which of the below mentioned statements is not true in this condition?
    1. The archive will be available as an object for the duration specified by the user during the restoration request
    2. The restored object’s storage class will be RRS (After the object is restored the storage class still remains GLACIER. Read more)
    3. The user can modify the restoration period only by issuing a new restore request with the updated period
    4. The user needs to pay storage for both RRS (restored) and Glacier (Archive) Rates
  6. Your department creates regular analytics reports from your company’s log files. All log data is collected in Amazon S3 and processed by daily Amazon Elastic Map Reduce (EMR) jobs that generate daily PDF reports and aggregated tables in CSV format for an Amazon Redshift data warehouse. Your CFO requests that you optimize the cost structure for this system. Which of the following alternatives will lower costs without compromising average performance of the system or data integrity for the raw data? [PROFESSIONAL]
    1. Use reduced redundancy storage (RRS) for PDF and CSV data in Amazon S3. Add Spot instances to Amazon EMR jobs. Use Reserved Instances for Amazon Redshift. (Spot instances impacts performance)
    2. Use reduced redundancy storage (RRS) for all data in S3. Use a combination of Spot instances and Reserved Instances for Amazon EMR jobs. Use Reserved instances for Amazon Redshift (Combination of the Spot and reserved with guarantee performance and help reduce cost. Also, RRS would reduce cost and guarantee data integrity, which is different from data durability )
    3. Use reduced redundancy storage (RRS) for all data in Amazon S3. Add Spot Instances to Amazon EMR jobs. Use Reserved Instances for Amazon Redshift (Spot instances impacts performance)
    4. Use reduced redundancy storage (RRS) for PDF and CSV data in S3. Add Spot Instances to EMR jobs. Use Spot Instances for Amazon Redshift. (Spot instances impacts performance)
  7. Which of the below mentioned options can be a good use case for storing content in AWS RRS?
    1. Storing mission critical data Files
    2. Storing infrequently used log files
    3. Storing a video file which is not reproducible
    4. Storing image thumbnails
  8. A newspaper organization has an on-premises application which allows the public to search its back catalogue and retrieve individual newspaper pages via a website written in Java. They have scanned the old newspapers into JPEGs (approx. 17TB) and used Optical Character Recognition (OCR) to populate a commercial search product. The hosting platform and software is now end of life and the organization wants to migrate its archive to AWS and produce a cost efficient architecture and still be designed for availability and durability. Which is the most appropriate? [PROFESSIONAL]
    1. Use S3 with reduced redundancy to store and serve the scanned files, install the commercial search application on EC2 Instances and configure with auto-scaling and an Elastic Load Balancer. (RRS impacts durability and commercial search would add to cost)
    2. Model the environment using CloudFormation. Use an EC2 instance running Apache webserver and an open source search application, stripe multiple standard EBS volumes together to store the JPEGs and search index. (Using EBS is not cost effective for storing files)
    3. Use S3 with standard redundancy to store and serve the scanned files, use CloudSearch for query processing, and use Elastic Beanstalk to host the website across multiple availability zones. (Standard S3 and Elastic Beanstalk provides availability and durability, Standard S3 and CloudSearch provides cost effective storage and search)
    4. Use a single-AZ RDS MySQL instance to store the search index and the JPEG images use an EC2 instance to serve the website and translate user queries into SQL. (RDS is not ideal and cost effective to store files, Single AZ impacts availability)
    5. Use a CloudFront download distribution to serve the JPEGs to the end users and Install the current commercial search product, along with a Java Container for the website on EC2 instances and use Route53 with DNS round-robin. (CloudFront needs a source and using commercial search product is not cost effective)
  9. A research scientist is planning for the one-time launch of an Elastic MapReduce cluster and is encouraged by her manager to minimize the costs. The cluster is designed to ingest 200TB of genomics data with a total of 100 Amazon EC2 instances and is expected to run for around four hours. The resulting data set must be stored temporarily until archived into an Amazon RDS Oracle instance. Which option will help save the most money while meeting requirements? [PROFESSIONAL]
    1. Store ingest and output files in Amazon S3. Deploy on-demand for the master and core nodes and spot for the task nodes.
    2. Optimize by deploying a combination of on-demand, RI and spot-pricing models for the master, core and task nodes. Store ingest and output files in Amazon S3 with a lifecycle policy that archives them to Amazon Glacier. (Master and Core must be RI or On Demand. Cannot be Spot)
    3. Store the ingest files in Amazon S3 RRS and store the output files in S3. Deploy Reserved Instances for the master and core nodes and on-demand for the task nodes. (Need better durability for ingest file. Spot instances can be used for task nodes for cost saving. RI will not provide cost saving in this case)
    4. Deploy on-demand master, core and task nodes and store ingest and output files in Amazon S3 RRS (Input must be in S3 standard)