Tag Archives: running

Announcing the Winners of the AWS Chatbot Challenge – Conversational, Intelligent Chatbots using Amazon Lex and AWS Lambda

Post Syndicated from Tara Walker original https://aws.amazon.com/blogs/aws/announcing-the-winners-of-the-aws-chatbot-challenge-conversational-intelligent-chatbots-using-amazon-lex-and-aws-lambda/

A couple of months ago on the blog, I announced the AWS Chatbot Challenge in conjunction with Slack. The AWS Chatbot Challenge was an opportunity to build a unique chatbot that helped to solve a problem or that would add value for its prospective users. The mission was to build a conversational, natural language chatbot using Amazon Lex and leverage Lex’s integration with AWS Lambda to execute logic or data processing on the backend.

I know that you all have been anxiously waiting to hear announcements of who were the winners of the AWS Chatbot Challenge as much as I was. Well wait no longer, the winners of the AWS Chatbot Challenge have been decided.

May I have the Envelope Please? (The Trumpets sound)

The winners of the AWS Chatbot Challenge are:

  • First Place: BuildFax Counts by Joe Emison
  • Second Place: Hubsy by Andrew Riess, Andrew Puch, and John Wetzel
  • Third Place: PFMBot by Benny Leong and his team from MoneyLion.
  • Large Organization Winner: ADP Payroll Innovation Bot by Eric Liu, Jiaxing Yan, and Fan Yang

 

Diving into the Winning Chatbot Projects

Let’s take a walkthrough of the details for each of the winning projects to get a view of what made these chatbots distinctive, as well as, learn more about the technologies used to implement the chatbot solution.

 

BuildFax Counts by Joe Emison

The BuildFax Counts bot was created as a real solution for the BuildFax company to decrease the amount the time that sales and marketing teams can get answers on permits or properties with permits meet certain criteria.

BuildFax, a company co-founded by bot developer Joe Emison, has the only national database of building permits, which updates data from approximately half of the United States on a monthly basis. In order to accommodate the many requests that come in from the sales and marketing team regarding permit information, BuildFax has a technical sales support team that fulfills these requests sent to a ticketing system by manually writing SQL queries that run across the shards of the BuildFax databases. Since there are a large number of requests received by the internal sales support team and due to the manual nature of setting up the queries, it may take several days for getting the sales and marketing teams to receive an answer.

The BuildFax Counts chatbot solves this problem by taking the permit inquiry that would normally be sent into a ticket from the sales and marketing team, as input from Slack to the chatbot. Once the inquiry is submitted into Slack, a query executes and the inquiry results are returned immediately.

Joe built this solution by first creating a nightly export of the data in their BuildFax MySQL RDS database to CSV files that are stored in Amazon S3. From the exported CSV files, an Amazon Athena table was created in order to run quick and efficient queries on the data. He then used Amazon Lex to create a bot to handle the common questions and criteria that may be asked by the sales and marketing teams when seeking data from the BuildFax database by modeling the language used from the BuildFax ticketing system. He added several different sample utterances and slot types; both custom and Lex provided, in order to correctly parse every question and criteria combination that could be received from an inquiry.  Using Lambda, Joe created a Javascript Lambda function that receives information from the Lex intent and used it to build a SQL statement that runs against the aforementioned Athena database using the AWS SDK for JavaScript in Node.js library to return inquiry count result and SQL statement used.

The BuildFax Counts bot is used today for the BuildFax sales and marketing team to get back data on inquiries immediately that previously took up to a week to receive results.

Not only is BuildFax Counts bot our 1st place winner and wonderful solution, but its creator, Joe Emison, is a great guy.  Joe has opted to donate his prize; the $5,000 cash, the $2,500 in AWS Credits, and one re:Invent ticket to the Black Girls Code organization. I must say, you rock Joe for helping these kids get access and exposure to technology.

 

Hubsy by Andrew Riess, Andrew Puch, and John Wetzel

Hubsy bot was created to redefine and personalize the way users traditionally manage their HubSpot account. HubSpot is a SaaS system providing marketing, sales, and CRM software. Hubsy allows users of HubSpot to create engagements and log engagements with customers, provide sales teams with deals status, and retrieves client contact information quickly. Hubsy uses Amazon Lex’s conversational interface to execute commands from the HubSpot API so that users can gain insights, store and retrieve data, and manage tasks directly from Facebook, Slack, or Alexa.

In order to implement the Hubsy chatbot, Andrew and the team members used AWS Lambda to create a Lambda function with Node.js to parse the users request and call the HubSpot API, which will fulfill the initial request or return back to the user asking for more information. Terraform was used to automatically setup and update Lambda, CloudWatch logs, as well as, IAM profiles. Amazon Lex was used to build the conversational piece of the bot, which creates the utterances that a person on a sales team would likely say when seeking information from HubSpot. To integrate with Alexa, the Amazon Alexa skill builder was used to create an Alexa skill which was tested on an Echo Dot. Cloudwatch Logs are used to log the Lambda function information to CloudWatch in order to debug different parts of the Lex intents. In order to validate the code before the Terraform deployment, ESLint was additionally used to ensure the code was linted and proper development standards were followed.

 

PFMBot by Benny Leong and his team from MoneyLion

PFMBot, Personal Finance Management Bot,  is a bot to be used with the MoneyLion finance group which offers customers online financial products; loans, credit monitoring, and free credit score service to improve the financial health of their customers. Once a user signs up an account on the MoneyLion app or website, the user has the option to link their bank accounts with the MoneyLion APIs. Once the bank account is linked to the APIs, the user will be able to login to their MoneyLion account and start having a conversation with the PFMBot based on their bank account information.

The PFMBot UI has a web interface built with using Javascript integration. The chatbot was created using Amazon Lex to build utterances based on the possible inquiries about the user’s MoneyLion bank account. PFMBot uses the Lex built-in AMAZON slots and parsed and converted the values from the built-in slots to pass to AWS Lambda. The AWS Lambda functions interacting with Amazon Lex are Java-based Lambda functions which call the MoneyLion Java-based internal APIs running on Spring Boot. These APIs obtain account data and related bank account information from the MoneyLion MySQL Database.

 

ADP Payroll Innovation Bot by Eric Liu, Jiaxing Yan, and Fan Yang

ADP PI (Payroll Innovation) bot is designed to help employees of ADP customers easily review their own payroll details and compare different payroll data by just asking the bot for results. The ADP PI Bot additionally offers issue reporting functionality for employees to report payroll issues and aids HR managers in quickly receiving and organizing any reported payroll issues.

The ADP Payroll Innovation bot is an ecosystem for the ADP payroll consisting of two chatbots, which includes ADP PI Bot for external clients (employees and HR managers), and ADP PI DevOps Bot for internal ADP DevOps team.


The architecture for the ADP PI DevOps bot is different architecture from the ADP PI bot shown above as it is deployed internally to ADP. The ADP PI DevOps bot allows input from both Slack and Alexa. When input comes into Slack, Slack sends the request to Lex for it to process the utterance. Lex then calls the Lambda backend, which obtains ADP data sitting in the ADP VPC running within an Amazon VPC. When input comes in from Alexa, a Lambda function is called that also obtains data from the ADP VPC running on AWS.

The architecture for the ADP PI bot consists of users entering in requests and/or entering issues via Slack. When requests/issues are entered via Slack, the Slack APIs communicate via Amazon API Gateway to AWS Lambda. The Lambda function either writes data into one of the Amazon DynamoDB databases for recording issues and/or sending issues or it sends the request to Lex. When sending issues, DynamoDB integrates with Trello to keep HR Managers abreast of the escalated issues. Once the request data is sent from Lambda to Lex, Lex processes the utterance and calls another Lambda function that integrates with the ADP API and it calls ADP data from within the ADP VPC, which runs on Amazon Virtual Private Cloud (VPC).

Python and Node.js were the chosen languages for the development of the bots.

The ADP PI bot ecosystem has the following functional groupings:

Employee Functionality

  • Summarize Payrolls
  • Compare Payrolls
  • Escalate Issues
  • Evolve PI Bot

HR Manager Functionality

  • Bot Management
  • Audit and Feedback

DevOps Functionality

  • Reduce call volume in service centers (ADP PI Bot).
  • Track issues and generate reports (ADP PI Bot).
  • Monitor jobs for various environment (ADP PI DevOps Bot)
  • View job dashboards (ADP PI DevOps Bot)
  • Query job details (ADP PI DevOps Bot)

 

Summary

Let’s all wish all the winners of the AWS Chatbot Challenge hearty congratulations on their excellent projects.

You can review more details on the winning projects, as well as, all of the submissions to the AWS Chatbot Challenge at: https://awschatbot2017.devpost.com/submissions. If you are curious on the details of Chatbot challenge contest including resources, rules, prizes, and judges, you can review the original challenge website here:  https://awschatbot2017.devpost.com/.

Hopefully, you are just as inspired as I am to build your own chatbot using Lex and Lambda. For more information, take a look at the Amazon Lex developer guide or the AWS AI blog on Building Better Bots Using Amazon Lex (Part 1)

Chat with you soon!

Tara

Announcement: IPS code

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/08/announcement-ips-code.html

So after 20 years, IBM is killing off my BlackICE code created in April 1998. So it’s time that I rewrite it.

BlackICE was the first “inline” intrusion-detection system, aka. an “intrusion prevention system” or IPS. ISS purchased my company in 2001 and replaced their RealSecure engine with it, and later renamed it Proventia. Then IBM purchased ISS in 2006. Now, they are formally canceling the project and moving customers onto Cisco’s products, which are based on Snort.

So now is a good time to write a replacement. The reason is that BlackICE worked fundamentally differently than Snort, using protocol analysis rather than pattern-matching. In this way, it worked more like Bro than Snort. The biggest benefit of protocol-analysis is speed, making it many times faster than Snort. The second benefit is better detection ability, as I describe in this post on Heartbleed.

So my plan is to create a new project. I’ll be checking in the starter bits into GitHub starting a couple weeks from now. I need to figure out a new name for the project, so I don’t have to rip off a name from William Gibson like I did last time :).

Some notes:

  • Yes, it’ll be GNU open source. I’m a capitalist, so I’ll earn money like snort/nmap dual-licensing it, charging companies who don’t want to open-source their addons. All capitalists GNU license their code.
  • C, not Rust. Sorry, I’m going for extreme scalability. We’ll re-visit this decision later when looking at building protocol parsers.
  • It’ll be 95% compatible with Snort signatures. Their language definition leaves so much ambiguous it’ll be hard to be 100% compatible.
  • It’ll support Snort output as well, though really, Snort’s events suck.
  • Protocol parsers in Lua, so you can use it as a replacement for Bro, writing parsers to extract data you are interested in.
  • Protocol state machine parsers in C, like you see in my Masscan project for X.509.
  • First version IDS only. These days, “inline” means also being able to MitM the SSL stack, so I’m gong to have to think harder on that.
  • Mutli-core worker threads off PF_RING/DPDK/netmap receive queues. Should handle 10gbps, tracking 10 million concurrent connections, with quad-core CPU.
So if you want to contribute to the project, here’s what I need:
  • Requirements from people who work daily with IDS/IPS today. I need you to write up what your products do well that you really like. I need to you write up what they suck at that needs to be fixed. These need to be in some detail.
  • Testing environment to play with. This means having a small server plugged into a real-world link running at a minimum of several gigabits-per-second available for the next year. I’ll sign NDAs related to the data I might see on the network.
  • Coders. I’ll be doing the basic architecture, but protocol parsers, output plugins, etc. will need work. Code will be in C and Lua for the near term. Unfortunately, since I’m going to dual-license, I’ll need waivers before accepting pull requests.
Anyway, follow me on Twitter @erratarob if you want to contribute.

Michael Reeves and the ridiculous Subscriber Robot

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/michael-reeves-subscriber-robot/

At the beginning of his new build’s video, YouTuber Michael Reeves discusses a revelation he had about why some people don’t subscribe to his channel:

The real reason some people don’t subscribe is that when you hit this button, that’s all, that’s it, it’s done. It’s not special, it’s not enjoyable. So how do we make subscribing a fun, enjoyable process? Well, we do it by slowly chipping away at the content creator’s psyche every time someone subscribes.

His fix? The ‘fun’ interactive Subscriber Robot that is the subject of the video.

Be aware that Michael uses a couple of mild swears in this video, so maybe don’t watch it with a child.

The Subscriber Robot

Just showing that subscriber dedication My Patreon Page: https://www.patreon.com/michaelreeves Personal Site: https://michaelreeves.us/ Twitter: https://twitter.com/michaelreeves08 Song: Summer Salt – Sweet To Me

Who is Michael Reeves?

Software developer and student Michael Reeves started his YouTube account a mere four months ago, with the premiere of his robot that shines lasers into your eyes – now he has 110k+ subscribers. At only 19, Michael co-owns and manages a company together with friends, and is set on his career path in software and computing. So when he is not making videos, he works a nine-to-five job “to pay for college and, y’know, live”.

The Subscriber Robot

Michael shot to YouTube fame with the aforementioned laser robot built around an Arduino. But by now he has also be released videos for a few Raspberry Pi-based contraptions.

Michael Reeves Raspberry Pi Subscriber Robot

Michael, talking us through the details of one of the worst ideas ever made

His Subscriber Robot uses a series of Python scripts running on a Raspberry Pi to check for new subscribers to Michael’s channel via the YouTube API. When it identifies one, the Pi uses a relay to make the ceiling lights in Michael’s office flash ten times a second while ear-splitting noise is emitted by a 102-decibel-rated buzzer. Needless to say, this buzzer is not recommended for home use, work use, or any use whatsoever! Moreover, the Raspberry Pi also connects to a speaker that announces the name of the new subscriber, so Michael knows who to thank.

Michael Reeves Raspberry Pi Subscriber Robot

Subscriber Robot: EEH! EEH! EEH! MoistPretzels has subscribed.
Michael: Thank you, MoistPretzels…

Given that Michael has gained a whopping 30,000 followers in the ten days since the release of this video, it’s fair to assume he is currently curled up in a ball on the office floor, quietly crying to himself.

If you think Michael only makes videos about ridiculous builds, you’re mistaken. He also uses YouTube to provide educational content, because he believes that “it’s super important for people to teach themselves how to program”. For example, he has just released a new C# beginners tutorial, the third in the series.

Support Michael

If you’d like to help Michael in his mission to fill the world with both tutorials and ridiculous robot builds, make sure to subscribe to his channel. You can also follow him on Twitter and support him on Patreon.

You may also want to check out the Useless Duck Company and Simone Giertz if you’re in the mood for more impractical, yet highly amusing, robot builds.

Good luck with your channel, Michael! We are looking forward to, and slightly dreading, more videos from one of our favourite new YouTubers.

The post Michael Reeves and the ridiculous Subscriber Robot appeared first on Raspberry Pi.

Analyzing AWS Cost and Usage Reports with Looker and Amazon Athena

Post Syndicated from Dillon Morrison original https://aws.amazon.com/blogs/big-data/analyzing-aws-cost-and-usage-reports-with-looker-and-amazon-athena/

This is a guest post by Dillon Morrison at Looker. Looker is, in their own words, “a new kind of analytics platform–letting everyone in your business make better decisions by getting reliable answers from a tool they can use.” 

As the breadth of AWS products and services continues to grow, customers are able to more easily move their technology stack and core infrastructure to AWS. One of the attractive benefits of AWS is the cost savings. Rather than paying upfront capital expenses for large on-premises systems, customers can instead pay variables expenses for on-demand services. To further reduce expenses AWS users can reserve resources for specific periods of time, and automatically scale resources as needed.

The AWS Cost Explorer is great for aggregated reporting. However, conducting analysis on the raw data using the flexibility and power of SQL allows for much richer detail and insight, and can be the better choice for the long term. Thankfully, with the introduction of Amazon Athena, monitoring and managing these costs is now easier than ever.

In the post, I walk through setting up the data pipeline for cost and usage reports, Amazon S3, and Athena, and discuss some of the most common levers for cost savings. I surface tables through Looker, which comes with a host of pre-built data models and dashboards to make analysis of your cost and usage data simple and intuitive.

Analysis with Athena

With Athena, there’s no need to create hundreds of Excel reports, move data around, or deploy clusters to house and process data. Athena uses Apache Hive’s DDL to create tables, and the Presto querying engine to process queries. Analysis can be performed directly on raw data in S3. Conveniently, AWS exports raw cost and usage data directly into a user-specified S3 bucket, making it simple to start querying with Athena quickly. This makes continuous monitoring of costs virtually seamless, since there is no infrastructure to manage. Instead, users can leverage the power of the Athena SQL engine to easily perform ad-hoc analysis and data discovery without needing to set up a data warehouse.

After the data pipeline is established, cost and usage data (the recommended billing data, per AWS documentation) provides a plethora of comprehensive information around usage of AWS services and the associated costs. Whether you need the report segmented by product type, user identity, or region, this report can be cut-and-sliced any number of ways to properly allocate costs for any of your business needs. You can then drill into any specific line item to see even further detail, such as the selected operating system, tenancy, purchase option (on-demand, spot, or reserved), and so on.

Walkthrough

By default, the Cost and Usage report exports CSV files, which you can compress using gzip (recommended for performance). There are some additional configuration options for tuning performance further, which are discussed below.

Prerequisites

If you want to follow along, you need the following resources:

Enable the cost and usage reports

First, enable the Cost and Usage report. For Time unit, select Hourly. For Include, select Resource IDs. All options are prompted in the report-creation window.

The Cost and Usage report dumps CSV files into the specified S3 bucket. Please note that it can take up to 24 hours for the first file to be delivered after enabling the report.

Configure the S3 bucket and files for Athena querying

In addition to the CSV file, AWS also creates a JSON manifest file for each cost and usage report. Athena requires that all of the files in the S3 bucket are in the same format, so we need to get rid of all these manifest files. If you’re looking to get started with Athena quickly, you can simply go into your S3 bucket and delete the manifest file manually, skip the automation described below, and move on to the next section.

To automate the process of removing the manifest file each time a new report is dumped into S3, which I recommend as you scale, there are a few additional steps. The folks at Concurrency labs wrote a great overview and set of scripts for this, which you can find in their GitHub repo.

These scripts take the data from an input bucket, remove anything unnecessary, and dump it into a new output bucket. We can utilize AWS Lambda to trigger this process whenever new data is dropped into S3, or on a nightly basis, or whatever makes most sense for your use-case, depending on how often you’re querying the data. Please note that enabling the “hourly” report means that data is reported at the hour-level of granularity, not that a new file is generated every hour.

Following these scripts, you’ll notice that we’re adding a date partition field, which isn’t necessary but improves query performance. In addition, converting data from CSV to a columnar format like ORC or Parquet also improves performance. We can automate this process using Lambda whenever new data is dropped in our S3 bucket. Amazon Web Services discusses columnar conversion at length, and provides walkthrough examples, in their documentation.

As a long-term solution, best practice is to use compression, partitioning, and conversion. However, for purposes of this walkthrough, we’re not going to worry about them so we can get up-and-running quicker.

Set up the Athena query engine

In your AWS console, navigate to the Athena service, and click “Get Started”. Follow the tutorial and set up a new database (we’ve called ours “AWS Optimizer” in this example). Don’t worry about configuring your initial table, per the tutorial instructions. We’ll be creating a new table for cost and usage analysis. Once you walked through the tutorial steps, you’ll be able to access the Athena interface, and can begin running Hive DDL statements to create new tables.

One thing that’s important to note, is that the Cost and Usage CSVs also contain the column headers in their first row, meaning that the column headers would be included in the dataset and any queries. For testing and quick set-up, you can remove this line manually from your first few CSV files. Long-term, you’ll want to use a script to programmatically remove this row each time a new file is dropped in S3 (every few hours typically). We’ve drafted up a sample script for ease of reference, which we run on Lambda. We utilize Lambda’s native ability to invoke the script whenever a new object is dropped in S3.

For cost and usage, we recommend using the DDL statement below. Since our data is in CSV format, we don’t need to use a SerDe, we can simply specify the “separatorChar, quoteChar, and escapeChar”, and the structure of the files (“TEXTFILE”). Note that AWS does have an OpenCSV SerDe as well, if you prefer to use that.

 

CREATE EXTERNAL TABLE IF NOT EXISTS cost_and_usage	 (
identity_LineItemId String,
identity_TimeInterval String,
bill_InvoiceId String,
bill_BillingEntity String,
bill_BillType String,
bill_PayerAccountId String,
bill_BillingPeriodStartDate String,
bill_BillingPeriodEndDate String,
lineItem_UsageAccountId String,
lineItem_LineItemType String,
lineItem_UsageStartDate String,
lineItem_UsageEndDate String,
lineItem_ProductCode String,
lineItem_UsageType String,
lineItem_Operation String,
lineItem_AvailabilityZone String,
lineItem_ResourceId String,
lineItem_UsageAmount String,
lineItem_NormalizationFactor String,
lineItem_NormalizedUsageAmount String,
lineItem_CurrencyCode String,
lineItem_UnblendedRate String,
lineItem_UnblendedCost String,
lineItem_BlendedRate String,
lineItem_BlendedCost String,
lineItem_LineItemDescription String,
lineItem_TaxType String,
product_ProductName String,
product_accountAssistance String,
product_architecturalReview String,
product_architectureSupport String,
product_availability String,
product_bestPractices String,
product_cacheEngine String,
product_caseSeverityresponseTimes String,
product_clockSpeed String,
product_currentGeneration String,
product_customerServiceAndCommunities String,
product_databaseEdition String,
product_databaseEngine String,
product_dedicatedEbsThroughput String,
product_deploymentOption String,
product_description String,
product_durability String,
product_ebsOptimized String,
product_ecu String,
product_endpointType String,
product_engineCode String,
product_enhancedNetworkingSupported String,
product_executionFrequency String,
product_executionLocation String,
product_feeCode String,
product_feeDescription String,
product_freeQueryTypes String,
product_freeTrial String,
product_frequencyMode String,
product_fromLocation String,
product_fromLocationType String,
product_group String,
product_groupDescription String,
product_includedServices String,
product_instanceFamily String,
product_instanceType String,
product_io String,
product_launchSupport String,
product_licenseModel String,
product_location String,
product_locationType String,
product_maxIopsBurstPerformance String,
product_maxIopsvolume String,
product_maxThroughputvolume String,
product_maxVolumeSize String,
product_maximumStorageVolume String,
product_memory String,
product_messageDeliveryFrequency String,
product_messageDeliveryOrder String,
product_minVolumeSize String,
product_minimumStorageVolume String,
product_networkPerformance String,
product_operatingSystem String,
product_operation String,
product_operationsSupport String,
product_physicalProcessor String,
product_preInstalledSw String,
product_proactiveGuidance String,
product_processorArchitecture String,
product_processorFeatures String,
product_productFamily String,
product_programmaticCaseManagement String,
product_provisioned String,
product_queueType String,
product_requestDescription String,
product_requestType String,
product_routingTarget String,
product_routingType String,
product_servicecode String,
product_sku String,
product_softwareType String,
product_storage String,
product_storageClass String,
product_storageMedia String,
product_technicalSupport String,
product_tenancy String,
product_thirdpartySoftwareSupport String,
product_toLocation String,
product_toLocationType String,
product_training String,
product_transferType String,
product_usageFamily String,
product_usagetype String,
product_vcpu String,
product_version String,
product_volumeType String,
product_whoCanOpenCases String,
pricing_LeaseContractLength String,
pricing_OfferingClass String,
pricing_PurchaseOption String,
pricing_publicOnDemandCost String,
pricing_publicOnDemandRate String,
pricing_term String,
pricing_unit String,
reservation_AvailabilityZone String,
reservation_NormalizedUnitsPerReservation String,
reservation_NumberOfReservations String,
reservation_ReservationARN String,
reservation_TotalReservedNormalizedUnits String,
reservation_TotalReservedUnits String,
reservation_UnitsPerReservation String,
resourceTags_userName String,
resourceTags_usercostcategory String  


)
    ROW FORMAT DELIMITED
      FIELDS TERMINATED BY ','
      ESCAPED BY '\\'
      LINES TERMINATED BY '\n'

STORED AS TEXTFILE
    LOCATION 's3://<<your bucket name>>';

Once you’ve successfully executed the command, you should see a new table named “cost_and_usage” with the below properties. Now we’re ready to start executing queries and running analysis!

Start with Looker and connect to Athena

Setting up Looker is a quick process, and you can try it out for free here (or download from Amazon Marketplace). It takes just a few seconds to connect Looker to your Athena database, and Looker comes with a host of pre-built data models and dashboards to make analysis of your cost and usage data simple and intuitive. After you’re connected, you can use the Looker UI to run whatever analysis you’d like. Looker translates this UI to optimized SQL, so any user can execute and visualize queries for true self-service analytics.

Major cost saving levers

Now that the data pipeline is configured, you can dive into the most popular use cases for cost savings. In this post, I focus on:

  • Purchasing Reserved Instances vs. On-Demand Instances
  • Data transfer costs
  • Allocating costs over users or other Attributes (denoted with resource tags)

On-Demand, Spot, and Reserved Instances

Purchasing Reserved Instances vs On-Demand Instances is arguably going to be the biggest cost lever for heavy AWS users (Reserved Instances run up to 75% cheaper!). AWS offers three options for purchasing instances:

  • On-Demand—Pay as you use.
  • Spot (variable cost)—Bid on spare Amazon EC2 computing capacity.
  • Reserved Instances—Pay for an instance for a specific, allotted period of time.

When purchasing a Reserved Instance, you can also choose to pay all-upfront, partial-upfront, or monthly. The more you pay upfront, the greater the discount.

If your company has been using AWS for some time now, you should have a good sense of your overall instance usage on a per-month or per-day basis. Rather than paying for these instances On-Demand, you should try to forecast the number of instances you’ll need, and reserve them with upfront payments.

The total amount of usage with Reserved Instances versus overall usage with all instances is called your coverage ratio. It’s important not to confuse your coverage ratio with your Reserved Instance utilization. Utilization represents the amount of reserved hours that were actually used. Don’t worry about exceeding capacity, you can still set up Auto Scaling preferences so that more instances get added whenever your coverage or utilization crosses a certain threshold (we often see a target of 80% for both coverage and utilization among savvy customers).

Calculating the reserved costs and coverage can be a bit tricky with the level of granularity provided by the cost and usage report. The following query shows your total cost over the last 6 months, broken out by Reserved Instance vs other instance usage. You can substitute the cost field for usage if you’d prefer. Please note that you should only have data for the time period after the cost and usage report has been enabled (though you can opt for up to 3 months of historical data by contacting your AWS Account Executive). If you’re just getting started, this query will only show a few days.

 

SELECT 
	DATE_FORMAT(from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate),'%Y-%m') AS "cost_and_usage.usage_start_month",
	COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0) AS "cost_and_usage.total_unblended_cost",
	COALESCE(SUM(CASE WHEN (CASE
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_reserved_unblended_cost",
	1.0 * (COALESCE(SUM(CASE WHEN (CASE
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.percent_spend_on_ris",
	COALESCE(SUM(CASE WHEN (CASE
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'Non RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_non_reserved_unblended_cost",
	1.0 * (COALESCE(SUM(CASE WHEN (CASE
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'Non RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.percent_spend_on_non_ris"
FROM aws_optimizer.cost_and_usage  AS cost_and_usage

WHERE 
	(((from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) >= ((DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))) AND (from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) < ((DATE_ADD('month', 6, DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))))))
GROUP BY 1
ORDER BY 2 DESC
LIMIT 500

The resulting table should look something like the image below (I’m surfacing tables through Looker, though the same table would result from querying via command line or any other interface).

With a BI tool, you can create dashboards for easy reference and monitoring. New data is dumped into S3 every few hours, so your dashboards can update several times per day.

It’s an iterative process to understand the appropriate number of Reserved Instances needed to meet your business needs. After you’ve properly integrated Reserved Instances into your purchasing patterns, the savings can be significant. If your coverage is consistently below 70%, you should seriously consider adjusting your purchase types and opting for more Reserved instances.

Data transfer costs

One of the great things about AWS data storage is that it’s incredibly cheap. Most charges often come from moving and processing that data. There are several different prices for transferring data, broken out largely by transfers between regions and availability zones. Transfers between regions are the most costly, followed by transfers between Availability Zones. Transfers within the same region and same availability zone are free unless using elastic or public IP addresses, in which case there is a cost. You can find more detailed information in the AWS Pricing Docs. With this in mind, there are several simple strategies for helping reduce costs.

First, since costs increase when transferring data between regions, it’s wise to ensure that as many services as possible reside within the same region. The more you can localize services to one specific region, the lower your costs will be.

Second, you should maximize the data you’re routing directly within AWS services and IP addresses. Transfers out to the open internet are the most costly and least performant mechanisms of data transfers, so it’s best to keep transfers within AWS services.

Lastly, data transfers between private IP addresses are cheaper than between elastic or public IP addresses, so utilizing private IP addresses as much as possible is the most cost-effective strategy.

The following query provides a table depicting the total costs for each AWS product, broken out transfer cost type. Substitute the “lineitem_productcode” field in the query to segment the costs by any other attribute. If you notice any unusually high spikes in cost, you’ll need to dig deeper to understand what’s driving that spike: location, volume, and so on. Drill down into specific costs by including “product_usagetype” and “product_transfertype” in your query to identify the types of transfer costs that are driving up your bill.

SELECT 
	cost_and_usage.lineitem_productcode  AS "cost_and_usage.product_code",
	COALESCE(SUM(cost_and_usage.lineitem_unblendedcost), 0) AS "cost_and_usage.total_unblended_cost",
	COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_data_transfer_cost",
	COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer-In')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_inbound_data_transfer_cost",
	COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer-Out')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_outbound_data_transfer_cost"
FROM aws_optimizer.cost_and_usage  AS cost_and_usage

WHERE 
	(((from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) >= ((DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))) AND (from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) < ((DATE_ADD('month', 6, DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))))))
GROUP BY 1
ORDER BY 2 DESC
LIMIT 500

When moving between regions or over the open web, many data transfer costs also include the origin and destination location of the data movement. Using a BI tool with mapping capabilities, you can get a nice visual of data flows. The point at the center of the map is used to represent external data flows over the open internet.

Analysis by tags

AWS provides the option to apply custom tags to individual resources, so you can allocate costs over whatever customized segment makes the most sense for your business. For a SaaS company that hosts software for customers on AWS, maybe you’d want to tag the size of each customer. The following query uses custom tags to display the reserved, data transfer, and total cost for each AWS service, broken out by tag categories, over the last 6 months. You’ll want to substitute the cost_and_usage.resourcetags_customersegment and cost_and_usage.customer_segment with the name of your customer field.

 

SELECT * FROM (
SELECT *, DENSE_RANK() OVER (ORDER BY z___min_rank) as z___pivot_row_rank, RANK() OVER (PARTITION BY z__pivot_col_rank ORDER BY z___min_rank) as z__pivot_col_ordering FROM (
SELECT *, MIN(z___rank) OVER (PARTITION BY "cost_and_usage.product_code") as z___min_rank FROM (
SELECT *, RANK() OVER (ORDER BY CASE WHEN z__pivot_col_rank=1 THEN (CASE WHEN "cost_and_usage.total_unblended_cost" IS NOT NULL THEN 0 ELSE 1 END) ELSE 2 END, CASE WHEN z__pivot_col_rank=1 THEN "cost_and_usage.total_unblended_cost" ELSE NULL END DESC, "cost_and_usage.total_unblended_cost" DESC, z__pivot_col_rank, "cost_and_usage.product_code") AS z___rank FROM (
SELECT *, DENSE_RANK() OVER (ORDER BY CASE WHEN "cost_and_usage.customer_segment" IS NULL THEN 1 ELSE 0 END, "cost_and_usage.customer_segment") AS z__pivot_col_rank FROM (
SELECT 
	cost_and_usage.lineitem_productcode  AS "cost_and_usage.product_code",
	cost_and_usage.resourcetags_customersegment  AS "cost_and_usage.customer_segment",
	COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0) AS "cost_and_usage.total_unblended_cost",
	1.0 * (COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.percent_spend_data_transfers_unblended",
	1.0 * (COALESCE(SUM(CASE WHEN (CASE
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'Non RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.unblended_percent_spend_on_ris"
FROM aws_optimizer.cost_and_usage_raw  AS cost_and_usage

WHERE 
	(((from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) >= ((DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))) AND (from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) < ((DATE_ADD('month', 6, DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))))))
GROUP BY 1,2) ww
) bb WHERE z__pivot_col_rank <= 16384
) aa
) xx
) zz
 WHERE z___pivot_row_rank <= 500 OR z__pivot_col_ordering = 1 ORDER BY z___pivot_row_rank

The resulting table in this example looks like the results below. In this example, you can tell that we’re making poor use of Reserved Instances because they represent such a small portion of our overall costs.

Again, using a BI tool to visualize these costs and trends over time makes the analysis much easier to consume and take action on.

Summary

Saving costs on your AWS spend is always an iterative, ongoing process. Hopefully with these queries alone, you can start to understand your spending patterns and identify opportunities for savings. However, this is just a peek into the many opportunities available through analysis of the Cost and Usage report. Each company is different, with unique needs and usage patterns. To achieve maximum cost savings, we encourage you to set up an analytics environment that enables your team to explore all potential cuts and slices of your usage data, whenever it’s necessary. Exploring different trends and spikes across regions, services, user types, etc. helps you gain comprehensive understanding of your major cost levers and consistently implement new cost reduction strategies.

Note that all of the queries and analysis provided in this post were generated using the Looker data platform. If you’re already a Looker customer, you can get all of this analysis, additional pre-configured dashboards, and much more using Looker Blocks for AWS.


About the Author

Dillon Morrison leads the Platform Ecosystem at Looker. He enjoys exploring new technologies and architecting the most efficient data solutions for the business needs of his company and their customers. In his spare time, you’ll find Dillon rock climbing in the Bay Area or nose deep in the docs of the latest AWS product release at his favorite cafe (“Arlequin in SF is unbeatable!”).

 

 

 

Raspbian Stretch has arrived for Raspberry Pi

Post Syndicated from Simon Long original https://www.raspberrypi.org/blog/raspbian-stretch/

It’s now just under two years since we released the Jessie version of Raspbian. Those of you who know that Debian run their releases on a two-year cycle will therefore have been wondering when we might be releasing the next version, codenamed Stretch. Well, wonder no longer – Raspbian Stretch is available for download today!

Disney Pixar Toy Story Raspbian Stretch Raspberry Pi

Debian releases are named after characters from Disney Pixar’s Toy Story trilogy. In case, like me, you were wondering: Stretch is a purple octopus from Toy Story 3. Hi, Stretch!

The differences between Jessie and Stretch are mostly under-the-hood optimisations, and you really shouldn’t notice any differences in day-to-day use of the desktop and applications. (If you’re really interested, the technical details are in the Debian release notes here.)

However, we’ve made a few small changes to our image that are worth mentioning.

New versions of applications

Version 3.0.1 of Sonic Pi is included – this includes a lot of new functionality in terms of input/output. See the Sonic Pi release notes for more details of exactly what has changed.

Raspbian Stretch Raspberry Pi

The Chromium web browser has been updated to version 60, the most recent stable release. This offers improved memory usage and more efficient code, so you may notice it running slightly faster than before. The visual appearance has also been changed very slightly.

Raspbian Stretch Raspberry Pi

Bluetooth audio

In Jessie, we used PulseAudio to provide support for audio over Bluetooth, but integrating this with the ALSA architecture used for other audio sources was clumsy. For Stretch, we are using the bluez-alsa package to make Bluetooth audio work with ALSA itself. PulseAudio is therefore no longer installed by default, and the volume plugin on the taskbar will no longer start and stop PulseAudio. From a user point of view, everything should still work exactly as before – the only change is that if you still wish to use PulseAudio for some other reason, you will need to install it yourself.

Better handling of other usernames

The default user account in Raspbian has always been called ‘pi’, and a lot of the desktop applications assume that this is the current user. This has been changed for Stretch, so now applications like Raspberry Pi Configuration no longer assume this to be the case. This means, for example, that the option to automatically log in as the ‘pi’ user will now automatically log in with the name of the current user instead.

One other change is how sudo is handled. By default, the ‘pi’ user is set up with passwordless sudo access. We are no longer assuming this to be the case, so now desktop applications which require sudo access will prompt for the password rather than simply failing to work if a user without passwordless sudo uses them.

Scratch 2 SenseHAT extension

In the last Jessie release, we added the offline version of Scratch 2. While Scratch 2 itself hasn’t changed for this release, we have added a new extension to allow the SenseHAT to be used with Scratch 2. Look under ‘More Blocks’ and choose ‘Add an Extension’ to load the extension.

This works with either a physical SenseHAT or with the SenseHAT emulator. If a SenseHAT is connected, the extension will control that in preference to the emulator.

Raspbian Stretch Raspberry Pi

Fix for Broadpwn exploit

A couple of months ago, a vulnerability was discovered in the firmware of the BCM43xx wireless chipset which is used on Pi 3 and Pi Zero W; this potentially allows an attacker to take over the chip and execute code on it. The Stretch release includes a patch that addresses this vulnerability.

There is also the usual set of minor bug fixes and UI improvements – I’ll leave you to spot those!

How to get Raspbian Stretch

As this is a major version upgrade, we recommend using a clean image; these are available from the Downloads page on our site as usual.

Upgrading an existing Jessie image is possible, but is not guaranteed to work in every circumstance. If you wish to try upgrading a Jessie image to Stretch, we strongly recommend taking a backup first – we can accept no responsibility for loss of data from a failed update.

To upgrade, first modify the files /etc/apt/sources.list and /etc/apt/sources.list.d/raspi.list. In both files, change every occurrence of the word ‘jessie’ to ‘stretch’. (Both files will require sudo to edit.)

Then open a terminal window and execute

sudo apt-get update
sudo apt-get -y dist-upgrade

Answer ‘yes’ to any prompts. There may also be a point at which the install pauses while a page of information is shown on the screen – hold the ‘space’ key to scroll through all of this and then hit ‘q’ to continue.

Finally, if you are not using PulseAudio for anything other than Bluetooth audio, remove it from the image by entering

sudo apt-get -y purge pulseaudio*

The post Raspbian Stretch has arrived for Raspberry Pi appeared first on Raspberry Pi.

“Public Figure” Threatened With Exposure Over Gay Piracy ‘Fine’

Post Syndicated from Andy original https://torrentfreak.com/public-figure-threatened-with-exposure-over-gay-piracy-fine-170817/

Flava Works is an Illinois-based company specializing in adult material featuring black and Latino men. It operates an aggressive anti-piracy strategy which has resulted in some large damages claims in the past.

Now, however, the company has found itself targeted by a lawsuit filed by one of its alleged victims. Filed in a California district court by an unnamed individual, it accuses Flava Works of shocking behavior relating to a claim of alleged piracy.

According to the lawsuit, ‘John Doe’ received a letter in early June from Flava Works CEO Phillip Bleicher, accusing him of Internet piracy. Titled “Settlement Demand and Cease and Desist”, the letter got straight to the point.

“Flava Works is aware that you have been ‘pirating’ the content from its website(s) for your own personal financial benefit,” the letter read.

[Update: ‘John Doe’ has now been identified as Marc Juris, President & General Manager of AMC-owned WE tv. All references to John Doe below refer to Juris. See note at footer]

As is often the case with such claims, Flava Works offered to settle with John Doe for a cash fee. However, instead of the few hundred or thousand dollars usually seen in such cases, the initial settlement amount was an astronomical $97,000. But that wasn’t all.

According to John Doe, Bleicher warned that unless the money was paid in ten days, Flava Works “would initiate litigation against [John Doe], publically accusing him of being a consumer and pirate of copyrighted gay adult entertainment.”

Amping up the pressure, Bleicher then warned that after the ten-day deadline had passed, the settlement amount of $97,000 would be withdrawn and replaced with a new amount – $525,000.

The lawsuit alleges that Bleicher followed up with more emails in which he indicated that there was still time to settle the matter “one on one” since the case hadn’t been assigned to an attorney. However, he warned John Doe that time was running out and that public exposure via a lawsuit would be the next step.

While these kinds of tactics are nothing new in copyright infringement cases, the amounts of money involved are huge, indicating something special at play. Indeed, it transpires that John Doe is a public figure in the entertainment industry and the suggestion is that Flava Works’ assessment of his “wealth and profile” means he can pay these large sums.

According to the suit, on July 6, 2017, Bleicher sent another email to John Doe which “alluded to [his] high-profile status and to the potential publicity that a lawsuit would bring.” The email went as far as threatening an imminent Flava Works press release, announcing that a public figure, who would be named, was being sued for pirating gay adult content.

Flava Works alleges that John Doe uploaded its videos to various BitTorrent sites and forums, but John Doe vigorously denies the accusations, noting that the ‘evidence’ presented by Flava Works fails to back up its claims.

“The materials do not reveal or expose infringement of any sort. [Flava Works’] real purpose in sending this ‘proof’ was to demonstrate just how humiliating it would be to defend against Flava Works’ scurrilous charges,” John Doe’s lawsuit notes.

“[Flava Works’] materials consist largely of screen shots of extremely graphic images of pornography, which [Flava Works] implies that [John Doe] has viewed — but which are completely irrelevant given that they are not Flava Works content. Nevertheless, Bleicher assured [John Doe] that these materials would all be included in a publicly filed lawsuit if he refused to accede to [Flava Works’] payment demands.”

From his lawsuit (pdf) it’s clear that John Doe is in no mood to pay Flava Works large sums of cash and he’s aggressively on the attack, describing the company’s demands as “criminal extortion.”

He concludes with a request for a declaration that he has not infringed Flava Works’ copyrights, while demanding attorneys’ fees and further relief to be determined by the court.

The big question now is whether Flava Works will follow through with its threats to exposure the entertainer, or whether it will drift back into the shadows to fight another day. Definitely one to watch.

Update: Flava Works has now followed through on its threat to sue Juris. A complaint filed iat an Illinois court accuses the TV executive of uploading Flava Works titles to several gay-focused torrent sites in breach of copyright. It demands $1.2m in damages.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

What’s the Diff: Programs, Processes, and Threads

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/whats-the-diff-programs-processes-and-threads/

let's talk about Threads

How often have you heard the term threading in relation to a computer program, but you weren’t exactly sure what it meant? How about processes? You likely understand that a thread is somehow closely related to a program and a process, but if you’re not a computer science major, maybe that’s as far as your understanding goes.

Knowing what these terms mean is absolutely essential if you are a programmer, but an understanding of them also can be useful to the average computer user. Being able to look at and understand the Activity Monitor on the Macintosh, the Task Manager on Windows, or Top on Linux can help you troubleshoot which programs are causing problems on your computer, or whether you might need to install more memory to make your system run better.

Let’s take a few minutes to delve into the world of computer programs and sort out what these terms mean. We’ll simplify and generalize some of the ideas, but the general concepts we cover should help clarify the difference between the terms.

Programs

First of all, you probably are aware that a program is the code that is stored on your computer that is intended to fulfill a certain task. There are many types of programs, including programs that help your computer function and are part of the operating system, and other programs that fulfill a particular job. These task-specific programs are also known as “applications,” and can include programs such as word processing, web browsing, or emailing a message to another computer.

Program

Programs are typically stored on disk or in non-volatile memory in a form that can be executed by your computer. Prior to that, they are created using a programming language such as C, Lisp, Pascal, or many others using instructions that involve logic, data and device manipulation, recurrence, and user interaction. The end result is a text file of code that is compiled into binary form (1’s and 0’s) in order to run on the computer. Another type of program is called “interpreted,” and instead of being compiled in advance in order to run, is interpreted into executable code at the time it is run. Some common, typically interpreted programming languages, are Python, PHP, JavaScript, and Ruby.

The end result is the same, however, in that when a program is run, it is loaded into memory in binary form. The computer’s CPU (Central Processing Unit) understands only binary instructions, so that’s the form the program needs to be in when it runs.

Perhaps you’ve heard the programmer’s joke, “There are only 10 types of people in the world, those who understand binary, and those who don’t.”

Binary is the native language of computers because an electrical circuit at its basic level has two states, on or off, represented by a one or a zero. In the common numbering system we use every day, base 10, each digit position can be anything from 0 to 9. In base 2 (or binary), each position is either a 0 or a 1. (In a future blog post we might cover quantum computing, which goes beyond the concept of just 1’s and 0’s in computing.)

Decimal—Base 10 Binary—Base 2
0 0000
1 0001
2 0010
3 0011
4 0100
5 0101
6 0110
7 0111
8 1000
9 1001

How Processes Work

The program has been loaded into the computer’s memory in binary form. Now what?

An executing program needs more than just the binary code that tells the computer what to do. The program needs memory and various operating system resources that it needs in order to run. A “process” is what we call a program that has been loaded into memory along with all the resources it needs to operate. The “operating system” is the brains behind allocating all these resources, and comes in different flavors such as macOS, iOS, Microsoft Windows, Linux, and Android. The OS handles the task of managing the resources needed to turn your program into a running process.

Some essential resources every process needs are registers, a program counter, and a stack. The “registers” are data holding places that are part of the computer processor (CPU). A register may hold an instruction, a storage address, or other kind of data needed by the process. The “program counter,” also called the “instruction pointer,” keeps track of where a computer is in its program sequence. The “stack” is a data structure that stores information about the active subroutines of a computer program and is used as scratch space for the process. It is distinguished from dynamically allocated memory for the process that is known as “the heap.”

diagram of how processes work

There can be multiple instances of a single program, and each instance of that running program is a process. Each process has a separate memory address space, which means that a process runs independently and is isolated from other processes. It cannot directly access shared data in other processes. Switching from one process to another requires some time (relatively) for saving and loading registers, memory maps, and other resources.

This independence of processes is valuable because the operating system tries its best to isolate processes so that a problem with one process doesn’t corrupt or cause havoc with another process. You’ve undoubtedly run into the situation in which one application on your computer freezes or has a problem and you’ve been able to quit that program without affecting others.

How Threads Work

So, are you still with us? We finally made it to threads!

A thread is the unit of execution within a process. A process can have anywhere from just one thread to many threads.

Process vs. Thread

diagram of threads in a process over time

When a process starts, it is assigned memory and resources. Each thread in the process shares that memory and resources. In single-threaded processes, the process contains one thread. The process and the thread are one and the same, and there is only one thing happening.

In multithreaded processes, the process contains more than one thread, and the process is accomplishing a number of things at the same time (technically, it’s almost at the same time—read more on that in the “What about Parallelism and Concurrency?” section below).

diagram of single and multi-treaded process

We talked about the two types of memory available to a process or a thread, the stack and the heap. It is important to distinguish between these two types of process memory because each thread will have its own stack, but all the threads in a process will share the heap.

Threads are sometimes called lightweight processes because they have their own stack but can access shared data. Because threads share the same address space as the process and other threads within the process, the operational cost of communication between the threads is low, which is an advantage. The disadvantage is that a problem with one thread in a process will certainly affect other threads and the viability of the process itself.

Threads vs. Processes

So to review:

  1. The program starts out as a text file of programming code,
  2. The program is compiled or interpreted into binary form,
  3. The program is loaded into memory,
  4. The program becomes one or more running processes.
  5. Processes are typically independent of each other,
  6. While threads exist as the subset of a process.
  7. Threads can communicate with each other more easily than processes can,
  8. But threads are more vulnerable to problems caused by other threads in the same process.

Processes vs. Threads — Advantages and Disadvantages

Process Thread
Processes are heavyweight operations Threads are lighter weight operations
Each process has its own memory space Threads use the memory of the process they belong to
Inter-process communication is slow as processes have different memory addresses Inter-thread communication can be faster than inter-process communication because threads of the same process share memory with the process they belong to
Context switching between processes is more expensive Context switching between threads of the same process is less expensive
Processes don’t share memory with other processes Threads share memory with other threads of the same process

What about Concurrency and Parallelism?

A question you might ask is whether processes or threads can run at the same time. The answer is: it depends. On a system with multiple processors or CPU cores (as is common with modern processors), multiple processes or threads can be executed in parallel. On a single processor, though, it is not possible to have processes or threads truly executing at the same time. In this case, the CPU is shared among running processes or threads using a process scheduling algorithm that divides the CPU’s time and yields the illusion of parallel execution. The time given to each task is called a “time slice.” The switching back and forth between tasks happens so fast it is usually not perceptible. The terms parallelism (true operation at the same time) and concurrency (simulated operation at the same time), distinguish between the two type of real or approximate simultaneous operation.

diagram of concurrency and parallelism

Why Choose Process over Thread, or Thread over Process?

So, how would a programmer choose between a process and a thread when creating a program in which she wants to execute multiple tasks at the same time? We’ve covered some of the differences above, but let’s look at a real world example with a program that many of us use, Google Chrome.

When Google was designing the Chrome browser, they needed to decide how to handle the many different tasks that needed computer, communications, and network resources at the same time. Each browser window or tab communicates with multiple servers on the internet to retrieve text, programs, graphics, audio, video, and other resources, and renders that data for display and interaction with the user. In addition, the browser can open many windows, each with many tasks.

Google had to decide how to handle that separation of tasks. They chose to run each browser window in Chrome as a separate process rather than a thread or many threads, as is common with other browsers. Doing that brought Google a number of benefits. Running each window as a process protects the overall application from bugs and glitches in the rendering engine and restricts access from each rendering engine process to others and to the rest of the system. Isolating JavaScript programs in a process prevents them from running away with too much CPU time and memory, and making the entire browser non-responsive.

Google made the calculated trade-off with a multi-processing design as starting a new process for each browser window has a higher fixed cost in memory and resources than using threads. They were betting that their approach would end up with less memory bloat overall.

Using processes instead of threads provides better memory usage when memory gets low. An inactive window is treated as a lower priority by the operating system and becomes eligible to be swapped to disk when memory is needed for other processes, helping to keep the user-visible windows more responsive. If the windows were threaded, it would be more difficult to separate the used and unused memory as cleanly, wasting both memory and performance.

You can read more about Google’s design decisions on Google’s Chromium Blog or on the Chrome Introduction Comic.

The screen capture below shows the Google Chrome processes running on a MacBook Air with many tabs open. Some Chrome processes are using a fair amount of CPU time and resources, and some are using very little. You can see that each process also has many threads running as well.

activity monitor of Google Chrome

The Activity Monitor or Task Manager on your system can be a valuable ally in helping fine-tune your computer or troubleshooting problems. If your computer is running slowly, or a program or browser window isn’t responding for a while, you can check its status using the system monitor. Sometimes you’ll see a process marked as “Not Responding.” Try quitting that process and see if your system runs better. If an application is a memory hog, you might consider choosing a different application that will accomplish the same task.

Windows Task Manager view

Made it This Far?

We hope this Tron-like dive into the fascinating world of computer programs, processes, and threads has helped clear up some questions you might have had.

The next time your computer is running slowly or an application is acting up, you know your assignment. Fire up the system monitor and take a look under the hood to see what’s going on. You’re in charge now.

We love to hear from you

Are you still confused? Have questions? If so, please let us know in the comments. And feel free to suggest topics for future blog posts.

The post What’s the Diff: Programs, Processes, and Threads appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

OK Google, be aesthetically pleasing

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/aesthetically-pleasing-ok-google/

Maker Andrew Jones took a Raspberry Pi and the Google Assistant SDK and created a gorgeous-looking, and highly functional, alternative to store-bought smart speakers.

Raspberry Pi Google AI Assistant

In this video I get an “Ok Google” voice activated AI assistant running on a raspberry pi. I also hand make a nice wooden box for it to live in.

OK Google, what are you?

Google Assistant is software of the same ilk as Amazon’s Alexa, Apple’s Siri and Microsoft’s Cortana. It’s a virtual assistant that allows you to request information, play audio, and control smart home devices via voice commands.

Infinite Looping Siri, Alexa and Google Home

One can barely see the iPhone’s screen. That’s because I have a privacy protection screen. Sorry, did not check the camera angle. Learn how to create your own loop, why we put Cortana out of the loop, and how to train Siri to an artificial voice: https://www.danrl.com/2016/12/01/looping-ais-siri-alexa-google-home.html

You probably have a digital assistant on your mobile phone, and if you go to the home of someone even mildly tech-savvy, you may see a device awaiting commands via a wake word such the device’s name or, for the Google Assistant, the phrase “OK, Google”.

Homebrew versions

Understanding the maker need to ‘put tech into stuff’ and upgrade everyday objects into everyday objects 2.0, the creators of these virtual assistants have allowed access for developers to run their software on devices such as the Raspberry Pi. This means that your common-or-garden homemade robot can now be controlled via voice, and your shed-built home automation system can have easy-to-use internet connectivity via a reliable, multi-device platform.

Andrew’s Google Assistant build

Andrew gives a peerless explanation of how the Google Assistant works:

There’s Google’s Cloud. You log into Google’s Cloud and you do a bunch of cloud configuration cloud stuff. And then on the Raspberry Pi you install some Python software and you do a bunch of configuration. And then the cloud and the Pi talk the clouds kitten rainbow protocol and then you get a Google AI assistant.

It all makes perfect sense. Though for more extra detail, you could always head directly to Google.

Andrew Jones Raspberry Pi OK Google Assistant

I couldn’t have explained it better myself

Andrew decided to take his Google Assistant-enabled Raspberry Pi and create a new body for it. One that was more aesthetically pleasing than the standard Pi-inna-box. After wiring his build and cannibalising some speakers and a microphone, he created a sleek, wooden body that would sit quite comfortably in any Bang & Olufsen shop window.

Find the entire build tutorial on Instructables.

Make your own

It’s more straightforward than Andrew’s explanation suggests, we promise! And with an array of useful resources online, you should be able to incorporate your choice of virtual assistants into your build.

There’s The Raspberry Pi Guy’s tutorial on setting up Amazon Alexa on the Raspberry Pi. If you’re looking to use Siri on your Pi, YouTube has a plethora of tutorials waiting for you. And lastly, check out Microsoft’s site for using Cortana on the Pi!

If you’re looking for more information on Google Assistant, check out issue 57 of The MagPi Magazine, free to download as a PDF. The print edition of this issue came with a free AIY Projects Voice Kit, and you can sign up for The MagPi newsletter to be the first to know about the kit’s availability for purchase.

The post OK Google, be aesthetically pleasing appeared first on Raspberry Pi.

Launch – AWS Glue Now Generally Available

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/launch-aws-glue-now-generally-available/

Today we’re excited to announce the general availability of AWS Glue. Glue is a fully managed, serverless, and cloud-optimized extract, transform and load (ETL) service. Glue is different from other ETL services and platforms in a few very important ways.

First, Glue is “serverless” – you don’t need to provision or manage any resources and you only pay for resources when Glue is actively running. Second, Glue provides crawlers that can automatically detect and infer schemas from many data sources, data types, and across various types of partitions. It stores these generated schemas in a centralized Data Catalog for editing, versioning, querying, and analysis. Third, Glue can automatically generate ETL scripts (in Python!) to translate your data from your source formats to your target formats. Finally, Glue allows you to create development endpoints that allow your developers to use their favorite toolchains to construct their ETL scripts. Ok, let’s dive deep with an example.

In my job as a Developer Evangelist I spend a lot of time traveling and I thought it would be cool to play with some flight data. The Bureau of Transportations Statistics is kind enough to share all of this data for anyone to use here. We can easily download this data and put it in an Amazon Simple Storage Service (S3) bucket. This data will be the basis of our work today.

Crawlers

First, we need to create a Crawler for our flights data from S3. We’ll select Crawlers in the Glue console and follow the on screen prompts from there. I’ll specify s3://crawler-public-us-east-1/flight/2016/csv/ as my first datasource (we can add more later if needed). Next, we’ll create a database called flights and give our tables a prefix of flights as well.

The Crawler will go over our dataset, detect partitions through various folders – in this case months of the year, detect the schema, and build a table. We could add additonal data sources and jobs into our crawler or create separate crawlers that push data into the same database but for now let’s look at the autogenerated schema.

I’m going to make a quick schema change to year, moving it from BIGINT to INT. Then I can compare the two versions of the schema if needed.

Now that we know how to correctly parse this data let’s go ahead and do some transforms.

ETL Jobs

Now we’ll navigate to the Jobs subconsole and click Add Job. Will follow the prompts from there giving our job a name, selecting a datasource, and an S3 location for temporary files. Next we add our target by specifying “Create tables in your data target” and we’ll specify an S3 location in Parquet format as our target.

After clicking next, we’re at screen showing our various mappings proposed by Glue. Now we can make manual column adjustments as needed – in this case we’re just going to use the X button to remove a few columns that we don’t need.

This brings us to my favorite part. This is what I absolutely love about Glue.

Glue generated a PySpark script to transform our data based on the information we’ve given it so far. On the left hand side we can see a diagram documenting the flow of the ETL job. On the top right we see a series of buttons that we can use to add annotated data sources and targets, transforms, spigots, and other features. This is the interface I get if I click on transform.

If we add any of these transforms or additional data sources, Glue will update the diagram on the left giving us a useful visualization of the flow of our data. We can also just write our own code into the console and have it run. We can add triggers to this job that fire on completion of another job, a schedule, or on demand. That way if we add more flight data we can reload this same data back into S3 in the format we need.

I could spend all day writing about the power and versatility of the jobs console but Glue still has more features I want to cover. So, while I might love the script editing console, I know many people prefer their own development environments, tools, and IDEs. Let’s figure out how we can use those with Glue.

Development Endpoints and Notebooks

A Development Endpoint is an environment used to develop and test our Glue scripts. If we navigate to “Dev endpoints” in the Glue console we can click “Add endpoint” in the top right to get started. Next we’ll select a VPC, a security group that references itself and then we wait for it to provision.


Once it’s provisioned we can create an Apache Zeppelin notebook server by going to actions and clicking create notebook server. We give our instance an IAM role and make sure it has permissions to talk to our data sources. Then we can either SSH into the server or connect to the notebook to interactively develop our script.

Pricing and Documentation

You can see detailed pricing information here. Glue crawlers, ETL jobs, and development endpoints are all billed in Data Processing Unit Hours (DPU) (billed by minute). Each DPU-Hour costs $0.44 in us-east-1. A single DPU provides 4vCPU and 16GB of memory.

We’ve only covered about half of the features that Glue has so I want to encourage everyone who made it this far into the post to go read the documentation and service FAQs. Glue also has a rich and powerful API that allows you to do anything console can do and more.

We’re also releasing two new projects today. The aws-glue-libs provide a set of utilities for connecting, and talking with Glue. The aws-glue-samples repo contains a set of example jobs.

I hope you find that using Glue reduces the time it takes to start doing things with your data. Look for another post from me on AWS Glue soon because I can’t stop playing with this new service.
Randall

AWS CloudHSM Update – Cost Effective Hardware Key Management at Cloud Scale for Sensitive & Regulated Workloads

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-cloudhsm-update-cost-effective-hardware-key-management/

Our customers run an incredible variety of mission-critical workloads on AWS, many of which process and store sensitive data. As detailed in our Overview of Security Processes document, AWS customers have access to an ever-growing set of options for encrypting and protecting this data. For example, Amazon Relational Database Service (RDS) supports encryption of data at rest and in transit, with options tailored for each supported database engine (MySQL, SQL Server, Oracle, MariaDB, PostgreSQL, and Aurora).

Many customers use AWS Key Management Service (KMS) to centralize their key management, with others taking advantage of the hardware-based key management, encryption, and decryption provided by AWS CloudHSM to meet stringent security and compliance requirements for their most sensitive data and regulated workloads (you can read my post, AWS CloudHSM – Secure Key Storage and Cryptographic Operations, to learn more about Hardware Security Modules, also known as HSMs).

Major CloudHSM Update
Today, building on what we have learned from our first-generation product, we are making a major update to CloudHSM, with a set of improvements designed to make the benefits of hardware-based key management available to a much wider audience while reducing the need for specialized operating expertise. Here’s a summary of the improvements:

Pay As You Go – CloudHSM is now offered under a pay-as-you-go model that is simpler and more cost-effective, with no up-front fees.

Fully Managed – CloudHSM is now a scalable managed service; provisioning, patching, high availability, and backups are all built-in and taken care of for you. Scheduled backups extract an encrypted image of your HSM from the hardware (using keys that only the HSM hardware itself knows) that can be restored only to identical HSM hardware owned by AWS. For durability, those backups are stored in Amazon Simple Storage Service (S3), and for an additional layer of security, encrypted again with server-side S3 encryption using an AWS KMS master key.

Open & Compatible  – CloudHSM is open and standards-compliant, with support for multiple APIs, programming languages, and cryptography extensions such as PKCS #11, Java Cryptography Extension (JCE), and Microsoft CryptoNG (CNG). The open nature of CloudHSM gives you more control and simplifies the process of moving keys (in encrypted form) from one CloudHSM to another, and also allows migration to and from other commercially available HSMs.

More Secure – CloudHSM Classic (the original model) supports the generation and use of keys that comply with FIPS 140-2 Level 2. We’re stepping that up a notch today with support for FIPS 140-2 Level 3, with security mechanisms that are designed to detect and respond to physical attempts to access or modify the HSM. Your keys are protected with exclusive, single-tenant access to tamper-resistant HSMs that appear within your Virtual Private Clouds (VPCs). CloudHSM supports quorum authentication for critical administrative and key management functions. This feature allows you to define a list of N possible identities that can access the functions, and then require at least M of them to authorize the action. It also supports multi-factor authentication using tokens that you provide.

AWS-Native – The updated CloudHSM is an integral part of AWS and plays well with other tools and services. You can create and manage a cluster of HSMs using the AWS Management Console, AWS Command Line Interface (CLI), or API calls.

Diving In
You can create CloudHSM clusters that contain 1 to 32 HSMs, each in a separate Availability Zone in a particular AWS Region. Spreading HSMs across AZs gives you high availability (including built-in load balancing); adding more HSMs gives you additional throughput. The HSMs within a cluster are kept in sync: performing a task or operation on one HSM in a cluster automatically updates the others. Each HSM in a cluster has its own Elastic Network Interface (ENI).

All interaction with an HSM takes place via the AWS CloudHSM client. It runs on an EC2 instance and uses certificate-based mutual authentication to create secure (TLS) connections to the HSMs.

At the hardware level, each HSM includes hardware-enforced isolation of crypto operations and key storage. Each customer HSM runs on dedicated processor cores.

Setting Up a Cluster
Let’s set up a cluster using the CloudHSM Console:

I click on Create cluster to get started, select my desired VPC and the subnets within it (I can also create a new VPC and/or subnets if needed):

Then I review my settings and click on Create:

After a few minutes, my cluster exists, but is uninitialized:

Initialization simply means retrieving a certificate signing request (the Cluster CSR):

And then creating a private key and using it to sign the request (these commands were copied from the Initialize Cluster docs and I have omitted the output. Note that ID identifies the cluster):

$ openssl genrsa -out CustomerRoot.key 2048
$ openssl req -new -x509 -days 365 -key CustomerRoot.key -out CustomerRoot.crt
$ openssl x509 -req -days 365 -in ID_ClusterCsr.csr   \
                              -CA CustomerRoot.crt    \
                              -CAkey CustomerRoot.key \
                              -CAcreateserial         \
                              -out ID_CustomerHsmCertificate.crt

The next step is to apply the signed certificate to the cluster using the console or the CLI. After this has been done, the cluster can be activated by changing the password for the HSM’s administrative user, otherwise known as the Crypto Officer (CO).

Once the cluster has been created, initialized and activated, it can be used to protect data. Applications can use the APIs in AWS CloudHSM SDKs to manage keys, encrypt & decrypt objects, and more. The SDKs provide access to the CloudHSM client (running on the same instance as the application). The client, in turn, connects to the cluster across an encrypted connection.

Available Today
The new HSM is available today in the US East (Northern Virginia), US West (Oregon), US East (Ohio), and EU (Ireland) Regions, with more in the works. Pricing starts at $1.45 per HSM per hour.

Jeff;

New Premier League Blocking Disrupts Pirate IPTV Providers

Post Syndicated from Andy original https://torrentfreak.com/new-premier-league-blocking-disrupts-pirate-iptv-providers-170814/

Top tier football in the UK is handled by the English Premier League (EPL) and broadcasting partners Sky and BT Sport. All face considerable problems with Internet piracy, through free web or Kodi-based streaming and premium IPTV feeds.

To mitigate the threat, earlier this year the Premier League obtained a unique High Court injunction which required ISPs such as Sky, BT, and Virgin to block ‘pirate’ football streams in real-time.

Although the success of the program was initially up for debate, the EPL reported it was able to block 5,000 server IP addresses that were streaming its content. When that temporary injunction ran out, the EPL went back to court for a new one, valid for the season that began this past weekend. There are signs the EPL may have upped its game.

As soon as the matches began on Saturday, issues were reported at several of the more prominent IPTV providers. Within minutes of the match streams going live, subscribers to affected services were met with black screens, causing anger and frustration. While some clearly knew that action was on the cards, relatively few had an effective plan in place.

One provider, which targets subscribers in the UK, scrambled to obtain new domain names, thinking that the existing domains had been placed on some kind of Premier League blacklist. While that may have indeed been the case, making a service more obscure in that sense was never going to outwit the systems deployed by the anti-piracy outfits involved.

Indeed, the provider in question was subjected to much chaos over both Saturday and Sunday, since it’s clear that large numbers of subscribers had absolutely no idea what was going on. Even if they understood that the EPL was blocking, the change of domain flat-footed the rest. The subsequent customer service chaos was not a pretty sight but would’ve been a pleasure for the EPL to behold.

An interesting side effect of this EPL action is that even if IPTV subscribers don’t care about football, many were affected this past weekend anyway.

TF is aware of at least three services (there are probably many more) that couldn’t service their UK customers with any other channels whatsoever while the Premier League games were being aired. This suggests that the IP addresses hit by the EPL and blocked by local ISPs belonged to the same servers carrying the rest of the content offered by the IPTV providers.

When the High Court handed down its original injunction it accepted that some non-Premier League content could be blocked at the same time but since that “consists almost exclusively of [infringing] commercial broadcast content such as other sports, films, and television programs,” there was little concern over collateral damage.

So the big question now is what can IPTV providers and/or subscribers do to tackle the threat?

The first interesting thing to note is not all of the big providers were affected this past weekend, so for many customers the matches passed by as normal. It isn’t clear whether EPL simply didn’t have all of the providers on the list or whether steps were taken to mitigate the threat, but that was certainly the case in a handful of cases.

Information passed to TF shows that at least a small number of providers were not only waiting for the EPL action but actually had a backup plan in place. This appears to have resulted in a minimum of disruption for their customers, something that will prove of interest to the many frustrated subscribers looking for a new service this morning.

While the past few days have been somewhat chaotic, other issues have been muddying the waters somewhat.

TF has learned that at least two, maybe three suppliers, were subjected to DDoS attacks around the time the matches were due to air. It seems unlikely that the EPL has been given permission to carry out such an attack but since the High Court injunction is secret in every way that describes its anti-piracy methods, that will remain a suspicion. In the meantime, rival IPTV services remain possible suspects.

Also, a major IPTV stream ‘wholesaler’ is reported to have had technical issues on Saturday, which affected its ability to serve lower-tier providers. Whether that was also linked to the Premier League action is unknown and TF couldn’t find any source willing to talk about the provider in any detail.

So, sports fans who rely on IPTV for their fix are wondering how things will pan out later this week. If this last weekend is anything to go by, disruption is guaranteed, but it will be less of a surprise given the problems of the last few days. While some don’t foresee huge problems, several providers are already advising customers that VPNs will be necessary.

An IPTV provider suggesting the use of VPNs

While a VPN will indeed solve the problem in most cases, for many subscribers that will amount to an additional expense, not to mention more time spent learning about VPNs, what they can do, and how they can be setup on the hardware they’re using for IPTV.

For users on Android devices running IPTV apps or Kodi-type setups, VPNs are both easy to install and use. However, Mag Box STB users cannot run a VPN directly on the device, meaning that they’ll need either a home router that can run a VPN or a smaller ‘travel’ type router with OpenVPN capabilities to use as a go-between.

Either way, costs are beginning to creep up, if IPTV providers can’t deal with the EPL’s blocking efforts. That makes the new cheaper football packages offered by various providers that little bit more attractive. But that was probably the plan all along.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Nazis, are bad

Post Syndicated from Eevee original https://eev.ee/blog/2017/08/13/nazis-are-bad/

Anonymous asks:

Could you talk about something related to the management/moderation and growth of online communities? IOW your thoughts on online community management, if any.

I think you’ve tweeted about this stuff in the past so I suspect you have thoughts on this, but if not, again, feel free to just blog about … anything 🙂

Oh, I think I have some stuff to say about community management, in light of recent events. None of it hasn’t already been said elsewhere, but I have to get this out.

Hopefully the content warning is implicit in the title.


I am frustrated.

I’ve gone on before about a particularly bothersome phenomenon that hurts a lot of small online communities: often, people are willing to tolerate the misery of others in a community, but then get up in arms when someone pushes back. Someone makes a lot of off-hand, off-color comments about women? Uses a lot of dog-whistle terms? Eh, they’re not bothering anyone, or at least not bothering me. Someone else gets tired of it and tells them to knock it off? Whoa there! Now we have the appearance of conflict, which is unacceptable, and people will turn on the person who’s pissed off — even though they’ve been at the butt end of an invisible conflict for who knows how long. The appearance of peace is paramount, even if it means a large chunk of the population is quietly miserable.

Okay, so now, imagine that on a vastly larger scale, and also those annoying people who know how to skirt the rules are Nazis.


The label “Nazi” gets thrown around a lot lately, probably far too easily. But when I see a group of people doing the Hitler salute, waving large Nazi flags, wearing Nazi armbands styled after the SS, well… if the shoe fits, right? I suppose they might have flown across the country to join a torch-bearing mob ironically, but if so, the joke is going way over my head. (Was the murder ironic, too?) Maybe they’re not Nazis in the sense that the original party doesn’t exist any more, but for ease of writing, let’s refer to “someone who espouses Nazi ideology and deliberately bears a number of Nazi symbols” as, well, “a Nazi”.

This isn’t a new thing, either; I’ve stumbled upon any number of Twitter accounts that are decorated in Nazi regalia. I suppose the trouble arises when perfectly innocent members of the alt-right get unfairly labelled as Nazis.

But hang on; this march was called “Unite the Right” and was intended to bring together various far right sub-groups. So what does their choice of aesthetic say about those sub-groups? I haven’t heard, say, alt-right coiner Richard Spencer denounce the use of Nazi symbology — extra notable since he was fucking there and apparently didn’t care to discourage it.


And so begins the rule-skirting. “Nazi” is definitely overused, but even using it to describe white supremacists who make not-so-subtle nods to Hitler is likely to earn you some sarcastic derailment. A Nazi? Oh, so is everyone you don’t like and who wants to establish a white ethno state a Nazi?

Calling someone a Nazi — or even a white supremacist — is an attack, you see. Merely expressing the desire that people of color not exist is perfectly peaceful, but identifying the sentiment for what it is causes visible discord, which is unacceptable.

These clowns even know this sort of thing and strategize around it. Or, try, at least. Maybe it wasn’t that successful this weekend — though flicking through Charlottesville headlines now, they seem to be relatively tame in how they refer to the ralliers.

I’m reminded of a group of furries — the alt-furries — who have been espousing white supremacy and wearing red armbands with a white circle containing a black… pawprint. Ah, yes, that’s completely different.


So, what to do about this?

Ignore them” is a popular option, often espoused to bullied children by parents who have never been bullied, shortly before they resume complaining about passive-aggressive office politics. The trouble with ignoring them is that, just like in smaller communitiest, they have a tendency to fester. They take over large chunks of influential Internet surface area like 4chan and Reddit; they help get an inept buffoon elected; and then they start to have torch-bearing rallies and run people over with cars.

4chan illustrates a kind of corollary here. Anyone who’s steeped in Internet Culture™ is surely familiar with 4chan; I was never a regular visitor, but it had enough influence that I was still aware of it and some of its culture. It was always thick with irony, which grew into a sort of ironic detachment — perhaps one of the major sources of the recurring online trope that having feelings is bad — which proceeded into ironic racism.

And now the ironic racism is indistinguishable from actual racism, as tends to be the case. Do they “actually” “mean it”, or are they just trying to get a rise out of people? What the hell is unironic racism if not trying to get a rise out of people? What difference is there to onlookers, especially as they move to become increasingly involved with politics?

It’s just a joke” and “it was just a thoughtless comment” are exceptionally common defenses made by people desperate to preserve the illusion of harmony, but the strain of overt white supremacy currently running rampant through the US was built on those excuses.


The other favored option is to debate them, to defeat their ideas with better ideas.

Well, hang on. What are their ideas, again? I hear they were chanting stuff like “go back to Africa” and “fuck you, faggots”. Given that this was an overtly political rally (and again, the Nazi fucking regalia), I don’t think it’s a far cry to describe their ideas as “let’s get rid of black people and queer folks”.

This is an underlying proposition: that white supremacy is inherently violent. After all, if the alt-right seized total political power, what would they do with it? If I asked the same question of Democrats or Republicans, I’d imagine answers like “universal health care” or “screw over poor people”. But people whose primary goal is to have a country full of only white folks? What are they going to do, politely ask everyone else to leave? They’re invoking the memory of people who committed genocide and also tried to take over the fucking world. They are outright saying, these are the people we look up to, this is who we think had a great idea.

How, precisely, does one defeat these ideas with rational debate?

Because the underlying core philosophy beneath all this is: “it would be good for me if everything were about me”. And that’s true! (Well, it probably wouldn’t work out how they imagine in practice, but it’s true enough.) Consider that slavery is probably fantastic if you’re the one with the slaves; the issue is that it’s reprehensible, not that the very notion contains some kind of 101-level logical fallacy. That’s probably why we had a fucking war over it instead of hashing it out over brunch.

…except we did hash it out over brunch once, and the result was that slavery was still allowed but slaves only counted as 60% of a person for the sake of counting how much political power states got. So that’s how rational debate worked out. I’m sure the slaves were thrilled with that progress.


That really only leaves pushing back, which raises the question of how to push back.

And, I don’t know. Pushing back is much harder in spaces you don’t control, spaces you’re already struggling to justify your own presence in. For most people, that’s most spaces. It’s made all the harder by that tendency to preserve illusory peace; even the tamest request that someone knock off some odious behavior can be met by pushback, even by third parties.

At the same time, I’m aware that white supremacists prey on disillusioned young white dudes who feel like they don’t fit in, who were promised the world and inherited kind of a mess. Does criticism drive them further away? The alt-right also opposes “political correctness”, i.e. “not being a fucking asshole”.

God knows we all suck at this kind of behavior correction, even within our own in-groups. Fandoms have become almost ridiculously vicious as platforms like Twitter and Tumblr amplify individual anger to deafening levels. It probably doesn’t help that we’re all just exhausted, that every new fuck-up feels like it bears the same weight as the last hundred combined.

This is the part where I admit I don’t know anything about people and don’t have any easy answers. Surprise!


The other alternative is, well, punching Nazis.

That meme kind of haunts me. It raises really fucking complicated questions about when violence is acceptable, in a culture that’s completely incapable of answering them.

America’s relationship to violence is so bizarre and two-faced as to be almost incomprehensible. We worship it. We have the biggest military in the world by an almost comical margin. It’s fairly mainstream to own deadly weapons for the express stated purpose of armed revolution against the government, should that become necessary, where “necessary” is left ominously undefined. Our movies are about explosions and beating up bad guys; our video games are about explosions and shooting bad guys. We fantasize about solving foreign policy problems by nuking someone — hell, our talking heads are currently in polite discussion about whether we should nuke North Korea and annihilate up to twenty-five million people, as punishment for daring to have the bomb that only we’re allowed to have.

But… violence is bad.

That’s about as far as the other side of the coin gets. It’s bad. We condemn it in the strongest possible terms. Also, guess who we bombed today?

I observe that the one time Nazis were a serious threat, America was happy to let them try to take over the world until their allies finally showed up on our back porch.

Maybe I don’t understand what “violence” means. In a quest to find out why people are talking about “leftist violence” lately, I found a National Review article from May that twice suggests blocking traffic is a form of violence. Anarchists have smashed some windows and set a couple fires at protests this year — and, hey, please knock that crap off? — which is called violence against, I guess, Starbucks. Black Lives Matter could be throwing a birthday party and Twitter would still be abuzz with people calling them thugs.

Meanwhile, there’s a trend of murderers with increasingly overt links to the alt-right, and everyone is still handling them with kid gloves. First it was murders by people repeating their talking points; now it’s the culmination of a torches-and-pitchforks mob. (Ah, sorry, not pitchforks; assault rifles.) And we still get this incredibly bizarre both-sides-ism, a White House that refers to the people who didn’t murder anyone as “just as violent if not more so“.


Should you punch Nazis? I don’t know. All I know is that I’m extremely dissatisfied with discourse that’s extremely alarmed by hypothetical punches — far more mundane than what you’d see after a sporting event — but treats a push for ethnic cleansing as a mere difference of opinion.

The equivalent to a punch in an online space is probably banning, which is almost laughable in comparison. It doesn’t cause physical harm, but it is a use of concrete force. Doesn’t pose quite the same moral quandary, though.

Somewhere in the middle is the currently popular pastime of doxxing (doxxxxxxing) people spotted at the rally in an attempt to get them fired or whatever. Frankly, that skeeves me out, though apparently not enough that I’m directly chastizing anyone for it.


We aren’t really equipped, as a society, to deal with memetic threats. We aren’t even equipped to determine what they are. We had a fucking world war over this, and now people are outright saying “hey I’m like those people we went and killed a lot in that world war” and we give them interviews and compliment their fashion sense.

A looming question is always, what if they then do it to you? What if people try to get you fired, to punch you for your beliefs?

I think about that a lot, and then I remember that it’s perfectly legal to fire someone for being gay in half the country. (Courts are currently wrangling whether Title VII forbids this, but with the current administration, I’m not optimistic.) I know people who’ve been fired for coming out as trans. I doubt I’d have to look very far to find someone who’s been punched for either reason.

And these aren’t even beliefs; they’re just properties of a person. You can stop being a white supremacist, one of those people yelling “fuck you, faggots”.

So I have to recuse myself from this asinine question, because I can’t fairly judge the risk of retaliation when it already happens to people I care about.

Meanwhile, if a white supremacist does get punched, I absolutely still want my tax dollars to pay for their universal healthcare.


The same wrinkle comes up with free speech, which is paramount.

The ACLU reminds us that the First Amendment “protects vile, hateful, and ignorant speech”. I think they’ve forgotten that that’s a side effect, not the goal. No one sat down and suggested that protecting vile speech was some kind of noble cause, yet that’s how we seem to be treating it.

The point was to avoid a situation where the government is arbitrarily deciding what qualifies as vile, hateful, and ignorant, and was using that power to eliminate ideas distasteful to politicians. You know, like, hypothetically, if they interrogated and jailed a bunch of people for supporting the wrong economic system. Or convicted someone under the Espionage Act for opposing the draft. (Hey, that’s where the “shouting fire in a crowded theater” line comes from.)

But these are ideas that are already in the government. Bannon, a man who was chair of a news organization he himself called “the platform for the alt-right”, has the President’s ear! How much more mainstream can you get?

So again I’m having a little trouble balancing “we need to defend the free speech of white supremacists or risk losing it for everyone” against “we fairly recently were ferreting out communists and the lingering public perception is that communists are scary, not that the government is”.


This isn’t to say that freedom of speech is bad, only that the way we talk about it has become fanatical to the point of absurdity. We love it so much that we turn around and try to apply it to corporations, to platforms, to communities, to interpersonal relationships.

Look at 4chan. It’s completely public and anonymous; you only get banned for putting the functioning of the site itself in jeopardy. Nothing is stopping a larger group of people from joining its politics board and tilting sentiment the other way — except that the current population is so odious that no one wants to be around them. Everyone else has evaporated away, as tends to happen.

Free speech is great for a government, to prevent quashing politics that threaten the status quo (except it’s a joke and they’ll do it anyway). People can’t very readily just bail when the government doesn’t like them, anyway. It’s also nice to keep in mind to some degree for ubiquitous platforms. But the smaller you go, the easier it is for people to evaporate away, and the faster pure free speech will turn the place to crap. You’ll be left only with people who care about nothing.


At the very least, it seems clear that the goal of white supremacists is some form of destabilization, of disruption to the fabric of a community for purely selfish purposes. And those are the kinds of people you want to get rid of as quickly as possible.

Usually this is hard, because they act just nicely enough to create some plausible deniability. But damn, if someone is outright telling you they love Hitler, maybe skip the principled hand-wringing and eject them.

New – AWS SAM Local (Beta) – Build and Test Serverless Applications Locally

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/new-aws-sam-local-beta-build-and-test-serverless-applications-locally/

Today we’re releasing a beta of a new tool, SAM Local, that makes it easy to build and test your serverless applications locally. In this post we’ll use SAM local to build, debug, and deploy a quick application that allows us to vote on tabs or spaces by curling an endpoint. AWS introduced Serverless Application Model (SAM) last year to make it easier for developers to deploy serverless applications. If you’re not already familiar with SAM my colleague Orr wrote a great post on how to use SAM that you can read in about 5 minutes. At it’s core, SAM is a powerful open source specification built on AWS CloudFormation that makes it easy to keep your serverless infrastructure as code – and they have the cutest mascot.

SAM Local takes all the good parts of SAM and brings them to your local machine.

There are a couple of ways to install SAM Local but the easiest is through NPM. A quick npm install -g aws-sam-local should get us going but if you want the latest version you can always install straight from the source: go get github.com/awslabs/aws-sam-local (this will create a binary named aws-sam-local, not sam).

I like to vote on things so let’s write a quick SAM application to vote on Spaces versus Tabs. We’ll use a very simple, but powerful, architecture of API Gateway fronting a Lambda function and we’ll store our results in DynamoDB. In the end a user should be able to curl our API curl https://SOMEURL/ -d '{"vote": "spaces"}' and get back the number of votes.

Let’s start by writing a simple SAM template.yaml:

AWSTemplateFormatVersion : '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Resources:
  VotesTable:
    Type: "AWS::Serverless::SimpleTable"
  VoteSpacesTabs:
    Type: "AWS::Serverless::Function"
    Properties:
      Runtime: python3.6
      Handler: lambda_function.lambda_handler
      Policies: AmazonDynamoDBFullAccess
      Environment:
        Variables:
          TABLE_NAME: !Ref VotesTable
      Events:
        Vote:
          Type: Api
          Properties:
            Path: /
            Method: post

So we create a [dynamo_i] table that we expose to our Lambda function through an environment variable called TABLE_NAME.

To test that this template is valid I’ll go ahead and call sam validate to make sure I haven’t fat-fingered anything. It returns Valid! so let’s go ahead and get to work on our Lambda function.

import os
import os
import json
import boto3
votes_table = boto3.resource('dynamodb').Table(os.getenv('TABLE_NAME'))

def lambda_handler(event, context):
    print(event)
    if event['httpMethod'] == 'GET':
        resp = votes_table.scan()
        return {'body': json.dumps({item['id']: int(item['votes']) for item in resp['Items']})}
    elif event['httpMethod'] == 'POST':
        try:
            body = json.loads(event['body'])
        except:
            return {'statusCode': 400, 'body': 'malformed json input'}
        if 'vote' not in body:
            return {'statusCode': 400, 'body': 'missing vote in request body'}
        if body['vote'] not in ['spaces', 'tabs']:
            return {'statusCode': 400, 'body': 'vote value must be "spaces" or "tabs"'}

        resp = votes_table.update_item(
            Key={'id': body['vote']},
            UpdateExpression='ADD votes :incr',
            ExpressionAttributeValues={':incr': 1},
            ReturnValues='ALL_NEW'
        )
        return {'body': "{} now has {} votes".format(body['vote'], resp['Attributes']['votes'])}

So let’s test this locally. I’ll need to create a real DynamoDB database to talk to and I’ll need to provide the name of that database through the enviornment variable TABLE_NAME. I could do that with an env.json file or I can just pass it on the command line. First, I can call:
$ echo '{"httpMethod": "POST", "body": "{\"vote\": \"spaces\"}"}' |\
TABLE_NAME="vote-spaces-tabs" sam local invoke "VoteSpacesTabs"

to test the Lambda – it returns the number of votes for spaces so theoritically everything is working. Typing all of that out is a pain so I could generate a sample event with sam local generate-event api and pass that in to the local invocation. Far easier than all of that is just running our API locally. Let’s do that: sam local start-api. Now I can curl my local endpoints to test everything out.
I’ll run the command: $ curl -d '{"vote": "tabs"}' http://127.0.0.1:3000/ and it returns: “tabs now has 12 votes”. Now, of course I did not write this function perfectly on my first try. I edited and saved several times. One of the benefits of hot-reloading is that as I change the function I don’t have to do any additional work to test the new function. This makes iterative development vastly easier.

Let’s say we don’t want to deal with accessing a real DynamoDB database over the network though. What are our options? Well we can download DynamoDB Local and launch it with java -Djava.library.path=./DynamoDBLocal_lib -jar DynamoDBLocal.jar -sharedDb. Then we can have our Lambda function use the AWS_SAM_LOCAL environment variable to make some decisions about how to behave. Let’s modify our function a bit:

import os
import json
import boto3
if os.getenv("AWS_SAM_LOCAL"):
    votes_table = boto3.resource(
        'dynamodb',
        endpoint_url="http://docker.for.mac.localhost:8000/"
    ).Table("spaces-tabs-votes")
else:
    votes_table = boto3.resource('dynamodb').Table(os.getenv('TABLE_NAME'))

Now we’re using a local endpoint to connect to our local database which makes working without wifi a little easier.

SAM local even supports interactive debugging! In Java and Node.js I can just pass the -d flag and a port to immediately enable the debugger. For Python I could use a library like import epdb; epdb.serve() and connect that way. Then we can call sam local invoke -d 8080 "VoteSpacesTabs" and our function will pause execution waiting for you to step through with the debugger.

Alright, I think we’ve got everything working so let’s deploy this!

First I’ll call the sam package command which is just an alias for aws cloudformation package and then I’ll use the result of that command to sam deploy.

$ sam package --template-file template.yaml --s3-bucket MYAWESOMEBUCKET --output-template-file package.yaml
Uploading to 144e47a4a08f8338faae894afe7563c3  90570 / 90570.0  (100.00%)
Successfully packaged artifacts and wrote output template to file package.yaml.
Execute the following command to deploy the packaged template
aws cloudformation deploy --template-file package.yaml --stack-name 
$ sam deploy --template-file package.yaml --stack-name VoteForSpaces --capabilities CAPABILITY_IAM
Waiting for changeset to be created..
Waiting for stack create/update to complete
Successfully created/updated stack - VoteForSpaces

Which brings us to our API:
.

I’m going to hop over into the production stage and add some rate limiting in case you guys start voting a lot – but otherwise we’ve taken our local work and deployed it to the cloud without much effort at all. I always enjoy it when things work on the first deploy!

You can vote now and watch the results live! http://spaces-or-tabs.s3-website-us-east-1.amazonaws.com/

We hope that SAM Local makes it easier for you to test, debug, and deploy your serverless apps. We have a CONTRIBUTING.md guide and we welcome pull requests. Please tweet at us to let us know what cool things you build. You can see our What’s New post here and the documentation is live here.

Randall

Automating Blue/Green Deployments of Infrastructure and Application Code using AMIs, AWS Developer Tools, & Amazon EC2 Systems Manager

Post Syndicated from Ramesh Adabala original https://aws.amazon.com/blogs/devops/bluegreen-infrastructure-application-deployment-blog/

Previous DevOps blog posts have covered the following use cases for infrastructure and application deployment automation:

An AMI provides the information required to launch an instance, which is a virtual server in the cloud. You can use one AMI to launch as many instances as you need. It is security best practice to customize and harden your base AMI with required operating system updates and, if you are using AWS native services for continuous security monitoring and operations, you are strongly encouraged to bake into the base AMI agents such as those for Amazon EC2 Systems Manager (SSM), Amazon Inspector, CodeDeploy, and CloudWatch Logs. A customized and hardened AMI is often referred to as a “golden AMI.” The use of golden AMIs to create EC2 instances in your AWS environment allows for fast and stable application deployment and scaling, secure application stack upgrades, and versioning.

In this post, using the DevOps automation capabilities of Systems Manager, AWS developer tools (CodePipeLine, CodeDeploy, CodeCommit, CodeBuild), I will show you how to use AWS CodePipeline to orchestrate the end-to-end blue/green deployments of a golden AMI and application code. Systems Manager Automation is a powerful security feature for enterprises that want to mature their DevSecOps practices.

Here are the high-level phases and primary services covered in this use case:

 

You can access the source code for the sample used in this post here: https://github.com/awslabs/automating-governance-sample/tree/master/Bluegreen-AMI-Application-Deployment-blog.

This sample will create a pipeline in AWS CodePipeline with the building blocks to support the blue/green deployments of infrastructure and application. The sample includes a custom Lambda step in the pipeline to execute Systems Manager Automation to build a golden AMI and update the Auto Scaling group with the golden AMI ID for every rollout of new application code. This guarantees that every new application deployment is on a fully patched and customized AMI in a continuous integration and deployment model. This enables the automation of hardened AMI deployment with every new version of application deployment.

 

 

We will build and run this sample in three parts.

Part 1: Setting up the AWS developer tools and deploying a base web application

Part 1 of the AWS CloudFormation template creates the initial Java-based web application environment in a VPC. It also creates all the required components of Systems Manager Automation, CodeCommit, CodeBuild, and CodeDeploy to support the blue/green deployments of the infrastructure and application resulting from ongoing code releases.

Part 1 of the AWS CloudFormation stack creates these resources:

After Part 1 of the AWS CloudFormation stack creation is complete, go to the Outputs tab and click the Elastic Load Balancing link. You will see the following home page for the base web application:

Make sure you have all the outputs from the Part 1 stack handy. You need to supply them as parameters in Part 3 of the stack.

Part 2: Setting up your CodeCommit repository

In this part, you will commit and push your sample application code into the CodeCommit repository created in Part 1. To access the initial git commands to clone the empty repository to your local machine, click Connect to go to the AWS CodeCommit console. Make sure you have the IAM permissions required to access AWS CodeCommit from command line interface (CLI).

After you’ve cloned the repository locally, download the sample application files from the part2 folder of the Git repository and place the files directly into your local repository. Do not include the aws-codedeploy-sample-tomcat folder. Go to the local directory and type the following commands to commit and push the files to the CodeCommit repository:

git add .
git commit -a -m "add all files from the AWS Java Tomcat CodeDeploy application"
git push

After all the files are pushed successfully, the repository should look like this:

 

Part 3: Setting up CodePipeline to enable blue/green deployments     

Part 3 of the AWS CloudFormation template creates the pipeline in AWS CodePipeline and all the required components.

a) Source: The pipeline is triggered by any change to the CodeCommit repository.

b) BuildGoldenAMI: This Lambda step executes the Systems Manager Automation document to build the golden AMI. After the golden AMI is successfully created, a new launch configuration with the new AMI details will be updated into the Auto Scaling group of the application deployment group. You can watch the progress of the automation in the EC2 console from the Systems Manager –> Automations menu.

c) Build: This step uses the application build spec file to build the application build artifact. Here are the CodeBuild execution steps and their status:

d) Deploy: This step clones the Auto Scaling group, launches the new instances with the new AMI, deploys the application changes, reroutes the traffic from the elastic load balancer to the new instances and terminates the old Auto Scaling group. You can see the execution steps and their status in the CodeDeploy console.

After the CodePipeline execution is complete, you can access the application by clicking the Elastic Load Balancing link. You can find it in the output of Part 1 of the AWS CloudFormation template. Any consecutive commits to the application code in the CodeCommit repository trigger the pipelines and deploy the infrastructure and code with an updated AMI and code.

 

If you have feedback about this post, add it to the Comments section below. If you have questions about implementing the example used in this post, open a thread on the Developer Tools forum.


About the author

 

Ramesh Adabala is a Solutions Architect in Southeast Enterprise Solution Architecture team at Amazon Web Services.

Deploy a Data Warehouse Quickly with Amazon Redshift, Amazon RDS for PostgreSQL and Tableau Server

Post Syndicated from Jorge A. Lopez original https://aws.amazon.com/blogs/big-data/deploy-a-data-warehouse-quickly-with-amazon-redshift-amazon-rds-for-postgresql-and-tableau-server/

One of the benefits of a data warehouse environment using both Amazon Redshift and Amazon RDS for PostgreSQL is that you can leverage the advantages of each service. Amazon Redshift is a high performance, petabyte-scale data warehouse service optimized for the online analytical processing (OLAP) queries typical of analytic reporting and business intelligence applications. On the other hand, a service like RDS excels at transactional OLTP workloads such as inserting, deleting, or updating rows.

In the recent JOIN Amazon Redshift AND Amazon RDS PostgreSQL WITH dblink post, we showed how you can deploy such an environment. Now, you can deploy a similar architecture using the Modern Data Warehouse on AWS Quick Start. The Quick Start is an automated deployment that uses AWS CloudFormation templates to launch, configure, and run the services required to deploy a data warehousing environment on AWS, based on Amazon Redshift and RDS for PostgreSQL.

The Quick Start also includes an instance of Tableau Server, running on Amazon EC2. This gives you the ability to host and serve analytic dashboards, workbooks and visualizations, supported by a trial license. You can play with the sample data source and dashboard, or create your own analyses by uploading your own data sets.

For more information about the Modern Data Warehouse on AWS Quick Start, download the full deployment guide. If you’re ready to get started, use one of the buttons below:

Option 1: Deploy Quick Start into a new VPC on AWS

Option 2: Deploy Quick Start into an existing VPC

If you have questions, please leave a comment below.


Next Steps

You can also join us for the webinar Unlock Insights and Reduce Costs by Modernizing Your Data Warehouse on AWS on Tuesday, August 22, 2017. Pearson, the education and publishing company, will present best practices and lessons learned during their journey to Amazon Redshift and Tableau.

Growing up alongside tech

Post Syndicated from Eevee original https://eev.ee/blog/2017/08/09/growing-up-alongside-tech/

IndustrialRobot asks… or, uh, asked last month:

industrialrobot: How has your views on tech changed as you’ve got older?

This is so open-ended that it’s actually stumped me for a solid month. I’ve had a surprisingly hard time figuring out where to even start.


It’s not that my views of tech have changed too much — it’s that they’ve changed very gradually. Teasing out and explaining any one particular change is tricky when it happened invisibly over the course of 10+ years.

I think a better framework for this is to consider how my relationship to tech has changed. It’s gone through three pretty distinct phases, each of which has strongly colored how I feel and talk about technology.

Act I

In which I start from nothing.

Nothing is an interesting starting point. You only really get to start there once.

Learning something on my own as a kid was something of a magical experience, in a way that I don’t think I could replicate as an adult. I liked computers; I liked toying with computers; so I did that.

I don’t know how universal this is, but when I was a kid, I couldn’t even conceive of how incredible things were made. Buildings? Cars? Paintings? Operating systems? Where does any of that come from? Obviously someone made them, but it’s not the sort of philosophical point I lingered on when I was 10, so in the back of my head they basically just appeared fully-formed from the æther.

That meant that when I started trying out programming, I had no aspirations. I couldn’t imagine how far I would go, because all the examples of how far I would go were completely disconnected from any idea of human achievement. I started out with BASIC on a toy computer; how could I possibly envision a connection between that and something like a mainstream video game? Every new thing felt like a new form of magic, so I couldn’t conceive that I was even in the same ballpark as whatever process produced real software. (Even seeing the source code for GORILLAS.BAS, it didn’t quite click. I didn’t think to try reading any of it until years after I’d first encountered the game.)

This isn’t to say I didn’t have goals. I invented goals constantly, as I’ve always done; as soon as I learned about a new thing, I’d imagine some ways to use it, then try to build them. I produced a lot of little weird goofy toys, some of which entertained my tiny friend group for a couple days, some of which never saw the light of day. But none of it felt like steps along the way to some mountain peak of mastery, because I didn’t realize the mountain peak was even a place that could be gone to. It was pure, unadulterated (!) playing.

I contrast this to my art career, which started only a couple years ago. I was already in my late 20s, so I’d already spend decades seeing a very broad spectrum of art: everything from quick sketches up to painted masterpieces. And I’d seen the people who create that art, sometimes seen them create it in real-time. I’m even in a relationship with one of them! And of course I’d already had the experience of advancing through tech stuff and discovering first-hand that even the most amazing software is still just code someone wrote.

So from the very beginning, from the moment I touched pencil to paper, I knew the possibilities. I knew that the goddamn Sistine Chapel was something I could learn to do, if I were willing to put enough time in — and I knew that I’m not, so I’d have to settle somewhere a ways before that. I knew that I’d have to put an awful lot of work in before I’d be producing anything very impressive.

I did it anyway (though perhaps waited longer than necessary to start), but those aren’t things I can un-know, and so I can never truly explore art from a place of pure ignorance. On the other hand, I’ve probably learned to draw much more quickly and efficiently than if I’d done it as a kid, precisely because I know those things. Now I can decide I want to do something far beyond my current abilities, then go figure out how to do it. When I was just playing, that kind of ambition was impossible.


So, I played.

How did this affect my views on tech? Well, I didn’t… have any. Learning by playing tends to teach you things in an outward sprawl without many abrupt jumps to new areas, so you don’t tend to run up against conflicting information. The whole point of opinions is that they’re your own resolution to a conflict; without conflict, I can’t meaningfully say I had any opinions. I just accepted whatever I encountered at face value, because I didn’t even know enough to suspect there could be alternatives yet.

Act II

That started to seriously change around, I suppose, the end of high school and beginning of college. I was becoming aware of this whole “open source” concept. I took classes that used languages I wouldn’t otherwise have given a second thought. (One of them was Python!) I started to contribute to other people’s projects. Eventually I even got a job, where I had to work with other people. It probably also helped that I’d had to maintain my own old code a few times.

Now I was faced with conflicting subjective ideas, and I had to form opinions about them! And so I did. With gusto. Over time, I developed an idea of what was Right based on experience I’d accrued. And then I set out to always do things Right.

That’s served me decently well with some individual problems, but it also led me to inflict a lot of unnecessary pain on myself. Several endeavors languished for no other reason than my dissatisfaction with the architecture, long before the basic functionality was done. I started a number of “pure” projects around this time, generic tools like imaging libraries that I had no direct need for. I built them for the sake of them, I guess because I felt like I was improving some niche… but of course I never finished any. It was always in areas I didn’t know that well in the first place, which is a fine way to learn if you have a specific concrete goal in mind — but it turns out that building a generic library for editing images means you have to know everything about images. Perhaps that ambition went a little haywire.

I’ve said before that this sort of (self-inflicted!) work was unfulfilling, in part because the best outcome would be that a few distant programmers’ lives are slightly easier. I do still think that, but I think there’s a deeper point here too.

In forgetting how to play, I’d stopped putting any of myself in most of the work I was doing. Yes, building an imaging library is kind of a slog that someone has to do, but… I assume the people who work on software like PIL and ImageMagick are actually interested in it. The few domains I tried to enter and revolutionize weren’t passions of mine; I just happened to walk through the neighborhood one day and decided I could obviously do it better.

Not coincidentally, this was the same era of my life that led me to write stuff like that PHP post, which you may notice I am conspicuously not even linking to. I don’t think I would write anything like it nowadays. I could see myself approaching the same subject, but purely from the point of view of language design, with more contrasts and tradeoffs and less going for volume. I certainly wouldn’t lead off with inflammatory puffery like “PHP is a community of amateurs”.

Act III

I think I’ve mellowed out a good bit in the last few years.

It turns out that being Right is much less important than being Not Wrong — i.e., rather than trying to make something perfect that can be adapted to any future case, just avoid as many pitfalls as possible. Code that does something useful has much more practical value than unfinished code with some pristine architecture.

Nowhere is this more apparent than in game development, where all code is doomed to be crap and the best you can hope for is to stem the tide. But there’s also a fixed goal that’s completely unrelated to how the code looks: does the game work, and is it fun to play? Yes? Ship the damn thing and forget about it.

Games are also nice because it’s very easy to pour my own feelings into them and evoke feelings in the people who play them. They’re mine, something with my fingerprints on them — even the games I’ve built with glip have plenty of my own hallmarks, little touches I added on a whim or attention to specific details that I care about.

Maybe a better example is the Doom map parser I started writing. It sounds like a “pure” problem again, except that I actually know an awful lot about the subject already! I also cleverly (accidentally) released some useful results of the work I’ve done thusfar — like statistics about Doom II maps and a few screenshots of flipped stock maps — even though I don’t think the parser itself is far enough along to release yet. The tool has served a purpose, one with my fingerprints on it, even without being released publicly. That keeps it fresh in my mind as something interesting I’d like to keep working on, eventually. (When I run into an architecture question, I step back for a while, or I do other work in the hopes that the solution will reveal itself.)

I also made two simple Pokémon ROM hacks this year, despite knowing nothing about Game Boy internals or assembly when I started. I just decided I wanted to do an open-ended thing beyond my reach, and I went to do it, not worrying about cleanliness and willing to accept a bumpy ride to get there. I played, but in a more experienced way, invoking the stuff I know (and the people I’ve met!) to help me get a running start in completely unfamiliar territory.


This feels like a really fine distinction that I’m not sure I’m doing justice. I don’t know if I could’ve appreciated it three or four years ago. But I missed making toys, and I’m glad I’m doing it again.

In short, I forgot how to have fun with programming for a little while, and I’ve finally started to figure it out again. And that’s far more important than whether you use PHP or not.