Tag Archives: BEC

Storing Encrypted Credentials In Git

Post Syndicated from Bozho original https://techblog.bozho.net/storing-encrypted-credentials-in-git/

We all know that we should not commit any passwords or keys to the repo with our code (no matter if public or private). Yet, thousands of production passwords can be found on GitHub (and probably thousands more in internal company repositories). Some have tried to fix that by removing the passwords (once they learned it’s not a good idea to store them publicly), but passwords have remained in the git history.

Knowing what not to do is the first and very important step. But how do we store production credentials. Database credentials, system secrets (e.g. for HMACs), access keys for 3rd party services like payment providers or social networks. There doesn’t seem to be an agreed upon solution.

I’ve previously argued with the 12-factor app recommendation to use environment variables – if you have a few that might be okay, but when the number of variables grow (as in any real application), it becomes impractical. And you can set environment variables via a bash script, but you’d have to store it somewhere. And in fact, even separate environment variables should be stored somewhere.

This somewhere could be a local directory (risky), a shared storage, e.g. FTP or S3 bucket with limited access, or a separate git repository. I think I prefer the git repository as it allows versioning (Note: S3 also does, but is provider-specific). So you can store all your environment-specific properties files with all their credentials and environment-specific configurations in a git repo with limited access (only Ops people). And that’s not bad, as long as it’s not the same repo as the source code.

Such a repo would look like this:

project
└─── production
|   |   application.properites
|   |   keystore.jks
└─── staging
|   |   application.properites
|   |   keystore.jks
└─── on-premise-client1
|   |   application.properites
|   |   keystore.jks
└─── on-premise-client2
|   |   application.properites
|   |   keystore.jks

Since many companies are using GitHub or BitBucket for their repositories, storing production credentials on a public provider may still be risky. That’s why it’s a good idea to encrypt the files in the repository. A good way to do it is via git-crypt. It is “transparent” encryption because it supports diff and encryption and decryption on the fly. Once you set it up, you continue working with the repo as if it’s not encrypted. There’s even a fork that works on Windows.

You simply run git-crypt init (after you’ve put the git-crypt binary on your OS Path), which generates a key. Then you specify your .gitattributes, e.g. like that:

secretfile filter=git-crypt diff=git-crypt
*.key filter=git-crypt diff=git-crypt
*.properties filter=git-crypt diff=git-crypt
*.jks filter=git-crypt diff=git-crypt

And you’re done. Well, almost. If this is a fresh repo, everything is good. If it is an existing repo, you’d have to clean up your history which contains the unencrypted files. Following these steps will get you there, with one addition – before calling git commit, you should call git-crypt status -f so that the existing files are actually encrypted.

You’re almost done. We should somehow share and backup the keys. For the sharing part, it’s not a big issue to have a team of 2-3 Ops people share the same key, but you could also use the GPG option of git-crypt (as documented in the README). What’s left is to backup your secret key (that’s generated in the .git/git-crypt directory). You can store it (password-protected) in some other storage, be it a company shared folder, Dropbox/Google Drive, or even your email. Just make sure your computer is not the only place where it’s present and that it’s protected. I don’t think key rotation is necessary, but you can devise some rotation procedure.

git-crypt authors claim to shine when it comes to encrypting just a few files in an otherwise public repo. And recommend looking at git-remote-gcrypt. But as often there are non-sensitive parts of environment-specific configurations, you may not want to encrypt everything. And I think it’s perfectly fine to use git-crypt even in a separate repo scenario. And even though encryption is an okay approach to protect credentials in your source code repo, it’s still not necessarily a good idea to have the environment configurations in the same repo. Especially given that different people/teams manage these credentials. Even in small companies, maybe not all members have production access.

The outstanding questions in this case is – how do you sync the properties with code changes. Sometimes the code adds new properties that should be reflected in the environment configurations. There are two scenarios here – first, properties that could vary across environments, but can have default values (e.g. scheduled job periods), and second, properties that require explicit configuration (e.g. database credentials). The former can have the default values bundled in the code repo and therefore in the release artifact, allowing external files to override them. The latter should be announced to the people who do the deployment so that they can set the proper values.

The whole process of having versioned environment-speific configurations is actually quite simple and logical, even with the encryption added to the picture. And I think it’s a good security practice we should try to follow.

The post Storing Encrypted Credentials In Git appeared first on Bozho's tech blog.

Some quick thoughts on the public discussion regarding facial recognition and Amazon Rekognition this past week

Post Syndicated from Dr. Matt Wood original https://aws.amazon.com/blogs/aws/some-quick-thoughts-on-the-public-discussion-regarding-facial-recognition-and-amazon-rekognition-this-past-week/

We have seen a lot of discussion this past week about the role of Amazon Rekognition in facial recognition, surveillance, and civil liberties, and we wanted to share some thoughts.

Amazon Rekognition is a service we announced in 2016. It makes use of new technologies – such as deep learning – and puts them in the hands of developers in an easy-to-use, low-cost way. Since then, we have seen customers use the image and video analysis capabilities of Amazon Rekognition in ways that materially benefit both society (e.g. preventing human trafficking, inhibiting child exploitation, reuniting missing children with their families, and building educational apps for children), and organizations (enhancing security through multi-factor authentication, finding images more easily, or preventing package theft). Amazon Web Services (AWS) is not the only provider of services like these, and we remain excited about how image and video analysis can be a driver for good in the world, including in the public sector and law enforcement.

There have always been and will always be risks with new technology capabilities. Each organization choosing to employ technology must act responsibly or risk legal penalties and public condemnation. AWS takes its responsibilities seriously. But we believe it is the wrong approach to impose a ban on promising new technologies because they might be used by bad actors for nefarious purposes in the future. The world would be a very different place if we had restricted people from buying computers because it was possible to use that computer to do harm. The same can be said of thousands of technologies upon which we all rely each day. Through responsible use, the benefits have far outweighed the risks.

Customers are off to a great start with Amazon Rekognition; the evidence of the positive impact this new technology can provide is strong (and growing by the week), and we’re excited to continue to support our customers in its responsible use.

-Dr. Matt Wood, general manager of artificial intelligence at AWS

1834: The First Cyberattack

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/1834_the_first_.html

Tom Standage has a great story of the first cyberattack against a telegraph network.

The Blanc brothers traded government bonds at the exchange in the city of Bordeaux, where information about market movements took several days to arrive from Paris by mail coach. Accordingly, traders who could get the information more quickly could make money by anticipating these movements. Some tried using messengers and carrier pigeons, but the Blanc brothers found a way to use the telegraph line instead. They bribed the telegraph operator in the city of Tours to introduce deliberate errors into routine government messages being sent over the network.

The telegraph’s encoding system included a “backspace” symbol that instructed the transcriber to ignore the previous character. The addition of a spurious character indicating the direction of the previous day’s market movement, followed by a backspace, meant the text of the message being sent was unaffected when it was written out for delivery at the end of the line. But this extra character could be seen by another accomplice: a former telegraph operator who observed the telegraph tower outside Bordeaux with a telescope, and then passed on the news to the Blancs. The scam was only uncovered in 1836, when the crooked operator in Tours fell ill and revealed all to a friend, who he hoped would take his place. The Blanc brothers were put on trial, though they could not be convicted because there was no law against misuse of data networks. But the Blancs’ pioneering misuse of the French network qualifies as the world’s first cyber-attack.

New – Pay-per-Session Pricing for Amazon QuickSight, Another Region, and Lots More

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-pay-per-session-pricing-for-amazon-quicksight-another-region-and-lots-more/

Amazon QuickSight is a fully managed cloud business intelligence system that gives you Fast & Easy to Use Business Analytics for Big Data. QuickSight makes business analytics available to organizations of all shapes and sizes, with the ability to access data that is stored in your Amazon Redshift data warehouse, your Amazon Relational Database Service (RDS) relational databases, flat files in S3, and (via connectors) data stored in on-premises MySQL, PostgreSQL, and SQL Server databases. QuickSight scales to accommodate tens, hundreds, or thousands of users per organization.

Today we are launching a new, session-based pricing option for QuickSight, along with additional region support and other important new features. Let’s take a look at each one:

Pay-per-Session Pricing
Our customers are making great use of QuickSight and take full advantage of the power it gives them to connect to data sources, create reports, and and explore visualizations.

However, not everyone in an organization needs or wants such powerful authoring capabilities. Having access to curated data in dashboards and being able to interact with the data by drilling down, filtering, or slicing-and-dicing is more than adequate for their needs. Subscribing them to a monthly or annual plan can be seen as an unwarranted expense, so a lot of such casual users end up not having access to interactive data or BI.

In order to allow customers to provide all of their users with interactive dashboards and reports, the Enterprise Edition of Amazon QuickSight now allows Reader access to dashboards on a Pay-per-Session basis. QuickSight users are now classified as Admins, Authors, or Readers, with distinct capabilities and prices:

Authors have access to the full power of QuickSight; they can establish database connections, upload new data, create ad hoc visualizations, and publish dashboards, all for $9 per month (Standard Edition) or $18 per month (Enterprise Edition).

Readers can view dashboards, slice and dice data using drill downs, filters and on-screen controls, and download data in CSV format, all within the secure QuickSight environment. Readers pay $0.30 for 30 minutes of access, with a monthly maximum of $5 per reader.

Admins have all authoring capabilities, and can manage users and purchase SPICE capacity in the account. The QuickSight admin now has the ability to set the desired option (Author or Reader) when they invite members of their organization to use QuickSight. They can extend Reader invites to their entire user base without incurring any up-front or monthly costs, paying only for the actual usage.

To learn more, visit the QuickSight Pricing page.

A New Region
QuickSight is now available in the Asia Pacific (Tokyo) Region:

The UI is in English, with a localized version in the works.

Hourly Data Refresh
Enterprise Edition SPICE data sets can now be set to refresh as frequently as every hour. In the past, each data set could be refreshed up to 5 times a day. To learn more, read Refreshing Imported Data.

Access to Data in Private VPCs
This feature was launched in preview form late last year, and is now available in production form to users of the Enterprise Edition. As I noted at the time, you can use it to implement secure, private communication with data sources that do not have public connectivity, including on-premises data in Teradata or SQL Server, accessed over an AWS Direct Connect link. To learn more, read Working with AWS VPC.

Parameters with On-Screen Controls
QuickSight dashboards can now include parameters that are set using on-screen dropdown, text box, numeric slider or date picker controls. The default value for each parameter can be set based on the user name (QuickSight calls this a dynamic default). You could, for example, set an appropriate default based on each user’s office location, department, or sales territory. Here’s an example:

To learn more, read about Parameters in QuickSight.

URL Actions for Linked Dashboards
You can now connect your QuickSight dashboards to external applications by defining URL actions on visuals. The actions can include parameters, and become available in the Details menu for the visual. URL actions are defined like this:

You can use this feature to link QuickSight dashboards to third party applications (e.g. Salesforce) or to your own internal applications. Read Custom URL Actions to learn how to use this feature.

Dashboard Sharing
You can now share QuickSight dashboards across every user in an account.

Larger SPICE Tables
The per-data set limit for SPICE tables has been raised from 10 GB to 25 GB.

Upgrade to Enterprise Edition
The QuickSight administrator can now upgrade an account from Standard Edition to Enterprise Edition with a click. This enables provisioning of Readers with pay-per-session pricing, private VPC access, row-level security for dashboards and data sets, and hourly refresh of data sets. Enterprise Edition pricing applies after the upgrade.

Available Now
Everything I listed above is available now and you can start using it today!

You can try QuickSight for 60 days at no charge, and you can also attend our June 20th Webinar.

Jeff;

 

Monitoring your Amazon SNS message filtering activity with Amazon CloudWatch

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/monitoring-your-amazon-sns-message-filtering-activity-with-amazon-cloudwatch/

This post is courtesy of Otavio Ferreira, Manager, Amazon SNS, AWS Messaging.

Amazon SNS message filtering provides a set of string and numeric matching operators that allow each subscription to receive only the messages of interest. Hence, SNS message filtering can simplify your pub/sub messaging architecture by offloading the message filtering logic from your subscriber systems, as well as the message routing logic from your publisher systems.

After you set the subscription attribute that defines a filter policy, the subscribing endpoint receives only the messages that carry attributes matching this filter policy. Other messages published to the topic are filtered out for this subscription. In this way, the native integration between SNS and Amazon CloudWatch provides visibility into the number of messages delivered, as well as the number of messages filtered out.

CloudWatch metrics are captured automatically for you. To get started with SNS message filtering, see Filtering Messages with Amazon SNS.

Message Filtering Metrics

The following six CloudWatch metrics are relevant to understanding your SNS message filtering activity:

  • NumberOfMessagesPublished – Inbound traffic to SNS. This metric tracks all the messages that have been published to the topic.
  • NumberOfNotificationsDelivered – Outbound traffic from SNS. This metric tracks all the messages that have been successfully delivered to endpoints subscribed to the topic. A delivery takes place either when the incoming message attributes match a subscription filter policy, or when the subscription has no filter policy at all, which results in a catch-all behavior.
  • NumberOfNotificationsFilteredOut – This metric tracks all the messages that were filtered out because they carried attributes that didn’t match the subscription filter policy.
  • NumberOfNotificationsFilteredOut-NoMessageAttributes – This metric tracks all the messages that were filtered out because they didn’t carry any attributes at all and, consequently, didn’t match the subscription filter policy.
  • NumberOfNotificationsFilteredOut-InvalidAttributes – This metric keeps track of messages that were filtered out because they carried invalid or malformed attributes and, thus, didn’t match the subscription filter policy.
  • NumberOfNotificationsFailed – This last metric tracks all the messages that failed to be delivered to subscribing endpoints, regardless of whether a filter policy had been set for the endpoint. This metric is emitted after the message delivery retry policy is exhausted, and SNS stops attempting to deliver the message. At that moment, the subscribing endpoint is likely no longer reachable. For example, the subscribing SQS queue or Lambda function has been deleted by its owner. You may want to closely monitor this metric to address message delivery issues quickly.

Message filtering graphs

Through the AWS Management Console, you can compose graphs to display your SNS message filtering activity. The graph shows the number of messages published, delivered, and filtered out within the timeframe you specify (1h, 3h, 12h, 1d, 3d, 1w, or custom).

SNS message filtering for CloudWatch Metrics

To compose an SNS message filtering graph with CloudWatch:

  1. Open the CloudWatch console.
  2. Choose Metrics, SNS, All Metrics, and Topic Metrics.
  3. Select all metrics to add to the graph, such as:
    • NumberOfMessagesPublished
    • NumberOfNotificationsDelivered
    • NumberOfNotificationsFilteredOut
  4. Choose Graphed metrics.
  5. In the Statistic column, switch from Average to Sum.
  6. Title your graph with a descriptive name, such as “SNS Message Filtering”

After you have your graph set up, you may want to copy the graph link for bookmarking, emailing, or sharing with co-workers. You may also want to add your graph to a CloudWatch dashboard for easy access in the future. Both actions are available to you on the Actions menu, which is found above the graph.

Summary

SNS message filtering defines how SNS topics behave in terms of message delivery. By using CloudWatch metrics, you gain visibility into the number of messages published, delivered, and filtered out. This enables you to validate the operation of filter policies and more easily troubleshoot during development phases.

SNS message filtering can be implemented easily with existing AWS SDKs by applying message and subscription attributes across all SNS supported protocols (Amazon SQS, AWS Lambda, HTTP, SMS, email, and mobile push). CloudWatch metrics for SNS message filtering is available now, in all AWS Regions.

For information about pricing, see the CloudWatch pricing page.

For more information, see:

Getting Rid of Your Mac? Here’s How to Securely Erase a Hard Drive or SSD

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/how-to-wipe-a-mac-hard-drive/

erasing a hard drive and a solid state drive

What do I do with a Mac that still has personal data on it? Do I take out the disk drive and smash it? Do I sweep it with a really strong magnet? Is there a difference in how I handle a hard drive (HDD) versus a solid-state drive (SSD)? Well, taking a sledgehammer or projectile weapon to your old machine is certainly one way to make the data irretrievable, and it can be enormously cathartic as long as you follow appropriate safety and disposal protocols. But there are far less destructive ways to make sure your data is gone for good. Let me introduce you to secure erasing.

Which Type of Drive Do You Have?

Before we start, you need to know whether you have a HDD or a SSD. To find out, or at least to make sure, you click on the Apple menu and select “About this Mac.” Once there, select the “Storage” tab to see which type of drive is in your system.

The first example, below, shows a SATA Disk (HDD) in the system.

SATA HDD

In the next case, we see we have a Solid State SATA Drive (SSD), plus a Mac SuperDrive.

Mac storage dialog showing SSD

The third screen shot shows an SSD, as well. In this case it’s called “Flash Storage.”

Flash Storage

Make Sure You Have a Backup

Before you get started, you’ll want to make sure that any important data on your hard drive has moved somewhere else. OS X’s built-in Time Machine backup software is a good start, especially when paired with Backblaze. You can learn more about using Time Machine in our Mac Backup Guide.

With a local backup copy in hand and secure cloud storage, you know your data is always safe no matter what happens.

Once you’ve verified your data is backed up, roll up your sleeves and get to work. The key is OS X Recovery — a special part of the Mac operating system since OS X 10.7 “Lion.”

How to Wipe a Mac Hard Disk Drive (HDD)

NOTE: If you’re interested in wiping an SSD, see below.

    1. Make sure your Mac is turned off.
    2. Press the power button.
    3. Immediately hold down the command and R keys.
    4. Wait until the Apple logo appears.
    5. Select “Disk Utility” from the OS X Utilities list. Click Continue.
    6. Select the disk you’d like to erase by clicking on it in the sidebar.
    7. Click the Erase button.
    8. Click the Security Options button.
    9. The Security Options window includes a slider that enables you to determine how thoroughly you want to erase your hard drive.

There are four notches to that Security Options slider. “Fastest” is quick but insecure — data could potentially be rebuilt using a file recovery app. Moving that slider to the right introduces progressively more secure erasing. Disk Utility’s most secure level erases the information used to access the files on your disk, then writes zeroes across the disk surface seven times to help remove any trace of what was there. This setting conforms to the DoD 5220.22-M specification.

  1. Once you’ve selected the level of secure erasing you’re comfortable with, click the OK button.
  2. Click the Erase button to begin. Bear in mind that the more secure method you select, the longer it will take. The most secure methods can add hours to the process.

Once it’s done, the Mac’s hard drive will be clean as a whistle and ready for its next adventure: a fresh installation of OS X, being donated to a relative or a local charity, or just sent to an e-waste facility. Of course you can still drill a hole in your disk or smash it with a sledgehammer if it makes you happy, but now you know how to wipe the data from your old computer with much less ruckus.

The above instructions apply to older Macintoshes with HDDs. What do you do if you have an SSD?

Securely Erasing SSDs, and Why Not To

Most new Macs ship with solid state drives (SSDs). Only the iMac and Mac mini ship with regular hard drives anymore, and even those are available in pure SSD variants if you want.

If your Mac comes equipped with an SSD, Apple’s Disk Utility software won’t actually let you zero the hard drive.

Wait, what?

In a tech note posted to Apple’s own online knowledgebase, Apple explains that you don’t need to securely erase your Mac’s SSD:

With an SSD drive, Secure Erase and Erasing Free Space are not available in Disk Utility. These options are not needed for an SSD drive because a standard erase makes it difficult to recover data from an SSD.

In fact, some folks will tell you not to zero out the data on an SSD, since it can cause wear and tear on the memory cells that, over time, can affect its reliability. I don’t think that’s nearly as big an issue as it used to be — SSD reliability and longevity has improved.

If “Standard Erase” doesn’t quite make you feel comfortable that your data can’t be recovered, there are a couple of options.

FileVault Keeps Your Data Safe

One way to make sure that your SSD’s data remains secure is to use FileVault. FileVault is whole-disk encryption for the Mac. With FileVault engaged, you need a password to access the information on your hard drive. Without it, that data is encrypted.

There’s one potential downside of FileVault — if you lose your password or the encryption key, you’re screwed: You’re not getting your data back any time soon. Based on my experience working at a Mac repair shop, losing a FileVault key happens more frequently than it should.

When you first set up a new Mac, you’re given the option of turning FileVault on. If you don’t do it then, you can turn on FileVault at any time by clicking on your Mac’s System Preferences, clicking on Security & Privacy, and clicking on the FileVault tab. Be warned, however, that the initial encryption process can take hours, as will decryption if you ever need to turn FileVault off.

With FileVault turned on, you can restart your Mac into its Recovery System (by restarting the Mac while holding down the command and R keys) and erase the hard drive using Disk Utility, once you’ve unlocked it (by selecting the disk, clicking the File menu, and clicking Unlock). That deletes the FileVault key, which means any data on the drive is useless.

FileVault doesn’t impact the performance of most modern Macs, though I’d suggest only using it if your Mac has an SSD, not a conventional hard disk drive.

Securely Erasing Free Space on Your SSD

If you don’t want to take Apple’s word for it, if you’re not using FileVault, or if you just want to, there is a way to securely erase free space on your SSD. It’s a little more involved but it works.

Before we get into the nitty-gritty, let me state for the record that this really isn’t necessary to do, which is why Apple’s made it so hard to do. But if you’re set on it, you’ll need to use Apple’s Terminal app. Terminal provides you with command line interface access to the OS X operating system. Terminal lives in the Utilities folder, but you can access Terminal from the Mac’s Recovery System, as well. Once your Mac has booted into the Recovery partition, click the Utilities menu and select Terminal to launch it.

From a Terminal command line, type:

diskutil secureErase freespace VALUE /Volumes/DRIVE

That tells your Mac to securely erase the free space on your SSD. You’ll need to change VALUE to a number between 0 and 4. 0 is a single-pass run of zeroes; 1 is a single-pass run of random numbers; 2 is a 7-pass erase; 3 is a 35-pass erase; and 4 is a 3-pass erase. DRIVE should be changed to the name of your hard drive. To run a 7-pass erase of your SSD drive in “JohnB-Macbook”, you would enter the following:

diskutil secureErase freespace 2 /Volumes/JohnB-Macbook

And remember, if you used a space in the name of your Mac’s hard drive, you need to insert a leading backslash before the space. For example, to run a 35-pass erase on a hard drive called “Macintosh HD” you enter the following:

diskutil secureErase freespace 3 /Volumes/Macintosh\ HD

Something to remember is that the more extensive the erase procedure, the longer it will take.

When Erasing is Not Enough — How to Destroy a Drive

If you absolutely, positively need to be sure that all the data on a drive is irretrievable, see this Scientific American article (with contributions by Gleb Budman, Backblaze CEO), How to Destroy a Hard Drive — Permanently.

The post Getting Rid of Your Mac? Here’s How to Securely Erase a Hard Drive or SSD appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Measuring the throughput for Amazon MQ using the JMS Benchmark

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/measuring-the-throughput-for-amazon-mq-using-the-jms-benchmark/

This post is courtesy of Alan Protasio, Software Development Engineer, Amazon Web Services

Just like compute and storage, messaging is a fundamental building block of enterprise applications. Message brokers (aka “message-oriented middleware”) enable different software systems, often written in different languages, on different platforms, running in different locations, to communicate and exchange information. Mission-critical applications, such as CRM and ERP, rely on message brokers to work.

A common performance consideration for customers deploying a message broker in a production environment is the throughput of the system, measured as messages per second. This is important to know so that application environments (hosts, threads, memory, etc.) can be configured correctly.

In this post, we demonstrate how to measure the throughput for Amazon MQ, a new managed message broker service for ActiveMQ, using JMS Benchmark. It should take between 15–20 minutes to set up the environment and an hour to run the benchmark. We also provide some tips on how to configure Amazon MQ for optimal throughput.

Benchmarking throughput for Amazon MQ

ActiveMQ can be used for a number of use cases. These use cases can range from simple fire and forget tasks (that is, asynchronous processing), low-latency request-reply patterns, to buffering requests before they are persisted to a database.

The throughput of Amazon MQ is largely dependent on the use case. For example, if you have non-critical workloads such as gathering click events for a non-business-critical portal, you can use ActiveMQ in a non-persistent mode and get extremely high throughput with Amazon MQ.

On the flip side, if you have a critical workload where durability is extremely important (meaning that you can’t lose a message), then you are bound by the I/O capacity of your underlying persistence store. We recommend using mq.m4.large for the best results. The mq.t2.micro instance type is intended for product evaluation. Performance is limited, due to the lower memory and burstable CPU performance.

Tip: To improve your throughput with Amazon MQ, make sure that you have consumers processing messaging as fast as (or faster than) your producers are pushing messages.

Because it’s impossible to talk about how the broker (ActiveMQ) behaves for each and every use case, we walk through how to set up your own benchmark for Amazon MQ using our favorite open-source benchmarking tool: JMS Benchmark. We are fans of the JMS Benchmark suite because it’s easy to set up and deploy, and comes with a built-in visualizer of the results.

Non-Persistent Scenarios – Queue latency as you scale producer throughput

JMS Benchmark nonpersistent scenarios

Getting started

At the time of publication, you can create an mq.m4.large single-instance broker for testing for $0.30 per hour (US pricing).

This walkthrough covers the following tasks:

  1.  Create and configure the broker.
  2. Create an EC2 instance to run your benchmark
  3. Configure the security groups
  4.  Run the benchmark.

Step 1 – Create and configure the broker
Create and configure the broker using Tutorial: Creating and Configuring an Amazon MQ Broker.

Step 2 – Create an EC2 instance to run your benchmark
Launch the EC2 instance using Step 1: Launch an Instance. We recommend choosing the m5.large instance type.

Step 3 – Configure the security groups
Make sure that all the security groups are correctly configured to let the traffic flow between the EC2 instance and your broker.

  1. Sign in to the Amazon MQ console.
  2. From the broker list, choose the name of your broker (for example, MyBroker)
  3. In the Details section, under Security and network, choose the name of your security group or choose the expand icon ( ).
  4. From the security group list, choose your security group.
  5. At the bottom of the page, choose Inbound, Edit.
  6. In the Edit inbound rules dialog box, add a role to allow traffic between your instance and the broker:
    • Choose Add Rule.
    • For Type, choose Custom TCP.
    • For Port Range, type the ActiveMQ SSL port (61617).
    • For Source, leave Custom selected and then type the security group of your EC2 instance.
    • Choose Save.

Your broker can now accept the connection from your EC2 instance.

Step 4 – Run the benchmark
Connect to your EC2 instance using SSH and run the following commands:

$ cd ~
$ curl -L https://github.com/alanprot/jms-benchmark/archive/master.zip -o master.zip
$ unzip master.zip
$ cd jms-benchmark-master
$ chmod a+x bin/*
$ env \
  SERVER_SETUP=false \
  SERVER_ADDRESS={activemq-endpoint} \
  ACTIVEMQ_TRANSPORT=ssl\
  ACTIVEMQ_PORT=61617 \
  ACTIVEMQ_USERNAME={activemq-user} \
  ACTIVEMQ_PASSWORD={activemq-password} \
  ./bin/benchmark-activemq

After the benchmark finishes, you can find the results in the ~/reports directory. As you may notice, the performance of ActiveMQ varies based on the number of consumers, producers, destinations, and message size.

Amazon MQ architecture

The last bit that’s important to know so that you can better understand the results of the benchmark is how Amazon MQ is architected.

Amazon MQ is architected to be highly available (HA) and durable. For HA, we recommend using the multi-AZ option. After a message is sent to Amazon MQ in persistent mode, the message is written to the highly durable message store that replicates the data across multiple nodes in multiple Availability Zones. Because of this replication, for some use cases you may see a reduction in throughput as you migrate to Amazon MQ. Customers have told us they appreciate the benefits of message replication as it helps protect durability even in the face of the loss of an Availability Zone.

Conclusion

We hope this gives you an idea of how Amazon MQ performs. We encourage you to run tests to simulate your own use cases.

To learn more, see the Amazon MQ website. You can try Amazon MQ for free with the AWS Free Tier, which includes up to 750 hours of a single-instance mq.t2.micro broker and up to 1 GB of storage per month for one year.

Project Floofball and more: Pi pet stuff

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/project-floofball-pi-pet-stuff/

It’s a public holiday here today (yes, again). So, while we indulge in the traditional pastime of barbecuing stuff (ourselves, mainly), here’s a little trove of Pi projects that cater for our various furry friends.

Project Floofball

Nicole Horward created Project Floofball for her hamster, Harold. It’s an IoT hamster wheel that uses a Raspberry Pi and a magnetic door sensor to log how far Harold runs.

Project Floofball: an IoT hamster wheel

An IoT Hamsterwheel using a Raspberry Pi and a magnetic door sensor, to see how far my hamster runs.

You can follow Harold’s runs in real time on his ThingSpeak channel, and you’ll find photos of the build on imgur. Nicole’s Python code, as well as her template for the laser-cut enclosure that houses the wiring and LCD display, are available on the hamster wheel’s GitHub repo.

A live-streaming pet feeder

JaganK3 used to work long hours that meant he couldn’t be there to feed his dog on time. He found that he couldn’t buy an automated feeder in India without paying a lot to import one, so he made one himself. It uses a Raspberry Pi to control a motor that turns a dispensing valve in a hopper full of dry food, giving his dog a portion of food at set times.

A transparent cylindrical hopper of dry dog food, with a motor that can turn a dispensing valve at the lower end. The motor is connected to a Raspberry Pi in a plastic case. Hopper, motor, Pi, and wiring are all mounted on a board on the wall.

He also added a web cam for live video streaming, because he could. Find out more in JaganK3’s Instructable for his pet feeder.

Shark laser cat toy

Sam Storino, meanwhile, is using a Raspberry Pi to control a laser-pointer cat toy with a goshdarned SHARK (which is kind of what I’d expect from the guy who made the steampunk-looking cat feeder a few weeks ago). The idea is to keep his cats interested and active within the confines of a compact city apartment.

Raspberry Pi Automatic Cat Laser Pointer Toy

Post with 52 votes and 7004 views. Tagged with cat, shark, lasers, austin powers, raspberry pi; Shared by JeorgeLeatherly. Raspberry Pi Automatic Cat Laser Pointer Toy

If I were a cat, I would definitely be entirely happy with this. Find out more on Sam’s website.

And there’s more

Michel Parreno has written a series of articles to help you monitor and feed your pet with Raspberry Pi.

All of these makers are generous in acknowledging the tutorials and build logs that helped them with their projects. It’s lovely to see the Raspberry Pi and maker community working like this, and I bet their projects will inspire others too.

Now, if you’ll excuse me. I’m late for a barbecue.

The post Project Floofball and more: Pi pet stuff appeared first on Raspberry Pi.

Use Slack ChatOps to Deploy Your Code – How to Integrate Your Pipeline in AWS CodePipeline with Your Slack Channel

Post Syndicated from Rumi Olsen original https://aws.amazon.com/blogs/devops/use-slack-chatops-to-deploy-your-code-how-to-integrate-your-pipeline-in-aws-codepipeline-with-your-slack-channel/

Slack is widely used by DevOps and development teams to communicate status. Typically, when a build has been tested and is ready to be promoted to a staging environment, a QA engineer or DevOps engineer kicks off the deployment. Using Slack in a ChatOps collaboration model, the promotion can be done in a single click from a Slack channel. And because the promotion happens through a Slack channel, the whole development team knows what’s happening without checking email.

In this blog post, I will show you how to integrate AWS services with a Slack application. I use an interactive message button and incoming webhook to promote a stage with a single click.

To follow along with the steps in this post, you’ll need a pipeline in AWS CodePipeline. If you don’t have a pipeline, the fastest way to create one for this use case is to use AWS CodeStar. Go to the AWS CodeStar console and select the Static Website template (shown in the screenshot). AWS CodeStar will create a pipeline with an AWS CodeCommit repository and an AWS CodeDeploy deployment for you. After the pipeline is created, you will need to add a manual approval stage.

You’ll also need to build a Slack app with webhooks and interactive components, write two Lambda functions, and create an API Gateway API and a SNS topic.

As you’ll see in the following diagram, when I make a change and merge a new feature into the master branch in AWS CodeCommit, the check-in kicks off my CI/CD pipeline in AWS CodePipeline. When CodePipeline reaches the approval stage, it sends a notification to Amazon SNS, which triggers an AWS Lambda function (ApprovalRequester).

The Slack channel receives a prompt that looks like the following screenshot. When I click Yes to approve the build promotion, the approval result is sent to CodePipeline through API Gateway and Lambda (ApprovalHandler). The pipeline continues on to deploy the build to the next environment.

Create a Slack app

For App Name, type a name for your app. For Development Slack Workspace, choose the name of your workspace. You’ll see in the following screenshot that my workspace is AWS ChatOps.

After the Slack application has been created, you will see the Basic Information page, where you can create incoming webhooks and enable interactive components.

To add incoming webhooks:

  1. Under Add features and functionality, choose Incoming Webhooks. Turn the feature on by selecting Off, as shown in the following screenshot.
  2. Now that the feature is turned on, choose Add New Webhook to Workspace. In the process of creating the webhook, Slack lets you choose the channel where messages will be posted.
  3. After the webhook has been created, you’ll see its URL. You will use this URL when you create the Lambda function.

If you followed the steps in the post, the pipeline should look like the following.

Write the Lambda function for approval requests

This Lambda function is invoked by the SNS notification. It sends a request that consists of an interactive message button to the incoming webhook you created earlier.  The following sample code sends the request to the incoming webhook. WEBHOOK_URL and SLACK_CHANNEL are the environment variables that hold values of the webhook URL that you created and the Slack channel where you want the interactive message button to appear.

# This function is invoked via SNS when the CodePipeline manual approval action starts.
# It will take the details from this approval notification and sent an interactive message to Slack that allows users to approve or cancel the deployment.

import os
import json
import logging
import urllib.parse

from base64 import b64decode
from urllib.request import Request, urlopen
from urllib.error import URLError, HTTPError

# This is passed as a plain-text environment variable for ease of demonstration.
# Consider encrypting the value with KMS or use an encrypted parameter in Parameter Store for production deployments.
SLACK_WEBHOOK_URL = os.environ['SLACK_WEBHOOK_URL']
SLACK_CHANNEL = os.environ['SLACK_CHANNEL']

logger = logging.getLogger()
logger.setLevel(logging.INFO)

def lambda_handler(event, context):
    print("Received event: " + json.dumps(event, indent=2))
    message = event["Records"][0]["Sns"]["Message"]
    
    data = json.loads(message) 
    token = data["approval"]["token"]
    codepipeline_name = data["approval"]["pipelineName"]
    
    slack_message = {
        "channel": SLACK_CHANNEL,
        "text": "Would you like to promote the build to production?",
        "attachments": [
            {
                "text": "Yes to deploy your build to production",
                "fallback": "You are unable to promote a build",
                "callback_id": "wopr_game",
                "color": "#3AA3E3",
                "attachment_type": "default",
                "actions": [
                    {
                        "name": "deployment",
                        "text": "Yes",
                        "style": "danger",
                        "type": "button",
                        "value": json.dumps({"approve": True, "codePipelineToken": token, "codePipelineName": codepipeline_name}),
                        "confirm": {
                            "title": "Are you sure?",
                            "text": "This will deploy the build to production",
                            "ok_text": "Yes",
                            "dismiss_text": "No"
                        }
                    },
                    {
                        "name": "deployment",
                        "text": "No",
                        "type": "button",
                        "value": json.dumps({"approve": False, "codePipelineToken": token, "codePipelineName": codepipeline_name})
                    }  
                ]
            }
        ]
    }

    req = Request(SLACK_WEBHOOK_URL, json.dumps(slack_message).encode('utf-8'))

    response = urlopen(req)
    response.read()
    
    return None

 

Create a SNS topic

Create a topic and then create a subscription that invokes the ApprovalRequester Lambda function. You can configure the manual approval action in the pipeline to send a message to this SNS topic when an approval action is required. When the pipeline reaches the approval stage, it sends a notification to this SNS topic. SNS publishes a notification to all of the subscribed endpoints. In this case, the Lambda function is the endpoint. Therefore, it invokes and executes the Lambda function. For information about how to create a SNS topic, see Create a Topic in the Amazon SNS Developer Guide.

Write the Lambda function for handling the interactive message button

This Lambda function is invoked by API Gateway. It receives the result of the interactive message button whether or not the build promotion was approved. If approved, an API call is made to CodePipeline to promote the build to the next environment. If not approved, the pipeline stops and does not move to the next stage.

The Lambda function code might look like the following. SLACK_VERIFICATION_TOKEN is the environment variable that contains your Slack verification token. You can find your verification token under Basic Information on Slack manage app page. When you scroll down, you will see App Credential. Verification token is found under the section.

# This function is triggered via API Gateway when a user acts on the Slack interactive message sent by approval_requester.py.

from urllib.parse import parse_qs
import json
import os
import boto3

SLACK_VERIFICATION_TOKEN = os.environ['SLACK_VERIFICATION_TOKEN']

#Triggered by API Gateway
#It kicks off a particular CodePipeline project
def lambda_handler(event, context):
	#print("Received event: " + json.dumps(event, indent=2))
	body = parse_qs(event['body'])
	payload = json.loads(body['payload'][0])

	# Validate Slack token
	if SLACK_VERIFICATION_TOKEN == payload['token']:
		send_slack_message(json.loads(payload['actions'][0]['value']))
		
		# This will replace the interactive message with a simple text response.
		# You can implement a more complex message update if you would like.
		return  {
			"isBase64Encoded": "false",
			"statusCode": 200,
			"body": "{\"text\": \"The approval has been processed\"}"
		}
	else:
		return  {
			"isBase64Encoded": "false",
			"statusCode": 403,
			"body": "{\"error\": \"This request does not include a vailid verification token.\"}"
		}


def send_slack_message(action_details):
	codepipeline_status = "Approved" if action_details["approve"] else "Rejected"
	codepipeline_name = action_details["codePipelineName"]
	token = action_details["codePipelineToken"] 

	client = boto3.client('codepipeline')
	response_approval = client.put_approval_result(
							pipelineName=codepipeline_name,
							stageName='Approval',
							actionName='ApprovalOrDeny',
							result={'summary':'','status':codepipeline_status},
							token=token)
	print(response_approval)

 

Create the API Gateway API

  1. In the Amazon API Gateway console, create a resource called InteractiveMessageHandler.
  2. Create a POST method.
    • For Integration type, choose Lambda Function.
    • Select Use Lambda Proxy integration.
    • From Lambda Region, choose a region.
    • In Lambda Function, type a name for your function.
  3.  Deploy to a stage.

For more information, see Getting Started with Amazon API Gateway in the Amazon API Developer Guide.

Now go back to your Slack application and enable interactive components.

To enable interactive components for the interactive message (Yes) button:

  1. Under Features, choose Interactive Components.
  2. Choose Enable Interactive Components.
  3. Type a request URL in the text box. Use the invoke URL in Amazon API Gateway that will be called when the approval button is clicked.

Now that all the pieces have been created, run the solution by checking in a code change to your CodeCommit repo. That will release the change through CodePipeline. When the CodePipeline comes to the approval stage, it will prompt to your Slack channel to see if you want to promote the build to your staging or production environment. Choose Yes and then see if your change was deployed to the environment.

Conclusion

That is it! You have now created a Slack ChatOps solution using AWS CodeCommit, AWS CodePipeline, AWS Lambda, Amazon API Gateway, and Amazon Simple Notification Service.

Now that you know how to do this Slack and CodePipeline integration, you can use the same method to interact with other AWS services using API Gateway and Lambda. You can also use Slack’s slash command to initiate an action from a Slack channel, rather than responding in the way demonstrated in this post.

Replacing macOS Server with Synology NAS

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/replacing-macos-server-with-synology-nas/

Synology NAS boxes backed up to the cloud

Businesses and organizations that rely on macOS server for essential office and data services are facing some decisions about the future of their IT services.

Apple recently announced that it is deprecating a significant portion of essential network services in macOS Server, as they described in a support statement posted on April 24, 2018, “Prepare for changes to macOS Server.” Apple’s note includes:

macOS Server is changing to focus more on management of computers, devices, and storage on your network. As a result, some changes are coming in how Server works. A number of services will be deprecated, and will be hidden on new installations of an update to macOS Server coming in spring 2018.

The note lists the services that will be removed in a future release of macOS Server, including calendar and contact support, Dynamic Host Configuration Protocol (DHCP), Domain Name Services (DNS), mail, instant messages, virtual private networking (VPN), NetInstall, Web server, and the Wiki.

Apple assures users who have already configured any of the listed services that they will be able to use them in the spring 2018 macOS Server update, but the statement ends with links to a number of alternative services, including hosted services, that macOS Server users should consider as viable replacements to the features it is removing. These alternative services are all FOSS (Free and Open-Source Software).

As difficult as this could be for organizations that use macOS server, this is not unexpected. Apple left the server hardware space back in 2010, when Steve Jobs announced the company was ending its line of Xserve rackmount servers, which were introduced in May, 2002. Since then, macOS Server has hardly been a prominent part of Apple’s product lineup. It’s not just the product itself that has lost some luster, but the entire category of SMB office and business servers, which has been undergoing a gradual change in recent years.

Some might wonder how important the news about macOS Server is, given that macOS Server represents a pretty small share of the server market. macOS Server has been important to design shops, agencies, education users, and small businesses that likely have been on Macs for ages, but it’s not a significant part of the IT infrastructure of larger organizations and businesses.

What Comes After macOS Server?

Lovers of macOS Server don’t have to fear having their Mac minis pried from their cold, dead hands quite yet. Installed services will continue to be available. In the fall of 2018, new installations and upgrades of macOS Server will require users to migrate most services to other software. Since many of the services of macOS Server were already open-source, this means that a change in software might not be required. It does mean more configuration and management required from those who continue with macOS Server, however.

Users can continue with macOS Server if they wish, but many will see the writing on the wall and look for a suitable substitute.

The Times They Are A-Changin’

For many people working in organizations, what is significant about this announcement is how it reflects the move away from the once ubiquitous server-based IT infrastructure. Services that used to be centrally managed and office-based, such as storage, file sharing, communications, and computing, have moved to the cloud.

In selecting the next office IT platforms, there’s an opportunity to move to solutions that reflect and support how people are working and the applications they are using both in the office and remotely. For many, this means including cloud-based services in office automation, backup, and business continuity/disaster recovery planning. This includes Software as a Service, Platform as a Service, and Infrastructure as a Service (Saas, PaaS, IaaS) options.

IT solutions that integrate well with the cloud are worth strong consideration for what comes after a macOS Server-based environment.

Synology NAS as a macOS Server Alternative

One solution that is becoming popular is to replace macOS Server with a device that has the ability to provide important office services, but also bridges the office and cloud environments. Using Network-Attached Storage (NAS) to take up the server slack makes a lot of sense. Many customers are already using NAS for file sharing, local data backup, automatic cloud backup, and other uses. In the case of Synology, their operating system, Synology DiskStation Manager (DSM), is Linux based, and integrates the basic functions of file sharing, centralized backup, RAID storage, multimedia streaming, virtual storage, and other common functions.

Synology NAS box

Synology NAS

Since DSM is based on Linux, there are numerous server applications available, including many of the same ones that are available for macOS Server, which shares conceptual roots with Linux as it comes from BSD Unix.

Synology DiskStation Manager Package Center screenshot

Synology DiskStation Manager Package Center

According to Ed Lukacs, COO at 2FIFTEEN Systems Management in Salt Lake City, their customers have found the move from macOS Server to Synology NAS not only painless, but positive. DSM works seamlessly with macOS and has been faster for their customers, as well. Many of their customers are running Adobe Creative Suite and Google G Suite applications, so a workflow that combines local storage, remote access, and the cloud, is already well known to them. Remote users are supported by Synology’s QuickConnect or VPN.

Business continuity and backup are simplified by the flexible storage capacity of the NAS. Synology has built-in backup to Backblaze B2 Cloud Storage with Synology’s Cloud Sync, as well as a choice of a number of other B2-compatible applications, such as Cloudberry, Comet, and Arq.

Customers have been able to get up and running quickly, with only initial data transfers requiring some time to complete. After that, management of the NAS can be handled in-house or with the support of a Managed Service Provider (MSP).

Are You Sticking with macOS Server or Moving to Another Platform?

If you’re affected by this change in macOS Server, please let us know in the comments how you’re planning to cope. Are you using Synology NAS for server services? Please tell us how that’s working for you.

The post Replacing macOS Server with Synology NAS appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

[$] Easier container security with entitlements

Post Syndicated from corbet original https://lwn.net/Articles/755238/rss

During KubeCon
+ CloudNativeCon Europe 2018
, Justin Cormack and Nassim Eddequiouaq presented
a proposal to simplify the setting of security parameters for containerized
applications.
Containers depend on a large set of intricate security primitives that can
have weird interactions. Because they are so hard to use, people often just
turn the whole thing off. The goal of the proposal is to make those
controls easier to understand and use; it is partly inspired by mobile apps
on iOS and Android platforms, an idea that trickled back into Microsoft and
Apple desktops. The time seems ripe to improve the field of
container security, which is in desperate need of simpler controls.

The devil wears Pravda

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/05/the-devil-wears-pravda.html

Classic Bond villain, Elon Musk, has a new plan to create a website dedicated to measuring the credibility and adherence to “core truth” of journalists. He is, without any sense of irony, going to call this “Pravda”. This is not simply wrong but evil.

Musk has a point. Journalists do suck, and many suck consistently. I see this in my own industry, cybersecurity, and I frequently criticize them for their suckage.

But what he’s doing here is not correcting them when they make mistakes (or what Musk sees as mistakes), but questioning their legitimacy. This legitimacy isn’t measured by whether they follow established journalism ethics, but whether their “core truths” agree with Musk’s “core truths”.

An example of the problem is how the press fixates on Tesla car crashes due to its “autopilot” feature. Pretty much every autopilot crash makes national headlines, while the press ignores the other 40,000 car crashes that happen in the United States each year. Musk spies on Tesla drivers (hello, classic Bond villain everyone) so he can see the dip in autopilot usage every time such a news story breaks. He’s got good reason to be concerned about this.

He argues that autopilot is safer than humans driving, and he’s got the statistics and government studies to back this up. Therefore, the press’s fixation on Tesla crashes is illegitimate “fake news”, titillating the audience with distorted truth.

But here’s the thing: that’s still only Musk’s version of the truth. Yes, on a mile-per-mile basis, autopilot is safer, but there’s nuance here. Autopilot is used primarily on freeways, which already have a low mile-per-mile accident rate. People choose autopilot only when conditions are incredibly safe and drivers are unlikely to have an accident anyway. Musk is therefore being intentionally deceptive comparing apples to oranges. Autopilot may still be safer, it’s just that the numbers Musk uses don’t demonstrate this.

And then there is the truth calling it “autopilot” to begin with, because it isn’t. The public is overrating the capabilities of the feature. It’s little different than “lane keeping” and “adaptive cruise control” you can now find in other cars. In many ways, the technology is behind — my Tesla doesn’t beep at me when a pedestrian walks behind my car while backing up, but virtually every new car on the market does.

Yes, the press unduly covers Tesla autopilot crashes, but Musk has only himself to blame by unduly exaggerating his car’s capabilities by calling it “autopilot”.

What’s “core truth” is thus rather difficult to obtain. What the press satisfies itself with instead is smaller truths, what they can document. The facts are in such cases that the accident happened, and they try to get Tesla or Musk to comment on it.

What you can criticize a journalist for is therefore not “core truth” but whether they did journalism correctly. When such stories criticize “autopilot”, but don’t do their diligence in getting Tesla’s side of the story, then that’s a violation of journalistic practice. When I criticize journalists for their poor handling of stories in my industry, I try to focus on which journalistic principles they get wrong. For example, the NYTimes reporters do a lot of stories quoting anonymous government sources in clear violation of journalistic principles.

If “credibility” is the concern, then it’s the classic Bond villain here that’s the problem: Musk himself. His track record on business statements is abysmal. For example, when he announced the Model 3 he claimed production targets that every Wall Street analyst claimed were absurd. He didn’t make those targets, he didn’t come close. Model 3 production is still lagging behind Musk’s twice adjusted targets.

https://www.bloomberg.com/graphics/2018-tesla-tracker/

So who has a credibility gap here, the press, or Musk himself?

Not only is Musk’s credibility problem ironic, so is the name he chose, “Pravada”, the Russian word for truth that was the name of the Soviet Union Communist Party’s official newspaper. This is so absurd this has to be a joke, yet Musk claims to be serious about all this.

Yes, the press has a lot of problems, and if Musk were some journalism professor concerned about journalists meeting the objective standards of their industry (e.g. abusing anonymous sources), then this would be a fine thing. But it’s not. It’s Musk who is upset the press’s version of “core truth” does not agree with his version — a version that he’s proven time and time again differs from “real truth”.

Just in case Musk is serious, I’ve already registered “www.antipravda.com” to start measuring the credibility of statements by billionaire playboy CEOs. Let’s see who blinks first.


I stole the title, with permission, from this tweet:

C is to low level

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/05/c-is-too-low-level.html

I’m in danger of contradicting myself, after previously pointing out that x86 machine code is a high-level language, but this article claiming C is a not a low level language is bunk. C certainly has some problems, but it’s still the closest language to assembly. This is obvious by the fact it’s still the fastest compiled language. What we see is a typical academic out of touch with the real world.

The author makes the (wrong) observation that we’ve been stuck emulating the PDP-11 for the past 40 years. C was written for the PDP-11, and since then CPUs have been designed to make C run faster. The author imagines a different world, such as where CPU designers instead target something like LISP as their preferred language, or Erlang. This misunderstands the state of the market. CPUs do indeed supports lots of different abstractions, and C has evolved to accommodate this.


The author criticizes things like “out-of-order” execution which has lead to the Spectre sidechannel vulnerabilities. Out-of-order execution is necessary to make C run faster. The author claims instead that those resources should be spent on having more slower CPUs, with more threads. This sacrifices single-threaded performance in exchange for a lot more threads executing in parallel. The author cites Sparc Tx CPUs as his ideal processor.

But here’s the thing, the Sparc Tx was a failure. To be fair, it’s mostly a failure because most of the time, people wanted to run old C code instead of new Erlang code. But it was still a failure at running Erlang.

Time after time, engineers keep finding that “out-of-order”, single-threaded performance is still the winner. A good example is ARM processors for both mobile phones and servers. All the theory points to in-order CPUs as being better, but all the products are out-of-order, because this theory is wrong. The custom ARM cores from Apple and Qualcomm used in most high-end phones are so deeply out-of-order they give Intel CPUs competition. The same is true on the server front with the latest Qualcomm Centriq and Cavium ThunderX2 processors, deeply out of order supporting more than 100 instructions in flight.

The Cavium is especially telling. Its ThunderX CPU had 48 simple cores which was replaced with the ThunderX2 having 32 complex, deeply out-of-order cores. The performance increase was massive, even on multithread-friendly workloads. Every competitor to Intel’s dominance in the server space has learned the lesson from Sparc Tx: many wimpy cores is a failure, you need fewer beefy cores. Yes, they don’t need to be as beefy as Intel’s processors, but they need to be close.

Even Intel’s “Xeon Phi” custom chip learned this lesson. This is their GPU-like chip, running 60 cores with 512-bit wide “vector” (sic) instructions, designed for supercomputer applications. Its first version was purely in-order. Its current version is slightly out-of-order. It supports four threads and focuses on basic number crunching, so in-order cores seems to be the right approach, but Intel found in this case that out-of-order processing still provided a benefit. Practice is different than theory.

As an academic, the author of the above article focuses on abstractions. The criticism of C is that it has the wrong abstractions which are hard to optimize, and that if we instead expressed things in the right abstractions, it would be easier to optimize.

This is an intellectually compelling argument, but so far bunk.

The reason is that while the theoretical base language has issues, everyone programs using extensions to the language, like “intrinsics” (C ‘functions’ that map to assembly instructions). Programmers write libraries using these intrinsics, which then the rest of the normal programmers use. In other words, if your criticism is that C is not itself low level enough, it still provides the best access to low level capabilities.

Given that C can access new functionality in CPUs, CPU designers add new paradigms, from SIMD to transaction processing. In other words, while in the 1980s CPUs were designed to optimize C (stacks, scaled pointers), these days CPUs are designed to optimize tasks regardless of language.

The author of that article criticizes the memory/cache hierarchy, claiming it has problems. Yes, it has problems, but only compared to how well it normally works. The author praises the many simple cores/threads idea as hiding memory latency with little caching, but misses the point that caches also dramatically increase memory bandwidth. Intel processors are optimized to read a whopping 256 bits every clock cycle from L1 cache. Main memory bandwidth is orders of magnitude slower.

The author goes onto criticize cache coherency as a problem. C uses it, but other languages like Erlang don’t need it. But that’s largely due to the problems each languages solves. Erlang solves the problem where a large number of threads work on largely independent tasks, needing to send only small messages to each other across threads. The problems C solves is when you need many threads working on a huge, common set of data.

For example, consider the “intrusion prevention system”. Any thread can process any incoming packet that corresponds to any region of memory. There’s no practical way of solving this problem without a huge coherent cache. It doesn’t matter which language or abstractions you use, it’s the fundamental constraint of the problem being solved. RDMA is an important concept that’s moved from supercomputer applications to the data center, such as with memcached. Again, we have the problem of huge quantities (terabytes worth) shared among threads rather than small quantities (kilobytes).

The fundamental issue the author of the the paper is ignoring is decreasing marginal returns. Moore’s Law has gifted us more transistors than we can usefully use. We can’t apply those additional registers to just one thing, because the useful returns we get diminish.

For example, Intel CPUs have two hardware threads per core. That’s because there are good returns by adding a single additional thread. However, the usefulness of adding a third or fourth thread decreases. That’s why many CPUs have only two threads, or sometimes four threads, but no CPU has 16 threads per core.

You can apply the same discussion to any aspect of the CPU, from register count, to SIMD width, to cache size, to out-of-order depth, and so on. Rather than focusing on one of these things and increasing it to the extreme, CPU designers make each a bit larger every process tick that adds more transistors to the chip.

The same applies to cores. It’s why the “more simpler cores” strategy fails, because more cores have their own decreasing marginal returns. Instead of adding cores tied to limited memory bandwidth, it’s better to add more cache. Such cache already increases the size of the cores, so at some point it’s more effective to add a few out-of-order features to each core rather than more cores. And so on.

The question isn’t whether we can change this paradigm and radically redesign CPUs to match some academic’s view of the perfect abstraction. Instead, the goal is to find new uses for those additional transistors. For example, “message passing” is a useful abstraction in languages like Go and Erlang that’s often more useful than sharing memory. It’s implemented with shared memory and atomic instructions, but I can’t help but think it couldn’t better be done with direct hardware support.

Of course, as soon as they do that, it’ll become an intrinsic in C, then added to languages like Go and Erlang.

Summary

Academics live in an ideal world of abstractions, the rest of us live in practical reality. The reality is that vast majority of programmers work with the C family of languages (JavaScript, Go, etc.), whereas academics love the epiphanies they learned using other languages, especially function languages. CPUs are only superficially designed to run C and “PDP-11 compatibility”. Instead, they keep adding features to support other abstractions, abstractions available to C. They are driven by decreasing marginal returns — they would love to add new abstractions to the hardware because it’s a cheap way to make use of additional transitions. Academics are wrong believing that the entire system needs to be redesigned from scratch. Instead, they just need to come up with new abstractions CPU designers can add.

[$] Case-insensitive filesystem lookups

Post Syndicated from jake original https://lwn.net/Articles/754508/rss

Case-insensitive file name lookups are a feature that is fairly frequently
raised at the Linux
Storage, Filesystem, and Memory-Management Summit (LSFMM). At the 2018
summit, Gabriel Bertazi proposed a new way to support
the feature, though it met with a rather skeptical reception—with one
notable exception. Ted Ts’o seemed favorably disposed to the idea, in part
because
it would potentially be a way to get rid of some longstanding Android ugliness:
wrapfs.

Monitoring for Everyone

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2018/05/23/monitoring-for-everyone/

Øredev – Carl Bergquist – Monitoring for Everyone What is monitoring?
What do the terms log, metric, and distributed tracing actually mean?
What makes a good alert?
Why should I care?
At a recent developer conference in Malmö, Sweden, I gave a presentation on monitoring and observability to discuss the high level concepts and common tools that are out there.
Monitoring and observability can easily become quite complex, but at the heart of it, we simply want to know how our systems are performing, and when performance drops – be able to find out why.

The Benefits of Side Projects

Post Syndicated from Bozho original https://techblog.bozho.net/the-benefits-of-side-projects/

Side projects are the things you do at home, after work, for your own “entertainment”, or to satisfy your desire to learn new stuff, in case your workplace doesn’t give you that opportunity (or at least not enough of it). Side projects are also a way to build stuff that you think is valuable but not necessarily “commercialisable”. Many side projects are open-sourced sooner or later and some of them contribute to the pool of tools at other people’s disposal.

I’ve outlined one recommendation about side projects before – do them with technologies that are new to you, so that you learn important things that will keep you better positioned in the software world.

But there are more benefits than that – serendipitous benefits, for example. And I’d like to tell some personal stories about that. I’ll focus on a few examples from my list of side projects to show how, through a sort-of butterfly effect, they helped shape my career.

The computoser project, no matter how cool algorithmic music composition, didn’t manage to have much of a long term impact. But it did teach me something apart from niche musical theory – how to read a bulk of scientific papers (mostly computer science) and understand them without being formally trained in the particular field. We’ll see how that was useful later.

Then there was the “State alerts” project – a website that scraped content from public institutions in my country (legislation, legislation proposals, decisions by regulators, new tenders, etc.), made them searchable, and “subscribable” – so that you get notified when a keyword of interest is mentioned in newly proposed legislation, for example. (I obviously subscribed for “information technologies” and “electronic”).

And that project turned out to have a significant impact on the following years. First, I chose a new technology to write it with – Scala. Which turned out to be of great use when I started working at TomTom, and on the 3rd day I was transferred to a Scala project, which was way cooler and much more complex than the original one I was hired for. It was a bit ironic, as my colleagues had just read that “I don’t like Scala” a few weeks earlier, but nevertheless, that was one of the most interesting projects I’ve worked on, and it went on for two years. Had I not known Scala, I’d probably be gone from TomTom much earlier (as the other project was restructured a few times), and I would not have learned many of the scalability, architecture and AWS lessons that I did learn there.

But the very same project had an even more important follow-up. Because if its “civic hacking” flavour, I was invited to join an informal group of developers (later officiated as an NGO) who create tools that are useful for society (something like MySociety.org). That group gathered regularly, discussed both tools and policies, and at some point we put up a list of policy priorities that we wanted to lobby policy makers. One of them was open source for the government, the other one was open data. As a result of our interaction with an interim government, we donated the official open data portal of my country, functioning to this day.

As a result of that, a few months later we got a proposal from the deputy prime minister’s office to “elect” one of the group for an advisor to the cabinet. And we decided that could be me. So I went for it and became advisor to the deputy prime minister. The job has nothing to do with anything one could imagine, and it was challenging and fascinating. We managed to pass legislation, including one that requires open source for custom projects, eID and open data. And all of that would not have been possible without my little side project.

As for my latest side project, LogSentinel – it became my current startup company. And not without help from the previous two mentioned above – the computer science paper reading was of great use when I was navigating the crypto papers landscape, and from the government job I not only gained invaluable legal knowledge, but I also “got” a co-founder.

Some other side projects died without much fanfare, and that’s fine. But the ones above shaped my “story” in a way that would not have been possible otherwise.

And I agree that such serendipitous chain of events could have happened without side projects – I could’ve gotten these opportunities by meeting someone at a bar (unlikely, but who knows). But we, as software engineers, are capable of tilting chance towards us by utilizing our skills. Side projects are our “extracurricular activities”, and they often lead to unpredictable, but rather positive chains of events. They would rarely be the only factor, but they are certainly great at unlocking potential.

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