# MQTT 5: Introduction to MQTT 5

Post Syndicated from The HiveMQ Team original https://www.hivemq.com/blog/mqtt-5-introduction-to-mqtt-5/

### Introduction to MQTT 5

Welcome to our brand new blog post series MQTT 5 – Features and Hidden Gems. Without doubt, the MQTT protocol is the most popular and best received Internet of Things protocol as of today (see the Google Trends Chart below), supporting large scale use cases ranging from Connected Cars, Manufacturing Systems, Logistics, Military Use Cases to Enterprise Chat Applications, Mobile Apps and connecting constrained IoT devices. Of course, with huge amounts of production deployments, the wish list for future versions of the MQTT protocol grew bigger and bigger.

MQTT 5 is by far the most extensive and most feature-rich update to the MQTT protocol specification ever. We are going to explore all hidden gems and protocol features with use case discussion and useful background information – one blog post at a time.

Be sure to read the MQTT Essentials Blog Post series first before diving into our new MQTT 5 series. To get the most out of the new blog posts, it’s important to have a basic understanding of the MQTT 3.1.1 protocol as we are going to highlight key changes as well as all improvements.

# About the Amazon Trust Services Migration

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/ses/669-2/

Amazon Web Services is moving the certificates for our services—including Amazon SES—to use our own certificate authority, Amazon Trust Services. We have carefully planned this change to minimize the impact it will have on your workflow. Most customers will not have to take any action during this migration.

The Amazon Trust Services Certificate Authority (CA) uses the Starfield Services CA, which has been valid since 2005. The Amazon Trust Services certificates are available in most major operating systems released in the past 10 years, and are also trusted by all modern web browsers.

If you send email through the Amazon SES SMTP interface using a mail server that you operate, we recommend that you confirm that the appropriate certificates are installed. You can test whether your server trusts the Amazon Trust Services CAs by visiting the following URLs (for example, by using cURL):

If you see a message stating that the certificate issuer is not recognized, then you should install the appropriate root certificate. You can download individual certificates from https://www.amazontrust.com/repository. The process of adding a trusted certificate to your server varies depending on the operating system you use. For more information, see “Adding New Certificates,” below.

## AWS SDKs and CLI

Recent versions of the AWS SDKs and the AWS CLI are not impacted by this change. If you use an AWS SDK or a version of the AWS CLI released prior to February 5, 2015, you should upgrade to the latest version.

## Potential Issues

If your system is configured to use a very restricted list of root CAs (for example, if you use certificate pinning), you may be impacted by this migration. In this situation, you must update your pinned certificates to include the Amazon Trust Services CAs.

The following sections list the steps you can take to install the Amazon Root CA certificates on your systems if they are not already present.

### macOS

To install a new certificate on a macOS server

1. Download the .pem file for the certificate you want to install from https://www.amazontrust.com/repository.
2. Change the file extension for the file you downloaded from .pem to .crt.
3. At the command prompt, type the following command to install the certificate: sudo security add-trusted-cert -d -r trustRoot -k /Library/Keychains/System.keychain /path/to/certificatename.crt, replacing /path/to/certificatename.crt with the full path to the certificate file.

### Windows Server

To install a new certificate on a Windows server

1. Download the .pem file for the certificate you want to install from https://www.amazontrust.com/repository.
2. Change the file extension for the file you downloaded from .pem to .crt.
3. At the command prompt, type the following command to install the certificate: certutil -addstore -f "ROOT" c:\path\to\certificatename.crt, replacing c:\path\to\certificatename.crt with the full path to the certificate file.

### Ubuntu

To install a new certificate on an Ubuntu (or similar) server

1. Download the .pem file for the certificate you want to install from https://www.amazontrust.com/repository.
2. Change the file extension for the file you downloaded from .pem to .crt.
3. Copy the certificate file to the directory /usr/local/share/ca-certificates/
4. At the command prompt, type the following command to update the certificate authority store: sudo update-ca-certificates

### Red Hat Enterprise Linux/Fedora/CentOS

To install a new certificate on a Red Hat Enterprise Linux (or similar) server

1. Download the .pem file for the certificate you want to install from https://www.amazontrust.com/repository.
2. Change the file extension for the file you downloaded from .pem to .crt.
3. Copy the certificate file to the directory /etc/pki/ca-trust/source/anchors/
4. At the command line, type the following command to enable dynamic certificate authority configuration: sudo update-ca-trust force-enable
5. At the command line, type the following command to update the certificate authority store: sudo update-ca-trust extract

# How to Easily Apply Amazon Cloud Directory Schema Changes with In-Place Schema Upgrades

In this blog post, I demonstrate how to perform an in-place schema upgrade and use schema versions in Cloud Directory. I add additional attributes to an existing facet and add a new facet to a schema. I then publish the new schema and apply it to running directories, upgrading the schema in place. I also show how to view the version history of a directory schema, which helps me to ensure my directory fleet is running the same version of the schema and has the correct history of schema changes applied to it.

Note: I share Java code examples in this post. I assume that you are familiar with the AWS SDK and can use Java-based code to build a Cloud Directory code example. You can apply the concepts I cover in this post to other programming languages such as Python and Ruby.

### Cloud Directory fundamentals

I will start by covering a few Cloud Directory fundamentals. If you are already familiar with the concepts behind Cloud Directory facets, schemas, and schema lifecycles, you can skip to the next section.

Facets: Groups of attributes. You use facets to define object types. For example, you can define a device schema by adding facets such as computers, phones, and tablets. A computer facet can track attributes such as serial number, make, and model. You can then use the facets to create computer objects, phone objects, and tablet objects in the directory to which the schema applies.

Schemas: Collections of facets. Schemas define which types of objects can be created in a directory (such as users, devices, and organizations) and enforce validation of data for each object class. All data within a directory must conform to the applied schema. As a result, the schema definition is essentially a blueprint to construct a directory with an applied schema.

Schema lifecycle: The four distinct states of a schema: Development, Published, Applied, and Deleted. Schemas in the Published and Applied states have version identifiers and cannot be changed. Schemas in the Applied state are used by directories for validation as applications insert or update data. You can change schemas in the Development state as many times as you need them to. In-place schema upgrades allow you to apply schema changes to an existing Applied schema in a production directory without the need to export and import the data populated in the directory.

### How to add attributes to a computer inventory application schema and perform an in-place schema upgrade

To demonstrate how to set up schema versioning and perform an in-place schema upgrade, I will use an example of a computer inventory application that uses Cloud Directory to store relationship data. Let’s say that at my company, AnyCompany, we use this computer inventory application to track all computers we give to our employees for work use. I previously created a ComputerSchema and assigned its version identifier as 1. This schema contains one facet called ComputerInfo that includes attributes for SerialNumber, Make, and Model, as shown in the following schema details.

Schema: ComputerSchema
Version: 1

Facet: ComputerInfo
Attribute: SerialNumber, type: Integer
Attribute: Make, type: String
Attribute: Model, type: String

AnyCompany has offices in Seattle, Portland, and San Francisco. I have deployed the computer inventory application for each of these three locations. As shown in the lower left part of the following diagram, ComputerSchema is in the Published state with a version of 1. The Published schema is applied to SeattleDirectory, PortlandDirectory, and SanFranciscoDirectory for AnyCompany’s three locations. Implementing separate directories for different geographic locations when you don’t have any queries that cross location boundaries is a good data partitioning strategy and gives your application better response times with lower latency.

The following code example creates the schema in the Development state by using a JSON file, publishes the schema, and then creates directories for the Seattle, Portland, and San Francisco locations. For this example, I assume the schema has been defined in the JSON file. The createSchema API creates a schema Amazon Resource Name (ARN) with the name defined in the variable, SCHEMA_NAME. I can use the putSchemaFromJson API to add specific schema definitions from the JSON file.

// The utility method to get valid Cloud Directory schema JSON
String validJson = getJsonFile("ComputerSchema_version_1.json")

String SCHEMA_NAME = "ComputerSchema";

String developmentSchemaArn = client.createSchema(new CreateSchemaRequest()
.withName(SCHEMA_NAME))
.getSchemaArn();

// Put the schema document in the Development schema
PutSchemaFromJsonResult result = client.putSchemaFromJson(new PutSchemaFromJsonRequest()
.withSchemaArn(developmentSchemaArn)
.withDocument(validJson));


The following code example takes the schema that is currently in the Development state and publishes the schema, changing its state to Published.

String SCHEMA_VERSION = "1";
String publishedSchemaArn = client.publishSchema(
new PublishSchemaRequest()
.withDevelopmentSchemaArn(developmentSchemaArn)
.withVersion(SCHEMA_VERSION))
.getPublishedSchemaArn();

// Our Published schema ARN is as follows
// arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:schema/published/ComputerSchema/1

The following code example creates a directory named SeattleDirectory and applies the published schema. The createDirectory API call creates a directory by using the published schema provided in the API parameters. Note that Cloud Directory stores a version of the schema in the directory in the Applied state. I will use similar code to create directories for PortlandDirectory and SanFranciscoDirectory.

String DIRECTORY_NAME = "SeattleDirectory";

CreateDirectoryResult directory = client.createDirectory(
new CreateDirectoryRequest()
.withName(DIRECTORY_NAME)
.withSchemaArn(publishedSchemaArn));

String directoryArn = directory.getDirectoryArn();
String appliedSchemaArn = directory.getAppliedSchemaArn();

// This code section can be reused to create directories for Portland and San Francisco locations with the appropriate directory names

// Our directory ARN is as follows
// arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:directory/XX_DIRECTORY_GUID_XX

// Our applied schema ARN is as follows
// arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:directory/XX_DIRECTORY_GUID_XX/schema/ComputerSchema/1


### Revising a schema

Now let’s say my company, AnyCompany, wants to add more information for computers and to track which employees have been assigned a computer for work use. I modify the schema to add two attributes to the ComputerInfo facet: Description and OSVersion (operating system version). I make Description optional because it is not important for me to track this attribute for the computer objects I create. I make OSVersion mandatory because it is critical for me to track it for all computer objects so that I can make changes such as applying security patches or making upgrades. Because I make OSVersion mandatory, I must provide a default value that Cloud Directory will apply to objects that were created before the schema revision, in order to handle backward compatibility. Note that you can replace the value in any object with a different value.

I also add a new facet to track computer assignment information, shown in the following updated schema as the ComputerAssignment facet. This facet tracks these additional attributes: Name (the name of the person to whom the computer is assigned), EMail (the email address of the assignee), Department, and department CostCenter. Note that Cloud Directory refers to the previously available version identifier as the Major Version. Because I can now add a minor version to a schema, I also denote the changed schema as Minor Version A.

Schema: ComputerSchema
Major Version: 1
Minor Version: A

Facet: ComputerInfo
Attribute: SerialNumber, type: Integer
Attribute: Make, type: String
Attribute: Model, type: Integer
Attribute: Description, type: String, required: NOT_REQUIRED
Attribute: OSVersion, type: String, required: REQUIRED_ALWAYS, default: "Windows 7"

Facet: ComputerAssignment
Attribute: Name, type: String
Attribute: EMail, type: String
Attribute: Department, type: String
Attribute: CostCenter, type: Integer

The following diagram shows the changes that were made when I added another facet to the schema and attributes to the existing facet. The highlighted area of the diagram (bottom left) shows that the schema changes were published.

The following code example revises the existing Development schema by adding the new attributes to the ComputerInfo facet and by adding the ComputerAssignment facet. I use a new JSON file for the schema revision, and for the purposes of this example, I am assuming the JSON file has the full schema including planned revisions.

// The utility method to get a valid CloudDirectory schema JSON
String schemaJson = getJsonFile("ComputerSchema_version_1_A.json")

// Put the schema document in the Development schema
PutSchemaFromJsonResult result = client.putSchemaFromJson(
new PutSchemaFromJsonRequest()
.withSchemaArn(developmentSchemaArn)
.withDocument(schemaJson));

The following code example performs an in-place schema upgrade of the Published schema with schema revisions (it adds new attributes to the existing facet and another facet to the schema). The upgradePublishedSchema API upgrades the Published schema with backward-compatible changes from the Development schema.

// From an earlier code example, I know the publishedSchemaArn has this value: "arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:schema/published/ComputerSchema/1"

// Upgrade publishedSchemaArn to minorVersion A. The Development schema must be backward compatible with
// the existing publishedSchemaArn.

String minorVersion = "A"

.withDevelopmentSchemaArn(developmentSchemaArn)
.withPublishedSchemaArn(publishedSchemaArn)
.withMinorVersion(minorVersion));

// The Published schema ARN after the upgrade shows a minor version as follows
// arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:schema/published/ComputerSchema/1/A

The following diagram shows the in-place schema upgrade for the SeattleDirectory directory. I am performing the schema upgrade so that I can reflect the new schemas in all three directories. As a reminder, I added new attributes to the ComputerInfo facet and also added the ComputerAssignment facet. After the schema and directory upgrade, I can create objects for the ComputerInfo and ComputerAssignment facets in the SeattleDirectory. Any objects that were created with the old facet definition for ComputerInfo will now use the default values for any additional attributes defined in the new schema.

I use the following code example to perform an in-place upgrade of the SeattleDirectory to a Major Version of 1 and a Minor Version of A. Note that you should change a Major Version identifier in a schema to make backward-incompatible changes such as changing the data type of an existing attribute or dropping a mandatory attribute from your schema. Backward-incompatible changes require directory data migration from a previous version to the new version. You should change a Minor Version identifier in a schema to make backward-compatible upgrades such as adding additional attributes or adding facets, which in turn may contain one or more attributes. The upgradeAppliedSchema API lets me upgrade an existing directory with a different version of a schema.

// This upgrades ComputerSchema version 1 of the Applied schema in SeattleDirectory to Major Version 1 and Minor Version A
// The schema must be backward compatible or the API will fail with IncompatibleSchemaException

.withDirectoryArn(directoryArn)

// The Applied schema ARN after the in-place schema upgrade will appear as follows
// arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:directory/XX_DIRECTORY_GUID_XX/schema/ComputerSchema/1

// This code section can be reused to upgrade directories for the Portland and San Francisco locations with the appropriate directory ARN

Note: Cloud Directory has excluded returning the Minor Version identifier in the Applied schema ARN for backward compatibility and to enable the application to work across older and newer versions of the directory.

The following diagram shows the changes that are made when I perform an in-place schema upgrade in the two remaining directories, PortlandDirectory and SanFranciscoDirectory. I make these calls sequentially, upgrading PortlandDirectory first and then upgrading SanFranciscoDirectory. I use the same code example that I used earlier to upgrade SeattleDirectory. Now, all my directories are running the most current version of the schema. Also, I made these schema changes without having to migrate data and while maintaining my application’s high availability.

### Schema revision history

I can now view the schema revision history for any of AnyCompany’s directories by using the listAppliedSchemaArns API. Cloud Directory maintains the five most recent versions of applied schema changes. Similarly, to inspect the current Minor Version that was applied to my schema, I use the getAppliedSchemaVersion API. The listAppliedSchemaArns API returns the schema ARNs based on my schema filter as defined in withSchemaArn.

I use the following code example to query an Applied schema for its version history.

// This returns the five most recent Minor Versions associated with a Major Version
ListAppliedSchemaArnsResult listAppliedSchemaArnsResult = client.listAppliedSchemaArns(new ListAppliedSchemaArnsRequest()
.withDirectoryArn(directoryArn)

// Note: The listAppliedSchemaArns API without the SchemaArn filter returns all the Major Versions in a directory

The listAppliedSchemaArns API returns the two ARNs as shown in the following output.

arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:directory/XX_DIRECTORY_GUID_XX/schema/ComputerSchema/1
arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:directory/XX_DIRECTORY_GUID_XX/schema/ComputerSchema/1/A

The following code example queries an Applied schema for current Minor Version by using the getAppliedSchemaVersion API.

// This returns the current Applied schema's Minor Version ARN

GetAppliedSchemaVersion getAppliedSchemaVersionResult = client.getAppliedSchemaVersion(new GetAppliedSchemaVersionRequest()
.withSchemaArn(upgradedAppliedSchemaArn));

The getAppliedSchemaVersion API returns the current Applied schema ARN with a Minor Version, as shown in the following output.

arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:directory/XX_DIRECTORY_GUID_XX/schema/ComputerSchema/1/A

If you have a lot of directories, schema revision API calls can help you audit your directory fleet and ensure that all directories are running the same version of a schema. Such auditing can help you ensure high integrity of directories across your fleet.

### Summary

If you have comments about this blog post, submit them in the “Comments” section below. If you have questions about implementing the solution in this post, start a new thread in the Directory Service forum or contact AWS Support.

– Mahendra

# The Pi Towers Secret Santa Babbage

Post Syndicated from Mark Calleja original https://www.raspberrypi.org/blog/secret-santa-babbage/

Tired of pulling names out of a hat for office Secret Santa? Upgrade your festive tradition with a Raspberry Pi, thermal printer, and everybody’s favourite microcomputer mascot, Babbage Bear.

## The name’s Santa. Secret Santa.

It’s that time of year again, when the cosiness gets turned up to 11 and everyone starts thinking about jolly fat men, reindeer, toys, and benevolent home invasion. At Raspberry Pi, we’re running a Secret Santa pool: everyone buys a gift for someone else in the office. Obviously, the person you buy for has to be picked in secret and at random, or the whole thing wouldn’t work. With that in mind, I created Secret Santa Babbage to do the somewhat mundane task of choosing gift recipients. This could’ve just been done with some names in a hat, but we’re Raspberry Pi! If we don’t make a Python-based Babbage robot wearing a jaunty hat and programmed to spread Christmas cheer, who will?

Ho ho ho!

## Mecha-Babbage Xmas shenanigans

The script the robot runs is pretty basic: a list of names entered as comma-separated strings is shuffled at the press of a GPIO button, then a name is popped off the end and stored as a variable. The name is matched to a photo of the person stored on the Raspberry Pi, and a thermal printer pinched from Alex’s super awesome PastyCam (blog post forthcoming, maybe) prints out the picture and name of the person you will need to shower with gifts at the Christmas party. (Well, OK — with one gift. No more than five quid’s worth. Nothing untoward.) There’s also a redo function, just in case you pick yourself: press another button and the last picked name — still stored as a variable — is appended to the list again, which is shuffled once more, and a new name is popped off the end.

Prototyping!

As the build was a bit of a rush job undertaken at the request of our ‘Director of Vibe’ Emily, there are a few things I’d like to improve about this functionality that I didn’t get around to — more on that later. To add some extra holiday spirit to the project at the last minute, I used Pygame to play a WAV file of Santa’s jolly laugh while Babbage chooses a name for you. The file is included in the GitHub repo along with everything else, because ‘tis the season, etc., etc.

Editor’s note: Considering these desk adornments, Mark’s Secret Santa gift-giver has a lot to go on.

Writing the code for Xmas Mecha-Babbage was fairly straightforward, though it uses some tricky bits for managing the thermal printer. You’ll need to install the drivers to make it go, as well as the CUPS package for managing the print hosting. You can find instructions for these things here, thanks to the wonderful Adafruit crew. Also, for reasons I couldn’t fathom, this will all only work on a Pi 2 and not a Pi 3, as there are some compatibility issues with the thermal printer otherwise. (I also tested the script on a Pi Zero W…no dice.)

## Building a Christmassy throne

The hardest (well, fiddliest) parts of making the whole build were constructing the throne and wiring the bear. Using MakerCase, Inkscape, a bit of ingenuity, and a laser cutter, I was able to rig up a Christmassy plywood throne which has a hole through the seat so I could run the wires down from Babbage and to the Pi inside. I finished the throne by rubbing a couple of fingers of beeswax into it; as well as making the wood shine just a little bit and protecting it against getting wet, this had the added bonus of making it smell awesome.

Next year’s iteration will be mulled wine–scented.

I next soldered two LEDs to some lengths of wire, and then ran the wires through holes at the top of the throne and down the back along a small channel I had carved with a narrow chisel to connect them to the Pi’s GPIO pins. The green LED will remain on as long as Babbage is running his program, and the red one will light up while he is processing your request. Once the red LED goes off again, the next person can have a go. I also laser-cut a final piece of wood to overlay the back of Babbage’s Xmas throne and cover the wiring a bit.

## Creating a Xmas cyborg bear

Taking two 6 mm tactile buttons, I clipped the spiky metal legs off one side of each (the buttons were going into a stuffed christmas toy, after all) and soldered a length of wire to each of the remaining legs. Next, I made a small incision into Babbage with my trusty Swiss army knife (in a place that actually made me cringe a little) and fed the buttons up into his paws. At some point in this process I was standing in the office wrestling with the bear and muttering to myself, which elicited some very strange looks from my colleagues.

Poor Babbage…

One thing to note here is to make sure the wires remain attached at the solder points while you push them up into Babbage’s paws. The first time I tried it, I snapped one of my connections and had to start again. It helped to remove some stuffing like a tunnel and then replace it afterward. Moreover, you can use your fingertip to support the joints as you poke the wire in. Finally, a couple of squirts of hot glue to keep Babbage’s furry cheeks firmly on the seat, and done!

Next year: Game of Thrones–inspired candy cane throne

## The Secret Santa Babbage masterpiece

The whole build process was the perfect holiday mix of cheerful and macabre, and while getting the thermal printer to work was a little time-consuming, the finished product definitely raised some smiles around the office and added a bit of interesting digital flavour to a staid office tradition. And it also helped people who are new to the office or from other branches of the Foundation to know for whom they will be buying a gift.

There are a few ways in which I’ll polish this project before next year, such as having the script write the names to external text files to create a record that will persist in case of a reboot, and maybe having Secret Santa Babbage play you a random Christmas carol when you squeeze his paw instead of just laughing merrily every time. (I also thought about adding electric shocks for those people who are on the naughty list, but HR said no. Bah, humbug!)

The code and laser cut plans for the whole build are available here. If you plan to make your own, let us know which stuffed toy you will be turning into a Secret Santa cyborg! And if you’ve been working on any other Christmas-themed Raspberry Pi projects, we’d like to see those too, so tag us on social media to share the festive maker cheer.

The post The Pi Towers Secret Santa Babbage appeared first on Raspberry Pi.

# Stretch for PCs and Macs, and a Raspbian update

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

Today, we are launching the first Debian Stretch release of the Raspberry Pi Desktop for PCs and Macs, and we’re also releasing the latest version of Raspbian Stretch for your Pi.

## For PCs and Macs

When we released our custom desktop environment on Debian for PCs and Macs last year, we were slightly taken aback by how popular it turned out to be. We really only created it as a result of one of those “Wouldn’t it be cool if…” conversations we sometimes have in the office, so we were delighted by the Pi community’s reaction.

Seeing how keen people were on the x86 version, we decided that we were going to try to keep releasing it alongside Raspbian, with the ultimate aim being to make simultaneous releases of both. This proved to be tricky, particularly with the move from the Jessie version of Debian to the Stretch version this year. However, we have now finished the job of porting all the custom code in Raspbian Stretch to Debian, and so the first Debian Stretch release of the Raspberry Pi Desktop for your PC or Mac is available from today.

## The new Stretch releases

As with the Jessie release, you can either run this as a live image from a DVD, USB stick, or SD card or install it as the native operating system on the hard drive of an old laptop or desktop computer. Please note that installing this software will erase anything else on the hard drive — do not install this over a machine running Windows or macOS that you still need to use for its original purpose! It is, however, safe to boot a live image on such a machine, since your hard drive will not be touched by this.

We’re also pleased to announce that we are releasing the latest version of Raspbian Stretch for your Pi today. The Pi and PC versions are largely identical: as before, there are a few applications (such as Mathematica) which are exclusive to the Pi, but the user interface, desktop, and most applications will be exactly the same.

For Raspbian, this new release is mostly bug fixes and tweaks over the previous Stretch release, but there are one or two changes you might notice.

## File manager

The file manager included as part of the LXDE desktop (on which our desktop is based) is a program called PCManFM, and it’s very feature-rich; there’s not much you can’t do in it. However, having used it for a few years, we felt that it was perhaps more complex than it needed to be — the sheer number of menu options and choices made some common operations more awkward than they needed to be. So to try to make file management easier, we have implemented a cut-down mode for the file manager.

Most of the changes are to do with the menus. We’ve removed a lot of options that most people are unlikely to change, and moved some other options into the Preferences screen rather than the menus. The two most common settings people tend to change — how icons are displayed and sorted — are now options on the toolbar and in a top-level menu rather than hidden away in submenus.

The sidebar now only shows a single hierarchical view of the file system, and we’ve tidied the toolbar and updated the icons to make them match our house style. We’ve removed the option for a tabbed interface, and we’ve stomped a few bugs as well.

One final change was to make it possible to rename a file just by clicking on its icon to highlight it, and then clicking on its name. This is the way renaming works on both Windows and macOS, and it’s always seemed slightly awkward that Unix desktop environments tend not to support it.

As with most of the other changes we’ve made to the desktop over the last few years, the intention is to make it simpler to use, and to ease the transition from non-Unix environments. But if you really don’t like what we’ve done and long for the old file manager, just untick the box for Display simplified user interface and menus in the Layout page of Preferences, and everything will be back the way it was!

## Battery indicator for laptops

One important feature missing from the previous release was an indication of the amount of battery life. Eben runs our desktop on his Mac, and he was becoming slightly irritated by having to keep rebooting into macOS just to check whether his battery was about to die — so fixing this was a priority!

We’ve added a battery status icon to the taskbar; this shows current percentage charge, along with whether the battery is charging, discharging, or connected to the mains. When you hover over the icon with the mouse pointer, a tooltip with more details appears, including the time remaining if the battery can provide this information.

While this battery monitor is mainly intended for the PC version, it also supports the first-generation pi-top — to see it, you’ll only need to make sure that I2C is enabled in Configuration. A future release will support the new second-generation pi-top.

## New PC applications

We have included a couple of new applications in the PC version. One is called PiServer — this allows you to set up an operating system, such as Raspbian, on the PC which can then be shared by a number of Pi clients networked to it. It is intended to make it easy for classrooms to have multiple Pis all running exactly the same software, and for the teacher to have control over how the software is installed and used. PiServer is quite a clever piece of software, and it’ll be covered in more detail in another blog post in December.

We’ve also added an application which allows you to easily use the GPIO pins of a Pi Zero connected via USB to a PC in applications using Scratch or Python. This makes it possible to run the same physical computing projects on the PC as you do on a Pi! Again, we’ll tell you more in a separate blog post this month.

Both of these applications are included as standard on the PC image, but not on the Raspbian image. You can run them on a Pi if you want — both can be installed from apt.

## How to get the new versions

New images for both Raspbian and Debian versions are available from the Downloads page.

It is possible to update existing installations of both Raspbian and Debian versions. For Raspbian, this is easy: just open a terminal window and enter

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

#### Updating Raspbian on your Raspberry Pi

It is slightly more complex for the PC version, as the previous release was based around Debian Jessie. You will need to edit the files /etc/apt/sources.list and /etc/apt/sources.list.d/raspi.list, using sudo to do so. In both files, change every occurrence of the word “jessie” to “stretch”. When that’s done, do the following:

sudo apt-get update
sudo dpkg --force-depends -r libwebkitgtk-3.0-common
sudo apt-get -f install
sudo apt-get install python3-thonny
sudo apt-get install sonic-pi=2.10.0~repack-rpt1+2
sudo apt-get install piserver
sudo apt-get install usbbootgui


At several points during the upgrade process, you will be asked if you want to keep the current version of a configuration file or to install the package maintainer’s version. In every case, keep the existing version, which is the default option. The update may take an hour or so, depending on your network connection.

As with all software updates, there is the possibility that something may go wrong during the process, which could lead to your operating system becoming corrupted. Therefore, we always recommend making a backup first.

Enjoy the new versions, and do let us know any feedback you have in the comments or on the forums!

The post Stretch for PCs and Macs, and a Raspbian update appeared first on Raspberry Pi.

Post Syndicated from ris original https://lwn.net/Articles/739946/rss

Greg Kroah-Hartman has released stable kernels 4.14.2, 4.13.16, 4.9.65, 4.4.101, 4.4.102, and 3.18.84. This is the last 4.13.y kernel and
users should upgrade to 4.14 now. For the two 4.4 updates Greg says:
[4.4.102] is a bugfix for an issue if PAGE_POISONING is enabled in
the kernel configuration. If you do not run your kernel with that option,
no need to upgrade, just stick with 4.4.101.

# HiveMQ 3.2.8 released

Post Syndicated from The HiveMQ Team original https://www.hivemq.com/blog/hivemq-3-2-8-released/

The HiveMQ team is pleased to announce the availability of HiveMQ 3.2.8. This is a maintenance release for the 3.2 series and brings the following improvements:

• Improved performance for payload disk persistence
• Improved performance for subscription disk persistence
• Improved exception handling in OnSubscribeCallback when an Exception is not caught by a plugin
• Fixed an issue where the metric for discarded messages “QoS 0 Queue not empty” was increased when a client is offline
• Fixed an issue where the convenience methods for a SslCertificate might return null for certain extensions
• Fixed an issue which could lead to the OnPubackReceivedCallback being executed when inflight queue is full
• Fixed an issue where a scheduled background cleanup job could cause an error in the logs
• Fixed an issue which could lead to an IllegalArgumentException when sending a QoS 0 message in a rare edge-case
• Fixed an issue where a error “Exception while handling batched publish request” was logged without reason

We recommend to upgrade if you are an HiveMQ 3.2.x user.

Have a great day,
The HiveMQ Team

# I Still Prefer Eclipse Over IntelliJ IDEA

Post Syndicated from Bozho original https://techblog.bozho.net/still-prefer-eclipse-intellij-idea/

Over the years I’ve observed an inevitable shift from Eclipse to IntelliJ IDEA. Last year they were almost equal in usage, and I have the feeling things are swaying even more towards IDEA.

IDEA is like the iPhone of IDEs – its users tell you that “you will feel how much better it is once you get used to it”, “are you STILL using Eclipse??”, “IDEA is so much better, I thought everyone has switched”, etc.

I’ve been using mostly Eclipse for the past 12 years, but in some cases I did use IDEA – when I was writing Scala, when I was writing Android, and most recently – when Eclipse failed to be ready for the Java 9 release, so after half a day of trying to get it working, I just switched to IDEA until Eclipse finally gets a working Java 9 version (with Maven and the rest of the stuff).

But I will get back to Eclipse again, soon. And I still prefer it. Not just because of all the key combinations I’ve internalized (you can reuse those in IDEA), but because there are still things I find worse in IDEA. Of course, IDEA has so much more cool features like code improvement suggestions and actually working plugins for everything. But at least some of the problems I see have to do with the more basic development workflow and experience. And you can’t compensate for those with sugarcoating. So here they are:

• Projects are not automatically built (by default), so you can end up with compilation errors that you don’t see until you open a non-compiling file or run a build. And turning the autobild on makes my machine crawl. I know I need an upgrade, but that’s not the point – not having “build on change” was a huge surprise to me the first time I tried IDEA. I recently complained about that on twitter and it turns out “it’s a feature”. The rationale seems to be that if you use refactoring, that shouldn’t happen. Well, there are dozens of cases when it does happen. Refactoring by adding a method parameter, by changing the type of a parameter, by removing a parameter (where the IDE can’t infer which parameter is removed based on the types), by changing return types. Also, a change in maven/gradle dependencies may introduces compilation issues that you don’t get to see. This is not a reasonable default at all, and I think the performance issues are the only reason it’s still the default. I think this makes the experience much worse.
• You can have only one project per screen. Maybe there are those small companies with greenfield projects where you only need one. But I’ve never been in a situation, where you don’t at least occasionally need a separate project. Be it an “experiments” one, a “tools” one, or whatever. And no, multi-module maven projects (which IDEA handles well) are not sufficient. So each time you need to step out of your main project, you launch another screen. Apart from the bad usability, it’s double the memory, double the fun.
• Speaking of memory, It seems to be taking more memory than Eclipse. I don’t have representative benchmarks of that, and I know that my 8 GB RAM home machine is way to small for development nowadays, but still.
• It feels less responsive and clunky. There is some minor delay that I can’t define well, but “I feel it”. I read somewhere that they were excessively repainting the screen elements, so that might be the explanation. Eclipse feels smoother (I know that’s not a proper argument, but I can’t be more precise)
• Due to some extra cleverness, I have “unused methods” and “never assigned fields” all around the project. It uses spring, so these methods and fields are controller methods and autowired fields. Maybe some spring plugin would take care of that, but spring is not the only framework that uses reflection. Even getters and setters on POJOs get the unused warnings. What’s the problem with those warnings? That warnings are devalued. They don’t mean anything now. There isn’t a “yellow” indicator on the class either, so you don’t actually see the amount of warnings you have. Eclipse displays warnings better, and the false positives are much less.
• The call hierarchy is slightly worse. But since that’s the most important IDE feature for me (alongside refactoring), it matters. It doesn’t give you the call hierarchy of default constructors that are not explicitly defined. Also, from what I’ve seen IDEA users don’t often use the call hierarchy feature. “Find usage” I think predates the call hierarchy, and is also much more visible through the UI, so some of the IDEA users don’t even know what a call hierarchy is. And repeatedly do “find usage”. That’s only partly the IDE’s fault.
• No search in the output console. Come one, why I do I have an IDE, where I have to copy the output and paste it in a text editor in order to search. Now, to clarify, the console does have search. But when I run my (spring-boot) application, it outputs stuff in a panel at the bottom that is not the console and doesn’t have search.
• CTRL+arrows by default jumps over whole words, and not camel cased words. This is configurable, but is yet another odd default. You almost always want to be able to traverse your variables word by word (in camel case), rather than skipping over the whole variable (method/class) name.
• A few years ago when I used it for Scala, the project never actually compiled. But I guess that’s more Scala’s fault than of the IDE

Apart from the first two, the rest are not major issues, I agree. But they add up. Ultimately, it’s a matter of personal choice whether you can turn a blind eye to these issues. But I’m getting back to Eclipse again. At some point I will propose improvements in the IntelliJ IDEA backlog and will check it again in a few years, I guess.

The post I Still Prefer Eclipse Over IntelliJ IDEA appeared first on Bozho's tech blog.

Post Syndicated from jake original https://lwn.net/Articles/738718/rss

Security updates have been issued by Arch Linux (lib32-openssl, libextractor, postgresql, and postgresql-old-upgrade), Debian (bchunk, postgresql-9.4, postgresql-9.6, postgresql-common, roundcube, and tomcat7), Gentoo (libxml2), SUSE (kvm, openssl1, and qemu), and Ubuntu (postgresql-common).

# piwheels: making “pip install” fast

Post Syndicated from Ben Nuttall original https://www.raspberrypi.org/blog/piwheels/

TL;DR pip install numpy used to take ages, and now it’s super fast thanks to piwheels.

The Python Package Index (PyPI) is a package repository for Python modules. Members of the Python community publish software and libraries in it as an easy method of distribution. If you’ve ever used pip install, PyPI is the service that hosts the software you installed. You may have noticed that some installations can take a long time on the Raspberry Pi. That usually happens when modules have been implemented in C and require compilation.

No more slacking off! pip install numpy takes just a few seconds now \o/

## Wheels for Python packages

A general solution to this problem exists: Python wheels are a standard for distributing pre-built versions of packages, saving users from having to build from source. However, when C code is compiled, it’s compiled for a particular architecture, so package maintainers usually publish wheels for 32-bit and 64-bit Windows, macOS, and Linux. Although Raspberry Pi runs Linux, its architecture is ARM, so Linux wheels are not compatible.

What Python wheels are not

Pip works by browsing PyPI for a wheel matching the user’s architecture — and if it doesn’t find one, it falls back to the source distribution (usually a tarball or zip of the source code). Then the user has to build it themselves, which can take a long time, or may require certain dependencies. And if pip can’t find a source distribution, the installation fails.

## Developing piwheels

In order to solve this problem, I decided to build wheels of every package on PyPI. I wrote some tooling for automating the process and used a postgres database to monitor the status of builds and log the output. Using a Pi 3 in my living room, I attempted to build wheels of the latest version of all 100 000 packages on PyPI and to host them on a web server on the Pi. This took a total of ten days, and my proof-of-concept seemed to show that it generally worked and was likely to be useful! You could install packages directly from the server, and installations were really fast.

This Pi 3 was the piwheels beta server, sitting atop my SSH gateway Pi 2 at home

I proceeded to plan for version 2, which would attempt to build every version of every package — about 750 000 versions in total. I estimated this would take 75 days for one Pi, but I intended to scale it up to use multiple Pis. Our web hosts Mythic Beasts provide dedicated Pi 3 hosting, so I fired up 20 of them to share the load. With some help from Dave Jones, who created an efficient queuing system for the builders, we were able make this run like clockwork. In under two weeks, it was complete! Read ALL about the first build run drama on my blog.

ALL the cloud Pis

## Improving piwheels

We analysed the failures, made some tweaks, installed some key dependencies, and ran the build again to raise our success rate from 76% to 83%. We also rebuilt packages for Python 3.5 (the new default in Raspbian Stretch). The wheels we build are tagged ‘armv7l’, but because our Raspbian image is compatible with all Pi models, they’re really ARMv6, so they’re compatible with Pi 3, Pi 2, Pi 1 and Pi Zero. This means the ‘armv6l’-tagged wheels we provide are really just the ARMv7 wheels renamed.

The wonderful piwheels monitor interface created by Dave

Now, you might be thinking “Why didn’t you just cross-compile?” I really wanted to have full compatibility, and building natively on Pis seemed to be the best way to achieve that. I had easy access to the Pis, and it really didn’t take all that long. Plus, you know, I wanted to eat my own dog food.

You might also be thinking “Why don’t you just apt install python3-numpy?” It’s true that some Python packages are distributed via the Raspbian/Debian archives too. However, if you’re in a virtual environment, or you need a more recent version than the one packaged for Debian, you need pip.

## How it works

Now that the piwheels package repository is running as a service, hosted on a Pi 3 in the Mythic Beasts data centre in London. The pip package in Raspbian Stretch is configured to use piwheels as an additional index, so it falls back to PyPI if we’re missing a package. Just run sudo apt upgrade to get the configuration change. You’ll find that pip installs are much faster now! If you want to use piwheels on Raspbian Jessie, that’s possible too — find the instructions in our FAQs. And now, every time you pip install something, your files come from a web server running on a Raspberry Pi (that capable little machine)!

Try it for yourself in a virtual environment:

sudo apt install virtualenv python3-virtualenv -y
virtualenv -p /usr/bin/python3 testpip
source testpip/bin/activate
pip install numpy

This takes about 20 minutes on a Pi 3, 2.5 hours on a Pi 1, or just a few seconds on either if you use piwheels.

If you’re interested to see the details, try pip install numpy -v for verbose output. You’ll see that pip discovers two indexes to search:

2 location(s) to search for versions of numpy:
* https://pypi.python.org/simple/numpy/
* https://www.piwheels.hostedpi.com/simple/numpy/

Then it searches both indexes for available files. From this list of files, it determines the latest version available. Next it looks for a Python version and architecture match, and then opts for a wheel over a source distribution. If a new package or version is released, piwheels will automatically pick it up and add it to the build queue.

How piwheels works

For the users unfamiliar with virtual environments I should mention that doing this isn’t a requirement — just an easy way of testing installations in a sandbox. Most pip usage will require sudo pip3 install {package}, which installs at a system level.

If you come across any issues with any packages from piwheels, please let us know in a GitHub issue.

## Taking piwheels further

We currently provide over 670 000 wheels for more than 96 000 packages, all compiled natively on Raspberry Pi hardware. Moreover, we’ll keep building new packages as they are released.

Note that, at present, we have built wheels for Python 3.4 and 3.5 — we’re planning to add support for Python 3.6 and 2.7. The fact that piwheels is currently missing Python 2 wheels does not affect users: until we rebuild for Python 2, PyPI will be used as normal, it’ll just take longer than installing a Python 3 package for which we have a wheel. But remember, Python 2 end-of-life is less than three years away!

Many thanks to Dave Jones for his contributions to the project, and to Mythic Beasts for providing the excellent hosted Pi service.

Related/unrelated, check out my poster from the PyCon UK poster session:

The post piwheels: making “pip install” fast appeared first on Raspberry Pi.

# HiveMQ 3.3.1 released

Post Syndicated from The HiveMQ Team original https://www.hivemq.com/blog/hivemq-3-3-1-released/

The HiveMQ team is pleased to announce the availability of HiveMQ 3.3.1. This is a maintenance release for the 3.3 series and brings the following improvements:

• Increased performance for offline message queues on disk
• Improved user experience when inspecting client details for TLS clients in the Web UI
• Improved user experience when creating a trace recording in the Web UI
• Improved logging for HiveMQ file persistence on Windows
• Fixed an issue which could lead to the OnPubackReceivedCallback being executed when inflight queue is full
• Fixed an issue when a message is created via the plugin system and added to an offline message queue
• Fixed an issue where the convenience methods for a SslCertificate might return null for certain extensions