# Surface Book 2 issues after running updates

This is definitely more of a note to myself, and my fellow Surface Book 2 users;

Lately mostly after installing Windows updates, the book is plagued by strange behavior.

A few things include:

• Difficulties to start up. When the power button has been pressed, it won’t quickly start up after hibernation. Most of the times you have to press a few times in a row to get the Surface to wake up. Pressing the detach button mostly is the quickest way to wake up in this situation.
• After a sleep the NVIDIA GTX 1050/1060 isn’t recognized after sleep. Detaching and then attaching may work to detect it again. A restart also works fine. So now and then a warning would pop up that there is a hardware problem with the base.
• Bad performance for detaching. It’d be easily possible to have to wait about 20 or 30 seconds for the base to detach. If at all. A restart mostly fixed these issues.

There is an old Reddit post I stumbled upon with people experiencing the same problem, and few fixes have been shared.

Go here for the Reddit post: https://www.reddit.com/r/Surface/comments/81qvp1/my_surface_book_2_having_issues_with_waking_up/

The top comment seemed to fix my problems. With the most recent updates I figured out that booting in UEFI mode (by pressing the power button and the volume up button) and exiting again solved my problems.

# ASP.NET Core 2.1 and GraphQL; Adding JWT Bearer validation to subscriptions

Please note before reading: this post flows over from implementation details of the graphql-dotnet project. If you’re like me, stuck on authorization for subscriptions, and want to know how I worked around it, read the post. If you just want authorization with subscriptions to work, copy past the code blocks.

The folks working on the graphql-dotnet library have done some amazing work on bringing the GraphQL specification to the .NET ecosystem. While the library is definitely not easy to implement, the resulting GraphQL API can be a delight to work with.

By now several additional libraries have been developed. Notoriously the authorization library. This library brings policies and permissions to your GraphQL API. This authorization library is not dependent on any specific authorization mechanism, as long as you can provide an implementation for the IProvideClaimsPrincipal interface which sole responsibility is providing GraphQL with a ClaimsPrincipal instance. Validation logic is provided by implementing the IValidationRule interface.

While this works fine for queries and mutations, at the time of writing the following issues with regards to making bearer authorization work on subscriptions:

• HTTP headers are provided in the payload of the first frame that goes over the WebSocket connection. This first frame has the type of connection_init. As the subscription subscriber is only invoked at the start frame, it might seem difficult at first sight to retrieve the JWT token from the payload of the connection_init frame.

If you want to know what is going on within a websocket connection, the developer tools inside browsers can show the individual frames on websocket connections these days, which is a nice way of getting to know some about the protocols flowing through :)

• Subscriptions hijack the UserContext property on the ResolveEventStream object to pass a MessageHandlingContext instance. Therefore, generic auth validation logic provided with an IValidationRule implementation cannot access the ClaimsPrincipal object on the UserContext, therefore failing verification.

## Retrieving the bearer token

While the GraphQL library looks pretty daunting at first sight, it also happens to be incredibly extensible at all points through the use of interfaces. One of these extension points happens to be the IOperationMessageListener, which acts on different messages received via the websocket connections, and therefore, indirectly on subscriptions. I have implemented the IOperationMessageListener in a way that the bearer token can be extracted from the connection_init frame.

public class SubscriptionInitializer : IOperationMessageListener
{
private IHttpContextAccessor _contextAccessor;
public SubscriptionInitializer(IHttpContextAccessor httpContextAccessor)
{
_contextAccessor = httpContextAccessor;
}

{
if (context.Terminated) return;

var message = context.Message;

if (message.Type == MessageType.GQL_CONNECTION_INIT)
{
JwtSecurityTokenHandler handler = new JwtSecurityTokenHandler();
var user = handler.ValidateToken(message.Payload.GetValue("Authorization").ToString().Replace("Bearer ", ""),
new TokenValidationParameters
{
ValidIssuer = "TODO: Enter your issuer",
ValidAudience = "TODO: Enter your audience",
},
out SecurityToken token);

if (!user.Identity.IsAuthenticated) await context.Terminate();

_contextAccessor.HttpContext.User = user;
}
}

{
}

{
}
}


## Passing on the IPrincipal

As seen in the code above it is not too difficult to validate a JWT for a subscription, but what if we’re using the authorization library and want our existing IValidationRule implementations also to apply for subscriptions?

To understand one of the possible ways we can pass this data to the IValidationRule implementations we have to dig into the DI system. The GraphQL library heavily relies on the DI mechanics, and the only thing we want to know is how IValidationRule objects are passed, or in this case, injected.

You don’t have to figure that out yourself. A type of IEnumerable<IValidationRule> is injected into the constructor of the DefaultGraphQLExecuter<TSchema> which is being registered in the DI as transient, therefore meaning that it is instantiated every time it is requested. Thankfully the IGraphQLExecuter<TSchema> is requested in the GraphQLHttpMiddleware<TSchema> middleware, so with every request we get a new executer. Great!

Because of these properties we can create our own object which is injected through the constructors both in the IOperationMessageListener and IValidationRule implementations. We will transfer the principal by means of this transport object. We can then populate the ClaimsPrincipal in the UserContext with the value we passed in this terrible, awefull, and just plain ugly way, but at least it’s better than duplicating code multiple times.

Now here comes the exciting part (As inspired by this commented out class, which I only discovered after I figured everything else out…). We can inject the IHttpContextAccessor into our class to have the ability to get the current HttpContext instance into our SubscriptionInitializer. Even more beautifull is the way we can pass the ClaimsPrincipal to the IValidationRule: We set the HttpContext.User property with our ClaimsPrincipal, after which it is available on the current HttpContext. How easy can life be sometimes.

Also, don’t forget to inject the IHttpContextAccessor into your IValidationRule in order to be able to access the IPrincipal.

# How to move data from MSSQL to ElasticSearch at galactic speeds

Shortly after the one billion mark has been reached with my side project I got a question from an old classmate whether I had ever heard about ElasticSearch. I did, but I’ve never had made any time to dive into it any further. This would be a nice moment to dive into it a bit further. In this post we’ll dive into the data feeding process I used to move some data out of MSSQL and into ElasticSearch.

At first I was a bit skeptical about nosql document storage. I heard good things about the performance of ElasticSearch and I’ve seen and loved Kibana for quick data visualization experiments. So lets jump in.

This post is inspired by / based on the post at https://instarea.com/2017/12/06/heavy-load-ms-sql-elasticsearch/ yet with a bit more code to get you up and running quickly.

We’re assuming that both the MSSQL and ElasticSearch databases are running on localhost. What we’re going to do in a nutshell is to export data from MSSQL to a JSON file and import this file in ElasticSearch.

## Preparing the source

Retrieving the data Since MSSQL 2016 it’s possible to export data directly from the database engine. That’s exactly what we’re going to do. There’s a few catches though.

1. The documents you want to import should be separated by a newline
2. Each document you want to import should be preceded by a command

The following type of command can be used to retrieve data in almost the correct format:

SELECT (SELECT
dbo.ToDateTime2(Ticks) AS 'timestamp'
, Location.Lat AS 'location.lat'
, Location.Long AS 'location.lon'
FOR JSON PATH, WITHOUT_ARRAY_WRAPPER)
FROM dbo.TableName


Every row you select will be a JSON object, or soon to be ElasticSearch document.

Exporting numeric values with a certain accuracy

When exporting numeric data MSSQL is usually trying to be overly accurate (Displaying doubles as 4.782966666666670e+001 for example). ElasticSearch can parse this as numeric data, but it is unable to parse these values as geo_point values. Besides this it makes the exported JSON file way bigger then it needs to be.

It is recommended to use the FORMAT function to select the number of decimal places you want to show. E.g. FORMAT(Location.Lat, ‘N5’) to retrieve a value with an accuracy of 5 decimal places.

You can store the result set from the SQL query in several ways. One way is by copy / pasting the results from the query window in SSMS or whatever your SQL editor is. Personally I find it more convenient, especially with large datasets, to use the bcp command in order to directly output the results to a file. bcp can be used in the following way:

bcp "YOUR SQL QUERY" queryout ./output.json -c -S "SERVER_LOCATION" -d DATABASE_NAME -U "DATABASE_USER" -P "DATABASE_USER_PASSWORD"


Every document which has to be indexed by ElasticSearch should be preceded by a command, though. We want to index the documents, but there are plenty of other commands you can use. See here for a list of commands.

We just use {"index":{}} as a command which tells ElasticSearch we want to index the document. We’ll define the index and type later on while indexing the document.

In order to precede all documents with a command the awk command can be used:

awk '{print "{\"index\":{}}";};1' fileToAddCommandsTo.json > outputFileWithCommandsAdded.json


## Importing the data

The resulting document is ready to be imported into ElasticSearch. Depending on the size of the output you might want to chop up the file in bits in order to make sure the bulk import still works.

The following script as found here can be used to chunk up your file and import the data into ElasticSearch. Please note you have to make the following changes to the document:

• The filename which contains the data you want to import. In this case output.json.
• The index name in the URL ([INDEX])
• The type name in the URL ([TYPE])
#!/bin/sh
LINES=400000
split -l $LINES output.json for xf in$(ls | grep x..$) do curl -o nul -H 'Content-Type: application/x-ndjson' -XPOST localhost:9200/[INDEX]/[TYPE]/_bulk --data-binary @$xf
rm $xf done  Hint: You can save the script above as import.sh. Execute it by either running sh import.sh or bash import.sh. I think either will work. I keep forgetting how to run scripts all the time. ## How fast is it? This fast. Netdata started complaining about the speed the (almost empty 2tb) drive was being written to. Without joke. It was fast. The file size was about 4.8gb, containing 17,075,262 documents. See the netdata screenshot for the performance hit it took. A rough scan shows the import began at 20:23:50 (without slicing the file, that is), and was done at 20:27:15. Which is 205 seconds. Dividing 17,075,262 by 205 amounts to a little more then 83,000 documents each second. Wanna know about another piece of magic? Check the index size. corstian@db:~$ curl -XGET "http://localhost:9200/_cat/shards?v"
index     shard prirep state         docs   store ip        node
.kibana   0     p      STARTED          2   6.8kb 127.0.0.1 VnzjBHy
positions 2     p      STARTED    3415189 478.9mb 127.0.0.1 VnzjBHy
positions 2     r      UNASSIGNED
positions 3     p      STARTED    3415823 477.7mb 127.0.0.1 VnzjBHy
positions 3     r      UNASSIGNED
positions 4     p      STARTED    3414075 476.3mb 127.0.0.1 VnzjBHy
positions 4     r      UNASSIGNED
positions 1     p      STARTED    3413247 478.2mb 127.0.0.1 VnzjBHy
positions 1     r      UNASSIGNED
positions 0     p      STARTED    3416929 479.4mb 127.0.0.1 VnzjBHy
positions 0     r      UNASSIGNED


There are five shards with around 480mb of data, which is about 2.4gb. I kid you not, that’s just half the size of the file we’ve just indexed! Ofcourse there’s the overhead in the indexed file of the commands we added but still. That’s truly amazing!

# Achieving one billion

Over the last years I’ve been working continuously with large data sets. Whether it’s about air quality, aircraft movements, plant growth or weather information. I love to process ‘pretty big data’ as I call it. Putting this data into data stores, processing it as fast as possible, preferably real-time or near real-time, and building cool applications on top of this data.

Yesterday is the day that one of my projects passed the ‘magic’ 1 billion record count. Lets look at all the stuff that got out of hand so badly that this could’ve happen.

## Fiddling with software

A really long time ago when I was just a little kiddo, possibly somewhere about 14 years old, I was playing with some PHP websites and scripts. Figuring out how things were working, trying to write some new code, and trying to get the computer to do what I was trying to achieve. It was at this time that I was trying to write a contact form which should’ve send the input in the form as email to my own mailbox. I’m not sure what I did wrong but in one way or another the script to send the mail got stuck in an infinite loop and started continuously sending mail to my own email address, which was hosted at my father’s company.

Five minutes later my father called me. They got a call from the ISP that they (me) were taking down their server(s?) with a mail bomb. These emails caused carnage everywhere in between my little web server and the receiving mail server (at my father’s company). In the timespan of only five minutes I ended up overloading a few mailservers (which were probably incorrectly configured either way), and about 80,000 emails in my own mailbox.

That’s only 25 mails per second.

This was the last moment ever I underestimated the power of computers.

Around the same time that I set off the email bomb I also learned about databases. Well, I knew there were tables, and I knew I could store and retrieve data with them, but don’t you dare ask me about foreign keys, indices and other technical details. What did I know? When a classmate showed me a database which contained a table with about 40,000 records I was amazed. How could one get to fill a database with so much information? I could not imagine I was ever about to create a database so big.

A while later I met some people who wanted to build an online platform which would help consumers find the perfect car for their needs based on a set of questions. For this system to work we needed a bit of data about different cars so I wrote a tool to scrape some websites and put this information in a database. After letting this scraper run for a few nights we ended up with information about 400,000 different types of cars. Note I still did not know about foreign keys and all that stuff, but the numbers were getting bigger.

## Growing bigger

When I was 17 years old I started writing software at my father’s company. It was this time that I wrote the base for a system which now processes information from about 1000 IoT like devices in real-time. It was at this time that I started learning about database internals because I had to. I needed it to keep the response times in an acceptable window.

I remember searching around on google about experiences people had with large data sets in order to be able to estimate the amount of resources needed. I was worried the whole thing would burn down as soon as 13 million records were processed. These numbers felt so big that I could not possibly imagine how much resources were needed to work with it. And amazingly it continued to work to this day, with 16 million records and counting.

## … and bigger

These days my biggest side project is about processing flight information from glider aircraft. When I started one and a half year ago I had no idea about how fast this project was about to grow. In less then a year the tool processed one billion data points in real-time, sometimes peaking at about 20,000 points / second, amounting to millions of points each day.

And then there are the learning moments in this traject. Like trashing a live database with 300 million records, and not having backups. The moments I’ve been trying to debug and fix the data processing beast without causing any downtime, and all moments I was busy doing performance optimization and I did not think I was going to make it. It’s funny how both the idea and the program evoluted at the same time. In the beginning I just wanted to process a small amount of data, and it took 10 seconds to process 7,000 records. Right now the same amount of information can be processed and distributed in just shy of a quarter of a second.

And this way the project starting to grow, and continued growing, regardless of the moments I just wanted to trash it because I did something stupid, or the moments I did not have any energy to continue working on it, or because of any other reason there was to stop.

And now it contains 1,000,000,000 data points. Actually I’m more proud about this achievement then I care to admit. It feels like an amazing milestone for me personally 🎉.

A quick calculation shows that these one billion data points represent more then 60 full years of experience flying aircraft in all kinds of conditions. This is more then 20,000 full days.

## What’s next?

Data alone is not useful. There is currently so much data stored that no one will understand the context of the data on itself without proper aggregation. One of the biggest goals for the future is to streamline the data aggregation and information distribution processes.

My goals are the following:

• Develop or set up an information processing pipeline which allows for rapid development of new data processing steps
• Develop API’s so that this information can be shared with the world
• Apply machine learning to this data set in order to be able to predict several things, including thermalling hotspots based on the weather conditions
• Develop a proper user interface and attract pilots to use this platform for their flight analysis needs

The goals above will probably take several years to achieve. Nevertheless, on to the next 10 billion data points and amazing data processing tools!

# Scaffolding an existing SQL database with Entity Framework Core in 5 minutes

Sometimes it’s nice to get a break from ‘legacy’ software. In this case we would like to get started using Identity Server 4 with an existing database running on SQL. Wouldn’t it be nice to get up and running in a few minutes? Hold on.

We’re using the dotnet cli for speed, and cross platform usefulness (OS X, Windows(?) and Linux). We assume you’ve booted your favorite terminal and you are in your solution folder. Buckle up buddy!

mkdir <YOUR_PROJECT_FOLDER>
cd <YOUR_PROJECT_FOLDER>
dotnet new classlib



After these packages have been installed we need to add the following two lines to the .csproj file in the current folder. These are required in order to use the Entity Framework tooling from the command line. Use your favorite text editor:

<ItemGroup>
<DotNetCliToolReference Include="Microsoft.EntityFrameworkCore.Tools.DotNet" Version="2.0.0" />
<DotNetCliToolReference Include="Microsoft.VisualStudio.Web.CodeGeneration.Tools" Version="2.0.0" />
</ItemGroup>


Ps. make sure these 4 lines are somewhere within the <Project> tags, or grouped with other <DotNetCliToolReference> tags eventually already in your file.

In order to be able to run migrations from this project we will configure it to be able to act as being startup project. There should be a line which is:

<TargetFramework>netstandard2.0</TargetFramework>


Change it to:

<TargetFrameworks>netcoreapp2.0;netstandard2.0</TargetFrameworks>


Just copy paste it for your speed, and sanity. Now you’re ready to scaffold your data model from the database. In order to do so:

dotnet ef dbcontext scaffold "<CONNECTION STRING>" Microsoft.EntityFrameworkCore.SqlServer


In case you get an error about the framework versions you need to install, just determine the current version by running the dotnet --info command and grabbing the value from under the Microsoft .NET Core Shared Framework Host line. Next add the following tag just under the <TargetFramework> tag and you’re good to go.

<RuntimeFrameworkVersion>2.0.5</RuntimeFrameworkVersion>