Sometimes when I want to write some technical posts or articles, I cannot find a proper place to post them. I know I can post them on this blog and I have been doing it all the time. But sometimes when you have a series of posts about a topic, especially when you want to keep those posts alive, have them versioned and update them from time to time, the blog post is not a very good way to organize them. For these kind of posts, writing them in the markdown format and keeping them in a git repo would be a more natural choice, like what azure-docs does. I don’t use markdown for this blog site because I quite like the Gutenberg editor of WordPress. It is quite good for the casual writings. WordPress also doesn’t have the versioning feature.
So I decided to create my own docs site. It is a static site built with mdbook and hosted on GitHub with GitHub Pages. mdbook is a tool to create online books from markdown files. It is written in Rust. Why I choose to use it is because it is just one single binary file and quite easy to use. I can easily use it in GitHub actions to build the site automatically.
I don’t know what I am going to put on my docs site. A rough idea is it would be for technical, especially Azure related, posts or articles I write, something not proper for a blog. The first one on the site is a series of tutorials which talks about step by step integration of Application Gateway, API Management and self-hosted gateway in an internal virtual network. It consists of multiple parts and is organized into sections. It is too heavy to be posted as blog posts.
So if I write something similar in the future, I will post it there.
With text fragments, browsers may implement an option to ‘Copy URL to here’ when the user opens the context menu on a text selection. The browser can then generate a URL with the text selection appropriately specified, and the recipient of the URL will have the specified text conveniently indicated.
The idea of creating this extension was originated from an internal discussion with my colleagues. As customer engineers, we need to share document links with customers quite often. Quoting the text from the document directly in the email could make the email awfully long, while just sending a link may not help customers quickly locate the information in a long document. We need a better way to share the information so that the customer can get it quickly and accurately.
Text fragment is a perfect solution for this case. It is a new feature supported by browsers natively, and it is available in the latest version of Chrome and Edge. We just need a way to easily generate a url with the text fragment. Browsers might provide such a feature in the future, but before that I hope this extension can help to address the needs.
The extension currently consists of 3 functions: copy the generated url, open the generated url in a new tab, and copy the text fragment and the generated url in the markdown format. I think these 3 features are more than enough for a single purpose extension.
Kubernetes’ Node Allocatable feature allows the cluster to reserve the resources of node for system daemons of OS and Kubernetes itself. For example, when I ran kubectl describe node for a node in my AKS cluster, I got the following capacity related output. The size of this node was Standard DS2 v2 which had 2 CPU cores and 7GB memory.
From the output, out of 2 cores and 7GB memory, there were 1900 millicores and 66% memory (4668348/7113660) allocatable to pods. The details about how these numbers were calculated are described in this document. In short, the configuration of node allocatable in AKS is as follows:
However, the above memory reservation doesn’t seem to applicable to Windows nodes. The following was the output when I ran the kubectl describe node for a Windows node. More memory was reserved for Windows nodes.
I’ve been using Microsoft PowerToys on my work machine for some time and find myself more and more rely on it every day. The name, PowerToys, is a very old name on Windows. The 1st version of PowerToys came with Windows 95 more than two decades ago. It’s actually a fantastic idea to build new the set of productivity tools on top of a legacy brand. It feels like the good old days were finally connected with the new era, and the history continues.
I cannot remember exactly when I used PowerToys for Windows last time. It could be the time around Windows 98 when I was still in college, long time ago. Honestly, I was not a fan of it at that time. Although I’ve forgotten why I didn’t like it, it was not something that I must install on my machine. But PowerToys for Windows 10 has changed my mind and it has made its way to my must-installed software list.
The two features that I used most in PowerToys are FancyZones and File Explorer Preview. As I’m using a 4k monitor, FancyZones helped me to better utilize the space of the monitor. It made me feel like the laptop screen becomes redundant that I turned it off most of the time. File Explorer Preview is an add-on of Windows file explorer. I can preview file content directly in Windows explorer with it. The best part is it supports markdown preview. Now I can read those README files without even opening them with markdown editors.
Another tool that I am going to use frequently is PowerToys Run which was just released with version 0.18. I always wanted to have such a tool to run apps quickly and I’ve already tried some 3rd tools. PowerToys Run looks quite promising.
The PowerToys is quite stable. I didn’t hit any problem with it since I started using it. So if you are on Windows 10 and haven’t tried it, maybe it’s the time. 🙂
In Azure API Management, when the HTTP method of a request doesn’t match the one defined in the corresponding operation, APIM returns status code HTTP 404 Resource Not Found to the client. For example, the OOTB Echo API defined the Retrieve resource (cached) operation with HTTP GET. If you call it with HTTP POST, you’ll get HTTP 404 Resource Not Found in the response.
The HTTP 404 returned by APIM in this scenario doesn’t really follow the definition of HTTP status code strictly. According to the definition, HTTP 405 Method Not Allowed is designated for this situation. There was a feedback for this issue to APIM team and it would be addressed in the future according to the response. But before that, we have to use some workaround to bypass this issue. Here is how you can do it with policies in APIM.
Handle the error
When APIM failed to identify an API or operation, it will raise a configuration error which will returns HTTP 404. What we need to do is to handle this error and change the status code to HTTP 405. In this way, you avoid the overhead of creating operations for each of the HTTP methods to handle the situation. The next question is on which scope the error should be handled. Depending on the configurations of your APIM, I think you can handle the error on either all operations or all APIs.
The policy code
The following policy code is a sample for Echo API all operations.
<when condition="@(context.LastError.Source == "configuration" && context.Request.Url.Path == "/echo/resource-cached")">
<set-status code="405" reason="Method not allowed" />
return new JObject(
new JProperty("status", "HTTP 405"),
new JProperty("message", "Method not allowed")
The tricky part is in the <when> condition. The first part of the condition is to check if this is a configuration error. If it is, the second part will test if the request is to Retrieve resource (cached) operation. The second test is to avoid the situation where a real HTTP 404 happens.
You may wonder why I used context.Request rather than context.Operation to test which operation it is. The reason is APIM sets context.Operation to null in this case because it cannot identify which operation it is (and that is why the configuration error happens).
You can use this walkaround to return HTTP 405 until APIM fixes its behavior.
SharePoint是我加入微软之后所专注的第一个服务器端产品。前前后后差不多有十年的时间，我都是围绕它展开工作的，也因此对它相当有感情。很多年前，我曾经写了几篇blog，介绍SharePoint的早期历史（SharePoint简史I, II, III）。2011年，我放弃了春节休假，跑去雷德蒙德参加了当时SharePoint的最高级别证书，Microsoft Certified Master for SharePoint，的培训和考试。我甚至一度以为，我的职业生涯会一直伴随SharePoint发展了。
If you have a custom image in a Shared Image Gallery and you want to use it to create a pool in Azure Batch, this document, Use the Shared Image Gallery to create a custom pool, provides a pretty good guidance for it. I followed it to test the scenario and hit two minor issues.
As it is mentioned in another doc, AAD authentication is also a prerequisite for using shared image gallery. If you use --shared-key-auth with az batch account login, you would hit an anthentication error with Azure Cli. I raised an issue for the document and hopefully a note will be added to it.
There is no sample code to demonstrate how to create a pool with shared image gallery with Python.
So I wrote a simple sample in Python. It is based on the latest version (9.0.0) of Azure Batch package for Python. And it uses a service principal for the AAD authentication. The custom image I used for test was built on top of Ubuntu 18.04-LTS. So the node agent sku is ubuntu 18.04. It needs to be changed accordingly if other os version is used.
# Import the required modules from the
# Azure Batch Client Library for Python
import azure.batch._batch_service_client as batch
import azure.batch.models as batchmodels
from azure.common.credentials import ServicePrincipalCredentials
# Specify Batch account credentials
account = "<batch-account-name>"
batch_url = "<batch-account-url>"
ad_client_id = "<client id of the SP>"
ad_tenant = "<tenant id>"
ad_secret = "<secret of the SP>"
# Pool settings
pool_id = "LinuxNodesSamplePoolPython"
vm_size = "STANDARD_D2_V3"
node_count = 1
# Initialize the Batch client
creds = ServicePrincipalCredentials(
config = batch.BatchServiceClientConfiguration(creds, batch_url)
client = batch.BatchServiceClient(creds, batch_url)
# Create the unbound pool
new_pool = batchmodels.PoolAddParameter(id=pool_id, vm_size=vm_size)
new_pool.target_dedicated = node_count
# Configure the start task for the pool
start_task = batchmodels.StartTask(
start_task.run_elevated = True
new_pool.start_task = start_task
# Create an ImageReference which specifies the Marketplace
# virtual machine image to install on the nodes.
ir = batchmodels.ImageReference(
virtual_machine_image_id="<resource id of the image version in sig>"
# Create the VirtualMachineConfiguration, specifying
# the VM image reference and the Batch node agent to
# be installed on the node.
vmc = batchmodels.VirtualMachineConfiguration(
# Assign the virtual machine configuration to the pool
new_pool.virtual_machine_configuration = vmc
# Create pool in the Batch service
Update: I polished the above sample code and pushed it into the document I mentioned at the beginning of this post via a PR. The Python sample code in that document is based on the one in this post.
Recently I spent more and more time on WSL 2. I ran a Ubuntu 18.04 on WSL 2 and mainly used the tools such as zsh, tmux and vim. To make fun with the environment, I customized them little by little, and now the environment looks like this:
As there are several customized dotfiles now, I put them together and created a simple script so that I can run a single script to get the environment ready. Now I have my own dotfiles repo and it is here. I will update it if I make other changes to these dotfiles in the future.
And I also shared the profile of my Windows Terminal here. Now I can easily get Windows Terminal and WSL 2 environment configured on any Windows 10 machine.