As in most IoT/cloud processing scenarios, we collect data for multiple producers, and then let a bunch of consumers process the data.
This is a very common design, every (professional) IoT solution I have seen recommends it, and for good reasons: when load increases (you have more producers, or each of them produces more traffic and/or bigger messages), you scale out: you have multiple, concurrent (competing) consumers.

You can do this easily if you decouple consumers and producers, by inserting something that "buffers" data in between the consumer and producer. The benefits are increased availability and load levelling.
An easy way to have this is to place a simple, durable data store in the middle. The design is clean: producers place data into the store, consumers take them out and process them. 
The data store is usually FIFO, and usually a Queue. 

In our case, it wasn't a queue. The design was similar, the implementation... different :)
Still durable, still FIFO-ish, but not a Queue in the sense of Azure Queues or Service Bus Queues or RabbitMQ (typical examples of queues used in IoT projects).

It was just a file server + a TCP client writing to an SQL DB in a very NoSQL-ish way (just a table of events, one event per row, shreded by date). Devices write data to an append-only file, copy them securely using an SSH/SCP tunnel, place them in a directory; a daemon (service) with a file-system watcher (inotify under Linux) takes new files and uploads them to a proper queue for processing; the TCP client notifies about relevant events (new file present, files queued but not uploaded yet, all uploaded, etc.).

Our goal is to change this structure with something much more standard, where possible, so we can use some modern middleware and ditch some old, buggy software. Or even "get rid" of the middleware and let some Cloud Service handle the details for us.
If you have something similar, in order to modernize it and/or move it to a Cloud environment such as Azure (or if you plan to make your move in the future), you want to use a modern, standard messaging protocol (AMQP, for example), and a middleware that understands it natively (RabbitMQ, Azure Events Hub, ...). But how do you use this protocol? How do you "change" your flow from the legacy devices (producers) to the consumers?
You have three options:
  1. Insert a "stub/proxy" on the server (or cloud) side. Devices talk to this stub using their native protocol (a custom TCP protocol, HTTP + XML payload, whatever). This "stub" usually scales well: you can code it as lightweight web server (an Azure Web Role, for example) and just throw in more machines if needed, and let a load balance (like HAProxy) distribute the load. It is important that this layer just acts as a "collector": take data, do basic validation, log, and throw it in a queue for processing. No processing of messages here, no SQL inserts, so we do not block and we can have rate leveling, survive to bursts, etc.
    This is the only viable solution if you cannot touch the device code.
  2. Re-write all the code on the device that "calls out" using the legacy protocol/wire format, and substitute it with something that talks in a standard supported by various brokers, like AMQP or MQTT. In this way, you can directly talk to the broker (Azure Event Hub, IoT Hub, RabbitMQ, ..), without the need of a stub. 
    This solution is viable only if you fully control the device firmware.
  3. Insert a "broker" or gateway on the device, and then redirect all existing TCP traffic to the gateway (using local sockets), and move the file manager/watcher to the device. Have multiple queues, based on priority of transmission. Have connection-sensitive policies (transmit only the high-priority queue under GPRS, for example). Provide also a way to call directly the broker for new code, so the broker itself will store data and events to files and handle their lifetime. Then use AMQP as the message transport: to the external obsever (the Queue), the devices talks AMQP natively.
    This is a "in the middle solution": you can code / add your own programs to the device, but you do not have to change the existing software.
In our case, the 3rd option is the best one. It gives us flexibility, the ability to work on a piece of functionality at the time while keeping some of the old software still running.
Plus, it makes it possible to implement some advanced controls over data transmission (a "built-in" way to transmit files in a reliable way, have messages with different priorities, transmission policies based on time/location/connection status, ...).
But why would you want to design a new piece of software that still writes to files, and not just keep a transmission (TX) queue in memory? For the same reason queues in the middleware or in the cloud are durable: fail and recover. Device fields are battery powered, work in harsh conditions, are operated by non-professional personnel. They can be shut down at any moment (no voltage, excess heat, manually turned off), and we have no guarantees all the messages have been transmitted already; GPRS connections can be really slow, or we may be in a location that has no connectivity at all at the moment.

I was surprised to discover that this kind of in-process, durable data structures are ... scarce!
I was only able to locate a few:
  • BigQueue (JVM): based on memory mapped files. Tuned for size, not reliability, but claims to be persistent and reliable.
  • Rhino.Queues.Storage.Disk (.NET): Rhino Queues are an experiment from the creator of the (very good) RavenDB. There is a follow up post on persistent transactional queues, as a generic base for durable engines (DB base).
  • Apache Mnemonic (JVM): "an advanced hybrid memory storage oriented library ... a non-volatile/durable Java object model and durable computing"
  • Imms (.NET): "is a powerful, high-performance library of immutable and persistent collections for the .NET Framework."
  • Akka streams + Akka persistence (JVM): two Akka modules, reactive streams and persistence, make it possible to implement a durable queue with a minimal amount of code. A good article can be found here.
  • Redis (Any lang): the famous in-memory DB provides periodic snapshots. You need to forget snapshot and go for the append-only file alternative, which is the fully-durable persistence strategy for Redis.

The last one is a bit stretched.. it is not in-process, but Redis is so lightweight, so common and so easy to port that it may be possible to run it (with some tweaks) on an embedded device. Not optimal, not my first choice (among the other problems, there is a RAM issue: what if the queue exceeds the memory size?), but probably viable if there is no alternative.

Most likely, given the memory and resource constraints of the devices, it would be wise to cook up our own alternative using C/Go and memory mapped files. This is an area of IoT were I have seen little work, so it would be an interesting new project to work on!

What is an "architect" anyway?

A little break from the series of posts on Pumpkin. Today I had to explain what I do for a living, and it was longer then I expected. But it gave me the time and opportunity to think about what I really do.

I always pause a second when people ask me "what is your job?"
I usually go for a very simple "software developer" or "programmer". After all if it is good enough for Scott Hanselman, I should be fine with it.

Since I have some experience, I sometimes may add "Senior" to it. But I don't feel that "Senior" anyway: I feel young, and I feel like I have always something new to learn, something new to do. "Senior" seems a little too accomplished to me.

Unfortunately, if you speak to people in the same or in a related field, this is rarely enough.
"But which is you role?" "Don't you manage a team of 7?" yes I do, but "manager" is way too nontechnical; "managing" the team, for me, is a mix of architectural and code reviews, coaching, mentoring, everything necessary to ensure the team delivers great things and customers are happy.

"So you are a tech lead!"
Well... I love the technical side of my job. I turned down good offers in the past because the "step-up" role was a pure management one, a common evil in Italy - management is the only "way up".
But my current role is made from pure technical parts (code, design, run sanity check -check the proper patterns are used and anti-patterns avoided, for example-) and also "soft" parts (be a bridge between customers and tech-speaking people, talk to upper management, customers and stakeholders, present figures and help making informed decision, advocate for my team).

This is why my "official" title of "Software architect" at my current job is kind of OK. It is technical, but not purely technical.

But what is an "architect", or a "team lead", anyway?

IMO, or at least in my case, it means being "Primus inter pares" and a "Servant leader".

"Primus inter pares" is a latin expression which roughly translates to "first among equals". It does not really matters if your leadership is sanctioned by the corporate ladder, or if it is an honorary title and you are formally equal to other members of their group. You act as a member of the group; you keep coding and share chores (debugging, bug fixing), otherwise you will lose what it really matters among developer: (unofficial) respect for your skills and knowledge.

Keep your hands dirty is key for me. I try to keep the balance between the technical and soft side of my job 50/50, and I code whenever I can. Because I like it (I think I will never give up coding, even if I win a billion euros and I can retire), and because I need it. It is like physical exercise, or training for a sport: both you and your body know when you need it, that you need it.

A good objective of leadership is to help those who are doing poorly to do well and to help those who are doing well to do even better.
– Jim Rohn, American entrepreneur.

Robert K. Greenleaf first coined the phrase "servant leadership" in his 1970 essay, "The Servant as a Leader."

As a servant leader, you are a "servant first".
In practice, I try to focus on the needs my team mates, before considering my own. I acknowledge other people's perspectives, give them the support they need to meet their work and personal goals, involve them in decisions where appropriate, and build a sense of community. I still call the shots (design-by-committee does not really work), but I listen before speaking, and use persuasion over authority.

A great side effect from acting in this way is that it gives you the necessary skills to deal with people "above" you: the ones you cannot use your authority upon, either because they are your boss, or because they are your peers (customers, for example). Your persuasion and reasoning skills are honed, and you are in a fantastic shape to be able to make your point, make your message pass, make them listen and consider what you say.

Cloud, at last!

After some time experimenting, studying, designing (but mostly: presenting possible scenarios to management), we are preparing to move a central part of our systems to the cloud!
Cost savings (especially OPEX - especially linked to sourcing: finding and hiring a good DBA is very hard!), increased availability and resistance to HW failures/catastrophes are the key points I presented to management to help them decide.

On the downside, to be ready to move will require a good engineering effort; our systems are very old, but the general architecture built during the years is sound. It was good (surprising and pleasant) to discover how we already used  many of the patterns listed in the Azure Cloud Design Patterns Architecture Guidance in our systems.

The legacy components of the system have been extensively extended during the years, and the new parts and paths developed since I joined the company in 2012 always followed a classic pattern which you may recognize from several IoT designs:
  •   Field devices -> Queue (Inbox/Outbox)
  •   Queue -> Processing -> SQL
  •   Commands -> Queue (Inbox) <- Device
More precisely:
  • Field devices communicate to a "central" server, which just collects the data, buffers them on a durable (temporary) store. Little or no processing here (basics validation only)
  • On different machines, "consume" the items in the temporary store: pull things from there, persist each event in an "append-only" data store (Event Sourcing)
  • Process the events: generate domain objects through a series of steps (3), from the append-only store events to the final objects persisted in SQL tables (Pipes and Filters)
  • Generate "synthesized" data for reporting and statistics queries (Materialized View)
The back-end is already decomposed in several "medium" services: not really "micro" services, but several HTTP-based services talking through a REST API.
These services are already quite robust: they have to, since they are already exposed to the Internet. In particular, they implement Cache-aside for performance, Circuit Breaker/Retry with exp. backoff when they talk to external services (and, in most cases, even when they talk internally to each other), sharding for big data, throttling for some of the public-facing APIs.

Technically, the challenge is so interesting. The architecture is really apt to be ported to the cloud, but to make it really competitive (and to minimize running costs), some pieces will have to be rewritten.
To make the transition as smooth as possible, initially most of the pieces will be less than optimal (mostly IaaS - VMs, SQL storage where NoSQL/Cloud storage would suffice, Compute instances, ..) but will be slowly rewritten to be more efficient, more "cloudy" (App Fabric, Tables, Functions, ...).

Really excited to have begun this journey!

Old school code writing (sort of)

As I mentioned in my previous post, online resources on Hosting are pretty scarce. 

Also, writing an Host for the CLR requires some in-depth knowledge of topics you do not usually see in your day-to-day programming, like for example IO completion ports. Same for AppDomains: there is plenty of documentation and resources compared to Hosting, but still some more advanced feature, and the underlying mechanisms (how does it work? How does a thread interact and knows of AppDomains?) are not something you can find in a forum. 

Luckily, I have been coding for enough time to have a programming library at home. Also, I have always been the kind of guy that wants to know not only how to use stuff, but how they really work, so I had plenty of books on how the CLR (and Windows) work at a low level. All the books I am going to list were already in my library!

The first one, a mandatory read, THE book on hosting the CLR:

Then, a couple of books from Richter:


The first one is very famous. I have the third edition (in Italian! :) ) which used to be titled "Advanced Windows". It is THE reference for the Win32 API.
If you go anywhere near CreateProcess and CreateThread, you need to have and read this book.

The second one has a title which is a bit misleading. It is acutally a "part 2" for the first one, focused on highly threaded, concurrent applications. It is the best explanation I have ever read of APCs and IO Completion Ports.


A couple of very good books on the CLR to understand Type Loading and AppDomains.
A "softer" read before digging into...


...the Internals. You need to know what a TEB is and how it works when you are chasing Threads as they cross AppDomains.
And you need all the insider knowledge you may get, if you need to debug cross-thread, managed-unmanaged transitions. And bugs spanning over asynchronous calls. 

My edition of the first book is actually called "Inside Windows NT". It is the second edition of the same book, which described the internals of NT3.1 (which was, despite the name, the first Windows running on the NT kernel), and was originally authored by Helen Custer. Helen worked closely with Dave Cutler's original NT team. My edition covers NT4, but it is still valid today. Actually, it is kind of fun to see how things evolved over the years: you can really see the evolution, how things changed with the transition from 32 to 64 bits (which my edition already covers, NT4 used to run on 64 bit Alphas), and how they changed it for security reasons. But the foundations and concepts are there: evolution, not revolution.


And finally two books that really helped me while writing replacements for the ITaks API. The first one to tell me how it should work, the second one telling me how to look inside the SSLCI for the relevant parts (how and when the Hosting code is called).

Of course, I did not read all these books before setting to work! But I have read them over the years, and having them in my bookshelf provided a quick and valuable reference during the development of my host for Pumpkin.
This is one of the (few) times when I'm grateful to have learned to program "before google", in the late '90/early '00. Reading a book was the only way to learn. It was slow, but it really fixed the concepts in my mind. 

Or maybe I was just younger :)

So, in the end, what went into Pumpkin?

Control was performed at compilation time or execution time? And if it is execution, using which technique?

In general, compilation has a big pro (you can notify immediately the snippet creator that he did something wrong, and even preventing the code block from becoming an executable snippet) and a big con (you control only the code that is written. What if the user code calls down some (legitimate) path in the BCL that results in a undesired behaviour?)

AppDomain sandboxing has some big pros (simple, designed with security in mind) and a big con (no "direct" way to control some resource usage, like thread time or CPU time).
Hosting has a big advantage (fine control of everything, also of "third" assemblies like the BCL) which is also the big disadvantage (you HAVE to do anything by yourself).

So each of them can handle the same issue with different efficacy. Consider the issue of controlling thread creation:
  • at compilation, you "catch" constructs that create a new thread (new Thread, Task.Factory.StartNew, ThreadPool.QueueUserWorkItem, ...)
    • you have to find all of them, and live with the code that creates a thread indirectly.
    • but you can do wonderful things, like intercepting calls to thread and sync primitives and substitute them - run them on your own scheduler!
  • at runtime, you:
    • (AppDomain) check periodically. Count new threads from the last check.
    • (hosting) you are notified of thread creation, so you monitor it.
    • (debugger) you are notified as well, and you can even suspend the user code immediately before/after.

Another example:
  • at compilation, you control which namespaces can be used (indirectly controlling the assembly)
  • at runtime you can control which assemblies are really loaded (you are either notified OR asked to load them - and you can prevent the loading)

What I ended up doing is to use a mix of techniques. 

In particular, I implemented some compiler checks.
Then, run the compiled IL on a separate AppDomain with a restricted PermissionSet (sandboxing).
Then, run all the managed code in an hosted CLR.

I am not crazy...

Guess who is using the same technique? (well, not compiler checks/rewriting, but AppDomain sandboxing + Hosting?)
A piece of software that has the same problem, i.e. running unknown, third party pieces of code from different parties in a reliable, efficient way: IIS.
There is very little information on the subject; it is not one of those things for which you have extensive documentation already available. Sure, MSDN has documented it (MSDN has documentation for everything, thankfully), but there is no tutorial, or Q&As on the subject on StackOverflow. But the pieces of information you find in blogs and articles suggests that this technology is used in two Microsoft products: SQL Server, for which the Hosting API was created, and IIS.

Also, this is a POC, so one of the goals is to let me explore different ways of doing the same thing, and assess robustness and speed of execution. Testing different technologies is part of the game :)

Copyright 2020 - Lorenzo Dematte