On REST services performance

Recently, I had to investigate a “performance issue” a customer was having with one of their web services.

To make it simple, the service is a REST API to get information about points of interest. The response is quite large (hundreds of KBs) but nothing exceptional.

Several clients can perform multiple requests for the same POI, and the response for a single POI is almost the same: it varies a little over time with real time updates (traffic, info, last minute additions or cancellations) but it is roughly the same. So, the code was already doing the right thing, and cached the answer for each POI.

Well.. more or less the right thing. For a single POI, with ~1000 sub-items, the response time for the first request was ~39 seconds. Subsequent requests required half a second, so the caching was working.

The API is for consumption by a service, so there is no need to be “responsive” (as in “users will need quick feedback or they will walk away”), but still: 39 seconds!

The API is implemented in Java (JAX-RS + JPA to be precise), so armed with the profiler of choice (VisualVM) I started hunting for hot spots. Here is a list of DOs and DON’Ts I compiled while investigating and fixing the issues which can came handy. The list is not tied to Java, it is very general!

  • DO instrument your code with Log calls with timings at enter/exit of “hot” functions.

  • DO it at a logging level you can leave on in production (e.g.: INFO. But then leave INFO on!)

  • If you didn’t do that… you don’t have times :( But you need timing to see where you need to improve, so DO use a profiler!

  • DON’T just go for the function you believe is the slowest: a profiler trace may surprise you.

  • DO use a profile with instrumentation, not with sampling. In my experience, sampling is never precise enough.

  • When you have found your hot spots, DO move all costly, repeated operations you find to a place where they are done once (a constructor or initialization method). In this case, the offender was an innocent looking Config.get(“db.name”) method. Just to get the DB name from a config class. Which ended up opening a property file, reading it, parsing it every time. The method was doing a lot under the hood, but would you have looked at it without the hint from a profiler? See the previous point :)

  • DO cache data that does not change, if you are reading it from a DB or web service. Cache on the output is the basics, but it is often not nearly enough. You have to avoid multiple lookups for the same resource inside a single request!

  • DON’T do a DB (or even a cache!) lookup if you can find another way to get the same information, even when you need to re-compute a result (i.e. spend CPU time). In this service, each POI sub-item could be categorized in one of two classes using some of its attributes. The old implementation used a subset of attributes that needed to be checked with a DB lookup; I changed it to use a different set of attributes that needed a (simple) computation.

  • DO load the cache in bulk for small-ish sets of data. In this service, each string which contained information to be displayed to the user was looked up in a DB of “special cases” using complex fallback rules, each time generating a less refined query (up to 4). If nothing was found (~80% of the times), a default string was loaded from a Web Service. This operation alone accounted for 10 seconds, or 25% of the total time. The “not default” DB contains just around 4k items; a bulk query for all the rows requires only 100ms, can be easily stored in memory, and doing the filtering and matching in memory costs just a few ms more.

  • DO use simple libraries: communication with other Web Services was done using a very easy to use but quite heavy library (Jersey + Jackson for JSON deserialization). I switched to a custom client written with OkHttp and GSON, and the net save was 4 whole seconds.

  • DO enable compression on the result (if the user agent says it supports compression - most do!)

  • DO minimize copy and allocations: in this case (but this advice applies to Java in general), I used streams instead of lists whenever possible, down to the response buffer.

  • DON’T use the DB, especially NOT the same DB you use for you primary data, to store “logs”. In this case, it was access logs for rate limiting. A client hitting the service hard could consume a lot of resources just to generate a 429 Too Many Requests response.
    Recording such an event to your primary DB is the perfect opportunity for a DoS attack.

Remember the times?

  • 39 seconds for the first request

  • 0.5 seconds for subsequent request son the same object


  • 1 second for the first request

  • 50 milliseconds (0.05 seconds) for subsequent requests on the same object

It is more than an order of magnitude. I’m quite happy, and so was the customer! The time can be brought time even further by ditching the ORM framework (JPA in this case) and going for native (JDBC) queries, changing some algorithms, using a different exchange format (e.g. protobuf instead of JSON), but with increasing effort and diminishing results. And for this customer, the result was already more than they asked for.

Recently I have watched with awe a couple of presentations on Docker on Windows. Wow… proper containers on the Windows kernel, I haven’t seen it coming! I thought that “porting” cgroups and namespaces from Linux was something hard to accomplish. Surely, all the bits where already almost there: Windows has had something similar to cgroups for resource control (Jobs, sets of processes which can enforce limits such as working set size, process priority, and end-of-job time limit on each process that is associated with the job) since NT 5.1 (XP/Windows Server 2003), and NT has had kernel namespaces since its beginning (NT 3.1). For details I recommend reading this excellent article: Inside NT Object Manager.

However, seeing this bits put together and exposed to the userland with a nice API, and Docker ported (not forked!) to use it, is something else.

Of course, I was instantly curious. How did they do that? The Windows Containers Documentation contains no clue: all you can find is a quick start.

There a couple of videos on presentations done at DockerCon EU 2015 and DockerCon 2016, but documentation is really scarce. Non-existent.

From the videos you understand that, as usual, Windows does not expose in an official way (at least, for now) the primitives needed to create the virtual environment for creating a container, but rather exposes a user-mode DLL with a simplified (and, hopefully, stable) API to create “Compute Systems”. One of the exposed functions is, for example, HcsCreateComputeSystem.

A search in MSDN for vmcompute.dll or, for example, HcsCreateComputeSystem, does reveal nothing… the only documentation is found in a couple of GitHub projects from Microsoft: hcsshim, a shim used by Docker to support Windows Containers by calling into the vmcompute.dll API, and dotnet-computevirtualization, a .NET assembly to access the vmcoumpute.dll API from managed languages.

Once it is documented, this “Compute Systems” API is surely something I want to try out for Pumpkin.

Meanwhile… there is a passage in the presentations and in the official Introducing Docker for Windows Server 2016 that left me with mixed feelings. You cannot use “FROM scratch”  to build your own image; you have to start with a “minimal” windows image.

Currently, Microsoft provides microsoft/windowsservercore or microsoft/nanoserver.

The Windows Server Core image comes with a mostly complete userland with the processes and DLLs found on a standard Windows Server Core install.  This image is very convenient: any Windows server software will run on it without modification, but it takes 10GB of disk space! The other base layer option is Nano Server, a new and very minimal Windows version with a pared-down Windows API. The API is not complete, but porting should be easy and it is less than 300MB.

But why do I need at least a 300MB image? The point of containers is to share the kernel of the host, isn’t it?

The explanation is buried in one of the DockerCon presentations, and it makes a lot of sense: the Win32 API is exposed to Windows programs through DLLs, not directly as syscalls.

(Note: I will call it the “Win32 API” even on x64, because there really isn’t any “Win64” API: it is the same! Just look at the names of the libraries: kernel32, gdi32, user32, …)

Of course, internally those DLLs will make syscalls to transition to kernel mode (sort of, more on this later), but the surface you program against in Windows is through user mode DLLs.

What really hit me is the sheer number of “basic” components required by Windows nowadays. I started to program using the Win32 API when there were only 3 of these DLLs. OK, 4 if you count advapi32.dll.

Sure, then there was ole32, if you wanted to use OLE, and comctr32, if you wanted “fancy” user controls, and Ws2_32 if you wanted sockets… But they were “optional”, while now without  crss.exe, lsass.exe, smss.exe, svchost, wininit, etc. you cannot even run a “no-op” executable.

Or can you?

I will take an extremely simple application, which “touches” (creates) a very specific file (no user input, to make things easier), and try to remove dependencies and see how far you can get (Spoiler alert: you can!)

I will divide my findings in three blog posts, one for each “step” I went through, and update this post with links every time I post a new one.

  • Step 1: no C runtime
  • Step 2: no Win32 API
  • Step 3: no Native API (no ntdll.dll)
For the impatient: there is a github repo with all the code here :)

An interesting scenario that I keep bumping into is: there is a device, which is typically "headless" (no significant UI), but it performs some specific, useful function ("IoT" device. Note the quotes). There is a web app, with a rich UI. Your customers wants them to "talk". 

I smell trouble at the "talk" part. 

  • Data collection + display? Sure, IoT is born to do exactly this.
  • Control the device, issuing commands? You have to be careful with this one, naive solutions bring a lot of trouble.
  • Getting interactive feedback? (Command - response - Web UI update) Let's talk about this, because it can be done, but it is not so straightforward.

The trouble with this scenario is that it looks so simple to non technical people (and less technical people alike... I heard an IT manager ask once how he can wire up his scanner to our web application, but that's another story). However, it is so easy to come up with bad or ugly solutions!

Fortunately, with a couple of new-ish technology bits and some good patterns, it is possible to come up with a good solution.

The good 

Well... Yeah, it you follow The Good, The Bad and the Ugly trinity, you have to start with "The Good". But I don't want to talk about the good already, it will spoil all the fun!

Let's come back to the good later.

The bad

Sending commands to a device is quite easy. You open a socket on the device, listening. You somehow know where the device is, which address it has (which is an interesting problem in its own, but I digress), so from your web server you just connect to that socket (address/port) and send your commands. You probably don't have a firewall, but if you have one, just punch a hole through it to let message pass through.


Even if you go trough all the effort of making it secure (using an SSH tunnel, for example, but I have seen plain text sockets with ASCII protocols. Open to the Internet.), you are exposing a single port, probably on a low-power device (like an ARM embedded device), possibly using a low-bandwidth channel (like GPRS). How much does it take to DoS it? Probably you don't even need the first D (as in DDoS), you could do it from a single machine.

But let's say you somehow try and cover this hole, maybe with a VPN, inserting a field gateway in front of your "IoT" device(s), or putting a VPN inside the devices themselves if they are powerful enough (and the aforementioned client exists for you platform/architecture. Good luck with some ARM v4 devices with 1MB (Mega) disk space I have seen, but I digress again).

Great, you are probably relieved because now you can have interactive feedback!

You see, it is easy. Your user click on the page. On the web server, inside the click handler (whatever this is: a controller, a handler, a servlet...) you open a socket and send a command trough TCP, and wait for a response. The client receives the command, process it, aswers back through the socket and closes the connection. The web server receives it, prepares an HTTP response and returns it to the web browser. Convenient!

Now you have thread affinity all the way: the same thread of execution spawns servers, programs, devices. Blocking threads is a performance bottleneck in any case but it is a big issue on a server. 


If the network is slow (and it will be), you may end up keeping the web server thread hanging for seconds. Let's forget about hanging the web browser UI (you can put up a nice animation, or use Ajax), but keeping a web server thread hung for seconds doing nothing is BAD. Like in "2 minutes, and your web server will crash for resource exhaustion" bad. 

The ugly

IoT is difficult, real-time web applications are difficult, so let's ditch them. 

We go back one or two decades, and write "desktop" applications. Functionalities provided by the former web applications are exposed as web services, and we consume them from our desktop app. Which is connected directly to the device. Which is not an "Internet of Things" device anymore, maybe a "Intranet of Things" device (I should probably register that term! :) ), if it is not connected by USB. 

It makes sense in a lot of cases, if the device and the PC/Tablet/whatever are co-located. But it imposes a lot of physical constraints (there is a direct connection between the device and the PCs/Tablets that can control that device). Also, if the app was a web app to begin with, there are probably good reasons for that: easy of deployment, developers familiar with the framework, runs on any modern web browser, ...

Especially if you discover that half your clients are using Windows PCs, the other half Linux, and a third half Android tablets. Now you need to build and maintain three different desktop applications. Which is an ugly mess.

Besides, how do you reach your "IoT"-device now, if it is on a private Intranet? How do you update it, collect diagnostics and logs in a central location? You can not, or you have to setup complicate firewall rules, policies, local update servers. Again, feasible, but ugly.

The good (finally)

The solution I came up with is to use WebSockets (or better, a multi-transport library like SignalR) + AMQP "response" queues to make it good.

AMQP is a messanging protocol. It is a raising standard, and it is implemented by many (most) queuing servers and event hubs (see my previous post). An interesting usage for AMQP is to create "response queues". A hint on how this might work is given, for example, in the RabbitMQ tutorial. The last tutorial in the series describes how to code an RPC mechanism using AMQP. 

The interesting part of the tutorial is in the client code:

   var consumer = new QueueingBasicConsumer(channel);

   var replyQueueName = channel.QueueDeclare(exclusive: true, autoDelete: true).QueueName;


   channel.BasicConsume(queue: replyQueueName,

                        noAck: true,

                        consumer: consumer);


   // Do "Something special" here with replyQueueName                          




      var ea = (BasicDeliverEventArgs)consumer.Queue.Dequeue();

      if(ea.BasicProperties.CorrelationId == corrId)


          return Encoding.UTF8.GetString(ea.Body);




The client declares a name-less (name is auto generated) queue. Plus, the queue should be deleted on disconnection. There are two flags for that: exclusive and autoDelete.

Than it does something with the queue name, and then continuously waits and reads messages from this queue.

The "something special" is: communicate to the server the device availability, and specify the name of the queue. This queue will be like an inbox for our device: a place on a server where who wants to communicate with us will place a message. The device (or better, a thread/task on the device) will wait for any incoming message, read it, and then dispatch it. The device will act accordingly.

It is important to note that the client is establishing the connection to a server (the queueing system server), not the other way around. This prevents the problem highlighted in the "Bad" section.

WebSockets (and related transport mechanism, like Server-Sent events, long polling, etc.) allow code on the server side to push content to the connected clients as it happens, in real-time.

So the device communicates with the web server using some standard way (direct HTTP POST to the server, or even better posting to a queue, and then having a worker read from the queue and POST to the web server, so you have queue-based load levelling, and the server pushes the update to the client. 

Note that the server knows which devices are "on" and can be controlled by the client (because the first thing a device does is to announce to the server its availability, and where it can be contacted), and it can also know which client is talking to which device, because traffic passes through the server:

Put the two together, and you have a working system for real time command + control of "IoT"-like devices, with real-time feedback and response, from a standard web application.

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.

Copyright 2020 - Lorenzo Dematte