HPTS 2015

I've been so busy travelling to conferences and customer engagements that I haven't had a chance to write about my trip to HPTS 2015. I've written several times about previous trips to this workshop and how it's my favourite of them all, so won't repeat. The workshop had the usual high standard of presentations and locating it at Asilomar is always a great way to focus the mind and conversations.

Because of its highly technical nature of the workshop I always like to use this event to try out new presentations - I know the feedback I receive will be constructive and worth hearing. This time my submission was essentially about what I'd written earlier this year concerning the evolution of application servers (application containers) driven by immutability and operating system containers, such as Docker. And I threw in a smattering of microservices since the topic is obviously relevant and I figured Adrian would be there! My presentation was well received and the feedback clearly showed that many people at the event agreed with it.

One other positive thing to come from the workshop and my presentation was that my co-traveller and long time friend/colleague, Professor Shrivastava, saw the presentation for the first time at the event. He understood it and whilst much of what was said I and others take for granted, he believes that there are groups of people that would find it interesting enough that we should write a paper. Writing papers with Santosh is something I enjoy and it has been a very fruitful collaboration over the years, so I look forward to this!

I also want to thank James because it was our discussions after I started my initial entries on the evolution of application servers that helped to focus and clarify my thinking.

High Integrity Software 2015 conference

I was asked to give one of the keynotes at this year's High Integrity Software Conference and I have to say that I enjoyed the entire event. It's probably one of the best technical conferences that I've been to for a while and I've been thinking about why that was the case. I think it's partly due to the fact that it was a very focussed themed event with multiple tracks for just a small part (4 talks) of the day so everyone at the event was able to concentrate on that main theme. In many ways it was similar to how conferences and workshops were "back in the day", before many of them seemed to need to try to appeal to everyone with all of the latests hot topics at the time.

The other thing that appealed to me was that I was asked to give a talk I hadn't given before: dependability issues for open source software. The presentation is now available and it was nice to be forced to put into a presentation things I've taken for granted for so many years. The feedback from the audience was very positive and then we were straight into a panel session on open source, which was also well attended with lots of great questions. Definitely a conference I'll remember for a long time and one I hope to go back to at some point.

Finally there was one presentation that stuck in my mind. It was by Professor Philip Koopman and worth reading. There's a video of a similar presentation he did previously and despite the fact it's not great quality, I recommend watching it if you're at all interested in dependable software for mission critical environments.

Heisenberg's back!

A long time ago (longer than I care to remember), I made the analogy between Heisenberg's Uncertainty Principle and large-scale data consistency (weak/eventual consistency). It got reported by InfoQ too. Over the weekend I came across a paper from friend/colleague Pat Helland where he made a similar analogy, so I figured I'd mention it here. What's that they say about "great minds" ;) ?

The modern production stack

Over a year ago I wrote the first of what was supposed to be the start of a series of articles on how research our industry had been doing years (decades) ago was relevant today and even in use today, i.e., had moved from blue-sky research into reality. I never got round to updating it, despite several valiant attempts. However, thanks to James I got to see a nice article called Anatomy of a Modern Production Stack, I probably don't need to, or at least not as much as I'd expected. And before anyone points out, yes this stuff is very similar to what James, Rob and the team have been doing with Fabric8, OpenShift etc.

Proud ...

I have two sons. They both make me proud on a daily basis. I've been away for a week at Summit and whilst there my youngest son, who's not quite 13, did something brave that made me very proud of him. I can't go into what it was except to say that he did it through the medium of social media - that annoyed me slightly but it seems to be the way of things today for a certain generation. I've told him that what he did was brave and made me proud but I figured I would also put it here so he can refer to it in the future; it's the closest I can get to social media.

Kids and asynchronous messaging

We've heard a lot recently about asynchronous frameworks. Typically what these really do is prevent or discourage blocking (synchronous) calls; such calls may be made, e.g., making an RPC to a remote service, but the sender thread/process either doesn't block (so doesn't actually make the call directly), or gets back some kind of token/promise that it can later present (locally or back to the receiver) in order to get the response when it needs it. Of course there's a lot more to this which I'm deliberately glossing over, but the point I'm trying to make it that most of these frameworks don't use the term "asynchronous" in the way we might understand it elsewhere. The FLP problem is a reality yet not one with which they tend to be concerned.

It's strange how the mind wanders when you're doing other things because this was precisely the problem I started to think about whilst cooking dinner the other day. With a lot of people believing that "asynchronous" is the way to go (and in many cases they're not necessarily wrong) I'm more often than not unsure as to whether they really understand the full implications. And whilst cooking I was getting frustrated with my kids, both of whom I'd tried contacting by SMS and neither of whom had responded.

So my mind wandered and I made the not-so-huge leap: I'm trying to communicate with them asynchronously. I sent each of them an SMS and I had no idea of it had arrived. Of course I could have added the SMS-delivery ack request to the messages but that can be disabled by the receiver. Therefore, until and unless they responded, I had absolutely no way to know if they'd got the message and were ignoring me, or if it had gone missing en route (SMS isn't reliable, after all). I sent more SMS messaged but with no responses I was left in the same quandary from before.

Now of course I could ring them but if they don't answer then all that does is mean I've deposited a message (voice) into some semi-persistent queue that they may or may not look at later. Another option would be to find someone who knows them and talk with them, asking for messages to be forwarded. By increasing the number of people I ask I increase the chances that one of them will eventually get through and I may receive a response (gossip protocols). However, ultimately until and unless they or a proxy (friend) for them responded (or came home) I couldn't really know directly whether or not they'd received my messages and acted upon them. Asynchronous is a bitch!

To conclude the story - they came home, had received the messages and didn't believe a response had been needed. Dinner was very tasty though!

A little more on transactions and microservices

I had some interesting discussions this week which made me want to write something more on transactions (really XA) and microservices.

Monoliths and a bit of a history lesson

Just another cross-post for those interested.

Update: one thing I forgot to make explicit but hopefully comes through in the above post and the earlier one on web-scale is that to suggest is that to suggest application servers (Java or something else) aren't sufficient for web-scale is difficult to justify since many high profile sites do run today quite well on them.

Web-scale: I do not think it means what you think it means!

When I blogged about transactions recently one of the comments referenced "web-scale" and how application servers aren't well suited for web-scale distributed applications. I'm going to write something specific about this later but it got me to thinking: what does that actually mean? To paraphrase Inigo Montoya "You keep using that term, I do not think it means what you think it means." Many people and groups use the term but there really isn't a common (agreed) definition. It's a cop out to claim that we don't need such a definition because everyone who is sufficiently "in the know" will "know it when they see it".

The web has been around now for a long time. Over the years it has grown considerably and yet many sites back in the late 1990's would not be considered "web-scale" today. But back then they almost certainly were. Despite what many people may suggest today, sites then and now got by well using "traditional" (or dated?) technologies such as relational databases, CORBA, probably even COBOL! Coping will millions of requests, terabytes of data etc.

It's only fair that our definition of "web-scale" should change over time in much the same way as our definition of things like "personal transport" change from "horse" to "car", or even "jet pack". But we don't even have a definition of "web-scale" that we can all get behind and then evolve. It's subjective and therefore fairly useless as a way of signifying whether anything is or is not suitable for the kinds of applications or services people develop for the web. Ideally we'd fix this before anyone (person, group, company) used the term again, but I doubt that'll happen.

Slightly frustrating article ...

I came across an article the other day from last year which tried to poke holes in transactions. I kept putting off writing about it but eventually decided I had to say something. So ... here it is.

Listening versus talking

As the song goes ... "To every thing there is a season, and a time to every purpose under the heaven". In any conversation or meeting there's a time to listen and a time to talk. Unfortunately in many environments there's an implicit peer pressure to be heard and be seen to be heard (!) even if what is then said doesn't advance the conversation. Yet often the right thing to do is listen, think and remain quiet until and unless you've got something to say which does add to the overall thread. But guess what? That can be harder to do than you might think, especially as meetings become very vocal and people verbally challenge each other to be the dominant individual.

It takes a lot of control in these situations to listen and not react, especially when there may be so many other people jostling to be heard and yet not really saying anything additive to the meeting. In fact others in such meetings may take silence as an indication of your disconnection from the conversation, or lack of understanding, which could lead you to want to say something (anything) just to prevent that kind of interpretation. But trust me ... Sometimes silence really is golden!

SOA done right?

I said something the other day in a presentation I was giving that I immediately regretted: I was asked how to position microservices and SOA for marketing, to which I responded "Microservices are SOA done right". A nice sound bite, but the reason I regret it is that someone could infer from it that anything termed a microservice is a good SOA citizen. That's not the case. Just as not everything termed SOA or REST was following good SOA or REST principles, so too will be the case with microservices. And the lack of a formal definition of microservices, whether in terms of recognisable SOA principles or something else, doesn't help matters.

So what should I have said? In hindsight, and remembering that it had to be short and to the point, I'd probably revisit and say something along the lines of "If the service follows good SOA principles then it's probably on the way to being the basis of a microservice". Not quite as snappy a response, but probably as far as I'd feel comfortable going in a single sentence. Of course it then could lead to follow up questions such as "What's the difference between a good SOA citizen/service and a microservice?" but then we're into a technical domain and no longer marketing.

Microservices and events

I wanted to write more on microservices (SOA) and specifically around reactive, event-orientation. We've heard a lot recently about frameworks such as Vert.x or Node.js which are reactive frameworks for Java, JavaScript and other languages (in the case of Vert.x). Developers are using them for a range of applications but because they present a reactive, event driven approach to building services and applications, it turns out they're also useful for microservices. Therefore, I wanted to give an indication of why they are useful for microservices and when I started to put proverbial pen to paper I found I was repeating a lot of what I'd written about before and specifically when I discussed some of the core reasons behind the future adaptive middleware platform.

Now I'm not suggesting that Vert.x, Node.js or any reactive, event-oriented framework or platform fulfills everything I mentioned in that earlier entry because I don't think they do, at least not today. However, they do represent a good basis from which to build. Of course that doesn't preclude existing architectures and associated software stacks from being used to build microservices. In fact I think a combination of these new approaches and existing mature stacks are the right way to go; with each new generation we learn the mistakes of the past and their successes, building upon the latter and hopefully not reproducing the former.

Unfortunately I'm not going to endorse the reactive manifesto, because I'm not entirely convinced that what's encompassed there really represents much more than good fault tolerance and message-driven architecture. For instance, responsive? I'd like to think that any useful platform should be responsive! I think we should add that to the "bleeding obvious" category. My definition of reactive would include fault tolerant, scalable, reliable, message-oriented (supporting synchronous and asynchronous), self-healing (resiliency is only part of that) and event-driven.

Therefore, when I say that reactive, event-oriented is a good fit for microservices (remember ... SOA) I'm talking about something slightly different than reactive as defined within the manifesto. It's all that and more. We've come a long way since the 1970's and 80's when things were typically synchronous and RPCs were the main paradigm. Even those are event driven if you think about it. But since then synchronous interactions fell out of favour, asynchronous rose in interest and so did event-orientation and associated efforts like CEP. Hopefully given what I wrote in that earlier article it should be fairly obvious what kind of platform characteristics I believe are necessary in the future - microservices is just a good approach that can take advantage of it.

As I said earlier though, I don't believe any implementation today really embodies where we need to be. There's a lot we can leverage from the past decades of enterprise software development but there is more work to be done. For instance, we hear a lot about asynchronous systems where in reality what most people are talking about is synchronous interactions performed by a separate thread to the main application (business logic) thread of control; there are known time bounds associated with work or interactions, even in a distributed environment. If we really want to talk about true asynchronous interactions, and I believe we do, then we are looking at unbounded interactions, where you simply do not know when a message is overdue. That causes a number of problems, including failure detection/suspicion (timeouts are a favourite way of trying to determine when a machine may have failed and now you can't use time as a factor), and distributed consensus, which has been proven to be unsolvable in an asynchronous environment.

Of course these may seem like purely theoretical or academic problems. But think about the kinds of environments we have today and which are growing in applicability: cloud, mobile, internet of things with sensors, gateways etc. These are inherently asynchronous, with unpredictable interaction patterns and message exchanges. Whilst synchronous or multi-threaded asynchronous may be a good stop-gap approach, it will inevitably present scalability and resiliency problems. True asynchronous, with all of the problems it represents, is the ultimate goal. But we're not there yet and it's going to take time as well as recognition by developers that further research and development is a necessity.

Theory of Everything

These days I watch more movies on a plane than anywhere else. One of those I saw yesterday on a trip to Boston was The Theory of Everything. Stephen Hawking has been a hero of mine from before I started at university doing my physics degree. I read a number of his papers and about him before I read his seminal book A Brief History Of Time. Several times.

Despite moving more into computing over the years I've continued to track physics, with books and papers by the likes of Penrose, Hawking and others, but obviously time gets in the way, which if you've read some of the same could be said to be quite ironic! Anyway, when I heard they were doing a film of Hawking's life I was a little nervous and decided it probably wasn't going to be something I'd watch. However, stuck on a 7 hour flight with not much else to do (laptop couldn't function in cattle-class due to person in front reclining) I decided to give it a go.

It's brilliant! Worth watching several times. The acting, the dialogue, the story, all come together. There's a sufficient mid of the human and physics to make to appeal to a wide audience. A singular film! (Sorry, a really bad pun!)

I also managed to watch The Imitation Game, which I think is probably just as good but for other reasons.

Transactions and microservices

Sometimes I think I have too many blogs to cover with articles. However, this time what I was writing about really did belong more in the JBoss Transactions team blog, so if you're interested in my thoughts on where transactions (ACID, compensation etc.) fit into the world of microservices, then check it out.

Containerless Microservices?

A few weeks ago I wrote about containerless development within Java. I said at the time that I don't really believe there's ever the absence of a container in one form or another, e.g., your operating system is a container. Then I wrote about containers and microservices. Different containers from those we're used to in the Java world, but definitely something more and more Java developers are becoming interested in. However, what I wanted to make clear here is that in no way are microservices (aka SOA) tied to (Linux) containers, whether Docker-based or some other variant.

Yes, those containers can make your life easier, particularly if you're in that DevOps mode and working in the Cloud. But just as not all software needs to run in the Cloud, neither do all services (whether micro or macro) need to be deployed into a (Linux) container. What I'm going to say will be in terms of Java, but it's really language agnostic: if you're looking to develop a service within Java then you already have great tools and 3rd party components to help you get there. The JVM is a wonderful container for all of your services that has had many years of skilled development within it to help make it reliable and perform. It also runs on a pretty much every operating system you can mention today and hardware ranging from constrained devices such as the Raspberry Pi, up to and including mainframes.

In the Java world we have a rich heritage of integration tools, such as Apache Camel and HornetQ. The community has built some of the best SOA tools and frameworks anywhere, so of course Java should be one of the top languages in which to develop microservices. If you're thinking about developing microservices in Java then you don't have to worry about using Linux containers: your unit of failure is the JVM. Start there and built upward. Don't throw in things (software components) that aren't strictly necessary. Remember what Einstein said: "Everything should be made as simple as possible, but not simpler."

However, as I said earlier, the Linux container can help you once you've got your microservice(s) up and running. Developing locally on your laptop, say, is unlikely to be the place where you want to start with something like Docker, especially if you're just learning how to create microservices in the first place. (Keep the number of variables to a minimum). But once you've got the service tested and working, Docker and its like represent a great way of packaging them up and deploying them elsewhere.

Microservices and state

In a previous entry I was talking about the natural unit of failure for a microservice(s) being the container (Docker or some other implementation). I touched briefly on state but only to say that we should assume stateless instances for the purpose of that article and we'd come back to state later. Well it's later and I've a few thoughts on the topic. First it's worth noting that if the container is the unit of failure, such that all servers within an image fail together, then it can also be the unit of replication. Again let's ignore durable state for now but it still makes sense to spin up multiple instances of the same image to handle load and provide fault tolerance (increased availability). In fact this is how cluster technologies such as Kubernetes work.

I've spent a lot of time over the years working in the areas of fault tolerance, replication and transactions; it doesn't matter whether we're talking about replicating objects, services, or containers, the principles are the same. This got me to thinking that something I wrote over 22 years ago might have some applicability today. Back then we were looking at distributed objects and strongly consistent replication using transactions. Work on weakly consistent replication protocols was in its infancy and despite the fact that today everyone seems to be offering them in one form or another and trying to use them within their applications, their general applicability is not as wide as you might believe; and pushing the problem of resolving replica inconsistencies up to the application developer isn't the best thing to do! However, once again this is getting off topic a bit and perhaps something else I'll come back to in a different article. For now let's assume if there are replicas then their states will be in sync (that doesn't require transactions, but they're a good approach).

In order to support a wide range of replication strategies, ranging from primary-copy (passive) through to available copies (active), we created a system whereby the object methods (code) was replicated separately from the object state. In this way the binaries representing the code was immutable and when activated they'd read the state from elsewhere; in fact because the methods could be replicated to different degress from the state, it was possible for multiple replicated methods (servers) to read their state from an unreplicated object state (store). I won't spoil the plot too much so the interested reader can take a look at the original material. However, I will add that there was a mechanism for naming and locating these various server and state replicas; we also investigated how you could place the replicas (dynamically) on various machines to obtain a desired level of availability, since availability isn't necessarily proportional to the number of replicas you've got.

If you're familiar with Kubernetes (other clustering implementations are possible) then hopefully this sounds familiar. There's a strong equivalency between the components and approaches that Kubernetes uses and what we had in 1993; of course other groups and systems are also similar and for good reasons - there are some fundamental requirements that must be met. Let's get back to how this all fits in with microservices, if that wasn't already obvious. As before, I'll talk about Kubernetes but if you're looking at some other implementation it should be possible to do a mental substitution.

Kubernetes assumes that the images it can spin up as replicas are immutable and identical, so it can pull an instance from any repository and place it on any node (machine) without having to worry about inconsistencies between replicas. Docker doesn't prevent you making changes to the state within a specific image but this results in a different image instance. Therefore, if your microservice(s) within an image maintain their state locally (within the image), you would have to ensure that this new image instance was replicated in the repositories that something like Kubernetes has access to when it creates the clusters of your microservices. That's not an impossible task, of course, but it does present some challenges, including how to distribute the updated image amongst the repositories in a timely manner - you wouldn't want a strongly consistent cluster to be created with different versions of the image because that means different states and hence not consistent, and how to ensure that state changes that happen at each Docker instance and result in a new image being created are in lock-step - one of the downsides of active replication is that it assumes determinism for the replica, i.e., given the same start state and the same set of messages in the same order, the same end state will result; not always possible if you have non-deterministic elements in your code, such as the time of day. There are a number of ways in which you can ensure consistency of state, but we're not just talking about the state of your service now, it's also got to include the entire Docker image.

Therefore, overall it can be a lot simpler to factor the binary that implements your algorithms for your microservices (aka the Docker image or 1993 object) from the state and consider the images within which the microservices reside to be immutable; any state changes that do occur must be saved (made durable) "off image" or be lost when the image is passivated, which could be fine for your services of course, if there's no need for state durability. Of course if you're using active replication then you still have to worry about determinism, but we're only considering state here and not the actual entire Docker image binary too. The way in which the states are kept consistent is covered by a range of protocols, which are well documented in the literature. Where the state is actually saved (the state store, object store, or whatever you want to call it) will depend upon your requirements for the microservice. There are the usual suspects, such as RDBMS, file system, NoSQL store, or even highly available (replicated) in memory data stores which have no persistent backup and rely upon the slim chance that a catastrophic failure will occur to wipe out all of the replicas (even persistent stores have a finite probability that they'll fail and you will lose your data). And of course the RDBMS, file system etc. should be replicated or you'll be putting all of your eggs in the same basket!

One final note (for now): so far we've been making the implicit assumption that each Docker image that contains your microservices in a cluster is identical and immutable. What if we relaxed the identical aspect slightly and allowed different implementations of the same service, written by different teams and potentially in different languages? Of course for simplicitly we should assume that these implementations can all read and write the same state (though even that limitation could be relaxed with sufficient thought). Each microservice in an image could be performing the same task, written against the same algorithm, but with the hopes that bugs or inaccuracies produced by one team were not replicated by others, i.e., this is n-versioning programming. Because these Docker images contain microservices that can deal with each other's states, all we have to do is ensure that Kubernetes (for instance) spins up sufficient versions of these heterogeneous images to give us a desired level of availability in the event of coding errors. That shouldn't be too hard to do since it's something research groups were doing back in the 1990's.

Microservices and the unit of failure

I've seen and heard people fixating on the "micro" bit of "microservices". Some people believe that a microservice should be no larger than "a few lines of code" or a "few megabytes" and there has been at least one discussion about nanoservices! I don't think we should fixate on the size but rather that old Unix addage from Doug McIlroy: "write programs that do one thing and do it well". Replace "programs" with "service". It doesn't matter if that takes 100 lines of code or 1000 (or more or less).

As I've said several times, I think the principles behind microservices aren't that far removed from "traditional" SOA, but what is driving the former is a significant change in the way we develop and deploy applications, aka DevOps, or even NoOps if you're tracking Netflix and others. Hand in hand with these changes come new processes, tools, frameworks and other software components, many of which are rapidly becoming part of the microservices toolkit. In some ways it's good to see SOA evolve in this way and we need to make sure we don't forget all of the good practices that we've learnt over the years - but that's a different issue.

Anyway, chief amongst those tools is the rapid evolution of container technolgoies, such as Docker (other implementations are available, of course!) For simplicity I'll talk about Docker in the rest of this article, but if you're using something else then you should be able to do a global substitution and have the same result. Docker is great at creating stable deployment instances for pretty much anything (as long as it runs in Linux, at the moment). For instance, you can distribute your product or project as a Docker image and the user can be sure it'll work as you intended because you went to the effort to ensure that any third party dependencies were taken care of at the point you built it; so even if that version of Foobar no longer exists in the world, if you had it and needed it when you built your image then that image will run just fine.

So it should be fairly obvious why container images, such as those based on Docker, make good deployment mechanisms for (micro) services. In just the same way as technologies such as OSGi did (and still do), you can package up your service and be sure it will run first time. But if you've ever looked at a Docker image you'll know that they're not exactly small; depending upon what's in them, they can range from 100s of megabytes of gigabytes in size. Now of course if you're creating microservices and are focussing on the size of the service, then you could be worried about this. However, as I mentioned before, I don't think size is the right metric on which to base the conclusion of whether a service fits into the "microservice" category. Furthermore, you've got to realise that there's a lot more in that image than the service you created, which could in fact be only a few 100s of lines of code: you've got the entire operating system, for a start!

Finally there's one very important reason why I think that despite the size of Docker images being rather large, you should still consider them for your (micro) service deployments: they make a great unit of failure. We rarely build and deploy a single service when creating applications. Typically an application will be built from a range of services, some built by different teams. These services will have differing levels of availability and reliability. They'll also have different levels of dependency between one another. Crucially there will be groupings of services which should fail together, or at least if one of them fails the others may as well fail because they can't be useful to the application (clients or other services) until the failed service has recovered.

In previous decades, and even today, we've looked at middleware systems that would automatically deploy related services on to the same machine and, where possible, into the same process instance, such that the failure of the process or machine would fail the unit. Furthermore, if you didn't know or understand these interdependencies a priori, some implementations could dynamically track them and migrate services closer to each other and maybe even on to the same machine eventually. Now this kind of dynamism is still useful in some environments, but with containers such as Docker you can now create those units of failures from the start. If you are building multiple microservices, or using them from other groups and organisations, within your applications or composite service(s), then do some thinking about how they are related and if they should fail as a unit then pull them together into a single image.

Note I haven't said anything about state here. Where is state stored? How does it remain consistent across failures? I'm assuming statelessness at the moment, so technologies such as Kubernetes can manage the failure and recovery of immutable (Docker) images. Once you inject state then some things may change, but let's cover that at another date and time.

Six Years as JBoss CTO

It's coming up to 6 years since Sacha asked me to take over from him as CTO. It was a privilege and it remains so today. Despite the facts that Arjuna was involved with JBoss for many years prior to my officially joining in 2005 and that my good friend/colleague Bob Bickel was a key member of JBoss, I didn't join JBoss officially until 2005. I found it to be a great company and a lot more than I ever expected! A decade on and now part of Red Hat, it remains an exciting place to work. I wake up each morning wondering what's in store for me as CTO and I still love it! Thanks Marc, Sacha and Bob!

Restrospecting: Sun slowly setting

I remember writing this entry a few years back (2010), but it appears I forgot to hit submit. Therefore, in the interests in completeness I'm going to post it even though it's 5 years old!

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So the deal is done and Sun Microsystems is no more and I'm filled with mixed emotions. Let's ignore my role within Red Hat/JBoss for now, as there are certainly some emotions tied up in that. It's sad to see Sun go and yet I'm pleased they haven't gone bust. I have a long history with Sun as a user and, through Arjuna, as a prospective partner. When I started my PhD back in the mid 1980's my first computer was a Whitechapel, the UK equivalent to the Sun 360 at the time. But within the Arjuna project the Sun workstation was king and to have one was certainly a status symbol. I remember each year when the new Sun catalogue came out or we had a new grant, we'd all look around for the next shiney new machine from Sun. We had 380s, Sparc, UltraSparc and others all the way through the 1990's (moving from SunOS through to the Solaris years). The Sun workstation and associated software was what you asipired to get, either directly or when someone left the project! In fact the Arjuna project was renowned within the Computing Department for always having the latest and greatest Sun kit.

The lustre started to dim in the 1990's when Linus put out the first Linux distribution and we got Pentium 133s for a research grant into distributed/parallel computing (what today people might call Grid or Cloud). When a P133 running Linux was faster than the latest Sun we knew the writing was on the wall. By the end of the decade most Sun equipment we had was at least 5 years old and there were no signs of it being replaced by more Sun machines.

Throughout those years we were also in touch with Sun around a variety of topics. For instance we talked with Jim Waldo about distributed systems and transactions, trying to persuade them not to develop their own transction implementation for Jini but to use ours. We also got the very first Spring operating system drop along with associated papers. We had invitations to speak at various Sun sites as well as the usual job offers. Once again, in those days Sun was the cool place to be seen and work.

But that all started to change as the hardware dominance waned. It was soon after Java came along, though I think that is coincidental. Although Solaris was still the best Unix variant around, Linux and NetBSD were good enough, at least in academia. Plus they were a heck of a lot cheaper and offered easier routes to do some interesting research and development, e.g., we started to look at reworking the Newcastle Connection in Linux, which would have been extremely difficult to do within Solaris.

Looking back I can safely say that I owe a lot to Sun. They were the best hardware and OS vendor in the late 1980's and early 1990's, providing me and others in our project with a great base on which to develop Arjuna and do our PhDs. In those days before Java came along they were already the de facto standard for academic research and develpment, at least with the international communities with which we worked. I came to X11, Interviews, network programming, C++ etc. all through Sun, and eventually Java of course. So as the Sun sinks slowly in the west I have to say a thanks to Sun for 20 years of pleasant memories.