Sunday, May 26, 2019

Supporting Ancient Software

With another round of fixes to Windows XP, the time is ripe for bloviating about supporting ancient stuff. Every software vendor has to decide what to do about supporting what they used to ship, as well as the broader ecosystem around them. Operating systems, databases, service providers. Maximize use of your new features, minimize maintenance of your old ones. Maximize the number of potential customers, minimize the amount of development time required.  Keeping support for old systems looks like it’s on the maximizing side at first, but it’s an exponentially scaled problem when combined with your own features. It’s worth considering how the vendors of those ecosystem parts do things.

Why do customers stay on an antiquated platform? Perhaps they can’t afford the upgrade job, or perhaps they’re focused elsewhere and willing to accept the risk. For a software vendor, the former is a questionable customer; landing and keeping them may be profitable, but it won’t be great margin. Ah, but the latter... a vendor can charge the latter appropriately for the work to be done through a special one-off development effort. Welcome to the world of extended support contracts.

“Oh come now”, one might say, “that is not charitable at all!” And it’s true, there are nuances: many customers depend on equipment that cannot be upgraded. It was sold as a unified system, its vendor will not provide an upgrade at all or at an affordable cost, and its vendor will not support updates to the system. This sucks. What is the manufacturer or hospital or university to do, fund a new robot or MRI or TEM vendor? And yet from the vendor’s perspective, the predicament of customers who can’t upgrade is not distinguishable from the customers who won’t. They’re still stuck on the dead branch, forced to pay what the market will bear or take the risk of going unpatched. Once again, we are in the world of extended support contracts.

So there’s patches for the dead and unsupported OS from time to time. Who makes them?

I suppose it’s possible that there’s an XP engineering team at Microsoft sitting around on mothballs waiting for the opportunity to fix this stuff, but I’m guessing that is not the case. I think it’s highly unlikely that these patches ever come entirely from a vendor’s internal development teams, because it would be wasteful to maintain the systems and processes to produce two different levels of supportability for a single product, much less maintain a dead product. It would be doubly expensive to pull developers off of the current Windows line into a one-off effort to fix the dead product. More likely, when it breaks badly enough to need fixing, a new development team parachutes in, figures it out, and posts a patch. I’d bet that development team is outsourced, too, at least to another team within the vendor.

That would mean every patch is a special snowflake, provided by giving source access to a services team that charges to sustain it. The vendor collects extra support contracts from X customers to pay for the super smokejumper team, recognizes that revenue every month, and about once a quarter has a patch built. Not hard to make that into a profitable, high-margin business. In fact, if a vendor kept this in-sourced and gambled on one or two developers to maintain their knowledge, they could even defer the cost of the super smokejumper team for quite some time.

A third party software vendor has the opportunity to make the same decision, of course; should they spend their developer time on extending support to old software, or new? The answer is driven by their customers, in theory — but the vendor must evaluate the value of each decision. For a vendor with a small customer base, each customer demanding an oddity can be a significant percentage of revenue potential. For a vendor with a large customer base, each oddity request can have a significant number of requesters.  What’s not clear is the associated margin opportunity versus opportunity cost. Worse, there won’t be associated requests for the obvious choices, because they’re obvious, and a PM would be mistaken to ignore them until customers have to request.

If the vendor embraces the requested oddity, putting aside the non-requested mainstream, the customer should theoretically pay extra for their decision to stay on the old platform; otherwise the vendor is eating their own opportunity cost. The dollars spent on patching old stuff or extending features to old stuff are taken directly from the budget to do new work with. And since most vendors don’t have internal permission to use external super smokejumpers, they’re pulling developers off of (say) Mongo support to build (say) DB/2 support. This adds context-switch costs to the overall pain load.

Adding salt to this wound, many vendors end up giving the customer a hefty discount while bending over backwards to provide one-off snowflake features, robbing their future Peter to pay the present Paul. It’s an easy decision to make when the company’s leaders allow profit-making to be deferred into the invisible future.

Sunday, May 5, 2019

Land and Expand Packaging Decisions


Subsets of packaged content are needed in different system classes. If you're pursuing a land-and-expand model, then you need to have a way to expand. One way is to ship a static monolith with features turned off. Another is to ship dynamic add-ons to your base product. 

Teams make these dynamic vs static decisions early, see https://www.monkeynoodle.org/2018/06/its-not-platform-without-partners.html. If the business went dynamic, then content names and versions that are already deployed must be visible. If on-prem with high availability & fault tolerance goals, this can be remarkably challenging.

During installation, upgrade, or removal operations, the admin must fully understand the infrastructure and know more about the internal workings of the packaged content than anyone desires. Proceeding without understanding produces unpredictable installs and high support burden.

Any enterprise vendor with this problem decides: hide the complexity and offer one big package (fully static linking, or shipping the whole product as a service), or expose the complexity and offer separate packages for every role? Plus regional availability problems in cloud.

Option 0 (do nothing): You might say, "this is a relatively infrequent problem; when a customer goes to distributed component infrastructure, we train heavily and plan for dedicated support allotments."

Option 1 (incremental): design the infrastructure so each component can announce what roles it uses, design the package so each file in it is associated with a role, design the package installer to install files that match roles. User repeats desired action on every component.

Option 2 (radical): As above, but a separate deployer policy enforcement service ensures packages are installed, updated, and removed from all infrastructure. User commits desired action once on the policy tool. This is easiest for Cloud-only organizations.


For a sick sort of fun, look at how many times operating systems and programming languages have recreated this wheel since 2000.

Sunday, March 24, 2019

What does your product disallow?

Product design sometimes opens an interesting can of worms: things that may be possible to do, but which the designer didn’t intend. Do you hide these paths or not? Will your user ultimately be frustrated, or satisfied?

The answer depends on whether your product’s design accurately sets and meets expectations for the majority of its user base. Let’s try a quadrant.

Vertical axis: complexity level of the typical user’s actual need
Horizontal axis: designer’s assumption of typical user’s need

Lower left: If the task that the user’s trying is simple and the designer has assumed this to be true, a prescriptive interface that hides everything but the happy path makes sense. Don’t offer options you haven’t planned for, and narrowly design for specific use cases. For example, the basic note taking app Bear has few options, easy discoverability, and a low bar to entry. I’m writing this article in it on an iPhone with a folding keyboard, and it’s a good tool for this task.

Upper right: If the user’s actual need is highly complex and the designer realizes this, then a wide open toolbox interface makes the most sense. Guard rails on the user’s experience are as likely to produce complaints as relief. The vim text editor is hard to use correctly without training, and its design makes no pretense towards friendly or easy. If I want to anonymize and tokenize a gigabyte of log files, vim is a good tool.

Upper left: If the user is doing something complex and the product does not support this complexity, they are unlikely to be happy using it to complete the task. I would find it very difficult to write a script or process logs with Bear on an iPhone. It does not support me or aid me in that task because its assumptions do not correctly align with what I will need to do that task. I’ll be frustrated in doing this task, but I’m still allowed to try.

Lower Right: If the user’s needs are low complexity and the designer’s assuming a high complexity need, the product is going to be very frustrating. For instance, using the vim text editor to take simple notes in a meeting is possible, but a user who is not familiar with the editor will struggle with its modes and may not even know how to save their file and exit.

Alignment is alignment, straightforward enough. The choices made in products for moments of non-alignment are more interesting. If the product overshoots the user’s need it frustrates with a lack of clarity. The user struggles to see if the product is able to do the task, exploring the interface and searching for an answer: should they invest more time into learning the product, or switch products? If the product undershoots the user’s need, it’s clearer, and the user moves on quickly.

So far so good with text editing. What about an enterprise-scale policy enforcement tool? For much of my career I’ve worked with tools that empower the enterprise to see what’s true and make it better. Some products have focused harder on different aspects of this mission, but everything I’ve ever sold has been able to cause massive damage if misused. What’s more, it’s not theoretical: some customer has done that damage, and all enterprise software vendors have off-the-record stories. That includes the everything as a service folks, of course, and commonly enough that stories about those accidents are public knowledge.

And yet, all of these products or services overshoot the complexity target and err on the side of flexibility. They may offer use-case specific wizards for specific tasks as extra cost add ons, but you can always get to the platform’s full capabilities if you’ve got administrative rights.

Why is that? People have often heard me say the flippant phrase “we sell chainsaws, up to the user to be careful”, but why does that resonate? As a vendor it might look like abdication of responsibilities... but it is a free market, in which make-X-easier startups fail every day. I think the reason is that protecting people from themselves is not a good look. It’s far more effective to produce the powerful product for complex stories, allow full access to that power, and add easier tools as extra cost options.

Saturday, January 26, 2019

Entities and Attributes


Quadrant models are useful organizing tools. Let’s use one to look at the problem of managing the attributes of entities in systems visibility. I’m not expecting to solve the problem, just usefully describe the playing field.

Horizontal axis:
* persistent entities with changing attributes
* ephemeral entities with static attributes

Vertical axis:
* Set the relationship at index time
* Set the relationship at search time

Let’s start with the old school entities model. Once upon a time managed computers were modular, high value devices. A server or desktop would be repaired or upgraded by replacing components. If its use case went away, the device would be repurposed to another use case. It did not vanish until accounting was certain it had fully depreciated and could be sold, donated, or scrapped. This state of affairs persists at the high end, so it’s still worth considering. Someone’s racking and stacking some pricey hardware to make things go.

Top Left: Persistent entities, Index time relationships


The computer (let’s call it THESEUS) has a timeline of footprints. Business Analytics teams can see it in their Enterprise Resource Planning (ERP) systems, contract tracking, and accounting systems. Facilities knows it by its power draw and heat load. Security Operations has interest in it, and their agents come and go with the vicissitudes of fortune. Lights On Doors Open (LODO) Operations cares the most about it, and tracks it closely as it serves each purpose of its lifecycle.

Each group’s view into the computer’s function is limited by their immediate needs. Most of the time, various teams are happy with their limited view into this entity. They are able to set any needed attribute to entity mappings at index time, when data about the device is collected. Changes don’t much matter, and can be manually updated or ignored.

Bottom Left: Persistent entities, Search time relationships


This works until change spills over between groups: for instance, if a missed recall for a faulty part leads to a hardware failure that starts a fire, Facilities and LODO will be equally interested in how they could have better coordinated with Business Analytics functions. “Where was this ball dropped?” The answer is often “changes in reality were lost because we don’t keep proper track of entities.” Of course no one states it like that. Rather, they say “we received the recall and sent it to the point of contact on record.” This scenario plays out in security as well, when incident responders can’t find out if an attacked device is safe to restart, or when a monitoring tool alert is how they learn that DevOps is rolling out a new service. These misses in visibility drive folks towards mapping attributes to entities at search time. Of course, no one says it like that; they say “we’re bringing updated data into our visibility tools.”

There’s a dirty secret in those tools though. Actively mapping attributes to entities entirely at search time is a hard problem to scale, and it gets even harder to do if you want to maintain that awareness into past records as well as the present. Few systems can handle “this was SVR42 before Tuesday, now it’s SVR69”. Add in that behavior has changed and the old model is still good for old records but a new model needs to be started, and most tools give up and start a new entity record. Sorry administrators and analysts, here are the tools for pruning stale entities from the system, good luck!

Lower Right: Ephemeral entities, Search time Relationships


And so a sea change: what if that whole set of reality based problems is outsourced, and the organization uses ephemeral virtual devices based on static configurations to perform its tasks? Amazon and Microsoft still have to worry about physical hardware, but the rest of us can just inject prebuilt software bundles into a management tool and let the load balancers figure it out. As long as logging is done properly and auditing can still be supported, this can be a great answer. The unpredictable nature of this world has precipitated a tsunami of heisenbugs, but unifying development and operations reduces the lag time for diagnosing those. Furthermore, the attribute to entity relationship really doesn’t matter; who cares what address or hostname a function was served from? All that matters is service level objectives and agreements: success, failure, completion time, and resource consumption. It’s a pretty ideal solution for anything where the entity is disposable: simply stop tracking it at all, and use temporary search time relationships based on the functions that were served to maintain visibility.

Upper Right: Ephemeral entities, Index time relationships


Although... that requires the analyst to know what they need to track up front. If the image doesn’t issue enough data attributes to answer a question, you’re out of luck. That’s annoying for internal visibility, but the internal folks aren’t the only ones asking questions. A hypothetical: there was an instance on Tuesday, let’s call it EPHEMERIDES. Interpol would like to know what it was doing at 10 AM UTC because it was apparently exploited and used for evil deeds. Or maybe not? Who knows? In the long-lived server world, we would have been dumping all output into a central system and could sort through it on demand, but now we just know that it was doing its intended job within acceptable parameters. That’s all we’d decided to monitor. If we’re not proactively tracking the organization’s activities from its infrastructure, we’ll have to track something else to achieve visibility. Why bother? Well, let’s talk about how you’re going to prove compliance with data privacy regulations or due diligence security assurances when you can’t say what happened where last Tuesday. “Trust us, we’re pretty sure it didn’t do anything bad in the few hours it was alive” may not wash in court. An easy solution is to dump whatever you can from these devices into the cheapest storage possible, with some index-time identifiers to make it hopefully retrievable later. And if that’s not possible? Oh well, at least we tried and the fines will probably be reduced.

* Top left is the legacy, revolution against the mainframe. It’s servers as pets, deploy then configure thinking.
* Bottom left, legacy incremented, insufficiently.
* Bottom right is the future, servers as cattle, configure then deploy thinking. Revolution against the Wintel and Lintel server world, of course.
* Top right, the future incremented, insufficiently.

Hopefully this has been fun and useful!

Sunday, January 13, 2019

How to manage a Proof of Concept

POCs as a concept are a response to customers getting oversold. As a vendor we’d rather skip the whole thing and trust our sales team to scope properly. As a customer we’d rather not spend time testing instead of doing. Sometimes they have to be done though, and it’s best for everyone to do it right. Right = tightly scoped and timeframed.

An ideal POC should look like a well-planned professional services engagement. The goal is established in writing before anyone gets on a plane. Infrastructure testing and go/no-go call Friday. Fly Monday. Kick off meeting Tuesday morning, installation rest of day. Wednesday and Thursday, go through the list of use cases and check them all off. Friday morning meeting to get the verbal, fly home, spend the next week with procurement instead of kicking the tires.

You should spend more time helping a customer or vendor define use cases up front than you allow for the POC.  If they can’t define use cases, you might still have a deal, but you’ve established that the product is not worth any actual dollars. That’s bad for the vendor obviously, but it also means the customer can’t get any internal attention for this project. Real use cases mean business value mean time and money allocations. If there is demonstrable value, there is easy justification for a fair price.

Given my one week frame, you’ve got a maximum of 16 hours for use cases. This is a bit more time than a circa 2018 Nicolas Cage binge. If it can be remote, great! Travel time can turn into work time for a maximum of 32 hours. That’s a play through of Far Cry 5. Planning ahead of time lets both sides think about how long each step will take. Estimate how long each use case will take to demonstrate, then double or triple that time. If you don’t need those hours, you’ll have time to get creative after the real work is done. Both sides should bring a punch list of extra things they’d like to show off or see.

This ideal model can have a couple of interesting wrinkles based on product maturity though. A young company with a single product has a straightforward agenda, but a mature company with many products on a shared platform has to pick and choose. Marketing being what it is, the customer’s excitement is also centered on the newest, highest-risk stuff! The reality is that these are things that haven’t been done before, at least by the feet on the ground, so they take even more time.

The only way to be successful in that case is to compartmentalize the platform use cases from the new shiny use cases so that you’re using the new stuff on a solid foundation. Everyone will be thankful in the end.

One last note on why this matters to customers; I’m describing the approach of quality field personnel, which is specifically intended to cast a product in its best light. This is good for customers because it makes the sale easy to explain and process. However, this is how a pro sales team gets their crap product over the line to beat out weak sales teams with potentially better solutions. If you care about the quality of the solution you’re going to be living with, it’s in your interest to understand and manage the POC process.

Sunday, December 23, 2018

Moving the transformation point of data

There’s a pattern that has become common knowledge, perhaps on its way to received wisdom. Endpoints pass their raw data off to storage as quickly as possible. Analysts then do their work against that storage using map reduced processors, automated and/or ad hoc.

This pattern has many benefits and is correct for many use cases. Ephemeral endpoints, such as elastically scaling containers, are able to emit data before it is lost. Machines under attack are able to emit data before the attacker deletes or corrupts it. Best of all, the analyst can explore the data, learn to ask smarter questions, and gradually improve the quality of their work.

The pattern also has a downside: staggering cost. Network load, storage, compute resources, license costs, service costs, and analyst time. It can all be worthwhile during the process of discovering a new root cause or isolating a security threat, but this is not an ideal way to perform operationalized tasks.

What if more of the analysis work is pushed to the endpoint where the raw data is generated?

That does not work for use cases where raw data must be saved, but those cases do not have to be as prevalent as assumed. It’s only like this because we work with data systems that fight against producing information.

We haul dozens of low fidelity lines around for every actual event, generating gigs of noise from every device to sort through. The distance from a given unit of event or metric data to the business impact it represents is huge; just ask anyone who’s collected data for a SIEM.

Bulk raw data collection is similar to running all your applications with logging level at Debug: it’s the right move when you need it, but a waste on the average day.

Alternatives are out there. The most obvious is metrics: it’s hard to argue that a single raw measurement is valuable. Generating a periodic histogram is higher signal, and a smaller data set to boot. Performing metrics analysis at the point of collection produces a better result for less effort.

“checking... is more efficient to do on the host as opposed to querying against a central metrics store... there was no need to store host metrics with that retention and resolution.“
https://eng.uber.com/observability-at-scale/

What about events? It’s true that a single event could be the needle in a haystack that explains a critical situation. It’s also true that these events can be lost to system failure or malicious activity. Unfortunately, it’s also true that these events are often nearly impossible to derive state from. That stream of debug events is great for forensics, but poor for analytics.

What if the endpoint were able to regularly report state in business-friendly terms? Less DURSLEy, more CAPS (http://www.monkeynoodle.org/2018/11/dursley-and-caps.html)? What would that require?

Administrators would need to describe the transformations that they need. Ideally, that’s writing configuration for an engine, just like they’re doing in analysis tools today. However, it would also work to write scripts; this could even be the optimal approach when native tools are faster than a cross-platform engine.

However the work is done, endpoints would need to be able to perform calculations to turn raw data into information. That almost certainly requires a data cache on the endpoint, so cache management becomes a probable requirement.

Of course, bidirectional data distribution, execution shell, and configuration management are required. Those are baseline requirements for any data collection mechanism though, even if some shortcut through baking into a golden image. Ditto for system identification and disambiguation.

So there are some gnarly problems in the infrastructure required to do endpoint-based business analysis, but nothing new, and no true show stoppers. The biggest challenge is at the point of highest value: converting data to business knowledge instead of gathering data for it’s own sake. https://open.nytimes.com/talking-technology-nick-rockwell-charity-majors-2acad1690dcf

Tuesday, December 18, 2018

Conflicts at Work



Work conflicts aren’t fun, but they come with the territory. Here’s a quick field guide for recognizing the type of conflict being observed.

conflict level 4: we disagree on a tactical implementation approach. I think we should write this in language foo and you think language bar. I think the configuration options should be in a vertical accordion and you think a horizontal tab bar. This level of disagreement is a mix of ego and misunderstanding. The more senior employee should start asking questions. "Why do you think that approach is better?" "Where have you seen it done that way before?" "What's the benefit that the user would see?" Ideally one or the other person will be convinced. If not, then go to the first shared manager for a decision that both parties will disagree-and-commit to.

conflict level 3: we disagree on a tactical ownership situation. My team and your team are both trying to solve the same problem. Both of us have sunk costs. This level of disagreement is an upstream fail that you now get to fix. The team leads need to review product market fit and decide: keep one solution, merge the solutions, keep both solutions, or kill both. Again, someone has to open this conversation, so the more senior employee should start.

conflict level 2: we disagree on role definition. I think the task at hand is my job, but you think it's yours. You think I should be doing something I'm not. I think you should be doing something you're not. Everyone around us is confused by mixed messaging and inconsistent results. We have to discuss who's doing which tasks in detail. If we can't fix it in one meeting, we have to involve our respective managers and get a decision made. One or both of us must disagree-and-commit ASAP.

conflict level 1: we disagree on strategic direction. I think the mission is wrong for the team and/or the company, and you are committed to doing it. You think my team is a waste of resources that needs to be redirected or disbanded. Again, we need to go to management for resolution, but one of us is probably looking for a job after that meeting. That’s assuming management gave a clear answer and someone wasn’t convinced of course! If the problem was just postponed, you might persist at conflict level 1 until you can’t bring yourself to walk into the office any more.

conflict level 0: we annoy each other. We speak in different ways, we don’t share common assumptions, we would rather not interact but we have to work together. This level of disagreement is easy, right? All ego? Just put the two individuals into a different context and encourage them to work out their differences? Sure, if everyone involved is dead certain this isn’t actually a diversity and inclusion issue. There’s a big difference between “you think I’m a jerk” and “you think I’m an unworthy human being”, but the two opinions can be hard to distinguish in constrained work scenarios where it’s not okay to have unsavory opinions. Maybe one or both people in this situation are working from something deeper than annoyance. Asking individual employees to work out their racism, sexism, and classism prejudices with each other is a recipe for disaster. If one of them has power over the other, that disaster is on roller skates.

Some common themes:

  • Conflict is going to persist until someone decides to stop it 
  • The more senior employee should act first and most graciously.
  • If the problem is beyond the scope of the people in conflict, bring in someone who can resolve it
And a warning: Disagree-and-commit agreements don’t always stick. If a person really cares so much that they’re willing to fight and can’t honestly work to further the other point of view, then this is the hill they’ll die on. Ideally they’d change teams or companies if the decision went against them. In our non-ideal world, changing jobs might not be an option and bad outcomes may ensue.