Claims in the Scheduler

One of the shortcomings of the current scheduler in OpenStack Nova is that there is a long interval from when the scheduler selects a suitable host for a new instance until the resources on that host are claimed so that they are no longer available. Now that resources are tracked in the Placement service, we want to move the claim closer to the time of host selection, in order to avoid (or eliminate) the race condition. I’m not going to explain the race condition here; if you’re reading this, I’m assuming this is well understood, so let me just summarize my concern: the current proposed design, as seen in the series starting with, could be made much better with some design changes.

At the recent Boston Summit, which I was unable to attend due to lack of funding by my employer, the design for this change was discussed, and the consensus was to have the scheduler return a list of hosts for each instance to the super conductor, and then have the super conductor attempt to claim the resources for the first host returned. If the allocation fails, the super conductor discards that host and tries to claim the resources on the second host. When it finally succeeds in a claim, it sends a message to that host to start building the instance, and that message will include the list of alternative hosts. If something happens that causes the build to fail, the compute node sends it back to its local conductor, which will unclaim the resources, and then try each of the alternates in order by first claiming the resources on that host, and if successful, sending the build request to that host. Only if all of the alternates fail will the request fail.

I believe that while this is an improvement, it could be better. I’d like to do two things differently:

  1. Have the scheduler claim the resources on the first selected host. If it fails, discard it and try the next. When it succeeds, find other hosts in the list of weighed hosts that are in the same cell as the selected host in order to provide the number of alternates, and return that list.
  2. Have the process asking the scheduler to select a host also provide the number of alternates, instead of having the scheduler use the current max_attempts config option value.

On the first point: the scheduler already has a representation of the resources that need to be claimed. If the super conductor does the claiming, it will have to re-generate that representation. Sure, that’s not all that demanding, but it sure makes for cleaner design to not repeat things. It also ensures that the super conductor gets a good host from the start. Let me give an example. If the scheduler returns a chosen host (without claiming) and two alternates (which is the standard behavior using the config option default), the conductor has no guarantee of getting a good host. In the event of a race, the first host may fail to allocate resources, and now there are only the two alternates to try. If the claim was done in the scheduler, though, when that first host failed it would have been discarded, and the the next host tried, until the allocation succeeded. Only then would the alternates be determined, and the super conductor could confidently pass on that build request to the chosen host. Simply put: by having the scheduler do the initial claim, the super conductor is guaranteed to get a good host.

Another problem, although much less critical, is that the scheduler still has the host do consume_from_request(). With the claim done in the conductor, there is no way to keep this working if the initial host fails. We will have consumed on that host, even though we aren’t building on it, and have not consumed on the host we actually select.

On the second point: we have spent a lot of time over the past few years trying to clean up the interface between Nova and the scheduler, and have made a great deal of progress on that front. Now I know that the dream of an independent scheduler is still just that: a dream. But I also know that the scheduler code has been greatly improved by defining a cleaner interface between it an Nova. One of the items that has been discussed is that the config option max_attempts doesn’t belong in the scheduler; instead, it really belongs in the conductor, and now that the conductor will be getting a list of hosts from the scheduler, the scheduler is out of the picture when it comes to retrying a failed build. The current proposal to not only leave that config option in the scheduler, but to make it dependent on it for its functioning, is something that once again makes the scheduler Nova-centric (and Nova-exclusive). It would be a much cleaner design to simply have the conductor ask for the number of hosts (chosen + alternates), and have the scheduler’s behavior use that number. Yes, it requires a change to the RPC interface, but that is to be expected if you are changing a fundamental behavior of the scheduler. And if the scheduler is ever moved into a module, all it is is another parameter. Really, that’s not a good reason to follow a poor design.

Since some of the principal people involved in this discussion are not available now, and I’m going to be away at PyCon for the next few days, Dan Smith suggested that I post a summary of my concerns so that all can read it and have an idea what the issues are. Then next week sometime when we are all around and have the time to discuss this, we can hash it out on #openstack-nova, or maybe in a hangout. I also have pushed a series that has all of the steps needed to make this happen, since it’s one thing to talk about a design, and it’s another to see the actual code. The series starts here: For some of the later patches I haven’t finished updating the tests to match the change in method signatures and returned value structures, but you should be able to get a good idea of the code changes I’m proposing.

Interop API Requirements

Lately the OpenStack Board of Directors and Technical Committee has placed a lot of emphasis on making OpenStack clouds from various providers “interoperable”. This is a very positive development, after years of different deployments adding various extensions and modifications to the upstream OpenStack code, which had made it hard to define just what it means to offer an “OpenStack Cloud”. So the Interop project (formerly known as DefCore) has been working for the past few years to create a series of objective tests that cloud deployers can run to verify that their cloud meets these interoperability standards.

As a member of the OpenStack API Working Group, though, I’ve had to think a lot about what interop means for an API. I’ll sum up my thoughts, and then try to explain why.

API Interoperability requires that all identical API calls return identical results when made to the same API version on all OpenStack clouds.

This may seem obvious enough, but it has implications that go beyond our current API guidelines. For example, we currently don’t recommend a version increase for changes that add things, such as an additional header or a new URL. After all, no one using the current version will be hurt by this, since they aren’t expecting those new things, and so their code cannot break. But this only considers the effect on a single cloud; when we factor in interoperability, things look very different.

Let’s consider the case where we have two OpenStack-based clouds, both running version 42 of an API. Cloud A is running the released version of the code, while Cloud B is tracking upstream master, which has recently added a new URL (which in the past we’ve said is OK). If we called that new URL on Cloud A, it will return a 404, since that URL had not been defined in the released version of the code. On Cloud B, however, since it is defined on the current code, it will return anything except a 404. So we have two clouds claiming to be running the same version of OpenStack, but making identical calls to them has very different results.

Note that when I say “identical” results, I mean structural things, such as response code, format of any body content, and response headers. I don’t mean that it will list the same resources, since it is expected that you can create different resources at will.

I’m sure this will be discussed further at next week’s PTG.


API Longevity

How long should an API, once released, be honored? This is a topic that comes up again and again in the OpenStack world, and there are strong opinions on both sides. On one hand are the absolutists, who insist that once a public API is released, it must be supported forever. There is never any justification for either changing or dropping that API. On the other hand, there are pragmatists, who think that APIs, like all software, should evolve over time, since the original code may be buggy, or the needs of its users have changed.

I’m not at either extreme. I think the best analogy is that I believe an API is like getting married: you put a lot of thought into it before you take the plunge. You promise to stick with it forever, even when it might be easier to give up and change things. When there are rough spots (and there will be), you work to smooth them out rather than bailing out.

But there comes a time when you have to face the reality that staying in the marriage isn’t really helping anyone, and that divorce is the only sane option. You don’t make that decision lightly. You understand that there will be some pain involved. But you also understand that a little short-term pain is necessary for long-term happiness.

And like a divorce, an API change requires extensive notification and documentation, so that everyone understands the change that is happening. Consumers of an API should never be taken by surprise, and should have as much advance notice as possible. When done with this in mind, an API divorce does not need to be a completely unpleasant experience for anyone.


Virtual Bike Sheds

Recently we’ve been doing a lot of work to revamp how the Nova Scheduler service manages the resources that are being requested in the cloud. The original design was very compute-centric, as the only thing we originally designed for was finding host machines that had enough CPU, disk, and RAM for the requested virtual machine. That design has been far too limiting, so in the past year we began making things simpler and more generic with the concept of Resource Providers. A resource provider is any entity that had something that could be shared in a virtual environment. Besides physical compute hosts, this would also handle shared storage, network resources, block storage, and anything else that could be virtualized. Those things that are being provided would be referred to as Resource Classes, and the amounts of each of those would be represented as integer amounts, making comparison simple (previously there were many complicated conditional code structures that were necessary to compare different types of things under the old model). These amounts are referred to as Inventory, and the consumed amounts of inventory are referred to as Allocations. Determining the available amount that a provider has of a particular resource class is a simple matter of subtracting the allocations from the inventory. This assumes, of course, that all of the inventory for a particular resource class is identical and interchangeable. (hint: they might not be!)

So far, everything seems straightforward enough. This model is designed to only address the quantitative aspect of resources; qualitative aspects are represented by boolean traits that can be assigned to resource providers (and only to resource providers). The classic example was different compute hosts that disk space available, where some was SSD and others were slower spinning disks. The disk space was all storage, measured in GB and treated equivalently. It was only the providers that were different, as distinguished by their differing traits.

However, once we began to consider more complex resources, things didn’t fit as well. SR-IOV devices, for example, allow their virtual functions (VFs) to be shared by virtual machines running on the host with the SR-IOV device. It is these VFs that are the actual resources provisioned to the virtual machines. Each compute node can also have multiple devices available, and they can be (and usually are) attached to different networks. So if we assume two devices that each offer 8 VFs, our typical model would have an inventory of 16 VFs for that resource provider.

It’s clear, though, that those 16 VFs are not interchangeable. A VM needs a VF attached to a particular network, and so we need to tell those two groups of VFs apart. The current solution being put forward tries to solve this by introducing a hierarchy of resource providers in a parent-child relationship, referred to as nested resource providers. In this approach, the compute host is the parent resource provider, with two child resource providers (the two SR-IOV devices). Each of those would have an inventory of 8 VFs, and we would distinguish them by assigning different traits to the child resource providers. While this approach does work, in my opinion it’s an unnecessary complication that is more of a workaround for two incorrect assumptions: that all inventory for a particular resource class is identical, and that traits describe resource providers.

The reason for this disconnect was that the original design of the resource provider/class model was too simple. It was based on a relation between the compute node and the inventory it controlled being flat, so that we could assign traits *of the inventory* to its provider, and it all worked. Think about it: is SSD vs.spinning disk really a trait of the compute node? Or is it a trait of the storage system? The iMac I have for our family has both SSD and spinning disk storage. If it were a compute node, what would its trait be set to? Clearly, saying that the storage type is a trait of the compute node is not correct. It is this error that requires the sort of complex workarounds such as nested resource providers.

So what it the alternative? I see two; there may be more. The first would be to make a separate ResourceClass for each type of resource. This has the advantage of preserving the notion that all inventory for a given resource class is interchangeable. In the SR-IOV case, there would be two classes of VFs (one for each network connection type), and the request to build a VM would specify which network the VF requires. Unfortunately, there are some who resist the idea of multiple resource classes for similar things; I believe that it’s an unfortunate result of naming them ‘classes’, since most of us who are experienced in OOP see that as bad class design. If they had been named ‘ResourceTypes’ instead, I doubt there would be as much resistance. The second approach doesn’t add more resource classes; instead, it would assign traits to the ResourceClass to distinguish among their respective inventories. While this may more accurately model the real world, it would require some changes to the inner workings of the placement engine, which assumes that all the inventory for a particular ResourceClass is interchangeable; it would now have to be class+traits that would be unique. It would also require extra calls to the traits API to find the right ResourceClass. That just seems like a lot of complication just to avoid making separate ResourceClasses.

Let’s imagine another example: Bike Shed As A Service! Our cloud provides virtual bike sheds using a Bike Shed ResourceProvider that can provide bike sheds on demand. There are a total of 32 bike sheds: 8 blue, 8 green, and 16 red (because red is the best color, obviously!). What would be the most practical way of representing them in the ResourceProvider framework? Can we really say that all the bike sheds are identical? Of course not! There is no way that a blue shed is anything like a prized red shed! So when I request my bike shed, of course I will specify “red bike shed”, not just any old shed.

The correct way to represent such a situation is to have a Bike Shed ResourceProvider, and it has 3 ResourceClasses: RedBikeShed, BlueBikeShed, and GreenBikeShed, each of which has an inventory of 16, 8, and 8 sheds, respectively. Contrast this with the nested resource provider proposal, which would have: A BikeShed ResourceProvider, with three child ResourceProviders, with traits of ‘red’, ‘blue’, and ‘green’ respectively, and each of which has separate inventories as above. Besides the inefficiency of the SQL joins required to query such a design, it really doesn’t reflect reality. There isn’t any such intermediary ‘provider’; it’s just an artifact of the workaround for an incorrect model.

To get back to the real-world SR-IOV example, it’s clear that the inventory of VFs for each device are not interchangeable, so therefore they belong to separate resource classes. We can bike shed on how to best name them (see what I did there?), but the end result would be an inventory of 8 VFs on network 1, and 8 VFs on network 2.

I know that the Bike Shed example is a very simple one, but one designed to show the problems with the nested approach. Let’s make sure that we aren’t digging ourselves into a design hole that will make things hard to work with as the placement engine design grows to incorporate all sorts of resources. Perhaps there may be a case that can only be solved with the nested approach, but I haven’t seen it yet.

Pair Development

If you’ve worked on large open source projects, one of the difficulties is dividing the workload. The goal, of course, is to spread it out so that every developer has a workload that will keep them busy, and everyone is working in sync towards a common goal. This isn’t easy in practice, as there is no top-down authority to hand out assignments and keep everyone on track, as there is in a corporate development environment. It requires a good deal of communication among the members of the team, as well as a good deal of trust.

This problem was brought to light recently in the Nova community. The issue was with the subteam working on the scheduler/placement engine, of which I’m a member. During the Newton development cycle, there was a significant bottleneck due to the fact that one person, Chris Dent, was responsible for a large chunk of work in designing and coding the Placement API and underlying engine, while the rest of us could only help by doing reviews after the code was written. And this isn’t a new thing: during Mitaka, it was Jay Pipes who was the bottleneck with the development of the Resource Providers concept, and in Liberty, it was Sylvain Bauza with the huge amount of work he did to integrate the Request Spec into Nova. Don’t get me wrong: I’m not criticizing any of these people, as they all did great work. Rather, I am expressing frustration that they bore the brunt of the load, when it didn’t have to be that way. I think that it is time to try a different approach in Ocata.

I propose that we use Pair Development. No, not Pair Programming – that’s an entirely different thing. Pair Development is when each “chunk” of work is not undertaken by a single developer, but rather to two. They discuss the path they want to take ahead of time, and instead of splitting the work, they both work on the same patches at the same time. Wait, you say – won’t this slow things down? I don’t believe that it will, for several reasons. First, when discussing a design, having multiple sets of eyes will reduce the number of dead ends, in the same way that bugs are reduced in pair programming by having both developers review the code as it is being written. Second, when a reviewer finds an issue with a patch, either developer can make the fix. This is an even greater benefit if the two developers are in different, but overlapping, time zones.

We also have as evidence the week before the most recent Feature Freeze: the placement stuff needed to get in before FF, and so a whole group of us pulled together to make that happen. Having a diverse set of eyes uncovered several edge cases and inconsistencies in the code, and those were resolved pretty quickly. We used IRC mostly, but had a Google Hangout at least once a day to discuss any outstanding, unresolved matters, so that we would all be on the same page. So yeah, the time pressure helped instill a bit of urgency in us all, but I think that it was having all of us own the code, not just Chris, that made things happen as well as they did. I know that I was familiar with the code, having reviewed much of it before, but now that I had to change it and test it myself, my understanding grew much deeper. It’s amazing how deeper you understand something when you touch it instead of just look at it.

Another benefit of pair development is that it provides much more continuity when one of the developers takes some time off. Instead of the progress getting put on hold, the other member of the development pair can continue along. It will also help to have more than one person know the new code intimately, so that when a behavior surfaces that is not expected, we aren’t depending on a single person to figure out what’s going on.

So for Ocata, let’s figure out the tasks, and make sure that each has two people assigned to it. I will wager that come the end of the cycle, it will help us accomplish much more than we have in previous releases.

Is Swift OpenStack?

There has been some discussion recently on the OpenStack Technical Committee about adding Golang as a “supported” language within OpenStack. This arose because the Swift project had recently run into some serious performance issues, which they solved by re-writing the bottleneck process in Golang with much success. I’m not writing here to debate the merits of making OpenStack more polyglot (it’s no secret that I oppose that), but instead, I want to address the issue of Swift not behaving like the rest of OpenStack.

Doug Hellman summarized this feeling well, originally writing it in a pastebin, but then copying it into a review comment on the TC proposal. Essentially, it says that while Swift makes some efforts to do things the “OpenStack Way”, it doesn’t hesitate to follow its own preferences when it chooses to.

I believe that there is good reason for this, and I think that people either don’t know or forget a lot of the history of OpenStack when they discuss Swift. Here’s some background to clarify:

Back in the late ’00s, Rackspace had a budding public cloud business (note: I worked for Rackspace from 2008-2014). It had bought Slicehost, a company with a closed-source VPS system that it used as the basis for its Cloud Servers product, and had developed a proprietary object storage system called NAST (Not Another S Three: S3, get it?). They began hitting limits with NAST fairly soon – it was simply too slow. So it was decided to write a new system with scalability in mind that would perform orders of magnitude better than NAST; this was named ‘Swift’ (for obvious reasons). Swift was developed in-house as a proprietary software project. The development team was a small, close-knit group of guys who had known each other for years. I joined the Swift development team briefly in 2009, but as I was the only team member working remotely, I was at a significant disadvantage, and found it really difficult to contribute much. When I learned that Rackspace was forming a distributed team to rewrite the Cloud Servers software, which was also beginning to hit scalability limits, I switched to that team. For a while we focused on keeping the Slicehost code running while starting to discuss the architecture of the new system. Meanwhile the Swift team continued to make strong progress, releasing Swift into production in the spring of 2010, several months before OpenStack was announced.

At roughly the same time, the other main part of OpenStack, Nova, was being started by some developers working for NASA. It worked, but it was, shall we say, a little rough in spots, and lacked some very important features. But since Nova had a lot of the things that Rackspace was looking for, we started talking with NASA about working together, which led to the creation of OpenStack. So while Rackspace was a major contributor to Nova development back then, from the beginning we had to work with people from a wide variety of companies, and it was this interaction that formed the basis of the open development process that is now the hallmark of OpenStack. Most of the projects in OpenStack today grew out of Nova (Glance, Neutron, Cinder), or are built on top of Nova (Trove, Heat, Watcher). So when we talk about the “OpenStack Way”, it really is more accurately thought of as the “Nova” way, since Nova was only half of OpenStack. These two original halves of OpenStack were built very differently, and that is reflected in their different cultures. So I don’t find it surprising that Swift behaves very differently. And while many more people work on it now than just the original team from Rackspace, many of that original team are still developing Swift today.

I do find it somewhat strange that Swift is being criticized for having “resisted following so many other existing community policies related to consistency”. They are and always have been distinct from Nova, and that goes for the community that sprang up around Nova. It feels really odd to ignore that history, and sweep Swift’s contributions away, or disparage their team’s intentions, because they work differently. So while I oppose the addition of languages other than Python for non-web and non-shell programming, I also feel that we should let Swift be Swift and let them continue to be a distinct part of OpenStack. Requiring Swift to behave like Nova and its offspring is as odd a thought as requiring Nova et. al. to run their projects like Swift.

Fragmented Data

(This is a follow-up to my earlier post on Distributed Data)

One of the more interesting design sessions today at the OpenStack Design Summit was focused on Nova Cells V2, which is the effort to rework the way cells work in Nova. Briefly, cells are a mechanism for allowing separate independent deployments to work as a single cloud, primarily as a way to provide horizontal scalability. They also have other uses for operators, but that’s the main reason for them. And as separate deployments, they have their own API service, conductor service, message queue, and database. There are several advantages that this kind of independence offers, with failure isolation being one of the biggest. By this I mean that something goes wrong and a cell is unreachable, it doesn’t affect the performance of the remaining cells.

There are tradeoffs with any approach, and this one is no different. One glaring issue that came up at that session is that there is no simple way to get a global view of your cloud. The example that was discussed was the common case of listing all your instances, which would require querying each cell independently, aggregating the results, and then sorting the aggregated records. For small clouds this process is negligible, but as the size grows, so does the overhead and complexity. It is particularly problematic for something that requires multiple calls, like pagination. Let’s consider a site with thousands of instances spread across dozens of cells. Typically when querying a large list like that, the API will return the first few, and include a link for the next batch. With a fragmented database, this will require some form of centralized caching approach, or, if that’s not feasible or the cache is stale, re-running the same costly query, aggregation, and sorting process for each page of data requested. With that, any gain that might have been realized by separating the databases will be more than offset by a need for a way to efficiently recombine that data. This isn’t only a cost for more memory/CPU for the API service to handle the aggregation and caching, which will only need to be borne by the larger cloud operating companies. It is an ongoing cost of complexity to the developers and maintainers of the Nova codebase to handle this, and every new part of Nova will be similarly difficult to fit.

There are other places where this fragmented database design will cause complexity, such as having the Scheduler require a database connection to every cell, and then query every cell on each request, followed by aggregating the results… see the pattern? Splitting a database to improve performance, or sharding, only makes sense if you shard along a line that logically separates the data so that each shard can be queried efficiently. We’re not doing that in the design of cells.

It’s not too late. There is a project that makes minimal changes to the oslo.db driver to allow replacing the SQLAlchemy and MySQL database that underpins Nova with a distributed database (they used Redis, but it doesn’t depend on Redis). It should really be investigated further before we create a huge pile of technical and design debt by fragmenting the data in Nova.

Distributed Data and Nova

Last year I wrote about the issues I saw with the design of the Nova Scheduler, and put forth a few proposals that I felt would address those issues. I’m not going to rehash them in depth here, but summarize instead:

  • The choice of having the state of compute nodes copied back to the scheduler over RPC was the source of the raciness observed when more than one scheduler was running. It would be better to have a database be the single source of truth.
  • The scheduler was created specifically for selecting hosts based on basic characteristics of VMs: RAM, disk, and VCPU. The growth of virtualization, though, has meant that we now need to select based on myriad other qualities of a host, and those don’t fit into the original ‘flavor’-based design. We could address that by creating Resource classes that encapsulated the knowledge of a resource’s characteristics, and which also “knew” how to both write the state of that resource to the database, and generate the query for selecting that resource from the database.
  • Nova spends an awful lot of effort trying to move state around, and to be honest, it doesn’t do it all that well. Instead of trying to re-invent a distributed data store, it should use something that is designed to do it, and which does it better than anything we could come up with.

But I’m pleased to report that some progress has been made, although not exactly in the manner that I believe will solve the issues long-term. True, there are now Resource classes that encapsulate the differences between different resources, but because the solution assumed that an SQL database was the only option, the classes reflect an inflexible structure that SQL demands. The process of squeezing all these different types of things into a rigid structure was brilliantly done, though, so it will most likely do just what is needed. But there is a glaring hole: the lack of a distributed data system. Until that issue is addressed, Nova developers will spend an inordinate amount of time trying to create one, and working around the limitations of an incomplete solution to this problem. Reading Chris Dent’s blog post on generic resource pools made this problem glaringly apparent to me: instead of a single, distributed data store, we are now making several separate databases: one in the API layer for data that applies across the cells, and a separate cell database for data that is just in that cell. And given that design choice, Chris is thinking about having a scheduler whose design mirrors that choice. This is simply adding complexity to deal with the complexity that has been added at another layer. Tracking the state of the cloud will now require knowing what bit of data is in which database, and I can guarantee you that as we move forward, this separation will be constantly changing as we run into situations where the piece of data we need is in the wrong place.

When I wrote last year, in the blog posts and subsequent mailing list discussions, I think the fatal mistake that I made was offering a solution instead of just outlining the problem. If I had limited it to “we need a distributed data store”, instead of “we need a distributed data store like Apache Cassandra“, I think much of the negative reaction could have been avoided. There are several such products out there, and who knows? Maybe one of them would be a much better solution than Cassandra. I only knew that I had gotten a proof-of-concept working with Cassandra, so I wanted to let everyone know that it was indeed possible. I was hoping that others would then present their preferred solution, and we could run a series of tests to evaluate them. And while several people did start discussing their ideas, the majority of the community heard ‘Cassandra’, which made them think ‘Java’, which soured the entire proposal in their minds.

So forget about Cassandra. It’s not the important thing. But please consider some distributed database for Nova instead of the current design. What does that design buy us, anyway? Failure isolation? So that if a cell goes down or is cut off from the internet, the rest can still continue? That’s exactly what distributed databases are designed to handle. Scalability? I doubt you could get much more scalable than Cassandra, which is used to run, among other things, Netflix and the Apple App Store. I’m sure that other distributed DBs scale as well or better than MySQL. And with a distributed DB, you can then drop the notion of a separate API database and separate cell databases that all have to coordinate with each other to get the information they need, and you can avoid the endless discussions about, say, whether the RequestSpec (the data representing a request to build a VM) belongs in the API layer (since it was received there) or in the cell DB (since that’s where the instance associated with it lives). The data is in the database. Write to it. Query it. Stop making things more complicated than they need to be.

Moving Forward (carefully)

It’s a classic problem in software development: how to change a system to make it better without breaking existing deployments. That’s the battle that comes up regularly in the OpenStack ecosystem, and there aren’t any simple answers.

On the one hand, you’ve released software that has a defined interface: if you call a particular API method with certain values, you expect a particular result. If one day making that exact same call has a different result, users will be angry, and rightfully so.

On the other hand, nobody ever releases perfect software. Maybe the call described above works, but does so in a very unintuitive way, and confuses a lot of new users, causing them a great deal of frustration. Or maybe a very similar call gives a wildly different result, surprising users who didn’t expect it. We could just leave them as is, but that isn’t a great option. The idea of iterative software is to constantly make things better with each release.

Enter microversions: a controlled, opt-in approach to revising the API. If this is a new concept, read Sean Dague’s excellent summary of microversions. The concept is simple enough: the API won’t ever change, unless you explicitly ask it to. Let’s take the example of an inconsistent API call that we want to make consistent with other similar calls: we make the change, bump the microversion (let’s call this microversion number 36, just for example), and we’re done! Existing code that relies on the old behavior continues to work, but anyone who wants to take advantage of the improved API just has to specify that they want to use microversion 36 or later in their request header, and they get the new behavior. Done! What could be simpler?

Well, there are potential problems. Let’s continue with the example above, and assume that later on some really cool new feature is added to the API. Let’s assume that this is added in microversion 42. A user who might want to use this new feature sets their headers to request microversion 42, but now they may have a problem if other code still expects the inconsistent call that existed in pre-36 versions of the API. In other words, moving to a new microversion to get one specific change requires that you also accept all of the changes that were added before that one!

In my opinion, that is a very small price to pay. Each microversion change has to be documented with a release note explaining the change, so before you jump into microversion 42, you have ample opportunity to learn what has changed in microversions 2-41, too. We really can’t spend too much mental effort on protecting the people who can’t be bothered to read the release notes, as the developers and reviewers have gone to great lengths to make sure that these changes are completely visible to anyone who cares to make the effort. We can’t assume that the way we did something years ago is going to work optimally forever; we need to be able to evolve the API as computing in general evolves, too. Static is just another word for ‘dead’ in this business. So let’s continue to provide a sane, controlled path forward for our users, and yes, it will take a little effort on their part, too. That’s perfectly OK.

Creating a Small-Scale Cassandra Cluster

My last post started a discussion about various possible ways to improve the Nova Scheduler, so I thought that I’d start putting together a proof-of-concept for the solution I proposed, namely, using Cassandra as the data store and replication mechanism. But there’s a problem: not everyone has the experience to set up Cassandra, so I thought I’d show you what I did. I’m using 3 small cloud instances on Digital Ocean, but you could set this up with some local VMs, too.

We’ll create 3 512MB droplets (that’s their term for VMs). The 512MB size is the smallest they offer (hey, this is POC, not production!). I named mine ‘cass0’, ‘cass1’, and ‘cass2’. Choose a region near you, and in the “Select Image” section, click on the “Applications” tab. In the lower right side of the various options, you should see one for Docker (as of this writing, it’s “Docker 1.8.3 on 14.04”). Select that, and then below that select the “Private Networking” option; this will allow your Cassandra nodes to communicate more efficiently with each other. Add your SSH key, and go! In about a minute the instances should be ready, so click on their name to get to the instance information page. Click the word ‘Settings’ along the left side of the page, and you will see both the public and private IP addresses for that instance. Record those, as we’ll need them in a bit. I’ll refer to them as $IP_PRIVn for the instance cass(n); e.g., $IP_PRIV2 is the private IP address for cass2.

If you are using something other than Digital Ocean, such as Virtual Box or Rackspace or anything else, and you don’t have access to an image with Docker pre-installed, you’ll have to install it using either sudo apt-get install docker-engine or sudo yum install docker-engine.

Once the droplets are running, ssh into them (I use cssh to make this easier), and run the usual apt-get updates to pull all the security fixes. Reboot. Reconnect to each droplet, and then grab the latest Cassandra image for Docker by running: docker pull cassandra:latest. [EDIT – I realized that without using volumes, restarting the node would lose all the data. So here are the corrected steps.] Then you’ll create directories to use for Cassandra’s data and logs:

mkdir data
mkdir log

To set up your Cassandra cluster, first ssh into the cass0 instance. Then run the following to create your container:

docker run --name node0 
    -v data:/var/lib/cassandra 
    -v log:/var/log/cassandra 
    -p 9042:9042 -p 7000:7000 
    -d cassandra:latest

If you’re not familiar with Docker, what this does is create a container with the name ‘node0’ from the image cassandra:latest. It creates two volumes (the sections beginning with the -v parameter: the first maps the local ‘data’ directory to the container’s ‘/var/lib/cassandra’ directory (where Cassandra stores its data), and the second maps the local ‘log’ directory to where Cassandra would normally write its logs. It passes in the private IP address in environment variable CASSANDRA_BROADCAST_ADDRESS; in Cassandra, the broadcast address is what that node should use to communicate. It also opens 2 ports: 9042 (the CQL query port) and 7000 (for intra-cluster communication). Now run docker ps -a to verify that the container is up and running.

For the other two nodes, you do something similar, but you also specify the CASSANDRA_SEEDS parameter to tell them how to join the cluster; this is the private IP address of the first node you just created. On cass1, run:

docker run --name node1 
    -v data:/var/lib/cassandra 
    -v log:/var/log/cassandra 
    -p 9042:9042 -p 7000:7000 
    -d cassandra:latest

Then on cass2 run:

docker run --name node2 
    -v data:/var/lib/cassandra 
    -v log:/var/log/cassandra 
    -p 9042:9042 -p 7000:7000 
    -d cassandra:latest

That’s it! You have a working 3-node Cassandra cluster. Now you can start playing around with it for your tests. I’m using the Python library for Cassandra to connect to my cluster, which you can install with pip install cassandra-driver. But working with that is the subject for another post in the future!