Graph Database Follow-up

I just published my series on Graph Databases a few hours ago, and have already had lots of hits. I guess people found the topic as interesting as I did! One reader, Jay Pipes, is one of the core developers of the Placement service I mentioned in that series, and probably the most heavily opinionated with regards to using MySQL. He is also a long-time friend. Jay asked some excellent questions at the end of the last post, and I didn’t feel that I could do them justice in the space of a WordPress reply box, so I created this post. Let’s get into it!

1) I note that you’ve combined allocations and inventory into a single object. How would we query for the individual consumers of resources?

The combination was for simplifying the illustration. As you can probably guess, a full Placement model would have Consumer objects that would have a [:CONSUMES] relation to the resource objects. So to illustrate this, I added another script to the GitHub repo: create_consumer.py. Since  relations can have properties too, we’d create an ‘amount’ property that would specify how much of a resource is being consumed by this particular consumption. The query would look something like this:

MATCH (disk:DISK_GB)<-[alloc:CONSUMES]-(con:Consumer)
WITH disk, disk.total - SUM(alloc.amount) AS avail
WHERE avail > 2000
RETURN disk, avail

This would return the disks whose total amount minus the sum of all the consumed amounts, is greater than 2000.

2) How many resources of each type are being consumed by a project or user or consumer? (i.e. usages, aggregated by a grouping mechanism – the thing that SQL does pretty well)

In this respect, it wouldn’t be all that different. Cypher does aggregates, too. Using the data generated by the create_consumer.py script, I can run the following query:

MATCH (con:Consumer)-[alloc:CONSUMES]->(r)
WHERE con.pk = 1
RETURN labels(r)[0] AS resource_class, sum(alloc.amount) AS usage

This query will return:

╒════════════════╤═══════════════════╕
│"resource_class"   │"usage"                │
╞════════════════╪═══════════════════╡
│"MEMORY_MB"        │1024                   │
├────────────────┼───────────────────┤
│"DISK_GB"          │500                    │
├────────────────┼───────────────────┤
│"VCPU"             │2                      │
└────────────────┴───────────────────┘

So as you can see, not a whole lot different than with SQL.

3) How do you ensure that multiple concurrent writers do not over-consume resources on a set of providers? We use a generation on the provider along with an atomic update-where-original-read-generation strategy to protect the placement DB objects. I’m curious how neo4j would allow that kind of operation.

Neo4j is ACID-compliant, and transactions either succeed completely or not at all. So I’m not really sure how much I can add to that. Setting the value of an object is trivial, updating that value is trivial, checking a supplied value with the one stored in the DB is trivial. So it would work identically to how it works in SQL.

In summary, I want to make two points: first, I’m not bashing MySQL. It’s an excellent database that I use extensively myself (well, I use MariaDB, but…). This WordPress site has a MariaDB backend. Second, this started as an attempt to scratch the itch I have felt since we began making Placement more and more complex. I wanted to find out if there was a better way to handle the sort of problems we have, and my previous reading on graph databases came to mind. It was tough to get used to thinking with a graph mindset. It reminded me of when I first started using Go after years of Python: when I tried to write Pythonic code in Go, it was terrible. Once I was able to drop the Python mindset and write Go the way it was designed, things were much, much easier to understand, and the code I wrote was much, much better. The same sort of “letting go” process had to happen before I was able to fully see what can be done with graph databases.

Placement Graph Examples

In the previous two posts in this series, I wrote about graph databases in general, and why I think that they are much more suited to modeling the data in the Placement service than relational DBs such as MySQL. In this post I’ll show a few examples of common Placement issues and how they are solved in Neo4j. The code for all of this is in my GitHub repo. I’ll cover the two biggest sources of complexity: Nested Resource Providers and Shared Providers.

Nested Resource Providers: this refers to the case where one ResourceProvider physically contains one or more other ResourceProvider, and it is this contained provider that is the source of the requested resources. This nesting forms a tree-like structure, and can go arbitrarily deep, although in practice it would be rare to see case where it was more than 4 levels deep. One such case that Placement needs to handle is a compute node containing NUMA nodes. With NUMA, some of the resources, such as disk, can be supplied by the ComputeNode, while others, such as RAM and VFs, come from the NUMA node. The response needs to not only include the selected ComputeNode, but the entire tree of ResourceProviders.

To illustrate this example, I’m going to create 50 plain compute nodes, and 50 that contain 2 NUMA nodes each. The plain computes will provide disk, RAM, VCPU and VFs, while on the NUMA computes, the compute node will only provide the disk; the RAM, VCPU, and VFs are provided by the NUMA node. The script will gradually decrease the amount of available resources by increasing the value assigned to ‘used’ as it runs. To make a script like this, I used the py2neo module; there are several Python wrappers for Neo4j; I chose this because it seemed simplest. The code is in the script named create_nested.py.

Let’s start with an easy one: get all the compute nodes that have at least 2000 MB of RAM:

MATCH (cn:ComputeNode)-[*]->(ram:MEMORY_MB)
WHERE ram.total - ram.used > 2000
RETURN cn, ram

Filtering on RAM result

Note that the query returned ComputeNodes (blue) that had the RAM (green) associated through a NUMA node (red) as well as those where the ComputeNode itself supplied the RAM.

You can match any number of filters with a single query. While it is possible to create long, complex queries in Cypher, it is simpler and more idiomatic to use the WITH clause to chain the results of one query with the results of the next:

MATCH p=(cnode:ComputeNode)-[*]->(ram:MEMORY_MB)
WHERE ram.total - ram.used > 4000
WITH p, cnode, ram
MATCH (cnode:ComputeNode)-[*]->(disk:DISK_GB)
WHERE disk.total - disk.used > 2000
RETURN p, cnode, disk, ram

So while this might look like a JOIN or UNION in SQL, it is interpreted by Cypher as a single query, and executed as such. This will return the following. Note that once again there are Compute Nodes both with and without NUMA nodes coming back from the same query.

Visual output of the query

Ok, you’re thinking, this is all very fine, but what am I going to do with a bunch of colored circles and lines? Fear not. This is just the visualization of the returned data. You can also see the results in plain text:

Text output of the query

Here it’s even clearer that even though there are only 4 ComputeNodes returned, there are 6 possible solutions, since each of the nodes with NUMA can satisfy the request with either NUMA node. This is returning the entire tree structure, which is what Placement requires for such queries.

When you run the query using py2neo, you get lists of Python dicts for the result:

[{'cnode': {'name': 'cn0000',
            'uuid': '29b401fd-9acd-4bbd-9f1d-428a5459c260'},
  'disk': {'name': 'disk0000',
           'total': 2048,
           'used': 0,
           'uuid': '605c622e-4f9a-4e47-9b34-73fbcba624fa'},
 'ram': {'name': 'ram0000',
         'total': 4096,
         'used': 0,
         'uuid': 'f0b1eca3-74bd-4a3f-a82c-a0fbb3298882'}},
 {'cnode': {'name': 'cn0001',
            'uuid': '629fb0ff-2a10-4686-a6a1-07405e7f7c01'},
  'disk': {'name': 'disk0001',
           'total': 2048,
           'used': 40,
           'uuid': '424414a2-5257-4747-abb5-96c407a2cbaf'},
  'ram': {'name': 'ram0001',
          'total': 4096,
          'used': 81,
          'uuid': '6ac584b6-4e6d-4d77-aaad-2d41a47db476'}},
 {'cnode': {'name': 'cnNuma0000',
            'uuid': 'b9e1450e-2845-4ecb-8011-f1fe16ae53be'},
  'disk': {'name': 'diskNuma0000',
            'total': 2048,
            'used': 0,
            'uuid': '7238e020-d628-4bdb-8549-8baffcf08271'},
  'ram': {'name': 'ramNumaA0000',
          'total': 4096,
          'used': 0,
          'uuid': 'e7bf8aed-3a22-4872-934f-2219f95258ed'}},
 {'cnode': {'name': 'cnNuma0000',
            'uuid': 'b9e1450e-2845-4ecb-8011-f1fe16ae53be'},
  'disk': {'name': 'diskNuma0000',
           'total': 2048,
           'used': 0,
           'uuid': '7238e020-d628-4bdb-8549-8baffcf08271'},
  'ram': {'name': 'ramNumaB0000',
          'total': 4096,
          'used': 0,
          'uuid': 'b1ac0a05-9e0b-4696-be8c-5cd5b6d4e876'}},
 {'cnode': {'name': 'cnNuma0001',
            'uuid': 'f082f2bb-18f9-4e50-b76f-09722dacff7a'},
  'disk': {'name': 'diskNuma0001',
           'total': 2048,
           'used': 40,
           'uuid': '2a7004b8-001c-4244-bf84-0a511f8a3eb1'},
  'ram': {'name': 'ramNumaA0001',
          'total': 4096,
          'used': 81,
          'uuid': '3a8170d3-be96-4029-be97-6a0d1d04f9d7'}},
 {'cnode': {'name': 'cnNuma0001',
            'uuid': 'f082f2bb-18f9-4e50-b76f-09722dacff7a'},
  'disk': {'name': 'diskNuma0001',
           'total': 2048,
           'used': 40,
           'uuid': '2a7004b8-001c-4244-bf84-0a511f8a3eb1'},
  'ram': {'name': 'ramNumaB0001',
          'total': 4096,
          'used': 81,
          'uuid': '32314631-428a-49dc-8331-54e91f9da23b'}}]

 

Let’s look at the other use case that complicates Placement: Shared Providers. The most common usage is when a large disk array is shared among many compute nodes. So to simulate that case, I created a single ComputeNode with local disk storage, and two without. Next I created 2 shared disk providers, one with a much greater capacity than the other. I also created 2  ComputeNodes, neither of which has local disk. The next step is to create an Aggregate that will be used to associate these diskless ComputeNodes with the shared disks. Now it really isn’t necessary to do this in Neo4j; I could simply associate the shared disks directly with the ComputeNodes. With graph databases this intermediate artifact to associate things is redundant, since relationships are first-class entities. But I’m adding this extra layer in order to make it more familiar with those who work with Placement today. The script for that is create_shared.py, and creates a deployment that looks like this:

shared disk layout

The local disk has 4000GB, the smaller shared disk has 10,000GB, and the larger shared disk has 100,000GB. Let’s run 3 queries, requesting 2,000GB, 8,000GB, and 50,000GB. The code for these queries is in ‘search_shared.py‘, and it looks like this:

MATCH (cnode:ComputeNode)-[*]-(gb:DISK_GB)
WHERE gb.total - gb.used > 50000
RETURN cnode, gb

If you have a sharp eye, you’ll notice something slightly different with this. Prior to this, queries had the format:

(obj)-[relation]->(obj)

In this one, the “arrow” on the right is gone:

(obj)-[relation]-(obj)

Relationships is Neo4j are always defined with a direction (e.g., (Alice)-[:KNOWS]->Bob). But you can query either with or without specifying the direction of the relation. In the shared provider case, there is no directional path between a ComputeNode and a SharedDisk, since they are both related as members of the Aggregate. But they are connected, and the Cypher language allows us to express that we don’t care about the direction of the relationship in some cases. This allows us to use the exact same query to return shared providers as well as local resources.

Finally, let’s combine the two above. In the create_nested_and_shared.py, I took the script for creating a bunch of ComputeNodes, both with and without NUMA, and then added in the shared disks. I associated the first ComputeNode (both with and without NUMA) with that aggregate. The script search_nested_and_shared.py queries nodes for both RAM and increasing amounts of disk. Here’s the query:

MATCH (cnode:ComputeNode)-[*]->(ram:MEMORY_MB)
WHERE ram.total - ram.used > 4000
WITH cnode, ram
MATCH (cnode:ComputeNode)-[*]-(disk:DISK_GB)
WHERE disk.total - disk.used > 2000
RETURN cnode, disk, ram

I ran that query 3 times, each requesting different disk amounts. Here’s the output for requests of 2,000GB, 8,000GB, and 20,000GB  :

Requesting small disk; found 15
[('cn0000', 'gb_small'),
 ('cnNuma0000', 'gb_small'),
 ('cnNuma0000', 'gb_small'),
 ('cn0000', 'gb_big'),
 ('cnNuma0000', 'gb_big'),
 ('cnNuma0000', 'gb_big'),
 ('cn0000', 'disk0000'),
 ('cnNuma0000', 'disk0000'),
 ('cnNuma0000', 'disk0000'),
 ('cn0001', 'disk0001'),
 ('cnNuma0000', 'diskNuma0000'),
 ('cnNuma0000', 'diskNuma0000'),
 ('cn0000', 'diskNuma0000'),
 ('cnNuma0001', 'diskNuma0001'),
 ('cnNuma0001', 'diskNuma0001')]

Requesting medium disk; found 6
[('cn0000', 'gb_small'),
 ('cnNuma0000', 'gb_small'),
 ('cnNuma0000', 'gb_small'),
 ('cn0000', 'gb_big'),
 ('cnNuma0000', 'gb_big'),
 ('cnNuma0000', 'gb_big')]

Requesting large disk; found 3
[('cn0000', 'gb_big'),
 ('cnNuma0000', 'gb_big'),
 ('cnNuma0000', 'gb_big')]

Note that the above was a single query that was identical in structure to the nested-only and shared-only queries. In other words, there was no complex SQL required, no joins, no auxiliary tables, no client-side combination of separate result sets – in other words, no heroic SQL-fu needed. The reason is that graph databases fit the problems of Placement much, much better than traditional relational DBs.

Performance: I suppose that without mentioning performance for these queries, it’s all pointless. I mean, what’s the good of simplified code if it takes forever to run? Well, if you notice, at the top of the create_nested.py script there is a constant NODE_COUNT. I ran my tests with the default setting of 50, which would simulate a deployment of 100 total servers (50 plain, 50 with NUMA). I have this running on a DigitalOcean VM with 2GB RAM, 30GB disk, and 1 VCPU.  When I ran the following query, which is the same one I ran earlier in this post, I got these results:

MATCH p=(cnode:ComputeNode)-[*]->(ram:MEMORY_MB)
WHERE ram.total - ram.used > 4000
WITH p, cnode, ram
MATCH (cnode:ComputeNode)-[*]->(disk:DISK_GB)
WHERE disk.total - disk.used > 2000
RETURN p, cnode, disk, ram

Returned: 78 records
Time: 4ms

OK, that’s wonderful. Now what if we upped that to 1,000 nodes each, or 2,000 total nodes. That’s a pretty good-sized cloud, and running the same query I got:

Returned: 1536 records
Time: 169ms

Not too bad! I couldn’t resist, and increased it to 10,000 nodes each, or 20,000 nodes total! First, please note that the script to create these nodes is terribly inefficient, and creating 20,000 nodes with all their related objects took over an hour. But once the data was there, running the same script returned:

Returned: 15354 records
Time: 831ms

So, even though I haven’t even created a single index, the performance is nothing to worry about.

Summary: Now I know that I didn’t touch Traits, Allocations, Inventory, etc., as I wanted this to be a simple introduction to the concepts of graph databases, and not create a drop-in replacement for the current Placement service. And while I don’t expect the OpenStack Placement team to ever consider anything other than MySQL, I hope you come away from this series at least a little intrigued, and take the time when starting a project to explore alternatives to what you’ve used before. Something that works well in one problem domain doesn’t necessarily work well in others. But when all you have is a hammer, every computing problem looks like a nail to you.

Graph Database Basics

It is not my intent here to create a full tutorial on using graph databases; I just want to convey enough understanding so that you might see why they are a better fit for placement than a relational DB.

The most basic thing to understand is that your whiteboard design is your database design. Here’s what I mean by that:

whiteboard diagram

This is the general form of the CREATE statement in Neo4j’s declarative language Cypher:

CREATE (obj1)-[relates_to]->(obj2)

And here is the specific code for creating that in Cypher:

CREATE (cnode:ComputeNode)-[:PROVIDES]->(mem:MEMORY_MB {
    total: 10000, used: 2000})
RETURN cnode, mem

Note that the syntax is close to ASCII diagramming, using parentheses to enclose nodes, square brackets to enclose relationships, dashes to connect them, and arrows to indicate the direction of the relationship. Also note that nodes and relationships can have properties; here I gave the memory a ‘total’ of 10,000, and a ‘used’ value of 2000. These can be used to filter results to only those that have the necessary amounts. I used the Placement standard resource class ‘MEMORY_MB’ for the RAM to make it more familiar.

Neo4j comes with a browser that lets you visualize your data. So when I ran the above code in the browser, I get this back:

Neo4j relationship diagram

Ok, I guess at this point it seems almost toy-like. So here’s something a bit more interesting:

Compute node with NUMA

This is a compute node (purple) that provides disk (green), and contains two NUMA nodes (yellow), each of which provides VCPU (orange), RAM (grey), and VFs (pink). The model as stored in the DB matches the real world, and doesn’t require expensive JOINs to retrieve.

And finally (just to show off a little),  I created 500 compute nodes and associated them with aggregates. In Nova/Placement lingo, an ‘aggregate’ is a way to associate things that have something in common. Here’s what one such model looks like:

Diagram of compute nodes and their aggregate

The pink dot in the center is an aggregate, with purple compute nodes attached to it, and attached to those compute nodes are green disks, yellow memory, grey VCPU, and red VFs. And though they are faint, the lines connecting those things are the key to why this works so well: you can traverse these relationships in any direction in a single query– no joins required. I’ll give some specific examples of querying nested and shared resources in the next post in this series.

Hammer and Nail

The work on the OpenStack Placement service has been getting more and more complex in the last few months, as the service has matured to handle more complex resources. At first, things were pretty basic: you had a ResourceProvider, and it had Inventory of various ResourceClasses. You told Placement how much you needed of one or more ResourceClasses, and it returned those providers that had enough to meet your request. Simple.

This was the world where the ResourceProviders were simple computers, and the resources were that computer’s RAM, disk, and VCPUs. You wanted a VM with so much RAM, disk, and VCPU, and Placement returned those compute nodes that could provide that. This was all implemented internally using common relational database techniques, and it was simple and fast.

Now we are starting to model more complex resources, and the complexity is growing rapidly. We have sharing providers, with the canonical example being a shared disk whose storage can be consumed by a number of compute nodes, so there needs to be a way to model that. There are also nested providers, such as a network virtual function (VF) that is provided by a NIC that is part of the computer. This means that when you ask Placement for an instance that has a VF, it has to know the relationship between the compute node (root provider), the NIC (resource provider), and the VF (resource), and return the whole structure. These nested relationships aren’t limited to just one level; when you add in things such as NUMA, there can be many such nesting levels.

The modeling complexity has now grown significantly, and the ability to implement this complexity using relational databases, while certainly still possible, requires solutions that are less and less readily comprehensible for anyone looking at the code for the first time. I worked as a SQL DBA for several years, so I’m not a stranger to SQL, but I need to read the code several times, usually with a pen and paper to diagram things, before I truly understand what each bit is doing. If you’d like to see what I mean, peruse the _get_trees_matching_all() method of nova/api/openstack/placement/objects/resource_provider.py. It’s truly amazing that that code works as well as it does, but drives home the point that overly complex solutions indicate that there is a poor fit between your model and the thing that you’re trying to model.

No database will ever get rid of the complexity, of course, but some are much better suited to handling relations of these types than traditional relational databases. At the last PTG, these discussions, as well as the drawings that were made to illustrate the relationships, made me think about another type of database: graph databases. So I started playing around with the most popular one, Neo4j. Graph databases use a relationship-first approach to storing and accessing data, and this seemed to better model the needs of Placement than an RDBMS.

I do realize that it is too late for OpenStack Placement to change something as fundamental as their data storage model. But after playing around with Neo4j for a little while, it’s obvious that it fits the problem domain better than MySQL does. I’ll demonstrate this in the next post in this series. But the current state of Placement reminds me of the old saying  “when all you have is a hammer…”.

Dublin PTG Recap

We recently held the OpenStack PTG for the Rocky cycle. The PTG ran from Monday to Friday, February 26 – March 2, in Dublin, Ireland. So of course the big stuff to write about would be the interesting meetings between the teams, and the discussions about future development, right? Wrong! The big news from the PTG: Snow! So much so that Jonathan Bryce created the hashtag #SnowpenStack to commemorate the event!

Yes, Ireland was gripped by a record cold snap and about 5 inches/12 cm. of snow. Sure, I know that those of you who live in places where everyone owns a snow shovel just read that and snickered, but if you don’t have the equipment and experience to deal with it, it is a very big deal. They were also forecasting over twice that, and seeing how hard it was for them to deal with what they got, I’m glad it was only that much.

Ireland newspaper headline
The warnings posted ahead of the big storm

Since the storm was considered an emergency situation, and people were told to go home and stay there, that meant that there was no staff available for the conference, and it had to be shut down early. The people who ran the venue, Croke Park, Ireland’s biggest sports stadium, were wonderful and did everything they could to accommodate us.

Wait, what? A tech conference in a stadium?  Turns out they also have conference facilities on the upper floors of the stadium, so it wasn’t so odd after all. There is a hotel across the street from the entrance to the stadium, but it was completely booked on the Friday/Saturday I would be arriving, due to an important Rugby match between Ireland and Wales at Croke Park on Saturday. So I ended up at a hotel about a mile walk from the stadium. Which was fine at first, but turned out to be a bit of a problem once it got cold and the snows came, as it made the walk to Croke Park fairly difficult. But enough about snow – on to the PTG!

On Monday the API-SIG had a room for a full day’s discussion. However, it was remotely located at one end of the stadium, and for a while it was just the cores who showed up. We were afraid that we would end up only talking amongst ourselves, but fortunately people began showing up shortly thereafter, and by the afternoon we had a pretty good crowd.

Probably the most contentious issue we discussed was how to create guidelines for “action” APIs. These are the API calls that are made to make something happen, such as rebooting a server. We already recommend using the RESTful approach, which is to POST to the resource, with the desired action in the body of the request. However, many people resist doing that for various reasons, and decry the recommended approach as being too “purist” for their tastes. As one of the goals for the API-SIG is to make OpenStack APIs more consistent, we decided to take a two-pronged approach: recommend the RESTful approach for all new APIs, and a more RPC-like approach for existing APIs. We will survey the OpenStack codebase to get some numbers as to the different ways this is being done now, and if there is an approach that is more common than others, we will recommend that existing APIs use that format.

We also discussed the version discovery documents that have been stalled in review for some time. The problem with them is that they are incredibly detailed, making your brain explode before you can get all the way through. I volunteered to write a quick summary document that will be easier for most people to digest, and have it link to the more detailed parts of the full document.

Tuesday was another cross-project day. I started the day checking out the Kubernetes SIG, and was very impressed at the amount of interest. The room was packed, and after a round of introductions, they started to divide up what they planned to work on that day. Since I had other sessions to go to, I left before the work started, and moved to the room for the Cyborg project. This project aims to provide management of various acceleration resources, such as FPGAs, GPUs, and the like. I have an interest in this both because of my work with the Placement service, and also because my employer sells hardware with these sorts of accelerators, and would like to have a good solution in place. The Cyborg folks had some questions about how things would be handled in Placement, and I did my best to answer them. However, I wasn’t sure how much the rest of the Nova team would want to alter the existing VM creation flow to accommodate Cyborg, so we brainstormed for a while and came up with an approach that involved the Cyborg agent monitoring notifications from Nova to detect when it needed to act. This would mean a lot more work for Cyborg, and would sometimes mean that a new VM that requested an accelerator may not have the accelerator available right away, but it had the advantage of not altering Nova. So imagine our surprise when the Nova-Cyborg joint meeting later that day rolled around, and the Nova cores were open to the idea of adding a blocking call in the build process to call out to Cyborg to do whatever preparation would be necessary to have the accelerator ready to go, so that when the VM is ready, any accelerators would also be ready to be used. I’m planning on staying in touch with the Cyborg team to help them however I can make this work.

On to Wednesday, not only did the Nova discussions begin, but the snow began to fall in Dublin.

Dublin morning
Dublin morning – the first snowfall of #SnowpenStack

As is the custom, we prepared an etherpad ahead of time with the various topics to discuss, and then organized it into a schedule so that we don’t rabbit-hole too deeply on any topic. If you look over that etherpad, you’ll see quite a bit of material to discuss. It would be silly for me to reproduce those topics and their conclusions here; instead, if you have an interest in Nova, reviewing that etherpad is the best way to get an understanding of what was decided (and what was not!).

The day’s discussions started off with Cells V2. Some of the more interesting topics were what to do when a cell goes down. For example, Nova should still be able to list all of a user’s instances even when a cell is down; they just won’t be able to interact with that instance through Nova. Another concern was more internal: are we going to remove the (few) upcalls from a cell to the outer-level API? While it has always been a design tenet that a cell cannot call the API-level services, it has been necessary in a few cases to bend that rule.

rooftop snow
The view from the area where lunch was served.

The afternoon was scheduled for Placement discussions, and there sure were enough of ’em! So much material to cover that it merited its own etherpad! And it’s a good thing we have an etherpad to record this stuff, because I’m writing this nearly two weeks after the fact, and I’ve already forgotten some of the things we discussed! So if you’re interested in any of the Placement discussions, that etherpad is probably your best source for information.

Thursday started off with the Nova-Cinder discussion. Now that multi-attach is a reality, we could finally focus on many of the other issues that have pushed to the background for a while. Again, for any particular topic, please refer to the Nova etherpad.

After that it was time for our team photo. We weren’t allowed onto the pitch at Croke Park, so the plan was to line up on the perimeter of the pitch to have the picture taken with the stadium in the background. But remember I mentioned that cold snap? Well, it was in full force, and we all bundled up to go outside for the photo.

Nova Team Photo Dublin

You think it was cold? 🙂 We had more discussions planned for the afternoon and Thursday, but by then we got word that they needed to have us all out of the stadium by 2pm so that they could send their workers home. The plan was to have people go back to their hotel, and the PTG would more or less continue with makeshift meeting areas in the hotel across the street from the stadium, where most attendees were staying. But since my hotel was further away, I headed back there and missed the rest of the events. All public transportation in Dublin had shut down!

bus sign shut down
All public transportation in Dublin was shut down for several days.

That also meant that Dublin Airport was shut down, canceling dozens of flights, including ours. We ended up having to stay in the hotel an extra 2 nights, and our hotel, the Maldron Parnell Square, was very accommodating. They kept their restaurant open, and some of the workers there told me that they couldn’t get home, so the hotel offered to put them up so that they could keep things running.

By Saturday things had cleared up enough that pretty much everything was open, and we rebooked our flight to leave Sunday. That left just enough time to enjoy a little more of what Dublin does best!

drinking guinness
Drinking a pint of Guinness, wearing my Irish wool sweater and Irish wool cap!

There was some discussion among the members of the OpenStack Board as to whether continuing to hold PTGs is a good idea. The main reason not to have them, in my opinion, is money. Without the flashy corporate sponsorships and expensive admission prices of the Summits, PTGs cost money to put on. It certainly isn’t because the PTG fails to meet its objective of bringing together the various development and deployment teams to make OpenStack better. Fortunately, the decision was to hold at least one more PTG, with the location still to be determined. Maybe by then enough people will realize that without a strong development process, all the fancy Summits in the world won’t make OpenStack better, and the PTGs are a critical part of that development process.