Buying a house in Montreal – the credit report

A few of the resources I read mentioned it was a good idea to get a credit report prior to visiting the mortgage lender/broker, so any inaccuracies can be corrected in advance. They indicate the credit report can be obtained free of charge through Equifax or TransUnion.

I went to Equifax (note this was before their 2017 breach – I obviously now recommend you go to TransUnion instead). And their home page is a bit scary offering products protecting you from identity theft (I guess that didn’t help or protect those affected in 2017). But I was only interested in the credit report and score (the score is not necessary but it’s a nice single-number summary of your credit and I though it worth it). They have an option to purchase on-line for $23 so I went with that.

But remember the report can be obtained for free? understandably, this is not terribly visible in their front page, but there it is: “You can receive a free credit file disclosure from Equifax Canada Co. via Canada Post“. That link will take you to a form you can fill out and mail or fax (Fax, really?). So for the cost of a stamp and a bit of waiting you can also have your free credit report.

I was impatient and paid the $23, only to get a scary error when trying to get the report, which necessitated calling Equifax, in the course of the call they tried to upsell me on their credit report monitoring service (it’s cheaper, they said, only $16/month instead of the $23 you’re paying – conveniently not mentioning than the $23 is a one-shot charge). Which product you choose is up to you, just remember to stand your ground if you’re not interested in the more expensive one.

The credit report indicated a reasonably high score and no unusual activity, and should look good to any prospective lenders, so this phase is complete and we’re good to go!

Buying a house in Montreal – the stress test

One of the changes to mortgage rules for 2016 in Canada is the creation of the “stress test“, meant to cool down hot real estate markets and keep people from buying houses that stretch their financial capabilities.

If you’re going for a high-ratio (less than 20% down payment) mortgage, lenders are required by law to check your payment capacity as if your loan interest rate were as high as the standard five-year rate (which currently is 4.94%), even if your eventual mortgage will actually be at a much lower (currently around 2-2.5%) rate.

The FCAC calculator makes it very easy to check what your maximum loan will be, once this rule is taken into account. Just enter your information and your expected interest rate to calculate your real maximum mortgage. Next, change the interest rate to 4.95% (I went super safe and put in 5%). It will tell you you won’t qualify, but you can now play with the maximum property value until it shows you you’re likely to be approved.

In my case, it resulted in a reduction of 18% in the maximum price I could afford, which is not terrible because all my previous calculations were taking this into account. Some people may be surprised, and discouraged out of the house hunting process by this, but if you know about this rule and factor it in your calculations prior to starting the process, you’ll know what to expect and how to compensate (get more money, save up for a larger down payment, lower your house price range).


Buying a house in Montreal – where to start?

So we decided to buy a house, what will the journey look like?

There are plenty of easily-googlable resources on the house buying process in Canada and in Québec more specifically (here’s the two most detailed I’ve seen: FCAC and CMHC), so I won’t try to repeat that information, but I’ll document the specifics of our process which will invariably result in a brief outline of the steps to follow.

Roughly what we’ll try to do:

  1. Get a relatively good family income so we can qualify for a reasonable mortgage loan.
  2. Build up a credit history.
  3. Save up for a down payment.

We’ve worked on those first three steps since we moved to Canada: I’ve been fortunate enough to have a stable and well-paid job, which has allowed us to use consumer credit responsibly, so should have a pretty good rating. It also allowed us to save for a down payment. So at this point we should be ready for the next parts of the process:

  1. See a lender to get financially checked and pre-approved for a loan. You can go for a well-known financial institution, perhaps your bank, or you can go to a mortgage broker, which is what I’m planning on doing.
  2. Once you know your price range, you can start looking at houses in your desired areas.

BUT before you can start with this, you should know roughly how much you can afford, be realistic with your inputs and use one of the available online calculators. I like this one which will tell you how much you should be able to afford, and this one which calculates your estimated payments. And this one is very simple but also very detailed as to the criteria used to estimate affordability. It makes sense to use this so you’re not disappointed when the broker tells you you can only afford a tiny shack in the boondocks :).

You should also have a pretty good idea of whether you like your target neighbourhood. Montreal is a geographically large city and neighbourhoods can differ, so it makes sense to check the ones you like and make a short list. If you don’t care where you buy, there’s something for almost any price range, but I don’t think that’s very common.

A possible problem with the neighbourhood you like is whether you can afford it. If you can’t just yet, there are two options: choose a different one or get more money (higher salary, larger down payment).

Once I identified our target neighbourhoods, I started scouring frequently, looking for houses in (and out of) our price range, checking their pictures and prices, nearby amenities, and comparing several possible neighbourhoods. We ended up discarding one of those, even though it was cheaper and had more inventory, because we decided we didn’t really like it that much. So we’re focusing on one of the other candidates, and also looking at adjacent neighbourhoods, which can be cheaper while still being closer to the amenities we want.

OK, so knowing how much we can afford (per the calculators) having located (and lived in) a neighborhood we like and knowing the approximate price range for homes here, and knowing it is within our affordability, I’m ready to hit the mortgage broker.


Weechat trigger sounds based on specific keywords

Weechat used to require some weird perl scripts to trigger on specific conditions, but since version 1.1 (from 2014) a trigger plugin can do all that without needing an external script.

This will create a trigger that runs a command when a specific word (or words) is mentioned in any channel you’re on:

Ansible task that retries

The task can be whatever you want: I used uri but it’s more usually shell or something like that. The main thing is that you need to use register so you’ll have something to check in the until: condition.


Mocking iterators

A colleague wanted to mock a Journal object which both has callable methods and works as an iterator itself. So it works like this:

We mocked it like this, to be able to pass an actual list of expected values the function will iterate over:

So mock_journal is both a mock proper, where methods can be called (and then asserted on), and an iterable, which when called repeatedly will yield elements of the __next__ side_effect.

Forcing Python Requests to connect to a specific IP address

Recently I ran into a script which tried to verify HTTPS connection and response to a specific IP address. The “traditional” way to do this is  (assuming I want on IP

This is useful if I want to specifically test how is responding; for instance, if is DNS round-robined to several IP addresses and I want to hit one of them specifically.

This also works for https requests if using Python <2.7.9 because older versions don’t do SNI and thus don’t pass the requested hostname as part of the SSL handshake.

However, Python >=2.7.9 and >=3.4.x conveniently added SNI support, breaking this hackish way of connecting to the IP, because the IP address embedded in the URL is passed as part of the SSL handshake, causing errors (mainly, the server returns a 400 Bad Request because the SNI host doesn’t match the one in the HTTP headers

The “easiest” way to achieve this is to force the IP address at the lowest possible level, namely when we do socket.create_connection. The rest of the “stack” is given the actual hostname. So the sequence is:

  1. Open a socket to
  2. SSL wrap this socket using the hostname.
  3. Do the rest of the HTTPS traffic, headers and all over this socket.

Unfortunately Requests hides the socket.create_connection call in the deep recesses of urllib3, so the specified chain of classes is needed to propagate the given dest_ip value all the way down the stack.

After wrestling with this for a bit, I wrote a TransportAdapter and accompanying stack of subclasses to be able to pass a specific IP for connection.

Use it like this:

There are a good number of subtleties on how it works, because it messes with the connection stack at all levels, I suggest you read the README to see how to use it in detail and whether it applies to you need. I even included a complete example script that uses this adapter.

Resources that helped:

Juju2 unit/service name autocompletion.

If juju1 and juju2 are installed on the same system, juju1’s bash auto completion breaks because it expects services where in juju2 they’re called applications.

Maybe juju2 has correct bash completion, but in the system I’m working on, only juju1 autocompletion was there, so I had to hack the autocomplete functions. Just added these at the end of .bashrc to override the ones in the juju1 package. Notice they work for both juju1 and juju2 by using dict.get() to not die if a particular key isn’t found.



Vegan picadillo

Vegan picadillo, served with fried white basmati rice
Vegan picadillo, served with fried white basmati rice

Picadillo is a traditional Mexican recipe, usually made with minced meat.  Seitan, however, makes a great substitute for minced meat, and since most of picadillo’s flavor comes from the sauce and reduction process, the flavor stays mostly similar.


  • Half a kg of Seitan (here’s the best recipe we’ve found – can be made well in advance as it keeps nicely in the fridge).
  • One large potato, diced
  • Two large carrots, diced
  • One cup cooked green peas
  • Two cups of vegetable broth
  • Two or three tomatoes (about 500g worth)
  • Two garlic cloves, finely chopped
  • One quarter onion, finely chopped
  • 1 teaspoon olive oil

Serves 6-8.

How to make:

Mince the seitan: Chop it into small dice, then run in small batches through a food processor on high, until you get a size similar to cooked, minced meat.

Prepare the sauce: Put the tomatoes, garlic and broth in the blender, blend for 1 minute or until smooth.

Do the thing: On a large (5L or more) pot, fry the onion with the olive oil until transparent. Once fried, dump the seitan, potato and carrot dice in the pot, dump the sauce and stir (it should initially look like a stew – if it’s drier, make some more sauce and add it to the pot). Set the heat to medium-high, bring the mixture to a boil and let simmer until the liquid is consumed and the carrots and potatoes are soft. BEWARE, there’ll come a point where you will need to start stirring to avoid burning the bottom part of the stew. This will happen even if the top seems to have enough liquid, so keep an eye on it. It should take 20-25 minutes to evaporate the sauce to the desired consistency.

When done, stir in the already-cooked green peas (so they remain firm, if you cook them in the stew they’ll go mushy).

Serve with white or red rice, or with corn tortillas.

Take me to your leader – Using Juju leadership for cron tasks in a multiunit service

I’m working on adding some periodic maintenance tasks to a service deployed using Juju. It’s a standard 3-tier web application with a number of Django application server units for load balancing and distribution.

Clearly the maintenance tasks’ most natural place to run is in one of these units, since they have all of the application’s software installed and doing the maintenance is as simple as running a “management command” with the proper environment set up.

A nice property we have by using Juju is that these application server units are just clones of each other, this allows scaling up/down very easily because the units are treated the same. However, the periodic maintenance stuff introduces an interesting problem, because we want only one of the units to run the maintenance tasks (no need for them to run several times). The maintenance scripts can conceivably be run in all units, even simultaneously (they do proper locking to avoid stepping on each other). And this would perhaps be OK if we only had 2 service units, but what if, as is the case, we have many more? there is still a single database and hitting it 5-10 times with what is essentially a redundant process sounded like an unacceptable tradeoff for the simplicity of the “just run them on each unit” approach.

We could also implement some sort of duplicate collapsing, perhaps by using something like rabbitmq and celery/celery beat to schedule periodic tasks. I refused to consider this since it seemed like swatting flies with a cannon, given that the first solution coming to mind is a one-line cron job. Why reinvent the wheel?

The feature that ended up solving the problem, thanks to the fine folks in Freenet’s #juju channel, is leadership, a feature which debuted in recent versions of Juju. Essentially, each service has one unit designated as the “leader” and it can be targeted with specific commands, queried by other units (‘ask this to my service’s leader’) and more importantly, unambiguously identified: a unit can determine whether it is the leader, and Juju events are fired when leadership changes, so units can act accordingly. Note that leadership is fluid and can change, so the charm needs to account for these changes. For example, if the existing leader is destroyed or has a charm hook error, it will be “deposed” and a new leader is elected from among the surviving units. Luckily all the details of this are handled by Juju itself, and charms/units need only hook on the leadership events and act accordingly.

So it’s then as easy as having the cron jobs run only on the leader unit, and not on the followers.

The simplistic way of using leadership to ensure only the leader unit performs an action was something like this in the crontab:

This uses juju-run with the unit’s name (which is hardcoded in the crontab – this is a detail of how juju run is used which I don’t love, but it works) to run the is-leader command in the unit. This will print out “True” if the executing unit is the leader, and False otherwise. So this will condition execution on the current unit being the leader.

Discussing this with my knowledgeable colleagues, a problem was pointed out: juju-run is blocking and could potentially stall if other Juju tasks are being run. This is possibly not a big deal but also not ideal, because we know leadership information changes infrequently and we also have specific events that are fired when it does change.

So instead, they suggested updating the crontab file when leadership changes, and hardcoding leadership status in the file. This way units can decide whether to actually run the command based on locally-available information which removes the lock on Juju.

The solution looks like this, when implemented using Ansible integration in the charm. I just added two tasks: One registers a variable holding is-leader output when either the config or leadership changes:

The second one fires on the same events and just uses the registered variable to write the crontabs appropriately. Note that Ansible’s “cron” plugin takes care of ensuring “crupdate” behavior for these crontab entries. Just be mindful if you change the “name” because Ansible uses that as the key to decide whether to update or create anew:

A created crontab file (in /etc/cron.d/roadmr-maintenance) looks like this:

A few notes about this. The IS_LEADER variable looks redundant. We could have put it directly in the comparison or simply wrote the crontab file only in the leader unit, removing it on the other ones. We specifically wanted the crontab to exist in all units and just be conditional on leadership. IS_LEADER makes it super obvious, right there in the crontab, whether the command will run. While redundant, we felt it added clarity.

Save for the actual value of IS_LEADER, the crontab is present and identical in all units. This helps people who log directly into the unit to understand what may be going on in case of trouble. Traditionally people log into the first unit; but what if that happens to not be the leader? If we write the crontab only on the leader and remove from other units, it will not be obvious that there’s a task running somewhere.

Charm Ansible integration magically runs tasks by tags identifying the hook events they should fire on. So by just adding the three tags, these events will fire in the specified order on config-changed, leader-elected and leader-settings-changed events.

The two leader hooks are needed because leader-elected is only fired on the actual leader unit; all the others get leader-settings-changed instead.

Last but not least, on’t forget to also declare the new hooks in your file, in the hooks declaration which now looks like this (see last two lines added):

Finally, I’d be remiss not to mention an existing bug in leadership event firing. Because of that, until leadership event functionality is fixed and 100% reliable, I wouldn’t use this technique for tasks which absolutely, positively need to be run without fail or the world will end. Here, I’m just using them for maintenance and it’s not a big deal if runs are missed for a few days. That said, if you need a 100% guarantee that your tasks will run, you’ll definitely want to implement something more robust and failproof than a simple crontab.

How to configure e-mail alerts with Munin

I had a hell of a time configuring Munin to send out e-mail alerts if values surpass specific thresholds. Many of the articles I found focused just on setting up the email command (which was the easy part), while few told me *how* to configure the per-service thresholds.

Once the thresholds are configured, you’ll see a green line for the warning threshold and a blue line for the critical one, like in this graph:


Some of Munin’s plugins already have configured thresholds (such as disk space monitoring which will send a warning at 92% usage and a critical alert at 96% or so). But others don’t, and I wanted to keep an eye on e.g. system load, network throughtput and outgoing e-mail.

The mail command can be configured in /etc/munin-conf.d/alerts.conf:

Next in /etc/munin.conf, under the specific host I want to receive alerts for, I did something like:

This will send alert if the postfix plugin’s volume surpasses 100k, if the load plugin’s load values surpass 1.0 or 5.0 (warning and critical, respectively) and if df plugin’s _dev_sda1 value is over 60% (this is disk usage).

Now here’s the tricky part: How to figure out what the plugin name is, and what the value from this plugin is? (if you get these wrong, you’ll get the dreaded UNKNOWN is UNKNOWN alert).

Just look in /etc/munin/plugins for the one that monitors the service you want alerts for. Then run it with munin-run, for example, for the memory plugin:

These are the values you have to use (so 500000000 will alert if active memory goes about 5GB).

A tricky one is diskstats:

In this case, use diskstats_utilization.sda_util.warning (so the value in “multigraph” is used as if it were the plugin name).

Easy mounting of host directories in lxc container.

This can be done manually as explained here. But I wanted to do this on one fell swoop, so this command worked:

If done frequently, a function may be useful. I’m too lazy to write that now but I’ll add it later.

Proxying Python file-like objects for fun and profit

As part of a project I’m working on, I wanted to be able to do some “side processing” while writing to a file-like object. The processing is basically checksumming on-the-fly. I’m essentially doing something like:

what I’d like is to be able to also get the data read from source and use hashlib’s update mechanism to get a checksum of the object. The easiest way to do it would be using temporary storage (an actual file or a StringIO), but I’d prefer to avoid that since the files can be quite large. The second way to do it is to read the source twice. But since that may come from a network, it makes no sense to read it twice just to get the checksum. A third way would be to have destination be a file-like derivative that updates an internal hash with each read block from source, and then provides a way to retrieve the hash.

Instead of creating my own file-like where I’d mostly be “passing through” all the calls to the underlying destination object (which incidentally also writes to a network resource), I decided to use padme which already should do most of what I need. I just needed to unproxy a couple of methods, add a new method to retrieve the checksum at the end, and presto.

A first implementation looks like this:

This however doesn’t work for reasons I was unable to fathom on my own:

This is clearly because super(sha256file, self) refers to the *class* and I need the *instance* which is the one with the write method. So Zygmunt helped me get a working version ready:

here’s the explanation of what was wrong:

– first of all the exception tells you that the super-object (which is a relative of base_proxy) has no write method. This is correct. A proxy is not a subclass of the proxied object’s class (some classes cannot be subclasses). The solution is to call the real write method. This can be accomplished with type(self).__proxiee__.write()

– second of all we need to be able to hold state, namely the hash attribute (I’ve renamed it to _hash but it’s irrelevant to the problem at hand). Proxy objects can store state, it’s just not terribly easy to do. The proxied object (here a file) may or may not be able to store state (here it cannot). The solution is to make it possible to access some of the state via standard means. The new (small) satateful_proxy class implements __setattr__ and __delattr__ in the same way __getattribute__ was always implemented. That is, those methods look at the __unproxied__ set to know if access should be routed to the original or to the proxy.
– the last problem is that __unproxied__ is only collected by the proxy_meta meta-class. It’s extremely hard to change that meta-class (because padme.proxy is not the real class that you ever use, it’s all a big fake to make proxy() both a function-like and class-like object.)

The really cool thing about all this is not so much that my code is now working, but that those ideas and features will make it into an upcoming version of Padme 🙂 So down the line the code should become a bit simpler.

Updating lxc image/container caches

One of lxc’s nice time-saving features is that, after initial container creation, it will cache the files it downloaded to do so, and when you create a new container using the same template/version/architecture, it will leverage the existing files and create the container with minimal downloads and really quickly.

A downside of this is that the cache can become stale; this is apparent when you want to install a package in a container and apt-get gives 404 errors indicating the version of the package the container knows about, is no longer available in the archive (most likely superseded by a new one).

This is easily fixed by always doing apt-get update in the container prior to any package installs/upgrades. However, it’s cumbersome, and if you’re creating dozens of new containers every day, the bandwidth and time spent re-downloading can quickly add up.

To update the “base image” or cache, which resides in /var/cache/lxc for each version, you can do two things.

most templates also support –flush-cache so if you’re calling lxc-create directly, just add an extra –flush-cache as template args (after –) and the cache will be flushed before making the container. Something like

this will obliterate the existing cache and re-download everything before creating the container.

If you want to update an existing cache do something like:

this will update the cache and all subsequently-created containers will know about the latest package versions.


WiFi interfaces on Ubuntu Server

Sometimes you may want to configure a wireless interface on a system with Ubuntu Server. The most common use case (for me, at least) is to run some tests with server, which require two network interfaces, on a laptop (it’s what I have available to play with) with an ethernet interface and a wireless interface. As long as Ubuntu sees the wireless interface, it’s quite easy to set things up so the wireless comes up at boot time.

You will probably need to set up the server to forward and masquerade the internal network (usually, the ethernet segment is the internal one, while the wireless counts as the “outside” interface). There are plenty of tutorials to do this over the internet, so I won’t extend this post by detailing that.

Of course, the wireless will grab a dynamic IP address, so use caution with that as the address may change (or, assign a static one from your router’s unused range). Anyway. Put this in /etc/network/interfaces:

Then you can do ifup wlan0 to bring the interface up. It should also come up automagically at boot time.