Mongo migration

For the past few months I’ve been at a terrific job, doing devops at a small SaaS company. Real quick, SaaS means “Software as a Service” & refers to companies that have a webapp that they either sell access to and/or set up a version of for their customers. There are a lot of challenges with doing devops for a company like this, trying to find the balance between the heavyweight solutions and the latest and greatest to find what’s right for us, all the while (personally speaking) doing a LOT of learning on the topic. That’s not to say that heavyweight versus the latest&greatest are opposed; there are a few more weights on that spinning disk, not the least of which is “what we were doing before was …”.

So what I’ve been working on for the last few weeks, somewhere between the old solution & the new hotness, has been a Mongo problem. We deal in data that must be scrubbed before we analyze it. So the way that works, is that the host captures data, then scrubs ALL data there, and then sends it on to our long-term storage database, and then all local data on that host is removed after a couple days. What we’ll do with all of this in five, ten years will hopefully be the subject of another post, but for now we are only dealing with about 30GB of data in the long-term storage DB, collected over the last couple years. Let’s call that “Storeo,” and the hosts that they come from “partner databases,” which is true enough.

We’ve developed a couple of schemas for Storeo, and we only upgrade our partners from one to the next with code releases. So we have a couple old versions of Storeo kicking around. The next piece of this story is that we have an analytics dashboard set up for each partner, which pulls from Storeo, based on a domain field in the data we get from each partner. There’s one for each version of Storeo that they (and we) have to refer to, which means multiple dashboards just to get all the info! So that’s foolish, yeah? As a result, a previous engineer wrote a Mongo migration script to migrate all data from version 1 to 2, and then from version 2 to 3, the current version. So there are two steps to this – first, to migrate all the legacy data up to the current version so everything can be analyzed in the same way, and second, to do this regularly so even if partners are using older versions, we roll that data up so there is ONE source of truth for all their data.

As happens occasionally, no one can quite remember how I got this project, but it’s been a ride. Mostly good, occasionally “how the hell does Mongo even work?”. Some of the problems I’ve gone through have been of a Mongo nature, some of them of a sysadmin nature, some of them just basic DBA. Many of these steps might make you scream, but I’m cataloguing them because I want to try to get down what all I’ve done and learned. When you are self-taught, your education comes in fits and starts and in no particular, and in sometimes infuriating (out of) order. So I’m going to do my best to show you all the things I did wrong, too.

Problem 1 – Where to Test

I wanted to test the migration locally, not on the production Storeo server, which continues to receive data from all our partner database. First, I fired up the mongodump docs and tried that. Well, I nearly immediately ran out of room, and deleted that dump/ directory with those contents. When I looked around with a df -h /, a command which shows you the disk file size on root, human-readable, the output was that there were only a couple gigs left. Well, I knew that dumping a 15GB database wasn’t going to work locally. So I investigated a lot of other options, like sending the mongodump to another server (technically possible), SSHing into the server but sending all dumped data to my local machine with plenty of space on it. This probably took a couple days of investigation between other tasks.

None of this really panned out (but I still think it should have), and my boss let me know that there’s a 300GB volume attached to Storeo, and I said, wait, but I didn’t see that, I looked for something like that, and they gently let me know not to give df any arguments in order to see all disks mounted on a server. With that, a df -h showed me the 300GB volume, mounted on /var/lib! Excellent. On a practical note, it’s extremely sensible to have all the data for your application stored on a volume rather than on some enormously provisioned server. When you use AWS, one volume is much the same as the next, so putting databases on their own volumes is pretty sensible. Keep your basic server’s disk very bare bones, put more complex stuff on modular disks that you can move around if you need.

So with that!! I made a directory for myself there to separate from the production stuff, confirmed that mongodump/mongorestore do NOT interrupt read/write operations, and made mongodumps of versions 1, 2 and 3. This took.. maybe an hour. Then, because they were still quite large (Mongo is very jealous of disk space), I tarballed & gzipped them to reduce them down to half a gig or so. We use magic-wormhole all the time at work (available with a quick pip install magic-wormhole [assuming you have Python and pip installed {but it doesn’t have to be just a Python thing, just like I use ag and that’s a super Perl-y tool}]) so I sent these tarballs to my local machine, untarred/ungzipped, and mongorestored to the versions of Storeo 1, 2, & 3 that I have locally to run our app on my own machine. This probably, with carefulness and lots of reading, took another couple hours. At this point we’re probably a week in.

Problem 2 – How to Test

At this point, I finally started testing the migration itself since everything was a safe copy and totally destructible. Also I retained the tarballs in case I ended up wanting to drop the database or fiddle with it in some unrecoverable way. I took a count of the documents being migrated, and of the space taken up by each DB (which was different than on prod – I thought until this week that those sizes should be constant from prod-mongodump-tarball-mongorestore, but that’s not true – apparently most databases are wiggly with their sizing). The migration script is a javascript script (how do you even say that) that you feed into mongo like so mongo migration1-to-2.js, within which you define dbSource and dbTarget. The source, in this case, is version 1 of Storeo, and the target is version 2. Each of these is a distinct database managed by Mongo. With great trepidation, I did iiiit. Ok, I’ve left a piece out. I, um, didn’t know how to run JS. Googling said “oh just give the path to the browser!” so I did and, uh – that didn’t work. You may be saying “Duh.” Look, I’ve never done any front-end at all, and have never touched javascript outside that Codecademy series I did on here a couple years back. With my tail between my legs I asked my boss again, & was told about the above, just mongo filename.js.

The script took three hours!! Gah! So I ran the next one, which took SEVEN (since it contained everything from the first one, too), and regular attention to the ssh session so I didn’t lose the process (don’t worry, linux-loving friends, I’ll get there, just keep reading). These two migrations took two business days. At this point, we started talking to the team who manages the data analysis dashboards for our partners to talk about some of the complexities. Because a) this isn’t a tool from Mongo, there are no public docs on it and b) you can only test Storeo performance after the data has been scrubbed and sent, even locally, we decided to set up a few demo servers to point to test versions of the database.

Remember the volume attached to Storeo on production? Whoo! I logged onto Storeo and learned a ton more about mongodump & mongorestore, and made teststoreo1, teststoreo2, and teststoreo3, exact mongodump/restore copies of versions 1, 2 & 3 of Storeo. Their sizes, again, were different, but we’ve learned that that’s ok! Mongo has a lot of guarantees, space management isn’t one of them, so pack extra disk and we’ll be fine. So because this took a lot of googling and careful testing, because the last thing I wanted to do was mongorestore back into the place I’d mongodumped from – at the time I wasn’t sure if mongorestore overwrites the disk entirely, and wanted to be cautious versus potential lost data. So, make the directory, mongdump into it while specifying the database. Then restore into a new database (with the same name as the directory you’ve just made – this isn’t mandatory but made it easier to trace) while feeding it the path where the mongodump lives.

mkdir teststoreo1 # make the directory
mongodump -d storeo1 teststoreo1/ # dump the database with the name storeo1 into the dir we just made 
... # this takes some time, depending of course on the size
mongorestore -d teststoreo1 teststoreo1/storeo1 # there could be a dump/ in front of this end path

So after doing this for the other two Storeo databases as well, a show dbs command in the Mongo shell outputs all three production Storeos, as well as all three test Storeos. This meant we were in a good place to do some final testing. There were a few more meetings assessing risk and the complexity of all the pieces of our infrastructure that touch Storeo, how you do. Because the function of Storeo is to continually take in stripped data, I had to ensure that we weren’t going to lose information being sent during the migration. Because it’s not an officially supported tool but instead something that we wrote in-house, and I hadn’t been able to find a tool that moves data from one mongo DB to another, it’s hard to know what will and won’t impact production, so I set up one of our demo servers to send its stripped data to teststoreo1, and then kicked off the migration from teststoreo1 to teststoreo2 to make sure there was no data loss. On that demo server, while the migration was migratin’, I made a bunch of new dummy data that I’d be able to trace back to this demo server. A few hours later, when the 1-to-2 migration was complete, sure enough there were a handful of documents in teststoreo1 that were new – they’d been held & NOT sent! With this, I was very happy with the migration script.

So I kicked off the following script with mongo migrate1-2.js, quit the process with ctrl-z, and put it in the background (after identifying it as job 1) with bg %1, so it wouldn’t be interrupted by my leaving the session (see?)..

'use strict';

var dbSource = connect("localhost/storeo1");
var dbTarget = connect("localhost/storeo2");

// The migration process could take so long that new documents may be created
// while the script is still running. We will move only the ones created
// before the start of the process
var now = new ISODate();

dbSource.collection_1.find().forEach(function(elem){
    elem.schemaVersion = 2; // this means each element is given the NEW schema version
    dbTarget.collection_1.insert(elem);
});

dbSource.collection_2.find({createTime: {$lt: now}}).forEach(function(elem){
    elem.schemaVersion = 2;
    dbTarget.collection_2.insert(elem);
});

dbSource.collection_3.find({timestamp: {$lt: now}}).forEach(function(elem){
    elem.schemaVersion = 2;
    dbTarget.collection_3.insert(elem);
});


dbSource.collection_1.remove({}); // this collection did not have a timestamp
dbSource.collection_2.remove({createTime: {$lt: now}});
dbSource.collection_3.remove({timestamp: {$lt: now}});

The second script was the same but for the definitions of dbSource and dbTarget to storeo2 and storeo3, respectively. As with the testing, the first one took about three hours, the second, seven. With each one, I kicked it off, then put it in the background, then checked on it… later. Because it’d been backgrounded (that’s a verb, sure), it wasn’t quiiiiite possible to tell when it was done. That could be fixed with some kind of output at the end of the script, but that’s not how I did it!

Then I set up a lil cron job there at the end to regularly move data from 1 to 2, and once that had run for the first time, then I set up the second cron job to move it from 2 to 3.

Who wants to talk about Mongo????????

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Exploring Dockerfiles

I’d like to continue the previous entry on Docker a little further. Last time we talked about the installation process & a little more, so this time we’re going to talk about the next part of getting started with Docker – writing a Dockerfile.

Here’s what we talked about last time, and with one odd little exception (why did I promise to talk about load testing…) we’re going to cover all these things!

So, next steps, make the container persistent – it isn’t yet, and play around with Dockerfiles, and just do a little more spying on the produced container itself & probably try to do some babby’s frist load testing things in there & spy on the container as a process without the box & all its processes within!

First let’s take a look at the Docker process we created last time. Just like at your native command line, docker commands all resemble low-level Linux commands, so just like you’d use ps to look at the processes running at any given time on your machine, you can use docker ps to see all the Docker processes it is managing at any given time. If you followed along last time you’ll see some that have been exited but which you don’t have access to – each time you run the docker run -it bash you get a new process. But the old ones are still there! The all flag will show us these Exited boxes with a docker ps -a.

rachel $ docker ps -a
CONTAINER ID        IMAGE                      COMMAND                  CREATED             STATUS                     PORTS               NAMES
b5de9583d7b3        fedora                     "bash"                   10 minutes ago      Exited (0) 3 seconds ago                       pedantic_morse
35192bfa05d4        images/cowsay-dockerfile   "/usr/games/cowsay *P"   2 hours ago         Exited (0) 2 hours ago                         gigantic_goldberg
a0e40d55125a        images/cowsayimage         "/usr/games/cowsay 'D"   3 hours ago         Exited (0) 3 hours ago                         jovial_mcnulty
d32381833772        debian                     "bash"                   3 hours ago         Exited (0) 3 hours ago                         cowsay

You’ll notice a few things, first that the names are a mix of adjective_noun, except one – the cowsay container example is from the excellent Using Docker where I’ve gained a lot of my recent Docker information. Their status is all Exited. Some of the container-specific commands are similar to the init.d service commands, like start, stop, and rm, so let’s start the desired container in that list up there. The container we’re going to start up is similar to the one we made before & is Fedora, though it is true that I only made it ~10m ago!

docker start pedantic_morse

So now the output of docker ps includes the container we just made. So how do we keep it? We commit it, just like with Git! Replace pedantic_morse with whatever name yours has been assigned beneath the NAMES column.

rachel $ docker commit pedantic_morse images/morse
sha256:b398fe28d7fd26a52e0947fc8eebb7614b8a8d6d19a5332359df167c9296c04f

So what we’ve done here is create an image from which we can create containers. images/morse is the image, pedantic_morse is the Docker process that we crafted it from. For every time we run the image images/morse, it creates a new Docker process, so at this point it’s still not persistent in ONE image, HOWEVER we can use this image to perform one-offs.

Clearly we’re not getting into the strength of Docker, yet. So now it’s time for a very basic Dockerfile. Just like Vagrantfile and Procfile & probably a few other similarly intended setup files, the D in Dockerfile is capitalized and there’s no extension to it, because remember – Linux doesn’t care about file extensions!

The main piece to know with Dockerfiles is that their syntax can be as minimal as you like, and personally I recommend making them non-complex – major structural pieces, and insert kickoff scripts or use some config management in the container itself for anything much more complicated. I reserve the right to change my mind on this later! And this is also more for next time to learn. But the way it looks, the RUN command will run any bash you put in it, but if you need anything more complex, the contents become a lot more murky, in my opinion. Simple is better than complex, but complex is better than complicated, so let’s do what we need to here.

For posterity and a simplistic example, here’s the first Dockerfile I ever wrote. (ed note: I trimmed this down because each line of a Dockerfile creates a new filesystem – try to truncate Dockerfile lines as much as possible)

FROM fedora:23
RUN /bin/bash
RUN echo "the dockerfile took!"

RUN dnf install -y wget tar man

MAINTAINER Rachel!

The output of this, which is a bit long to post, pulls down version 23 of Fedora, uses bash for the following commands, prints “the dockerfile took!” to stdout, and then installs those three packages. I’m unsure why some of those aren’t present in a base Fedora image, but it doesn’t appear to be related to what I’m working on in this blog post, so we’ll leave it be for now.

This is about ten times longer than I thought it would be, woohoo! I hope you learned something, please please let me know if I’ve missed the mark on anything, cheers!

Tune in next time and we’ll talk about a more complicated Dockerfile, and syncing it up to… something 🙂 come back and you’ll find out what!

Lesson 43

Ok, it’s starting to get pretty complex! As best as I can understand it at the moment, a class is a a sort of super structure that can be used more flexibly than can a simple function, that other variables and structures can be flexibly swapped in and out of. I am fairly lost on a good amount of the syntax, like the __init__ and the self.name = name, to name a few. But I’ve decided to go ahead and start copying in the code for this exercise, though I don’t entirely understand it yet. Evidently it always takes a long time to really grok OOP. So that’s ok.

Some things I notice: The classes call a generic object seem to be hierarchically above others, e.g. class Scene(object) (rather than class Death(Scene) which calls Scene()). I don’t know if this is important or not, but it is how Zed’s exercise is going.

And at this point, I’ve pushed through, even not being totally sure of what I’m doing, and punched in all the code, learning snippets here and there, and I’ve checked it all over, and it doesn’t run. I am frustrated. I have read the last few exercises several times where he has started discussing classes and object-oriented programming, and I’m really not getting the hang of it. Someone told me, when I shared that I was trying to start learning what the meat and potatoes of modern programming, OOP, really is, that it took learning another language before he really grokked what the deal was. Barring that, I just keep trying to read about it. I know I can get it, but I don’t have it right now, and I can’t finish this lesson of LPTHW, and I’m frustrated. So I shall move on and try to come back later.

Lesson 41

Oh, wow, Exercise 41. This is great. The code is complicated and I’m still working through it, with gems like:

for sentence in snippet, phrase:
result = sentence[:]

Wow-ee, why is there a colon between the two brackets!
EDIT: This was a CSQ! Here’s what Zed sez:

What does result = sentence[:] do?
That’s a Python way of copying a list. You’re using the list slice syntax [:] to effectively make a slice from the very first element to the very last one.

Also, don’t forget this lil guy:

PHRASE_FIRST = False
if len(sys.argv) == 2 and sys.argv[1] == "english":
PHRASE_FIRST = True

argv is used to import a file, so I can only guess that it is related to the url that the module urlopen imports. the len operation is easy enough to parse, but I don’t really know why sys is in there. [Edit: duh-doy, sys is just the imported module and sys.argv calls it.] The rest of it, honestly, I have not much idea about the rest of it other than the fact that we’re using some Boolean conditionals.

At any rate, while some of the code is confusing, it became significantly less so once I ran oop_test.py. It’s a test! As in, guess the right answer test! It scrapes the text of this learn code the hard way list of words to make up class names in order to ask what the relation is amongst all cited.

I am going to spend some time with this! The test itself is enjoyable to run. And THEN I shall try to really scrape some understanding of the code into my noggin : )

Edit: THIS IS MY TWENTIETH POST!! HOORAY!! wow!

Heroku Hero Haiku

Heroku Haiku
Are not easy to write, no :
You can tell by lame.

Ok, that might be a bit of a stretch, but I think it’s an appropriately silly start to my Heroku knowledge! Today, I will learn to use Heroku, a platform-as-a-service (PaaS) cloud application that, as far as I can tell so far, acts as a sort of auxiliary server for development.

I am going to start on the quickstart and then moved on to the headier document entitled How Heroku Works. I’ll be documenting my process in this entry, so, stay tuned! (I’m talking to YOU, happy-hour-free loner out there!)

Update 1:

Ok, first update, I’m finally putting git onto my machine. It was (or at least seemed) awkward on windows, since the command line is just a whole other beast over there, so now I’ve got it on the linux end. fabulous; all I did was type git into bash & it said “if you want to download this just type sudo apt-get git” and that is so amazingly helpfully easy. I think this will be a whole new reason to use github, which I haven’t been doing for a while because I’ve been so into the CLI lately and I only ever used github’s browser interface. Progress, of a type!

And now I’m trying to flash-dash convert a python program I wrote in 3.3 to 2.7! raw_input is weird! ok, a half hour later & I’ve got that locked down, the differences between which you can see at its github repository.

Update 2:

And now I’ve got the heroku toolbelt for linux on the machine, here. Why why why does it seem so much more approachable on linux? Why is the scrolling text of installation so very comforting to me? Why, indeed, is the sky blue. I actually did the boneheaded thing of accidentally typing my master pw in with capslock on, really! so that is a funny/dumb thing for me to have to remember. [edit: since fixed] and with my successfully converted python 2.7 program, which I have cloned to my system via the heroku dashboard helpful page, I’m now on the git push heroku master, the entire logistics of which I am as yet unsure.

Ah crum. it went through the git push heroku master, counting objects, compressing them, writing them, and THEN it has said

Push rejected, no Cedar-supported app detected
To git@heroku.com:stormy-scrubland-8935.git
! [remote rejected] master -> master (pre-receive hook declined)
error: failed to push some refs to 'git@heroku.com:stormy-scrubland-8935.git'

but doing the heroku open still worked just fine. I do not know what cedar nor “stormy-scrubland” mean, though I’m guessing the latter is some kind of version of cedar, or some version of heroku. it seems like that kind of cute hacker convention in line with “llooming llama ubuntu” or whatever the L was called, ha ha! edit: ok, weird. stormy-scrubland-8935 is the name of my app on heroku? somehow? that’s an odd name generator. [edit: I think that this is because I did not create a heroku environment before pushing the git change, with a quick heroku create beforehand.]

Update 3:

Now I’ll be installing Flask, required on the Getting Started [in heroku] With Python page. It says to have pip, virtualenv, and of course, python installed in advance. I have already installed these items, so that is fortunate. Now, I’ve activated the virtual environment (venv), and while I’m glad to know that I can’t borf (borf is a VERY technical term) my computer irrevocably while within the virtual env, I am Worried that I have already done so, & that with any programming & new set of tools to download, I should have already been putting it all in a virtual environment. BLAST. ok, now I need to find out if my entire environment is salvageable, haha! I have heard about this linux problem, that is to say, when you start a new project but instead end up necessitating a machine-wide wipe and OS reinstall. Oh jeez. Ok!

And now I’ve figured out virtualenv. It was giving me tons of crazy errors, like “nothing in “*”” which means that there was nothing in the indicated path, as that’s what the asterisk means, and also “nothing of *.pyo” and “nothing of *.pyc” which are python compiler file extensions, so I got out of the virtualenv I’d made above & deleted it, and started over using virtualenv venv --distribute --no-site-packages. then it worked! now, whenever I’m screwing around with any programming, I know how to create a virtualenv and I won’t (further) irrevocably borf (still a very technical term) my machine!!

FINAL Update: WOW! What a terrific exercise. Many hiccoughs along the road, but now, at the end, I have learned so so much. For example, now I can tell you what Heroku really does – it’s something that will take in and run your program, but is separate from actual domain/web hosting. Though the tutorial does host it on heroku, it’s obviously intended for your own site, ha ha! Also, I’m now totally comfortable with virtualenv, which is a really crucial step for me. There’s more, but I’ll reserve it for a future update! Thank you for reading this megapost!

brb trying to install django

wish me luck!

EDIT: so. this installation has been Really Hard. finally, having installed wsig, virtualenv, and virtualenvwrapper, I finally installed pip to handle python-related downloads, and while muddling yet again (really – for like the tenth time over the last couple days) through trying to just download django, my friend suggested, “well, why not just sudo pip install django?” HA! beautiful! so that worked, immediately, and now I have django on my linux box. woo-hoo!

now to USE it, to make a django-structured (probably wrong terminology) personal site, to showcase my ~tAlEnTs~! aaaahhhh