05 January 2018
0 comments
Docker,
Mozilla,
Web development,
Python
tl;dr; Whatsdeployed.io is an impressively simple web app to help web developers and web ops people quickly see what GitHub commits have made it into your Dev, Stage or Prod environment. Today it got a facelift.
The code is now more than 5 years old and has served me well. It's weird to talk too positively about the app because I actually wrote it but because it's so simple in terms of design and effort it feels less personal to talk about it.
Here's what's in the facelift
- Upgraded to Bootstrap 4.
- Instead of relying on downloading a heavy Glyphicon web font, just to display a single checkmark, that's now a simple image.
- Ability to use a GitHub developer personal token to avoid rate limitations on GitHub's API.
- The first lookup to get all commits is now done via the Flask app to use my auth token to avoid the rate limit.
- Much better error handling if any of the underlying
requests.get() that the Flask app does, fails. Also includes which URL it failed on.
- Basic validation to prevent submitting the main form without typing anything in.
- You can hack on it with Docker. Thanks @willkg.
- Improved the code that extracts Bugzilla bug numbers out of commit messages. Thanks @edmorely.
- Refreshed screenshots in the
README.md
- A brand new introduction text on the home page for people who end up on the site not knowing what it is.
- If any XHR errors happen figuring out the "culprits", you now get a pretty error describing this instead of swallowing it all.
Please let me know if there's anything broken or missing.
13 December 2017
0 comments
Mozilla,
Web development
When I read Swedish news I usually open DN.se. At least I used to. Opening it used to feel like an "investment". It takes too long to load and it makes the browser all janky because of the weight of all ad videos and whatnot.
With Tracking Protection, which is built in to recent versions of Firefox, all of those load speed concerns goes away pretty much.
Without Tracking Protection
- 322 requests
- 8.78MB transferred
- 16.12s to finish fully loading
With Tracking Protection
- 67 requests
- 1.15MB transferred
- 8.42s to finish fully loading
I've blogged about this before but I'm just so excited I felt like repeating it. With Tracking Protection now enabled it actually feels fun to open heavy-on-ads news sites again. Perhaps even worth paying for.
29 November 2017
18 comments
Go,
Mozilla,
Linux,
Python
This web app I'm working on gets a blob of bytes from a HTTP POST. The nature of the blob is a 100MB to 1,100MB blob of a zip file. What my app currently does is that it takes this byte buffer, uses Python's built in zipfile to extract all its content to a temporary directory. A second function then loops over the files within this extracted tree and processes each file in multiple threads with concurrent.futures.ThreadPoolExecutor. Here's the core function itself:
@metrics.timer_decorator('upload_dump_and_extract')
def dump_and_extract(root_dir, file_buffer):
zf = zipfile.ZipFile(file_buffer)
zf.extractall(root_dir)
So far so good.
Speed Speed Speed
I quickly noticed that this is amounting to quite a lot of time spent doing the unzip and the writing to disk. What to do????
At first I thought I'd shell out to good old unzip. E.g. unzip -d /tmp/tempdirextract /tmp/input.zip but that has two flaws:
1) I'd first have to dump the blob of bytes to disk and do the overhead of shelling out (i.e. Python subprocess)
2) It's actually not faster. Did some experimenting and got the same results at Alex Martelli in this Stackoverflow post
What about disk speed? Yeah, this is likely to be a part of the total time. The servers that run the symbols.mozilla.org service runs on AWS EC2 c4.large. This only has EBS (Elastic Block Storage). However, AWS EC2 c3.large looks interesting since it's using SSD disks. That's probably a lot faster. Right?
Note! For context, the kind of .zip files I'm dealing with contain many small files and often 1-2 really large ones.
EC2s Benchmarking
I create two EC2 nodes to experiment on. One c3.large and one c4.large. Both running Ubuntu 16.04.
Next, I have this little benchmarking script which loops over a directory full of .zip files between 200MB-600MB large. Roughly 10 of them. It then loads each one, one at a time, into memory and calls the dump_and_extract. Let's run it on each EC2 instance:
On c4.large
c4.large$ python3 fastest-dumper.py /tmp/massive-symbol-zips
138.2MB/s 291.1MB 2.107s
146.8MB/s 314.5MB 2.142s
144.8MB/s 288.2MB 1.990s
84.5MB/s 532.4MB 6.302s
146.6MB/s 314.2MB 2.144s
136.5MB/s 270.7MB 1.984s
85.9MB/s 518.9MB 6.041s
145.2MB/s 306.8MB 2.113s
127.8MB/s 138.7MB 1.085s
107.3MB/s 454.8MB 4.239s
141.6MB/s 251.2MB 1.774s
Average speed: 127.7MB/s
Median speed: 138.2MB/s
Average files created: 165
Average directories created: 129
On c3.large
c3.large$ python3 fastest-dumper.py -t /mnt/extracthere /tmp/massive-symbol-zips
105.4MB/s 290.9MB 2.761s
98.1MB/s 518.5MB 5.287s
108.1MB/s 251.2MB 2.324s
112.5MB/s 294.3MB 2.615s
113.7MB/s 314.5MB 2.767s
106.3MB/s 291.5MB 2.742s
104.8MB/s 291.1MB 2.778s
114.6MB/s 248.3MB 2.166s
114.2MB/s 248.2MB 2.173s
105.6MB/s 298.1MB 2.823s
106.2MB/s 297.6MB 2.801s
98.6MB/s 521.4MB 5.289s
Average speed: 107.3MB/s
Median speed: 106.3MB/s
Average files created: 165
Average directories created: 127
What the heck!? The SSD based instance is 23% slower!
I ran it a bunch of times and the average and median numbers are steady. c4.large is faster than c3.large at unzipping large blobs to disk. So much for that SSD!
Something Weird Is Going On
It's highly likely that the unzipping work is CPU bound and that most of those, for example, 5 seconds is spent unzipping and only a small margin is the time it takes to write to disk.
If the unzipping CPU work is the dominant "time consumer" why is there a difference at all?!
Or, is the "compute power" the difference between c3 and c4 and disk writes immaterial?
For the record, this test clearly demonstrates that the locally mounted SSD drive is 600% faster than ESB.
c3.large$ dd if=/dev/zero of=/tmp/1gbtest bs=16k count=65536
65536+0 records in
65536+0 records out
1073741824 bytes (1.1 GB, 1.0 GiB) copied, 16.093 s, 66.7 MB/s
c3.large$ sudo dd if=/dev/zero of=/mnt/1gbtest bs=16k count=65536
65536+0 records in
65536+0 records out
1073741824 bytes (1.1 GB, 1.0 GiB) copied, 2.62728 s, 409 MB/s
Let's try again. But instead of using c4.large and c3.large, let's use the beefier c4.4xlarge and c3.4xlarge. Both have 16 vCPUs.
c4.4xlarge
c4.4xlarge$ python3 fastest-dumper.py /tmp/massive-symbol-zips
130.6MB/s 553.6MB 4.238s
149.2MB/s 297.0MB 1.991s
129.1MB/s 529.8MB 4.103s
116.8MB/s 407.1MB 3.486s
147.3MB/s 306.1MB 2.077s
151.9MB/s 248.2MB 1.634s
140.8MB/s 292.3MB 2.076s
146.8MB/s 288.0MB 1.961s
142.2MB/s 321.0MB 2.257s
Average speed: 139.4MB/s
Median speed: 142.2MB/s
Average files created: 148
Average directories created: 117
c3.4xlarge
c3.4xlarge$ python3 fastest-dumper.py -t /mnt/extracthere /tmp/massive-symbol-zips
95.1MB/s 502.4MB 5.285s
104.1MB/s 303.5MB 2.916s
115.5MB/s 313.9MB 2.718s
105.5MB/s 517.4MB 4.904s
114.1MB/s 288.1MB 2.526s
103.3MB/s 555.9MB 5.383s
114.0MB/s 288.0MB 2.526s
109.2MB/s 251.2MB 2.300s
108.0MB/s 291.0MB 2.693s
Average speed: 107.6MB/s
Median speed: 108.0MB/s
Average files created: 150
Average directories created: 119
What's going on!? The time it takes to unzip and write to disk is, on average, the same for c3.large as c3.4xlarge!
Is Go Any Faster?
I need a break. As mentioned above, the unzip command line program is not any better than doing it in Python. But Go is faster right? Right?
Please first accept that I'm not a Go programmer even though I can use it to build stuff but really my experience level is quite shallow.
Here's the Go version. Critical function that does the unzipping and extraction to disk here:
func DumpAndExtract(dest string, buffer []byte, name string) {
size := int64(len(buffer))
zipReader, err := zip.NewReader(bytes.NewReader(buffer), size)
if err != nil {
log.Fatal(err)
}
for _, f := range zipReader.File {
rc, err := f.Open()
if err != nil {
log.Fatal(err)
}
defer rc.Close()
fpath := filepath.Join(dest, f.Name)
if f.FileInfo().IsDir() {
os.MkdirAll(fpath, os.ModePerm)
} else {
// Make File
var fdir string
if lastIndex := strings.LastIndex(fpath, string(os.PathSeparator)); lastIndex > -1 {
fdir = fpath[:lastIndex]
}
err = os.MkdirAll(fdir, os.ModePerm)
if err != nil {
log.Fatal(err)
}
f, err := os.OpenFile(
fpath, os.O_WRONLY|os.O_CREATE|os.O_TRUNC, f.Mode())
if err != nil {
log.Fatal(err)
}
defer f.Close()
_, err = io.Copy(f, rc)
if err != nil {
log.Fatal(err)
}
}
}
}
And the measurement is done like this:
size := int64(len(content))
t0 := time.Now()
DumpAndExtract(tmpdir, content, filename)
t1 := time.Now()
speed := float64(size) / t1.Sub(t0).Seconds()
It's not as sophisticated (since it's only able to use /tmp) but let's just run it see how it compares to Python:
c4.4xlarge$ mkdir ~/GO
c4.4xlarge$ export GOPATH=~/GO
c4.4xlarge$ go get github.com/pyk/byten
c4.4xlarge$ go build unzips.go
c4.4xlarge$ ./unzips /tmp/massive-symbol-zips
56MB/s 407MB 7.27804954
74MB/s 321MB 4.311504933
75MB/s 288MB 3.856798853
75MB/s 292MB 3.90972474
81MB/s 248MB 3.052652168
58MB/s 530MB 9.065985117
59MB/s 554MB 9.35237202
75MB/s 297MB 3.943132388
74MB/s 306MB 4.147176578
Average speed: 70MB/s
Median speed: 81MB/s
So... Go is, on average, 40% slower than Python in this scenario. Did not expect that.
In Conclusion
No conclusion. Only confusion.
I thought this would be a lot clearer and more obvious. Yeah, I know it's crazy to measure two things at the same time (unzip and disk write) but the whole thing started with a very realistic problem that I'm trying to solve. The ultimate question was; will the performance benefit from us moving the web servers from AWS EC2 c4.large to c3.large and I think the answer is no.
UPDATE (Nov 30, 2017)
Here's a horrible hack that causes the extraction to always go to /dev/null:
class DevNullZipFile(zipfile.ZipFile):
def _extract_member(self, member, targetpath, pwd):
# member.is_dir() only works in Python 3.6
if member.filename[-1] == '/':
return targetpath
dest = '/dev/null'
with self.open(member, pwd=pwd) as source, open(dest, "wb") as target:
shutil.copyfileobj(source, target)
return targetpath
def dump_and_extract(root_dir, file_buffer, klass):
zf = klass(file_buffer)
zf.extractall(root_dir)
And here's the outcome of running that:
c4.4xlarge$ python3 fastest-dumper.py --dev-null /tmp/massive-symbol-zips
170.1MB/s 297.0MB 1.746s
168.6MB/s 306.1MB 1.815s
147.1MB/s 553.6MB 3.765s
132.1MB/s 407.1MB 3.083s
145.6MB/s 529.8MB 3.639s
175.4MB/s 248.2MB 1.415s
163.3MB/s 321.0MB 1.965s
162.1MB/s 292.3MB 1.803s
168.5MB/s 288.0MB 1.709s
Average speed: 159.2MB/s
Median speed: 163.3MB/s
Average files created: 0
Average directories created: 0
I ran it a few times to make sure the numbers are stable. They are. This is on the c4.4xlarge.
So, the improvement of writing to /dev/null instead of the ESB /tmp is 15%. Kinda goes to show how much of the total time is spent reading the ZipInfo file object.
For the record, the same comparison on the c3.4xlarge was 30% improvement when using /dev/null.
Also for the record, if I replace that line shutil.copyfileobj(source, target) above with pass, the average speed goes from 159.2MB/s to 112.8GB/s but that's not a real value of any kind.
UPDATE (Nov 30, 2017)
Here's the same benchmark using c5.4xlarge instead. So, still EBS but...
"3.0 GHz Intel Xeon Platinum processors with new Intel Advanced Vector Extension 512 (AVX-512) instruction set"
Let's run it on this supposedly faster CPU:
c5.4xlarge$ python3 fastest-dumper.py /tmp/massive-symbol-zips
165.6MB/s 314.6MB 1.900s
163.3MB/s 287.7MB 1.762s
155.2MB/s 278.6MB 1.795s
140.9MB/s 513.2MB 3.643s
137.4MB/s 556.9MB 4.052s
134.6MB/s 531.0MB 3.946s
165.7MB/s 314.2MB 1.897s
158.1MB/s 301.5MB 1.907s
151.6MB/s 253.8MB 1.674s
146.9MB/s 502.7MB 3.422s
163.7MB/s 288.0MB 1.759s
Average speed: 153.0MB/s
Median speed: 155.2MB/s
Average files created: 150
Average directories created: 119
So that is, on average, 10% faster than c4.4xlarge.
Is it 10% more expensive? For a 1-year reserved instance, it's $0.796 versus $0.68 respectively. I.e. 15% more expensive. In other words, in this context it's 15% more $$$ for 10% more processing power.
UPDATE (Jan 24, 2018)
I can almost not believe it!
Thanks you Oliver who discovered (see comment below) a blaring mistake in my last conclusion. The for reserved instances (which is what we use on my Mozilla production servers) the c5.4xlarge is actually cheaper than c4.4xlarge. What?!
In my previous update I compared c4.4xlarge and c5.4xlarge and concluded that c5.4xlarge is 10% faster but 15% more expensive. That actually made sense. Fancier servers, more $$$. But it's not like that in the real world. See for yourself:
c4.4xlarge
c5.4xlarge
01 November 2017
0 comments
Mozilla,
Web development
In Firefox it's really easy to switch Tracking Protection on and off. I usually have it off in my main browser because, as a web developer, it often gives me an insight into what others would see and that's often helpful.
Tracking Protection as a means for performance boost as been mentioned many times before to not just avoid leaving digital footprints but also a way to browse faster.
An Example of Performance
I just wanted to show such an example. In both examples I load a blog post on seriouseats.com which has real content and lots of images (11 non-ad images, totalling ~600KB).
First, without Tracking Protection
Next, with Tracking Protection
This is the Network waterfall view of all the requests needed to make up the page. The numbers at the very bottom are the interesting ones.
Without Tracking Protection
- 555 requests
- 8.61MB transferred
- Total loading time 59 seconds
With Tracking Protection
- 48 requests
- 1.87MB transferred
- Total loading time 3.3 seconds
It should be pointed out that the first version, without Tracking Protection, actually is document complete at about 20 seconds. That means that page loading icon in the browser toolbar stops spinning. That's because internally it starts triggering more downloads (for ads) by JavaScript that executes when the page load event is executed. So you can already start reading the main content and all content related images are ready at this point but it's still ~30 seconds of excessive browser and network activity just to download the ads.
Enabling Tracking Protection in Firefox is really easy. It's not an addon or anything else that needs to be installed. Just click Preferences, then click the Privacy & Security tab, scroll down a little and look for Tracking Protection. There choose the Always option. That's it.
Discussion
Tracking Protection is big and involved topic. It digs into the realm of privacy and the right to your digital footprint. It also digs into the realm of making it harder (or easier, depending on how you phrase it) for content creators to generate revenue to be able to keep create content.
The takeaway is that it can mean many things but for people who just want to browse the web much much faster it can be just about performance.
06 September 2017
0 comments
Mozilla,
Django,
Web development,
Python
(Thanks Miles Crabil not only for being an awesome Ops person but also for reviewing this blog post!)
My project over the summer, here at Mozilla, has been a project called Mozilla Symbol Server. It's a web service that uploads C++ symbol files, downloads C++ symbol files and symbolicates C++ crash stacktraces. It went into production last week which was fun but there's still lots of work to do on adding beyond-parity features and more optimizations.
What Is Mozilla Symbol Server?
The code name for this project is Tecken and it's written in Python (Django, Gunicorn) and uses PostgreSQL, Redis and Celery. The frontend is entirely static and developed (almost) as a separate project within. The frontend is written in React (using create-react-app and react-router). Everything is run as Docker containers. And if you ask me more details about how it's configured/deployed I'm afraid I have to defer to the awesome Mozilla CloudOps team.
One the challenges I faces developing Tecken is that symbol downloads need to be fast to handle high volumes of traffic. Today I did some load testing on our stage deployment and managed to start 14 concurrent clients that bombarded our staging server with realistic HTTPS GET queries based on log files. It's actually 7 + 1 + 4 + 2 concurrent clients. 7 of them from a m3.2xlarge EC2 node (8 vCPUs), 1 from a m3.large EC2 node (1 vCPU), 2 from two separate NYC based DigitalOcean personal servers and 2 clients here from my laptop on my home broadband. Basically, each loadtest script process got its own CPU.
It's hard to know how much more each client could push if it wasn't slowed down. Either way, the server managed to sustain about 330 requests per second. Our production baseline goal is to able to handle at least 40 requests per second.
After running for a while the caches started getting warm but about 1-5% of requests do have to make a boto3 roundtrip to an S3 bucket located on the other side of America in Oregon. There is also a ~5% penalty in that some requests trigger a write to a central Redis ElastiCache server. That's cheaper than the boto3 S3 call but still hefty latency costs to pay.
The ELB in our staging environment spreads the load between 2 c4.large (2 vCPUs, 3.75GB RAM) EC2 web heads. Each running with preloaded Gunicorn workers between Nginx and Django. Each web head has its own local memcached server to share memory between each worker but only local to the web head.
Is this a lot?
How long is a rope? Hard to tell. Tecken's performance is certainly more than enough and by the sheer fact that it was only just production deployed last week tells me we can probably find a lot of low-hanging fruit optimizations on the deployment side over time.
One way of answering that is to compare it with our lightest endpoint. One that involves absolutely no external resources. It's just pure Python in the form of ELB → Nginx → Gunicorn → Django. If I run hey from the same server I did the load testing I get a topline of 1,300 requests per second.
$ hey -n 10000 -c 10 https://symbols.stage.mozaws.net/__lbheartbeat__
Summary:
Total: 7.6604 secs
Slowest: 0.0610 secs
Fastest: 0.0018 secs
Average: 0.0075 secs
Requests/sec: 1305.4199
...
That basically means that all the extra "stuff" (memcache key prep, memcache key queries and possible other high latency network requests) it needs to do in the Django view takes up roughly 3x the time it takes the absolute minimal Django request-response rendering.
Also, if I use the same technique to bombard a single URL, but one that actually involves most code steps but is definitely able to not require any slow ElastiCache writes or boto3 S3 reads you I get 800 requests per second:
$ hey -n 10000 -c 10 https://symbols.stage.mozaws.net/advapi32.pdb/5EFB9BF42CC64024AB64802E467394642/advapi32.sy
Summary:
Total: 12.4160 secs
Slowest: 0.0651 secs
Fastest: 0.0024 secs
Average: 0.0122 secs
Requests/sec: 805.4150
Total data: 300000 bytes
Size/request: 30 bytes
...
Lesson learned
It's a recurring reminder that performance is almost all about latency. If not RAM or disk it's networking. See the graph of the "Max CPU Used" which basically shows that CPU of user, system and stolen ("CPU spent waiting for the hypervisor to service another virtual CPU") never sum totalling over 50%.
04 March 2017
0 comments
PostgreSQL,
Mozilla,
Python
tl;dr; In other words, you can now have multiple servers with crontabber, all talking to a central PostgreSQL for its state, and not have to worry about jobs being started more than exactly once. This will be super useful if your crontabber apps are such that they kick of stored procedures that would freak out if run more than once with the same parameters.
crontabber is an advanced Python program to run cron-like applications in a predictable way. Every app is a Python class with a run method. Example here. Until version 0.18 you had to do locking outside but now the locking has been "internalized". Meaning, if you open two terminals and run python crontabber.py --admin.conf=myconfig.ini in both you don't have to worry about it starting the same apps in parallel.
General, business logic locking
Every app has a state. It's stored in PostgreSQL. It looks like this:
# \d crontabber
Table "public.crontabber"
Column | Type | Modifiers
--------------+--------------------------+-----------
app_name | text | not null
next_run | timestamp with time zone |
first_run | timestamp with time zone |
last_run | timestamp with time zone |
last_success | timestamp with time zone |
error_count | integer | default 0
depends_on | text[] |
last_error | json |
ongoing | timestamp with time zone |
Indexes:
"crontabber_unique_app_name_idx" UNIQUE, btree (app_name)
The last column, ongoing used to be just for the "curiosity". For example, in Socorro we used that to display a flashing message about which jobs are ongoing right now.
As of version 0.18, this ongoing column is actually used to NOT run apps again. Basically, when started, crontabber figures out which app to run next (assuming it's time to run it) and now the first thing it does is look up if it's ongoing already, and if it is the whole crontabber application exits with an error code of 3.
Sub-second locking
What might happen is that two separate servers which almost perfectly synchronoized clocks might have cron run crontabber at the "exact" same time. Or rather, only a few milliseconds apart. But the database is central so what might happen is that two distinct PostgreSQL connection tries to send a... UPDATE crontabber SET ongoing=now() WHERE app_name='some-app-name' at the very same time.
So how is this solved? The answer is row-level locking. The magic sauce is here. You make a select, by app_name with a suffix of FOR UPDATE WAIT. Imagine two distinct PostgreSQL connections sending this:
BEGIN;
SELECT ongoing FROM crontabber WHERE app_name = 'my-app-name'
FOR UPDATE NOWAIT;
-- do some other stuff in Python
UPDATE crontabber SET ongoing = now() WHERE app_name = 'my-app-name';
COMMIT;
One of them will succeed the other will raise an error. Now all you need to do is catch that raised error, check that it's a row-level locking error and not some other general error. Instead of worrying about the raised error you just accept it and exit the program early.
This screenshot of a test.sql script demonstrates this:
Two terminals lined up and I start one and quickly switch and start the other one
Another way to demonstrate this is to use psycopg2 in a little script:
import threading
import psycopg2
def updater():
connection = psycopg2.connect('dbname=crontabber_exampleapp')
cursor = connection.cursor()
cursor.execute("""
SELECT ongoing FROM crontabber WHERE app_name = 'bar'
FOR UPDATE NOWAIT
""")
cursor.execute("""
UPDATE crontabber SET ongoing = now() WHERE app_name = 'bar'
""")
print("JOB CAN START!")
connection.commit()
# Use threads to simulate starting two connections virtually
# simultaneously.
threads = [
threading.Thread(target=updater),
threading.Thread(target=updater),
]
for thread in threads:
thread.start()
for thread in threads:
thread.join()
The output of this is:
▶ python /tmp/test.py
JOB CAN START!
Exception in thread Thread-1:
Traceback (most recent call last):
...
OperationalError: could not obtain lock on row in relation "crontabber"
With threads, you never know exactly which one will work and which one will not. In this case it was Thread-1 that sent its SQL a couple of nanoseconds too late.
In conclusion...
As of version 0.18 of crontabber, all locking is now dealt with inside crontabber. You still kick off crontabber from cron or crontab but if your cron does kick it off whilst it's still in the midst of running a job, it will simply exit with an error code of 2 or 3.
In other words, you can now have multiple servers with crontabber, all talking to a central PostgreSQL for its state, and not have to worry about jobs being started more than exactly once. This will be super useful if your crontabber apps are such that they kick of stored procedures that would freak out if run more than once with the same parameters.
13 December 2016
1 comment
Python,
Web development,
Django,
Mozilla,
Javascript
Earlier this year we started using Fanout.io in Air Mozilla to enhance the experience for users awaiting content updates. Here I hope to flesh out its details a bit to inspire others to deploy a similar solution.
What It Is
First of all, Fanout.io is basically a service that handles your WebSockets. You put in some of Fanout's JavaScript into your site that handles a persistent WebSocket connection between your site and Fanout.io. And to push messages to your user you basically send them to Fanout.io from the server and they "forward" it to the WebSocket.
The HTML page looks like this:
<html>
<body>
<h1>Web Page</h1>
<!-- replace the FANOUT_REALM_ID with the ID you get in the Fanout.io admin page -->
<script
src="https://{{ FANOUT_REALM_ID }}.fanoutcdn.com/bayeux/static/faye-browser-1.1.2-fanout1-min.js"
></script>
<script src="fanout.js"></script>
</body>
</html>
And the fanout.js script looks like this:
window.onload = function() {
// replace the FANOUT_REALM_ID with the ID you get in the Fanout.io admin page
var client = new Faye.Client('https://{{ FANOUT_REALM_ID }}.fanoutcdn.com/bayeux')
client.subscribe('/mycomments', function(data) {
console.log('Incoming updated data from the server:', data);
})
};
And in server it looks something like this:
from django.conf import settings
import fanout
fanout.realm = settings.FANOUT_REALM_ID
fanout.key = settings.FANOUT_REALM_KEY
def post_comment(request):
"""A django view function that saves the posted comment"""
text = request.POST['comment']
saved_comment = Comment.objects.create(text=text, user=request.user)
fanout.publish('mycomments', {'new_comment': saved_comment.id})
return http.JsonResponse({'comment_posted': True})
Note that, in the client-side code, there's no security since there's no authentication. Any client can connect to any channel. So it's important that you don't send anything sensitive. In fact, you should think of this pattern simply as a hint that something has changed. For example, here's a slightly more fleshed out example of how you'd use the subscription.
window.onload = function() {
// replace the FANOUT_REALM_ID with the ID you get in the Fanout.io admin page
var client = new Faye.Client('https://{{ FANOUT_REALM_ID }}.fanoutcdn.com/bayeux')
client.subscribe('/mycomments', function(data) {
if (data.new_comment) {
// server says a new comment has been posted in the server
$.json('/comments', function(response) {
$('#comments .comment').remove();
$.each(response.comments, function(comment) {
$('<div class="comment">')
.append($('<p>').text(comment.text))
.append($('<span>').text('By: ' + comment.user.name))
.appendTo('#comments');
});
});
}
})
};
Yes, I know jQuery isn't hip but it demonstrates the pattern well. Also, in the real world you might not want to ask the server for all comments (and re-render) but instead do an AJAX query to get all new comments since some parameter or something.
Why It's Awesome
It's awesome because you can have a simple page that updates near instantly when the server's database is updated. The alternative would be to do a setInterval loop that frequently does an AJAX query to see if there's new content to update. This is cumbersome because it requires a lot heavier AJAX queries. You might want to make it secure so you engage sessions that need to be looked up each time. Or, since you're going to request it often you have to write a very optimized server-side endpoint that is cheap to query often.
And last but not least, if you rely on an AJAX loop interval, you have to pick a frequency that your server can cope with and it's likely to be in the range of several seconds or else it might overload the server. That means that updates are quite delayed.
But maybe most important, you don't need to worry about running a WebSocket server. It's not terribly hard to do one yourself on your laptop with a bit of Node Express or Tornado but now you have yet another server to maintain and it, internally, needs to be connected to a "pub-sub framework" like Redis or a full blown message queue.
Alternatives
Fanout.io is not the only service that offers this. The decision to use Fanout.io was taken about a year ago and one of the attractive things it offers is that it's got a freemium option which is ideal for doing local testing. The honest truth is that I can't remember the other justifications used to chose Fanout.io over its competitors but here are some alternatives that popped up on a quick search:
It seems they all (including Fanout.io) has freemium plans, supports authentication, REST APIs (for sending and for querying connected clients' stats).
There are also some more advanced feature packed solutions like Meteor, Firebase and GunDB that act more like databases that are connected via WebSockets or alike. For example, you can have a database as a "conduit" for pushing data to a client. Meaning, instead of sending the data from the server directly you save it in a database which syncs to the connected clients.
Lastly, I've heard that Heroku has a really neat solution that does something similar whereby it sets up something similar as an extension.
Let's Get Realistic
The solution sketched out above is very simplistic. There are a lot more fine-grained details that you'd probably want to zoom in to if you're going to do this properly.
Throttling
In Air Mozilla, we call fanout.publish(channel, message) from a post_save ORM signal. If you have a lot of saves for some reason, you might be sending too many messages to the client. A throttling solution, per channel, simply makes sure your "callback" gets called only once per channel per small time frame. Here's the solution we employed:
window.Fanout = (function() {
var _locks = {};
return {
subscribe: function subscribe(channel, callback) {
_client.subscribe(channel, function(data) {
if (_locks[channel]) {
// throttled
return;
}
_locks[channel] = true;
callback(data);
setTimeout(function() {
_locks[channel] = false;
}, 500);
});
};
}
})();
Subresource Integrity
Subresource integrity is an important web security technique where you know in advance a hash of the remote JavaScript you include. That means that if someone hacks the result of loading https://cdn.example.com/somelib.js the browser compares the hash of that with a hash mentioned in the <script> tag and refuses to load it if the hash doesn't match.
In the example of Fanout.io it actually looks like this:
<script
src="https://{{ FANOUT_REALM_ID }}.fanoutcdn.com/bayeux/static/faye-browser-1.1.2-fanout1-min.js"
crossOrigin="anonymous"
integrity="sha384-/9uLm3UDnP3tBHstjgZiqLa7fopVRjYmFinSBjz+FPS/ibb2C4aowhIttvYIGGt9"
></script>
The SHA you get from the Fanout.io documentation. It requires, and implies, that you need to use an exact version of the library. You can't use it like this: <script src="https://cdn.example/somelib.latest.min.js" ....
WebSockets vs. Long-polling
Fanout.io's JavaScript client follows a pattern that makes it compatible with clients that don't support WebSockets. The first technique it uses is called long-polling. With this the server basically relys on standard HTTP techniques but the responses are long lasting instead. It means the request simply takes a very long time to respond and when it does, that's when data can be passed.
This is not a problem for modern browsers. They almost all support WebSocket but you might have an application that isn't a modern browser.
Anyway, what Fanout.io does internally is that it first creates a long-polling connection but then shortly after tries to "upgrade" to WebSockets if it's supported. However, the projects I work only need to support modern browsers and there's a trick to tell Fanout to go straight to WebSockets:
var client = new Faye.Client('https://{{ FANOUT_REALM_ID }}.fanoutcdn.com/bayeux', {
// What this means is that we're opting to have
// Fanout *start* with fancy-pants WebSocket and
// if that doesn't work it **falls back** on other
// options, such as long-polling.
// The default behaviour is that it starts with
// long-polling and tries to "upgrade" itself
// to WebSocket.
transportMode: 'fallback'
});
Fallbacks
In the case of Air Mozilla, it already had a traditional solution whereby it does a setInterval loop that does an AJAX query frequently.
Because the networks can be flaky or because something might go wrong in the client, the way we use it is like this:
var RELOAD_INTERVAL = 5; // seconds
if (typeof window.Fanout !== 'undefined') {
Fanout.subscribe('/' + container.data('subscription-channel-comments'), function(data) {
// Supposedly the comments have changed.
// For security, let's not trust the data but just take it
// as a hint that it's worth doing an AJAX query
// now.
Comments.load(container, data);
});
// If Fanout doesn't work for some reason even though it
// was made available, still use the regular old
// interval. Just not as frequently.
RELOAD_INTERVAL = 60 * 5;
}
setInterval(function() {
Comments.reload_loop(container);
}, RELOAD_INTERVAL * 1000);
Use Fanout Selectively/Progressively
In the case of Air Mozilla, there are lots of pages. Some don't ever need a WebSocket connection. For example, it might be a simple CRUD (Create Update Delete) page. So, for that I made the whole Fanout functionality "lazy" and it only gets set up if the page has some JavaScript that knows it needs it.
This also has the benefit that the Fanout resource loading etc. is slightly delayed until more pressing things have loaded and the DOM is ready.
You can see the whole solution here. And the way you use it here.
Have Many Channels
You can have as many channels as you like. Don't create a channel called comments when you can have a channel called comments-123 where 123 is the ID of the page you're on for example.
In the case of Air Mozilla, there's a channel for every single page. If you're sitting on a page with a commenting widget, it doesn't get WebSocket messages about newly posted comments on other pages.
Conclusion
We've now used Fanout for almost a year in our little Django + jQuery app and it's been great. The management pages in Air Mozilla use AngularJS and the integration looks like this in the event manager page:
window.Fanout.subscribe('/events', function(data) {
$scope.$apply(lookForModifiedEvents);
});
Fanout.io's been great to us. Really responsive support and very reliable. But if I were to start a fresh new project that needs a solution like this I'd try to spend a little time to investigate the competitors to see if there are some neat features I'd enjoy.
UPDATE
Fanout reached out to help explain more what's great about Fanout.io
"One of Fanout's biggest differentiators is that we use and promote open technologies/standards. For example, our service supports the open Bayeux protocol, and you can connect to it with any compatible client library, such as Faye. Nearly all competing services have proprietary protocols. This "open" aspect of Fanout aligns pretty well with Mozilla's values, and in fact you'd have a hard time finding any alternative that works the same way."
03 May 2016
1 comment
Python,
Web development,
Django,
Mozilla
tl;dr; Use raven's ThreadedRequestsHTTPTransport transport class to send Google Analytics pageview trackings asynchronously to Google Analytics to collect pageviews that aren't actually browser pages.
We have an API on our Django site that was not designed from the ground up. We had a bunch of internal endpoints that were used by the website. So we simply exposed those as API endpoints that anybody can query. All we did was wrap certain parts carefully as to not expose private stuff and we wrote a simple web page where you can see a list of all the endpoints and what parameters are needed. Later we added auth-by-token.
Now the problem we have is that we don't know which endpoints people use and, as equally important, which ones people don't use. If we had more stats we'd be able to confidently deprecate some (for easier maintanenace) and optimize some (to avoid resource overuse).
Our first attempt was to use statsd to collect metrics and display those with graphite. But it just didn't work out. There are just too many different "keys". Basically, each endpoint (aka URL, aka URI) is a key. And if you include the query string parameters, the number of keys just gets nuts. Statsd and graphite is better when you have about as many keys as you have fingers on one hand. For example, HTTP error codes, 200, 302, 400, 404 and 500.
Also, we already use Google Analytics to track pageviews on our website, which is basically a measure of how many people render web pages that have HTML and JavaScript. Google Analytic's UI is great and powerful. I'm sure other competing tools like Mixpanel, Piwik, Gauges, etc are great too, but Google Analytics is reliable, likely to stick around and something many people are familiar with.
So how do you simulate pageviews when you don't have JavaScript rendering? The answer; using plain HTTP POST. (HTTPS of course). And how do you prevent blocking on sending analytics without making your users have to wait? By doing it asynchronously. Either by threading or a background working message queue.
Threading or a message queue
If you have a message queue configured and confident in its running, you should probably use that. But it adds a certain element of complexity. It makes your stack more complex because now you need to maintain a consumer(s) and the central message queue thing itself. What if you don't have a message queue all set up? Use Python threading.
To do the threading, which is hard, it's always a good idea to try to stand on the shoulder of giants. Or, if you can't find a giant, find something that is mature and proven to work well over time. We found that in Raven.
Raven is the Python library, or "agent", used for Sentry, the open source error tracking software. As you can tell by the name, Raven tries to be quite agnostic of Sentry the server component. Inside it, it has a couple of good libraries for making threaded jobs whose task is to make web requests. In particuarly, the awesome ThreadedRequestsHTTPTransport. Using it basically looks like this:
import urlparse
from raven.transport.threaded_requests import ThreadedRequestsHTTPTransport
transporter = ThreadedRequestsHTTPTransport(
urlparse.urlparse('https://ssl.google-analytics.com/collect'),
timeout=5
)
params = {
...more about this later...
}
def success_cb():
print "Yay!"
def failure_cb(exception):
print "Boo :("
transporter.async_send(
params,
headers,
success_cb,
failure_cb
)
The call isn't very different from regular plain old requests.post.
About the parameters
This is probably the most exciting part and the place where you need some thought. It's non-trivial because you might need to put some careful thought into what you want to track.
Your friends is: This documentation page
There's also the Hit Builder tool where you can check that the values you are going to send make sense.
Some of the basic ones are easy:
"Protocol Version"
Just set to v=1
"Tracking ID"
That code thing you see in the regular chunk of JavaScript you put in the head, e.g tid=UA-1234-Z
"Data Source"
Optional word you call this type of traffic. We went with ds=api because we use it to measure the web API.
The user ones are a bit more tricky. Basically because you don't want to accidentally leak potentially sensitive information. We decided to keep this highly anonymized.
"Client ID"
A random UUID (version 4) number that identifies the user or the app. Not to be confused with "User ID" which is basically a string that identifies the user's session storage ID or something. Since in our case we don't have a user (unless they use an API token) we leave this to a new random UUID each time. E.g. cid=uuid.uuid4().hex This field is not optional.
"User ID"
Some string that identifies the user but doesn't reveal anything about the user. For example, we use the PostgreSQL primary key ID of the user as a string. It just means we can know if the same user make several API requests but we can never know who that user is. Google Analytics uses it to "lump" requests together. This field is optional.
Next we need to pass information about the hit and the "content". This is important. Especially the "Hit type" because this is where you make your manually server-side tracking act as if the user had clicked around on the website with a browser.
"Hit type"
Set this to t=pageview and it'll show up Google Analytics as if the user had just navigated to the URL in her browser. It's kinda weird to do this because clearly the user hasn't. Most likely she's used curl or something from the command line. So it's not really a pageview but, on our end, we have "views" in the webserver that produce information to the user. Some of it is HTML and some of it is JSON, in terms of output format, but either way they're sending us a URL and we respond with data.
"Document location URL"
The full absolute URL of that was used. E.g. https://www.example.com/page?foo=bar. So in our Django app we set this to dl=request.build_absolute_uri(). If you have a site where you might have multiple domains in use but want to collect them all under just 1 specific domain you need to set dh=example.com.
"Document Host Name" and "Document Path"
I actually don't know what the point of this is if you've already set the "Document location URL".
"Document Title"
In Google Analytics you can view your Content Drilldown by title instead of by URL path. In our case we set this to a string we know from the internal Python class that is used to make the API endpoint. dt='API (%s)'%api_model.__class__.__name__.
There are many more things you can set, such as the clients IP, the user agent, timings, exceptions. We chose to NOT include the user's IP. If people using the JavaScript version of Google Analytics can set their browser to NOT include the IP, we should respect that. Also, it's rarely interesting to see where the requests for a web API because it's often servers' curl or requests that makes the query, not the human.
Sample implementation
Going back to the code example mentioned above, let's demonstrate a fuller example:
import urlparse
from raven.transport.threaded_requests import ThreadedRequestsHTTPTransport
transporter = ThreadedRequestsHTTPTransport(
urlparse.urlparse('https://ssl.google-analytics.com/collect'),
timeout=5
)
# Remember, this is a Django, but you get the idea
domain = settings.GOOGLE_ANALYTICS_DOMAIN
if not domain or domain == 'auto':
domain = RequestSite(request).domain
params = {
'v': 1,
'tid': settings.GOOGLE_ANALYTICS_ID,
'dh': domain,
't': 'pageview,
'ds': 'api',
'cid': uuid.uuid4().hext,
'dp': request.path,
'dl': request.build_request_uri(),
'dt': 'API ({})'.format(model_class.__class__.__name__),
'ua': request.META.get('HTTP_USER_AGENT'),
}
def success_cb():
logger.info('Successfully informed Google Analytics (%s)', params)
def failure_cb(exception):
logger.exception(exception)
transporter.async_send(
params,
headers,
success_cb,
failure_cb
)
How to unit test this
The class we're using, ThreadedRequestsHTTPTransport has, as you might have seen, a method called async_send. There's also one, with the exact same signature, called sync_send which does the same thing but in a blocking fashion. So you could make your code look someting silly like this:
def send_tracking(page_title, request, async=True):
# ...same as example above but wrapped in a function...
function = async and transporter.async_send or transporter.sync_send
function(
params,
headers,
success_cb,
failure_cb
)
And then in your tests you pass in async=False instead.
But don't do that. The code shouldn't be sub-serviant to the tests (unless it's for the sake of splitting up monster-long functions).
Instead, I recommend you mock the inner workings of that ThreadedRequestsHTTPTransport class so you can make the whole operation synchronous. For example...
import mock
from django.test import TestCase
from django.test.client import RequestFactory
from where.you.have import pageview_tracking
class TestTracking(TestCase):
@mock.patch('raven.transport.threaded_requests.AsyncWorker')
@mock.patch('requests.post')
def test_pageview_tracking(self, rpost, aw):
def mocked_queue(function, data, headers, success_cb, failure_cb):
function(data, headers, success_cb, failure_cb)
aw().queue.side_effect = mocked_queue
request = RequestFactory().get('/some/page')
with self.settings(GOOGLE_ANALYTICS_ID='XYZ-123'):
pageview_tracking('Test page', request)
# Now we can assert that 'requests.post' was called.
# Left as an exercise to the reader :)
print rpost.mock_calls
This is synchronous now and works great. It's not finished. You might want to write a side effect for the requests.post so you can have better control of that post. That'll also give you a chance to potentially NOT return a 200 OK and make sure that your failure_cb callback function gets called.
How to manually test this
One thing I was very curious about when I started was to see how it worked if you really ran this for reals but without polluting your real Google Analytics account. For that I built a second little web server on the side, whose address I used instead of https://ssl.google-analytics.com/collect. So, change your code so that https://ssl.google-analytics.com/collect is not hardcoded but a variable you can change locally. Change it to http://localhost:5000/ and start this little Flask server:
import time
import random
from flask import Flask, abort, request
app = Flask(__name__)
app.debug = True
@app.route("/", methods=['GET', 'POST'])
def hello():
print "- " * 40
print request.method, request.path
print "ARGS:", request.args
print "FORM:", request.form
print "DATA:", repr(request.data)
if request.args.get('sleep'):
sec = int(request.args['sleep'])
print "** Sleeping for", sec, "seconds"
time.sleep(sec)
print "** Done sleeping."
if random.randint(1, 5) == 1:
abort(500)
elif random.randint(1, 5) == 1:
# really get it stuck now
time.sleep(20)
return "OK"
if __name__ == "__main__":
app.run()
Now you get an insight into what gets posted and you can pretend that it's slow to respond. Also, you can get an insight into how your app behaves when this collection destination throws a 5xx error.
How to really test it
Google Analytics is tricky to test in that they collect all the stuff they collect then they take their time to process it and it then shows up the next day as stats. But, there's a hack! You can go into your Google Analytics account and click "Real-Time" -> "Overview" and you should see hits coming in as you're testing this. Obviously you don't want to do this on your real production account, but perhaps you have a stage/dev instance you can use. Or, just be patient :)
26 February 2016
0 comments
Python,
Web development,
Mozilla
Last year I developed a web app called "Whatsdeployed". It's one of those rare one-afternoon-hacks that turns out to be really really useful. I use it every [work]day. And I've heard many people say they use it too.
At the time I built it, it only supported comparing multiple instance. E.g. a production and a dev site. Or a test, stage and production. But oftentimes, especially for smaller projects, you might only just have your one deployed site.
So I've now made it possible so you can compare just 1 site against your github.com master branch.
For example: whatsdeployed.io/s-Sir
Or whatsdeployed.io/s-J14
What these do, is simply comparing what git sha revision is deployed on those side-projects, compared to the latest git sha on the master branch on github.com.
27 January 2016
0 comments
Python,
Web development,
Mozilla,
Javascript
Here's the final demo.
What I did was, I used the Bugzilla REST APIs to download all bugs for a specific product. Then I bulk-uploaded then to Autocompeter.com and lastly built a simply web front-end.
When you "download all" bugs with the Bugzilla REST API, it might be capped but I don't know what the limit is. The trick is to not download ALL bugs for the product in one big fat query, but to find out what all components are for that product and then download for each. The Python code is here.
Everyone's Invited to Play
So first you need to sign in on https://autocompeter.com using your GitHub account. Then you can generate a Auth-Key by picking a domain. The domain can be anything really. I picked bugzilla.mozilla.org but you can use whatever you like.
Then, when you have an Auth-Key you need to know the name of the product (or products) and run the script like this:
python download.py 7U4eFYH5cqR15m3ekuxkzaUR Socorro
Once you've done that, fork my codepen and replace the domain and any other references to the product.
Caveats
To make this really useful, you'd have to run it more often. Perhaps you can hook it up to a cron job or something and make it so that you only download, from the REST API, things that have changed since the last time you did a big download. Then you can let the cron job run frequently.
If you want really hot results, you could hook up a server-side service that consumes the Bugzfeed websocket.
Last but not least; this will never list private/secure bugs. Only publically available stuff.
The Future
If people enjoy it perhaps we can change the front-end demo so it's not hardcoded to one specific product ("Socorro" in my case). And it can be made pretty.
And the data would need to be downloaded and re-submitted more frequently. A quick Heroku app mayhaps?
