A blog and website by Peter Bengtsson

Filtered home page!
Currently only showing blog entries under the category: Web development. Clear filter

Experimenting with Guetzli

24 May 2017 0 comments   MacOSX, Web development, Linux

tl;dr; Guetzli, the new JPEG compression program from Google can save a bytes with little loss of quality.

Inspired by this blog post about Guetzli I thought I'd try it out with something that's relevant to my project, 300x300 JPGs that can be heavily compressed.

So I installed it (with Homebrew) on my MacBook Pro (late 2013) and picked 7 JPGs I had, and use in SongSearch. Which is interesting because these JPEGs have already been compressed once. They are taken from converting from much larger PNGs with PIL (Pillow) at quality rating 80%. In other words, this is Guetzli on top of PIL.

I ran one iteration for every image for the following qualities: 85%, 90%, 95%, 99%, 100%.

The results on the size are as follows:

Image Average Size (bytes) % Smaller
original 23497.0 0
85% 16025.4 32%
90% 18829.4 20%
95% 21338.1 9.2%
99% 22705.3 3.4%
100% 22919.7 2.5%

So, for example, if you choose the 90% quality you save, on average, 4,667B (4.6KB).

As you might already know, Guetzli is incredibly memory hungry and very very slow. On average each image compression took on average 4-6 seconds (higher quality, shorter times). Meaning, if you like Guetzli you probably need to build around it so that the compression happens in a build step or async somewhere and ideally you don't want to run too many compressions in parallel as it might cause CPU and memory overloading.

Now, how does it look?

Go to and stare at the screen to see if you can A) see which one is more compressed and B) if the one that is more compressed is too low quality.

What do you think?

Is it worth it?

Is the quality drop too much to save 10% on image sizes?

Please share your thoughts. Perhaps we can re-do this experiment with some slightly larger JPGs.

Fastest Redis configuration for Django

11 May 2017 0 comments   Django, Web development, Linux, Python

I have an app that does a lot of Redis queries. It all runs in AWS with ElastiCache Redis. Due to the nature of the app, it stores really large hash tables in Redis. The application then depends on querying Redis for these. The question is; What is the best configuration possible for the fastest service possible?

Note! Last month I wrote Fastest cache backend possible for Django which looked at comparing Redis against Memcache. Might be an interesting read too if you're not sold on Redis.


All options are variations on the compressor, serializer and parser which are things you can override in django-redis. All have an effect on the performance. Even compression, for if the number of bytes between Redis and the application is smaller, then it should have better network throughput.

Without further ado, here are the variations:

    "default": {
        "BACKEND": "django_redis.cache.RedisCache",
        "LOCATION": config('REDIS_LOCATION', 'redis://') + '/0',
        "OPTIONS": {
            "CLIENT_CLASS": "django_redis.client.DefaultClient",
    "json": {
        "BACKEND": "django_redis.cache.RedisCache",
        "LOCATION": config('REDIS_LOCATION', 'redis://') + '/1',
        "OPTIONS": {
            "CLIENT_CLASS": "django_redis.client.DefaultClient",
            "SERIALIZER": "django_redis.serializers.json.JSONSerializer",
    "ujson": {
        "BACKEND": "django_redis.cache.RedisCache",
        "LOCATION": config('REDIS_LOCATION', 'redis://') + '/2',
        "OPTIONS": {
            "CLIENT_CLASS": "django_redis.client.DefaultClient",
            "SERIALIZER": "fastestcache.ujson_serializer.UJSONSerializer",
    "msgpack": {
        "BACKEND": "django_redis.cache.RedisCache",
        "LOCATION": config('REDIS_LOCATION', 'redis://') + '/3',
        "OPTIONS": {
            "CLIENT_CLASS": "django_redis.client.DefaultClient",
            "SERIALIZER": "django_redis.serializers.msgpack.MSGPackSerializer",
    "hires": {
        "BACKEND": "django_redis.cache.RedisCache",
        "LOCATION": config('REDIS_LOCATION', 'redis://') + '/4',
        "OPTIONS": {
            "CLIENT_CLASS": "django_redis.client.DefaultClient",
            "PARSER_CLASS": "redis.connection.HiredisParser",
    "zlib": {
        "BACKEND": "django_redis.cache.RedisCache",
        "LOCATION": config('REDIS_LOCATION', 'redis://') + '/5',
        "OPTIONS": {
            "CLIENT_CLASS": "django_redis.client.DefaultClient",
            "COMPRESSOR": "django_redis.compressors.zlib.ZlibCompressor",
    "lzma": {
        "BACKEND": "django_redis.cache.RedisCache",
        "LOCATION": config('REDIS_LOCATION', 'redis://') + '/6',
        "OPTIONS": {
            "CLIENT_CLASS": "django_redis.client.DefaultClient",
            "COMPRESSOR": "django_redis.compressors.lzma.LzmaCompressor"

As you can see, they each have a variation on the OPTIONS.PARSER_CLASS, OPTIONS.SERIALIZER or OPTIONS.COMPRESSOR.

The default configuration is to use redis-py and to pickle the Python objects to a bytestring. Pickling in Python is pretty fast but it has the disadvantage that it's Python specific so you can't have a Ruby application reading the same Redis database.

The Experiment

Note how I have one LOCATION per configuration. That's crucial for the sake of testing. That way one database is all JSON and another is all gzip etc.

What the benchmark does is that it measures how long it takes to READ a specific key (called benchmarking). Then, once it's done that it appends that time to the previous value (or [] if it was the first time). And lastly it writes that list back into the database. That way, towards the end you have 1 key whose value looks something like this: [0.013103008270263672, 0.003879070281982422, 0.009411096572875977, 0.0009970664978027344, 0.0002830028533935547, ..... MANY MORE ....].

Towards the end, each of these lists are pretty big. About 500 to 1,000 depending on the benchmark run.

In the experiment I used wrk to basically bombard the Django server on the URL /random (which makes a measurement with a random configuration). On the EC2 experiment node, it finalizes around 1,300 requests per second which is a decent number for an application that does a fair amount of writes.

The way I run the Django server is with uwsgi like this:

uwsgi --http :8000 --wsgi-file fastestcache/ --master --processes 4 --threads 2

And the wrk command like this:

wrk -d30s  ""

(that, by default, runs 2 threads on 10 connections)

At the end of starting the benchmarking, I open http://localhost:8000/summary which spits out a table and some simple charts.

An Important Quirk

Time measurements over time
One thing I noticed when I started was that the final numbers' average was very different from the medians. That would indicate that there are spikes. The graph on the right shows the times put into that huge Python list for the default configuration for the first 200 measurements. Note that there are little spikes but generally quite flat over time once it gets past the beginning.

Sure enough, it turns out that in almost all configurations, the time it takes to make the query in the beginning is almost order of magnitude slower than the times once the benchmark has started running for a while.

So in the test code you'll see that it chops off the first 10 times. Perhaps it should be more than 10. After all, if you don't like the spikes you can simply look at the median as the best source of conclusive truth.

The Code

The benchmarking code is here. Please be aware that this is quite rough. I'm sure there are many things that can be improved, but I'm not sure I'm going to keep this around.

The Equipment

The ElastiCache Redis I used was a cache.m3.xlarge (13 GiB, High network performance) with 0 shards and 1 node and no multi-zone enabled.

The EC2 node was a m4.xlarge Ubuntu 16.04 64-bit (4 vCPUs and 16 GiB RAM with High network performance).

Both the Redis and the EC2 were run in us-west-1c (North Virginia).

The Results

Here are the results! Sorry if it looks terrible on mobile devices.

root@ip-172-31-2-61:~# wrk -d30s  "" && curl ""
Running 30s test @
  2 threads and 10 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency     9.19ms    6.32ms  60.14ms   80.12%
    Req/Sec   583.94    205.60     1.34k    76.50%
  34902 requests in 30.03s, 2.59MB read
Requests/sec:   1162.12
Transfer/sec:     88.23KB
                         TIMES        AVERAGE         MEDIAN         STDDEV
json                      2629        2.596ms        2.159ms        1.969ms
msgpack                   3889        1.531ms        0.830ms        1.855ms
lzma                      1799        2.001ms        1.261ms        2.067ms
default                   3849        1.529ms        0.894ms        1.716ms
zlib                      3211        1.622ms        0.898ms        1.881ms
ujson                     3715        1.668ms        0.979ms        1.894ms
hires                     3791        1.531ms        0.879ms        1.800ms

Best Averages (shorter better)
██████████████████████████████████████████████████████████████   2.596  json
█████████████████████████████████████                            1.531  msgpack
████████████████████████████████████████████████                 2.001  lzma
█████████████████████████████████████                            1.529  default
███████████████████████████████████████                          1.622  zlib
████████████████████████████████████████                         1.668  ujson
█████████████████████████████████████                            1.531  hires
Best Medians (shorter better)
███████████████████████████████████████████████████████████████  2.159  json
████████████████████████                                         0.830  msgpack
████████████████████████████████████                             1.261  lzma
██████████████████████████                                       0.894  default
██████████████████████████                                       0.898  zlib
████████████████████████████                                     0.979  ujson
█████████████████████████                                        0.879  hires

Size of Data Saved (shorter better)
█████████████████████████████████████████████████████████████████  60K  json
██████████████████████████████████████                             35K  msgpack
████                                                                4K  lzma
█████████████████████████████████████                              35K  default
█████████                                                           9K  zlib
████████████████████████████████████████████████████               48K  ujson
█████████████████████████████████████                              34K  hires

Discussion Points


This experiment has lead me to the conclusion that the best serializer is msgpack and the best compression is zlib. That is the best configuration for django-redis.

msgpack has implementation libraries for many other programming languages. Right now that doesn't matter for my application but if msgpack is both faster and more versatile (because it supports multiple languages) I conclude that to be the best serializer instead.

Web Console trick to get all URLs into your clipboard

27 April 2017 0 comments   Javascript, Web development

This isn't rocket science in the world of Web Development but I think it's so darn useful that if you've been unlucky to miss this, it's worth mentioning one more time.

Suppose you're on a site with lots of links. Like
And you want to get a list of all URLs that contain word "react" in the URL pathname.
And you want to get this into your clipboard.

Here's what you do:

  1. Open your browsers Web Console. In Firefox it's Alt+Cmd+K on OSX (or F12). In Chrome it's Alt+Cmd+J.

  2. Type in the magic: copy([...document.querySelectorAll('a')].map(a => a.href).filter(u => u.match(/react/i)))

  3. Hit Enter, go to a text editor and paste like regular.

It should look something like this:


Web Console in Firefox
The example is just that. An example. The cool thing about this is:

The limit is your imagination. You can also do things like copy([...document.querySelectorAll('a')].filter(a => a.textContent.match(/react/i)).map(a => a.href)) and you filter by the links' text.

A decent Elasticsearch search engine implementation

09 April 2017 0 comments   Django, Web development, Python

The title is a bit of an understatement because I think it's pretty good. It's not perfect and it's not guaranteed to scale, but it works pretty well. Especially on search term typos.

This, my own blog, now has a search engine built with Elasticsearch using the Python library elasticsearch-dsl. The algorithm (if you can call it that) is my own afternoon hack invention. Before I explain how it works try out a couple of searches:

Try a couple of searches:

(each search appends &debug-search for extended output)

Also, by default it uses Elasticsearch's match_phrase so when you search for a multi-word thing, it requires a match on each term. E.g. date format which finds Date formatting, date formats etc.

But if you search for something where the whole phrase can't match, it splits up the search an uses a match operator instead (minus any stop words).


This solution is very much focussed on typos. One thing I really dislike in non-Google search engines is when you make a search and nothing is found and it says "Did you mean ...?". Quite likely I did, but why do I have to click it? Can't it just be clicked for me?

Also, if there's ambiguity and possibly some results based on what you typed and multiple potential "Did you mean...?". Why not just blend them alltogether like Google does? Here is my attempt to solve that. Come with me...

Figuring Out ALL Search Terms

So if you type "Firefix" (not "Firefox", also scroll to the bottom to see the debug table) then maybe, that's an actual word that might be in the database. Then by using the Elasticsearch's Suggesters it figures out alternative spellings based on frequency distributions within the indexed content. This lookup is actually really fast. So now it figures out three alternative ways to spell this term:

And, very arbitrarily I pick a score for the default term that the user typed in. Let's pick 1.1. Doesn't matter gravely and it's up for future tuning. The initial goal is to not bury this spelling alternative too far back.

Here's how to run the suggester for every defined doc type and generate a list of other search terms tuples (minimum score >=0.6).

search_terms = [(1.1, q)]
_search_terms = set([q])
doc_type_keys = (
    (BlogItemDoc, ('title', 'text')),
    (BlogCommentDoc, ('comment',)),
for doc_type, keys in doc_type_keys:
    suggester =
    for key in keys:
        suggester = suggester.suggest('sugg', q, term={'field': key})
    suggestions = suggester.execute_suggest()
    for each in suggestions.sugg:
        if each.options:
            for option in each.options:
                if option.score >= 0.6:
                    better = q.replace(each['text'], option['text'])
                    if better not in _search_terms:

Eventually we get a list (once sorted) that looks like this:

search_terms = [(1.1 'firefix'), (0.9, 'firefox'), (0.7, 'firefli'), (0.7, 'firfox')]

The only reason the code sorts this by the score is in case there are crazy-many search terms. Then we might want to chop off some and only use the 5 highest scoring spelling alternatives.

Building The Boosted OR-query

In this scenario, we're searching amongst blog posts. The title is likely to be a better match than the body. If the title mentions it we probably want to favor that over those where it's only mentioned in the body.

So to build up the OR-query we'll boost the title more than the body ("text" in this example) and we'll build it up using all possible search terms and boost them based on their score. Here's the complete query.

strategy = 'match_phrase'
if original_q:
    strategy = 'match'
search_term_boosts = {}
for i, (score, word) in enumerate(search_terms):
    # meaning the first search_term should be boosted most
    j = len(search_terms) - i
    boost = 1 * j * score
    boost_title = 2 * boost
    search_term_boosts[word] = (boost_title, boost)
    match = Q(strategy, title={
        'query': word,
        'boost': boost_title,
    }) | Q(strategy, text={
        'query': word,
        'boost': boost,
    if matcher is None:
        matcher = match
        matcher |= match

search_query = search_query.query(matcher)

The core is that it does Q('match_phrase' title='firefix', boost=2X) | Q('match_phrase', text='firefix', boost=X).

Here's another arbitrary number. The number 2. It means that the "title" is 2 times more important than the "text".

And that's it! Now every match is scored based on how suggester's score and whether it be matched on the "title" or the "text" (or both). Elasticsearch takes care of everything else. The default is to sort by the _score as ultimately dictated by Lucene.

Match Phrase or Match

In this implementation it tries to match using a match phrase query which basically tries to find matches where every word in the query matches.

The cheap solution here is to basically keep whole search function as is, but if absolutely nothing is found with a match_phrase, and there were multiple words, then just recurse over one more time and do it with a match query instead.

This could probably be improved and do the match_phrase first with higher boost and do the match too but with a lower boost. All in one big query.

Want A Copy?

Note, this copy is quite a mess! It's a personal side-project which is an excuse for experimentation and goofing around.

The full search function is here.

Please don't judge me for the scrappiness of the code but please share your thoughts on this being a decent application of Elasticsearch for smallish datasets like a blog.

Fastest cache backend possible for Django

07 April 2017 5 comments   Web development, Linux, Python

tl;dr; Redis is twice as fast as memcached as a Django cache backend when installed using AWS ElastiCache. Only tested for reads.

Django has a wonderful caching framework. I think I say "wonderful" because it's so simple. Not because it has a hundred different bells or whistles. Each cache gets a name (e.g. "mymemcache" or "redis append only"). The only configuration you generally have to worry about is 1) what backed and 2) what location.

For example, to set up a memcached backend:

# this in
    'default': {
        'BACKEND': 'django.core.cache.backends.memcached.MemcachedCache',
        'KEY_PREFIX': 'myapp',
        'LOCATION': config('MEMCACHED_LOCATION', ''),

With that in play you can now do:

>>> from django.core.cache import caches
>>> caches['default'].set('key', 'value', 60)  # 60 seconds
>>> caches['default'].get('key')

Django comes without built-in backend called django.core.cache.backends.locmem.LocMemCache which is basically a simply Python object in memory with no persistency between Python processes. This one is of course super fast because it involves no further network (local or remote) beyond the process itself. But it's not really useful because if you care about performance (which you probably are if you're here because of the blog post title) because it can't be reused amongst processes.

Anyway, the most common backends to use are:

These are semi-persistent and built for extremely fast key lookups. They can both be reached over TCP or via a socket.

What I wanted to see, is which one is fastest.

The Experiment

First of all, in this blog post I'm only measuring the read times of the various cache backends.

Here's the Django view function that is the experiment:

from django.conf import settings
from django.core.cache import caches

def run(request, cache_name):
    if cache_name == 'random':
        cache_name = random.choice(settings.CACHE_NAMES)
    cache = caches[cache_name]
    t0 = time.time()
    data = cache.get('benchmarking', [])
    t1 = time.time()
    if random.random() < settings.WRITE_CHANCE:
        data.append(t1 - t0)
        cache.set('benchmarking', data, 60)
    if data:
        avg = 1000 * sum(data) / len(data)
        avg = 'notyet'
    # print(cache_name, '#', len(data), 'avg:', avg, ' size:', len(str(data)))
    return http.HttpResponse('{}\n'.format(avg))

It records the time to make a cache.get read and depending settings.WRITE_CHANCE it also does a write (but doesn't record that).
What it records is a list of floats. The content of that piece of data stored in the cache looks something like this:

  1. [0.0007331371307373047]
  2. [0.0007331371307373047, 0.0002570152282714844]
  3. [0.0007331371307373047, 0.0002570152282714844, 0.0002200603485107422]

So the data grows from being really small to something really large. If you run this 1,000 times with settings.WRITE_CACHE of 1.0 the last time it has to fetch a list of 999 floats out of the cache backend.

You can either test it with 1 specific backend in mind and see how fast Django can do, say, 10,000 of these. Here's one such example:

$ wrk -t10 -c400 -d10s
Running 10s test @
  10 threads and 400 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency    76.28ms  155.26ms   1.41s    92.70%
    Req/Sec   349.92    193.36     1.51k    79.30%
  34107 requests in 10.10s, 2.56MB read
  Socket errors: connect 0, read 0, write 0, timeout 59
Requests/sec:   3378.26
Transfer/sec:    259.78KB

$ wrk -t10 -c400 -d10s
Running 10s test @
  10 threads and 400 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency    96.87ms  183.16ms   1.81s    95.10%
    Req/Sec   213.42     82.47     0.91k    76.08%
  21315 requests in 10.09s, 1.57MB read
  Socket errors: connect 0, read 0, write 0, timeout 32
Requests/sec:   2111.68
Transfer/sec:    159.27KB

$ wrk -t10 -c400 -d10s
Running 10s test @
  10 threads and 400 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency    84.93ms  148.62ms   1.66s    92.20%
    Req/Sec   262.96    138.72     1.10k    81.20%
  25271 requests in 10.09s, 1.87MB read
  Socket errors: connect 0, read 0, write 0, timeout 15
Requests/sec:   2503.55
Transfer/sec:    189.96KB

But an immediate disadvantage with this is that the "total final rate" (i.e. requests/sec) is likely to include so many other factors. However, you can see that LocMemcache got 3378.26 req/s, MemcachedCache got 2111.68 req/s and RedisCache got 2503.55 req/s.

The code for the experiment is available here:

The Infra Setup

I created an AWS m3.xlarge EC2 Ubuntu node and two nodes in AWS ElastiCache. One 2-node memcached cluster based on cache.m3.xlarge and one 2-node 1-replica Redis cluster also based on cache.m3.xlarge.

The Django server was run with uWSGI like this:

uwsgi --http :8000 --wsgi-file fastestcache/  --master --processes 6 --threads 10

The Results

Instead of hitting one backend repeatedly and reporting the "requests per second" I hit the "random" endpoint for 30 seconds and let it randomly select a cache backend each time and once that's done, I'll read each cache and look at the final massive list of timings it took to make all the reads. I run it like this:

wrk -t10 -c400 -d30s && curl
...wrk output redacted...

                         TIMES        AVERAGE         MEDIAN         STDDEV
memcached                 5738        7.523ms        4.828ms        8.195ms
default                   3362        0.305ms        0.187ms        1.204ms
redis                     4958        3.502ms        1.707ms        5.591ms

Best Averages (shorter better)
█████████████████████████████████████████████████████████████  7.523  memcached
██                                                             0.305  default
████████████████████████████                                   3.502  redis

Things to note:

Other Things To Test

Perhaps pylibmc is faster than python-memcached.

                         TIMES        AVERAGE         MEDIAN         STDDEV
pylibmc                   2893        8.803ms        6.080ms        7.844ms
default                   3456        0.315ms        0.181ms        1.656ms
redis                     4754        3.697ms        1.786ms        5.784ms

Best Averages (shorter better)
██████████████████████████████████████████████████████████████   8.803  pylibmc
██                                                               0.315  default
██████████████████████████                                       3.697  redis

Using pylibmc didn't make things much faster. What if we we pit memcached against pylibmc?:

                         TIMES        AVERAGE         MEDIAN         STDDEV
pylibmc                   3005        8.653ms        5.734ms        8.339ms
memcached                 2868        8.465ms        5.367ms        9.065ms

Best Averages (shorter better)
█████████████████████████████████████████████████████████████  8.653  pylibmc
███████████████████████████████████████████████████████████    8.465  memcached

What about that fancy hiredis Redis Python driver that's supposedly faster?

                         TIMES        AVERAGE         MEDIAN         STDDEV
redis                     4074        5.628ms        2.262ms        8.300ms
hiredis                   4057        5.566ms        2.296ms        8.471ms

Best Averages (shorter better)
███████████████████████████████████████████████████████████████  5.628  redis
██████████████████████████████████████████████████████████████   5.566  hiredis

These last two results are both surprising and suspicious. Perhaps the whole setup is wrong. Why wouldn't the C-based libraries be faster? Is it so incredibly dwarfed by the network I/O in the time between my EC2 node and the ElastiCache nodes?

In Conclusion

I personally like Redis. It's not as stable as memcached. On a personal server I've run for years the Redis server sometimes just dies due to corrupt memory and I've come to accept that. I don't think I've ever seen memcache do that.

But there are other benefits with Redis as a cache backend. With the django-redis library you have really easy access to the raw Redis connection and you can do much more advanced data structures. You can also cache certain things indefinitely. Redis also supports storing much larger strings than memcached (1MB for memcached and 512MB for Redis).

The conclusion is that Redis is faster than memcached by a factor of 2. Considering the other feature benefits you can get out of having a Redis server available, it's probably a good choice for your next Django project.

Bonus Feature

In big setups you most likely have a whole slur of web heads that are servers that do nothing by handle web requests. And these are configured to talk to databases and caches over the near network. However, so many of us have cheap servers on DigitalOcean or Linode where we run web servers, relational databases and cache servers all on the same machine. (I do. This blog is one of those where there is Nginx, Redis, memcached and PostgreSQL on a 4GB DigitalOcean NYC SSD machine).

So here's one last test where I installed a local Redis and a local memcached on the EC2 node itself:

$ cat .env | grep

Here are the results:

                         TIMES        AVERAGE         MEDIAN         STDDEV
memcached                 7366        3.456ms        1.380ms        5.678ms
default                   3716        0.263ms        0.189ms        1.002ms
redis                     5582        2.334ms        0.639ms        4.965ms

Best Averages (shorter better)
█████████████████████████████████████████████████████████████  3.456  memcached
████                                                           0.263  default
█████████████████████████████████████████                      2.334  redis

The conclusion of that last benchmark is that Redis is still faster and it's roughly 1.8x faster to run these backends on the web head than to use ElastiCache. Perhaps that just goes to show how amazingly fast the AWS inter-datacenter fiber network is!

More CSS load performance experiments (of a React app)

25 March 2017 0 comments   ReactJS, Javascript, Web development

tl;dr; More experiments with different ways to lazy load CSS with and without web fonts. Keeping it simple is almost best but not good enough.


Last week I blogged about Web Performance Optimization of a Single-Page-App and web fonts in which I experimented with tricks to delay the loading of some massive CSS file. The app is one of those newfangled single-page-app where JavaScript (React in this case) does ALL the DOM rendering, which is practical but suffers the first-load experience.

Again, let's iterate that single-page-apps built with fully fledged JavaScript frameworks isn't the enemy. The hope is that your visitors will load more than just one page. If users load it once, any further click within the app might involve some "blocking" AJAX but generally the app feels great since a click doesn't require a new page load.

Again, let's iterate what the ideal is. Ideally the page should load and be visually useful by the server sending all the DOM you almost need and once loaded, some JavaScript can turn the app "alive" so that further clicks just update the DOM instead of reloading. But that's not trivial. Things like react-router (especially version 4) makes it a lot easier but you now need a NodeJS server to render the pages server-side and you can't just easily take your "build" directory and put into a CDN like Firebase or CloudFront.

When I first started these experiements I was actually looking at what I can do with the JavaScript side to make first loads faster. Turns out fixing your CSS render blocking load and assessing use of web fonts has a bigger "bang for the buck".

Go Straight to the Results

Visual comparison on
Visual comparison of 6 different solutions

Same visual but as a comparative video

Stare at that for a second or two. Note that some versions start rendering something earlier than others. Some lose some of their rendered content and goes blank. Some change look significantly more than once before the final rendered result. As expected they almost all take the same total time till the final thing is fully rendered with DOM and all CSS loaded.

What Each Experimental Version Does


A regular webapp straight out of the ./build directory built with Create React App. Blocking CSS, blocking JS and no attempts to show any minimal DOM stuff first.

Optimized Version 1

Inline CSS with basic stuff like background color and font color. CSS links are lazy loaded using tricks from loadCSS.

Optimized Version 2

Same as Optimized version 1 but both the JavaScript that lazy loads the CSS and the React JavaScript has a defer on it to prevent render blockage.

Optimized Version 3

Not much of an optimization. More of a test. Takes the original version but moves the <link rel="stylesheet" ... tags to just above the </body> tag. So below the <script src="..."> tags. Note how, around the 15 second mark, this renders the DOM (by the JavaScript code) before the CSS has loaded and it gets ugly. Note also how much longer this one takes because it has to context switch and go back to CSS loading and re-rendering after the DOM is built by the JavaScript.

Optimized Version 4

All custom web font loading removed. Bye bye "Lato" font-family. Yes you could argue that it looks worse but I still believe performance makes users more happy ultimately than prettiness (not true for all sites, e.g. a fashion web app).
Actually this version is copied from the Optimized version 1 but with the web fonts removed. The final winner in my opinion.

Optimized Version 5

Out of curiousity, let's remove the web fonts one more time but do it based on the Original. So no web fonts but CSS still render blocking.

So, Which Is Best?

I would say Optimized version 4. Remove the web font and do the loadCSS trick of loading the CSS after some really basic rendering but do that before loading the JavaScript. This is what I did to Podcast Time except slightly improved the initial server HTML so that it says "Loading..." immediately and I made the header nav look exactly like the one you get when everything is fully loaded.

Also, Chrome is quite a popular browser after all and the first browser that supports <link rel="preload" ...>. That means that when Chrome (and Opera 43 and Android Browser) parses the initial HTML and sees <link rel="preload" href="foo.css"> it can immediately start downloading that file and probably start parsing it too (in parallel). The visual comparison I did here on WebPagetest used Chrome (on 3G Emerging Markets) so that gives the optimized versions above a slight advantage only for Chrome.

What's Next?

To really make your fancy single-page-app fly like greased lightning you should probably do two things:

  1. Server-side render the HTML but perhaps do it in such a way that if your app depends on a lot of database data, skip that so you at least send a decent amount of HTML so when React/Angular/YouNameIt starts executing it doesn't have to do much DOM rendering and can just go straight to a loading message whilst it starts fetching the actual AJAX data that needs to be displayed. Or if it's a news article or blog post, include that in the server rendered HTML but let the client-side load other things like comments or a sidebar with related links.

  2. Tools like critical that figures out the critical above-the-fold CSS don't work great with single-page-apps where the DOM is generated by loaded JavaScript but you can probably hack around that by writing some scripts that generate a DOM as a HTML string by first loading the app in PhantomJS in various ways. That way you can inline all the CSS needed even after the JavaScript has built the DOM. And once that's loaded, then you can lazy load in the remaining CSS (e.g. bootstrap.min.css) to have all styles ready when the user starts clicking around on stuff.

Most important is the sites' functionality and value but hopefully these experiments have shown that you can get pretty good mileage from your standard build of a single-page-app without having to do too much tooling.

Web Performance Optimization of a Single-Page-App and web fonts

16 March 2017 0 comments   ReactJS, Javascript, Web development

tl;dr; Don't worry so much about your bloated JavaScript and how to async it. Worry about your CSS and your Web fonts. Here I demonstrate a web performance optimization story with results demonstrating the improvements.

The app playground

I have a little app called Podcast Time. It's a single-page-app that renders all the functionality in React with the help of MobX and I use Bootstrap 4 and Bootswatch to make it "pretty". And that Bootswatch theme I chose depends on the Lato font by Google Fonts.

I blogged about it last month. Check it out if you're curious what it actually does. As with almost all my side-projects, it ultimately becomes a playground to see how I can performance optimize it.

sourcemap of combined .js file
The first thing I did was add a Service Worker so that consecutive visits all draws from a local cache. But I fear most visitors (all 52 of them (I kid, I don't know)) come just once and don't benefit from the Service Worker. The compiled and minified JavaScript weighs 124 KB gzipped (440 KB uncompressed) and the CSS weighs 20 KB gzipped (151 KB uncompressed). Not only that but the bootstrap.min.css contained an external import to Google Fonts's CSS that loads the external font files. And lastly, the JavaScript is intense. It's almost 0.5 MB of heavy framework machinery. Of that JavaScript about 45 KB which is "my code". By the way, as ES6 code it's actually 66 KB that my fingers have typed. So that's an interesting factoid about the BabelJS conversion to ES5 code.

Anyway, to make site ultra fast the bestest thing would be to rewrite all the JavaScript in vanilla JavaScript and implement, with browser quirks (read: polyfills), HTML5 pushState etc. Also, I guess I should go through the bootstrap.css file and extract exactly only the selectors I'm actually using. But I'm not going to do that. That would be counter productive and I wouldn't have a chance to add the kind of features I have and can add. So that fat JavaScript and that fat CSS needs to be downloaded.

So what can I do to make that slightly less painful? ...when Service Workers isn't an option.

1. The baseline

Here's the test results on

Click on the first Filmstrip View and notice that the rendering both starts and finishes between the 3.0 and 3.5 second mark. The test is done using Chrome on what they call "Mobile 3G Fast".

2. Have some placeholder content whilst it's downloading

I poked around at the CSS to get some really basics out of it. The background color, the font color, the basic grid. I then copied that into the index.html as an inline stylesheet. You can see it here.

The second thing I did was to replace all <link rel="stylesheet" href="..."> meta tags with this:

<link rel="preload" href="FOO.css" as="style" onload="this.rel='stylesheet'">
<noscript><link rel="stylesheet" href="FOO.css"></noscript>

Also, near the bottom of the index.html I took the loadCSS.js and cssrelpreload.js scripts from Filament Group's loadCSS solution.

And lastly I copied the header text and the sub-title from the DOM, once rendered, plus added a little "Loading..." paragraph and put that into the DOM element that React mounts into once fully loaded.

Now the cool effect is that the render blocking CSS is gone. CSS loads AFTER some DOM rendering has happened. Yes, there's going to be an added delay when the browser has to re-render everything all over again, but at least now it appears that the site is working whilst you're waiting for the site to fully load. Much more user-friendly if you ask me.

Here's the test results on

A lot of blankness
Click on the first Filmstrip View and notice that some rendering started at the 0.9 second mark (instead of 3 seconds in the previous version). Then, after the browser notices that it's not the right font so it blanks the DOM, waits for the web font to load and around the time the web font is loaded, the JavaScript is loaded and ready so it starts rendering the final version at around the 2.8 second mark.

Note that the total time from start to finish is still the same. 3.1 second vs. 3.5 second. It's just that in this second version the user gets some indication that the site is alive and loading. I think the pulsating "Loading..." message puts the user at ease and although they're unhappy it still hasn't loaded I think it buys me a smidge of patience.

3. Hang on! Are web fonts really that important?

That FOIT is still pretty annoying though. When the browser realizes that the font is wrong, it makes the DOM blank until the right font has been loaded. My poor user has to stare at a blank screen for 1.3 seconds instad of seeing the "Loading..." message. Not only that but instead of just saying "Loading..." this could be an opportunity to display something more "constructive" that the user can actually start consuming with their eyes and brains. Then, by the time the full monty has been downloaded and ready the user will not have been waiting in vain.

So how important are those web fonts really? It's a nerdy app where search, content and aggregates matter more than the easthetic impression. Sure, a sexy looking site (no matter what the context/environment) does yield a better impression but there's tradeoffs to be made. If I can load it faster, my hunch is that that'll be more impressive than a pretty font. Secs sells.

I know there are possible good hacks to alleviate these FOIT problems. For example, in "How We Load Web Fonts Progressively" they first set the CSS to point to a system font-family, then, with a JavaScript polyfill they can know when the external web font has been downloaded and then they change the CSS. Cute and cool but it means yet another piece of JavaScript that has to be loaded. It also means "FOUT" (Flash Of Unstyled Text) which can disturb the view quite a lot if the changed font changes layout shifts.

Anyway, I decided I'll prefer fast loading over pretty font so I removed all font family hacks and references to the external font. Bye bye!

Here's the test results on

Click on the first Filmstrip View and notice that the rendering starts already at 1.2 seconds and stay with the "Loading..." message all the way till around 3.0 seconds when both the CSS and the full JavaScript has been loaded.

Side note; Lato vs. Helvetica Neue vs. Helvetica

You decide, is the Lato font that much prettier?



Helvetica Neue

Helvetica Neue



In Conclusion

PageSpeed Insights
I started this exploration trying to decide if there are better ways I can load my JavaScript files. I did some experiments with some server-side loading and async and defer. I scrutinized my ES6 code to see if there are things I can cut out. Perhaps I should switch to Preact instead of React but then I'd have to own the whole es-lint, babel and webpack config hell that create-react-app has eliminated and I'd get worried about not being able to benefit from awesome libraries like MobX and various routing libraries. However, when I was doing various measurements I noticed that all the tricks I tried, for a better loading experience, dwarfed in comparison to the render blocking CSS and the web font loading.

Service Workers is to web apps, what smartphone native apps are to mobile web pages in terms of loading performance. But Service Workers only work if you have a lot of repeat users.

If you want a fast loading experience and still have the convenience of building your app in a powerful JavaScript framework; focus on your CSS and your web font loading. - How Much Time Do Your Podcasts Take To Listen To?

13 February 2017 3 comments   ReactJS, Javascript, Django, Web development, Python

tl;dr; It's a web app where you search and find the podcasts you listen to. It then gives you a break down how much time that requires to keep up, per day, per week and per month. on Firefox iOS
First I wrote some scripts to scrape various sources of podcasts. This is basically a RSS feed URL from which you can fetch the name and an image. And with some cron jobs you can download and parse each podcast feed and build up an index of how many episodes they have and how long each episode is. Together with each episodes "publish date" you can easily figure out an average of how much content each podcast puts out over time.

Suppose you listen to JavaScript Air, Talk Python To Me and Google Cloud Platform Podcast for example, that means you need to listen to podcasts for about 8 minutes per day to keep up.

The Back End

The technology is exciting. The backend is a Django 1.10 server. It manages a PostgreSQL database of all the podcasts, episodes, cron jobs etc. Through Django ORM signals is packages up each podcast with its metadata and stores it in an Elasticsearch database. All the communication between Django and ElasticSearch is done with Elasticsearch DSL.

Also, all the downloading and parsing of feeds is done as background tasks in Celery. This got really interesting/challenging because sooo many podcasts are poorly marked up and many a times the only way to find out how long an episode is is to use ffmpeg to probe it and that takes time.

Another biggish challenge is that fact that often things simply don't work because of networks being what they are, unreliable. So you have to re-attempt network calls without accidentally getting caught in infinite loops of accidentally putting a bad/broken RSS feed back into the background queue again and again and again.

The Front End

Actually, the first prototype of this app was written with Django as the front end plus some jQuery to tie things together. On a plane ride, and as an excuse to learn it, I re-wrote the whole thing in React with Redux. To be honest, I never really enjoyed that and it felt like everything was hard and I had to do more jumping-around-files than actual coding. In particular, Redux is nice but when you have a lot of AJAX both inside components and upon mounting it gets quite messy in my humble opinion.

So, on another plane ride (to Hawaii, so I had more time) I re-wrote it from scratch but this time using three beautiful pieces of front end technology: create-react-app, Mobx and mobx-router. Suddenly it became fun again. Mobx (or Redux or something "fluxy") is necessary if you want fancy pushState URLs AND a central (aka global) state management.

To be perfectly honest, I never actually tried combining Mobx with something like react-router or if it's even possible. But with mobx-router it's quite neat. You write a "views route map" (see example) where you can kick off AJAX before entering (and leaving) routes. Then you use that to populate a global store and now all components can be almost entirely about simply rendering the store. There is some AJAX within the mounted components (e.g. the search and autocomplete).

Plotly graph
On the home page, there's a chart that rather unscientifically plots episode durations over time as a line chart. I'm trying a library called Plotly which is actually a online app for building charts but they offer a free JavaScript library too for generating graphs. Not entirely sure how I feel about it yet but apart from looking a big crowded on mobile, it's working really well.

A Killer Feature

This is a pattern I've wanted to build but never managed to get right. The way to get data about a podcast (and its episodes) is to do an Elasticsearch search. From the homepage you basically call /find?q=Planet%20money when you search. That gives you almost all the information you need. So you store that in the global store. Then, if the user clicks on that particular podcast to go to its "perma page" you can simply load that podcast's individual route and you don't need to do something like /find?id=727 because you already have everything you need. If the user then opens that page in a new tab or reloads you now have to fetch just the one podcast, so you simply call /find?id=727. In other words, subsequent page loads load instantly! (Basically, it updates the store's podcast object upon clicking any of the podcasts iterated over from the listing. Code here)

And to top that - and this is where a good router shines - if you make a search or something, click something and click back since you have a global store of state, you can simply reuse that without needing another AJAX query.

The State of the Future

First of all, this is a fun little side project and it's probably buggy. My goal is not to make money on it but to build up a graph. Every time someone uses the site and finds the podcasts they listen to that slowly builds up connections. If you listen to "The Economist", "Planet Money" and "Freakonomics", that tie those together loosely. It's hard to programmatically know that those three podcasts are "related" but they are by "peoples' taste".

The ultimate goal of this is; now I can recommend other podcasts based on a given set. It's a little bit like LastFM used to work. Using Audioscrobbler LastFM was able to build up a graph based on what people preferred to listen to and using that network of knowledge they can recommend things you have not listened to but probably would appreciate.

At the moment, there's a simple Picks listing of "lists" (aka "picks") that people have chosen. With enough time and traffic I'll try to use Elasticsearch's X-Pack Graph capabilities to develop a search engine based on this.

At the time of writing, I've indexed 4,669 podcasts, spanning 611,025 episodes which equates to 549,722 hours of podcast content.

The Code

The front end code is available on and is relatively neat and tidy. The most interesting piece is probably the views/index.js which is the "controller" of things. That's where it decides which component to render, does the AJAX queries and manages the global store.

The back end code is a bit messier. It's done as an "app" as part of this very blog. The way the Elasticsearch indexing is configured is here and the hotch potch code for scraping and parsing RSS feeds is here.

Please try it out and show me your selection. You can drop feedback here.

Autocompeter is Dead. Long live Autocompeter!

09 January 2017 0 comments   Go, Web development, Python

About 2 years ago I launched It was two parts:

1) A autocompeter.js pure JavaScript solution to add autocomplete to a search input field.
2) A REST API where you can submit titles with a HTTP header key, and a fancy autocomplete search.

Only Rewrote the Go + Redis part

The second part has now been completely re-written. The server was originally written in Go and used Redis. Now it's Django and ElasticSearch.

The ultimate reason for this was that Redis was, by far, the biggest memory consumer on my shared DigitalOcean server. The way it worked was that every prefix of every word in every title was indexes as a key. For example the words p, pe, pet, pete, peter and peter$ are all keys and they point to an array of IDs that you then look up to get the distinct set of titles and their URLs. This makes it really really fast but since redis doesn't support namespaces, or multiple columns it means that for every prefix it needs a prefix of its own for the domain they belong to. So the hash for is eb9f747 so the strings to store are instead eb9f747p, eb9f747pe, eb9f747pet, eb9f747pete, eb9f747peter and eb9f747peter$.

ElasticSearch on the other hand has ALL of this built in deep in Lucene. AND you can filter. So the way it's queried now instead is something like this:

search =
search = search.filter('term',
search = search.query(Q('match_phrase', title=request.GET['q']))
search = search.sort('-popularity', '_score')
search = search[:size]
response = search.execute()

And here's how the mapping is defined:

from elasticsearch_dsl import (

edge_ngram_analyzer = analyzer(
            'edge_ngram_filter', type='edgeNGram',
            min_gram=1, max_gram=20

class TitleDoc(DocType):
    id = Keyword()
    domain = Keyword(required=True)
    url = Keyword(required=True, index=False)
    title = Text(
    popularity = Float()
    group = Keyword()

I'm learning ElasticSearch rapidly but I still feel like I have so much to learn. This solution I have here is quite good and I'm pretty happy with the results but I bet there's a lot of things I can learn to make it even better.

Why Ditch Go?

I actually had a lot of fun building the first server version of Autocompeter in Go but Django is just so many times more convenient. It's got management commands, ORM, authentication system, CSRF protection, awesome error reporting, etc. All built in! With Go I had to build everything from scratch.

Also, I felt like the important thing here is the JavaScript client and the database. Now that I've proven this to work with Django and elasticsearch-dsl I think it wouldn't be too hard to re-write the critical query API in Go or in something like Sanic for maximum performance.

All Dockerized

Oh, one of the reasons I wanted to do this new server in Python is because I want to learn Docker better and in particular Docker with Python projects.

The project is now entirely contained in Docker so you can start the PostgreSQL, ElasticSearch 5.1.1 and Django with docker-compose up. There might be a couple of things I've forgot to document for how to configure things but this is actually the first time I've developed something entirely in Docker.

ElasticSearch 5 in Travis-CI

06 January 2017 0 comments   Web development, Linux, Python

tl;dr; Here's a working .travis.yml file that works with ElasticSearch 5.1.1

I had to jump through hoops to get Travis-CI to run with ElasticSearch 5.1.1 and I thought I'd share. If you just do:

  - elasticsearch

This is from the Travis-CI documentation but this installs ElasticSearch 1.4. Not good enough. The instructions on the same page for using higher versions did not work for me.

To get a specific version you need to download it yourself and install it with dpkg -i but the problem is that if you want to use ElasticSearch version 5, you need to have Java 1.8. The short answer is that this is how you install Java 1.8:

      - oracle-java8-set-default

But now you need to sudo so you need to add sudo: true in your .travis.yml. Bummer, because it makes the build a bit slower. However, a necessary evil.

The critical line I use to install it is this:

curl -O && \
sudo dpkg -i --force-confnew elasticsearch-5.1.1.deb && \
sudo service elasticsearch start

I thought I could "upgrade" the existing install, but that breaks thinks. In other words you have to remove the services: - elasticsearch line or else it can't upgrade.

Now, during debugging I was not getting errors on the line:

sudo service elasticsearch start

So I add this to be sure the right version got installed:

curl -v http://localhost:9200/

and then I can see that the right version was installed. It should look something like this:

* About to connect() to localhost port 9200 (#0)
*   Trying connected
> GET / HTTP/1.1
> User-Agent: curl/7.22.0 (x86_64-pc-linux-gnu) libcurl/7.22.0 OpenSSL/1.0.1 zlib/ libidn/1.23 librtmp/2.3
> Host: localhost:9200
> Accept: */*
< HTTP/1.1 200 OK
< content-type: application/json; charset=UTF-8
< content-length: 327
  "name" : "m_acpqT",
  "cluster_name" : "elasticsearch",
  "cluster_uuid" : "b4_KnK6KQmSx64C9o-81Ug",
  "version" : {
    "number" : "5.1.1",
    "build_hash" : "5395e21",
    "build_date" : "2016-12-06T12:36:15.409Z",
    "build_snapshot" : false,
    "lucene_version" : "6.3.0"
  "tagline" : "You Know, for Search"
* Connection #0 to host localhost left intact
* Closing connection #0

Note the line that says "number" : "5.1.1",.

So, yay! Hopefully this will help someone else because it took me quite a while to get right.