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Best EXPLAIN ANALYZE benchmark script

19 April 2018 0 comments   PostgreSQL, Python

https://gist.github.com/peterbe/966effb3f357258ddda5aa8ac385b418


tl;dr; Use best-explain-analyze.py to benchmark a SQL query in Postgres.

I often benchmark SQL by extracting the relevant SQL string, prefix it with EXPLAIN ANALYZE, putting it into a file (e.g. benchmark.sql) and then running psql mydatabase < benchmark.sql. That spits out something like this:

                                                           QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------
 Index Scan using main_song_ca949605 on main_song  (cost=0.43..237.62 rows=1 width=4) (actual time=1.586..1.586 rows=0 loops=1)
   Index Cond: (artist_id = 27451)
   Filter: (((name)::text % 'Facing The Abyss'::text) AND (id <> 2856345))
   Rows Removed by Filter: 170
 Planning time: 3.335 ms
 Execution time: 1.701 ms
(6 rows)

Cool. So you study the steps of the query plan and look for "Seq Scan" and various sub-optimal uses of heaps and indices etc. But often, you really want to just look at the Execution time milliseconds number. Especially if you might have to slightly different SQL queries to compare and contrast.

However, as you might have noticed, the number on the Execution time varies between runs. You might think nothing's changed but Postgres might have warmed up some internal caches or your host might be more busy or less busy. To remedy this, you run the EXPLAIN ANALYZE select ... a couple of times to get a feeling for an average. But there's a much better way!

best-explain-analyze.py

Check this out: best-explain-analyze.py

Download it into your ~/bin/ and chmod +x ~/bin/best-explain-analyze.py. I wrote it just this morning so don't judge!

Now, when you run it it runs that thing 10 times (by default) and reports the best Execution time, its mean and its median. Example output:

▶ best-explain-analyze.py songsearch dummy.sql
EXECUTION TIME
    BEST    1.229ms
    MEAN    1.489ms
    MEDIAN  1.409ms
PLANNING TIME
    BEST    1.994ms
    MEAN    4.557ms
    MEDIAN  2.292ms

The "BEST" is an important metric. More important than mean or median.

Raymond Hettinger explains it better than I do. His context is for benchmarking Python code but it's equally applicable:

"Use the min() rather than the average of the timings. That is a recommendation from me, from Tim Peters, and from Guido van Rossum. The fastest time represents the best an algorithm can perform when the caches are loaded and the system isn't busy with other tasks. All the timings are noisy -- the fastest time is the least noisy. It is easy to show that the fastest timings are the most reproducible and therefore the most useful when timing two different implementations."

Efficient many-to-many field lookup in Django REST Framework

11 April 2018 0 comments   PostgreSQL, Django, Python


The basic setup

Suppose you have these models:

from django.db import models


class Category(models.Model):
    name = models.CharField(max_length=100)


class Blogpost(models.Model):
    title = models.CharField(max_length=100)
    categories = models.ManyToManyField(Category)

Suppose you hook these up Django REST Framework and list all Blogpost items. Something like this:

# urls.py
from rest_framework import routers
from . import views


router = routers.DefaultRouter()
router.register(r'blogposts', views.BlogpostViewSet)
# views.py
from rest_framework import viewsets

class BlogpostViewSet(viewsets.ModelViewSet):
    queryset = Blogpost.objects.all().order_by('date')
    serializer_class = serializers.BlogpostSerializer

What's the problem?

Then, if you execute this list (e.g. curl http://localhost:8000/api/blogposts/) what will happen, on the database, is something like this:

SELECT "app_blogpost"."id", "app_blogpost"."title" FROM "app_blogpost";

SELECT "app_category"."id", "app_category"."name" FROM "app_category" INNER JOIN "app_blogpost_categories" ON ("app_category"."id" = "app_blogpost_categories"."category_id") WHERE "app_blogpost_categories"."blogpost_id" = 1025;
SELECT "app_category"."id", "app_category"."name" FROM "app_category" INNER JOIN "app_blogpost_categories" ON ("app_category"."id" = "app_blogpost_categories"."category_id") WHERE "app_blogpost_categories"."blogpost_id" = 193;
SELECT "app_category"."id", "app_category"."name" FROM "app_category" INNER JOIN "app_blogpost_categories" ON ("app_category"."id" = "app_blogpost_categories"."category_id") WHERE "app_blogpost_categories"."blogpost_id" = 757;
SELECT "app_category"."id", "app_category"."name" FROM "app_category" INNER JOIN "app_blogpost_categories" ON ("app_category"."id" = "app_blogpost_categories"."category_id") WHERE "app_blogpost_categories"."blogpost_id" = 853;
SELECT "app_category"."id", "app_category"."name" FROM "app_category" INNER JOIN "app_blogpost_categories" ON ("app_category"."id" = "app_blogpost_categories"."category_id") WHERE "app_blogpost_categories"."blogpost_id" = 1116;
SELECT "app_category"."id", "app_category"."name" FROM "app_category" INNER JOIN "app_blogpost_categories" ON ("app_category"."id" = "app_blogpost_categories"."category_id") WHERE "app_blogpost_categories"."blogpost_id" = 1126;
SELECT "app_category"."id", "app_category"."name" FROM "app_category" INNER JOIN "app_blogpost_categories" ON ("app_category"."id" = "app_blogpost_categories"."category_id") WHERE "app_blogpost_categories"."blogpost_id" = 964;
SELECT "app_category"."id", "app_category"."name" FROM "app_category" INNER JOIN "app_blogpost_categories" ON ("app_category"."id" = "app_blogpost_categories"."category_id") WHERE "app_blogpost_categories"."blogpost_id" = 591;
SELECT "app_category"."id", "app_category"."name" FROM "app_category" INNER JOIN "app_blogpost_categories" ON ("app_category"."id" = "app_blogpost_categories"."category_id") WHERE "app_blogpost_categories"."blogpost_id" = 1112;
SELECT "app_category"."id", "app_category"."name" FROM "app_category" INNER JOIN "app_blogpost_categories" ON ("app_category"."id" = "app_blogpost_categories"."category_id") WHERE "app_blogpost_categories"."blogpost_id" = 1034;
...

Obviously, it depends on how you define that serializers.BlogpostSerializer class, but basically, as it loops over the Blogpost, for each and every one, it needs to make a query to the many-to-many table (app_blogpost_categories in this example).

That's not going to be performant. In fact, it might be dangerous on your database if the query of blogposts gets big, like requesting a 100 or 1,000 records. Fetching 1,000 rows from the app_blogpost table might be cheap'ish but doing 1,000 selects with JOIN is never going to be cheap. It adds up horribly.

How you solve it

The trick is to only do 1 query on the many-to-many field's table, 1 query on the app_blogpost table and 1 query on the app_category table.

First you have to override the ViewSet.list method. Then, in there you can do exactly what you need.

Here's the framework for this change:

# views.py
from rest_framework import viewsets

class BlogpostViewSet(viewsets.ModelViewSet):
    # queryset = Blogpost.objects.all().order_by('date')
    serializer_class = serializers.BlogpostSerializer

    def get_queryset(self):
        # Chances are, you're doing something more advanced here 
        # like filtering.
        Blogpost.objects.all().order_by('date')

    def list(self, request, *args, **kwargs):
        response = super().list(request, *args, **kwargs)
        # Where the magic happens!

        return response

Next, we need to make a mapping of all Category.id -1-> Category.name. But we want to make sure we do only on the categories that are involved in the Blogpost records that matter. You could do something like this:

category_names = {}
for category in Category.objects.all():
    category_names[category.id] = category.name

But to avoid doing a lookup of category names for those you never need, use the query set on Blogpost. I.e.

qs = self.get_queryset()
all_categories = Category.objects.filter(
    id__in=Blogpost.categories.through.objects.filter(
        blogpost__in=qs
    ).values('category_id')
)
category_names = {}
for category in all_categories:
    category_names[category.id] = category.name

Now you have a dictionary of all the Category IDs that matter.

Note! The above "optimization" assumes that it's worth it. Meaning, if the number of Category records in your database is huge, and the Blogpost queryset is very filtered, then it's worth only extracting a subset. Alternatively, if you only have like 100 different categories in your database, just do the first variant were you look them up "simplestly" without any fancy joins.

Next, is the mapping of Blogpost.id -N-> Category.name. To do that you need to build up a dictionary (int to list of strings). Like this:

categories_map = defaultdict(list)
for m2m in Blogpost.categories.through.objects.filter(blogpost__in=qs):
    categories_map[m2m.blogpost_id].append(
        category_names[m2m.category_id]
    )

So what we have now is a dictionary whose keys are the IDs in self.get_queryset() and each value is a list of a strings. E.g. ['Category X', 'Category Z'] etc.

Lastly, we need to put these back into the serialized response. This feels a little hackish but it works:

for each in response.data:
    each['categories'] = categories_map.get(each['id'], [])

The whole solution looks something like this:

# views.py
from rest_framework import viewsets

class BlogpostViewSet(viewsets.ModelViewSet):
    # queryset = Blogpost.objects.all().order_by('date')
    serializer_class = serializers.BlogpostSerializer

    def get_queryset(self):
        # Chances are, you're doing something more advanced here 
        # like filtering.
        Blogpost.objects.all().order_by('date')

    def list(self, request, *args, **kwargs):
        response = super().list(request, *args, **kwargs)
        qs = self.get_queryset()
        all_categories = Category.objects.filter(
            id__in=Blogpost.categories.through.objects.filter(
                blogpost__in=qs
            ).values('category_id')
        )
        category_names = {}
        for category in all_categories:
            category_names[category.id] = category.name

        categories_map = defaultdict(list)
        for m2m in Blogpost.categories.through.objects.filter(blogpost__in=qs):
            categories_map[m2m.blogpost_id].append(
                category_names[m2m.category_id]
            )

        for each in response.data:
            each['categories'] = categories_map.get(each['id'], [])

        return response

It's arguably not very pretty but doing 3 tight queries instead of doing as many queries as you have records is much better. O(c) is better than O(n).

Discussion

Perhaps the best solution is to not run into this problem. Like, don't serialize any many-to-many fields.

Or, if you use pagination very conservatively, and only allow like 10 items per page then it won't be so expensive to do one query per every many-to-many field.

Show size of every PostgreSQL database you have

07 February 2018 0 comments   PostgreSQL


tl;dr; SELECT pg_database.datname, pg_database_size(pg_database.datname), pg_size_pretty(pg_database_size(pg_database.datname)) FROM pg_database ORDER by 2 DESC;

I recently had to transfer all my postgres data for my local databases on the laptop. Although I, unfortunately, can't remember what it was before I deleted a bunch of databases, now after some clean up, all my PostgreSQL databases weighs 12GB.

To find out the size of all your database, start psql like this:

$ psql postgres

Then run this:

SELECT pg_database.datname, 
pg_database_size(pg_database.datname), 
pg_size_pretty(pg_database_size(pg_database.datname)) 
FROM pg_database ORDER by 2 DESC;

Here's what it looked like on my laptop:

postgres=# SELECT pg_database.datname,
postgres-# pg_database_size(pg_database.datname),
postgres-# pg_size_pretty(pg_database_size(pg_database.datname))
postgres-# FROM pg_database ORDER by 2 DESC;
         datname          | pg_database_size | pg_size_pretty
--------------------------+------------------+----------------
 songsearch               |      10689224876 | 10194 MB
 air_mozilla_org          |        355639812 | 339 MB
 kl                       |        239297028 | 228 MB
 kl2                      |        239256068 | 228 MB
 peterbecom               |        191914500 | 183 MB
 kintobench               |        125968556 | 120 MB
 airmozilla               |         41640452 | 40 MB
 socorro_webapp           |         32530948 | 31 MB
 tecken                   |         26706092 | 25 MB
 dailycookie              |         12935684 | 12 MB
 autocompeter             |         12313092 | 12 MB
 socorro_integration_test |         11428356 | 11 MB
 breakpad                 |         11313668 | 11 MB
 test_peterbecom          |          9298436 | 9081 kB
 battleshits              |          9028100 | 8817 kB
 thuawood2                |          8716804 | 8513 kB
 thuawood                 |          8667652 | 8465 kB
 fastestdb                |          8012292 | 7825 kB
 premailer                |          7676420 | 7497 kB
 crontabber               |          7586308 | 7409 kB
 postgres                 |          7536812 | 7360 kB
 crontabber_exampleapp    |          7488004 | 7313 kB
 whatsdeployed            |          7414276 | 7241 kB
 socorro_test             |          7315972 | 7145 kB
 template1                |          7307780 | 7137 kB
 template0                |          7143940 | 6977 kB
(26 rows)

Conditional aggregation in Django 2.0

12 January 2018 4 comments   PostgreSQL, Django, Python


Django 2.0 came out a couple of weeks ago. It now supports "conditional aggregation" which is SQL standard I didn't even know about.

Before

So I have a Django app which has an endpoint that generates some human-friendly stats about the number of uploads (and their total size) in various different time intervals.

First of all, this is how it set up the time intervals:

today = timezone.now()
start_today = today.replace(hour=0, minute=0, second=0)
start_yesterday = start_today - datetime.timedelta(days=1)
start_this_month = today.replace(day=1)
start_this_year = start_this_month.replace(month=1)

And then, for each of these, there's a little function that returns a dict for each time interval:

def count_and_size(qs, start, end):
    sub_qs = qs.filter(created_at__gte=start, created_at__lt=end)
    return {
        'count': sub_qs.count(),
        'total_size': sub_qs.aggregate(size=Sum('size'))['size'],
}

numbers['uploads'] = {
    'today': count_and_size(upload_qs, start_today, today),
    'yesterday': count_and_size(upload_qs, start_yesterday, start_today),
    'this_month': count_and_size(upload_qs, start_this_month, today),
    'this_year': count_and_size(upload_qs, start_this_year, today),
}

What you get is exactly 2 x 4 = 8 queries. One COUNT and one SUM for each time interval. E.g.

SELECT SUM("upload_upload"."size") AS "size" 
FROM "upload_upload" 
WHERE ("upload_upload"."created_at" >= ...

SELECT COUNT(*) AS "__count" 
FROM "upload_upload" 
WHERE ("upload_upload"."created_at" >= ...

...6 more queries...

Middle

Oops. I think this code comes from a slightly rushed job. We can do the COUNT and the SUM at the same time for each query.

# New, improved count_and_size() function!
def count_and_size(qs, start, end):
    sub_qs = qs.filter(created_at__gte=start, created_at__lt=end)
    return sub_qs.aggregate(
        count=Count('id'),
        total_size=Sum('size'),
    )

numbers['uploads'] = {
    'today': count_and_size(upload_qs, start_today, today),
    'yesterday': count_and_size(upload_qs, start_yesterday, start_today),
    'this_month': count_and_size(upload_qs, start_this_month, today),
    'this_year': count_and_size(upload_qs, start_this_year, today),
}

Much better, now there's only one query per time bucket. So 4 queries in total. E.g.

SELECT COUNT("upload_upload"."id") AS "count", SUM("upload_upload"."size") AS "total_size" 
FROM "upload_upload" 
WHERE ("upload_upload"."created_at" >= ...

...3 more queries...

After

But we can do better than that! Instead, we use conditional aggregation. The syntax gets a bit hairy because there's so many keyword arguments, but I hope I've indented it nicely so it's easy to see how it works:

def make_q(start, end):
    return Q(created_at__gte=start, created_at__lt=end)

q_today = make_q(start_today, today)
q_yesterday = make_q(start_yesterday, start_today)
q_this_month = make_q(start_this_month, today)
q_this_year = make_q(start_this_year, today)

aggregates = upload_qs.aggregate(
    today_count=Count('pk', filter=q_today),
    today_total_size=Sum('size', filter=q_today),

    yesterday_count=Count('pk', filter=q_yesterday),
    yesterday_total_size=Sum('size', filter=q_yesterday),

    this_month_count=Count('pk', filter=q_this_month),
    this_month_total_size=Sum('size', filter=q_this_month),

    this_year_count=Count('pk', filter=q_this_year),
    this_year_total_size=Sum('size', filter=q_this_year),
)
numbers['uploads'] = {
    'today': {
        'count': aggregates['today_count'],
        'total_size': aggregates['today_total_size'],
    },
    'yesterday': {
        'count': aggregates['yesterday_count'],
        'total_size': aggregates['yesterday_total_size'],
    },
    'this_month': {
        'count': aggregates['this_month_count'],
        'total_size': aggregates['this_month_total_size'],
    },
    'this_year': {
        'count': aggregates['this_year_count'],
        'total_size': aggregates['this_year_total_size'],
    },
}

Voila! One single query to get all those pieces of data.
The SQL sent to PostgreSQL looks something like this:

SELECT 
  COUNT("upload_upload"."id") FILTER (WHERE ("upload_upload"."created_at" >= ...)) AS "today_count", 
  SUM("upload_upload"."size") FILTER (WHERE ("upload_upload"."created_at" >= ...)) AS "today_total_size",

  COUNT("upload_upload"."id") FILTER (WHERE ("upload_upload"."created_at" >= ...)) AS "yesterday_count", 
  SUM("upload_upload"."size") FILTER (WHERE ("upload_upload"."created_at" >= ...)) AS "yesterday_total_size", 

  ...

FROM "upload_upload";

Is this the best thing to do? I'm starting to have my doubts.

Watch Out!

When I take this now 1 monster query for a spin with an EXPLAIN ANALYZE prefix I notice something worrying!

                                                    QUERY PLAN
-------------------------------------------------------------------------------------------------------------------
 Aggregate  (cost=74.33..74.34 rows=1 width=16) (actual time=0.587..0.587 rows=1 loops=1)
   ->  Seq Scan on upload_upload  (cost=0.00..62.13 rows=813 width=16) (actual time=0.012..0.210 rows=813 loops=1)
 Planning time: 0.427 ms
 Execution time: 0.674 ms
(4 rows)

A sequential scan! That's terrible. The created_at column is indexed in a BTREE so why can't it use the index.

The short answer is: I don't know!
I've uploaded a reduced, but still complete, example demonstrating this in a gist. It's very similar to the example in the stackoverflow question I asked.

So what did I do? I went back to the "middle" solution. One SELECT query per time bucket. So 4 queries in total, but at least all 4 is able to use an index.

Synonyms with elasticsearch-dsl

05 December 2017 0 comments   PostgreSQL, Web development, Python

https://www.elastic.co/guide/en/elasticsearch/guide/current/synonyms.html


The documentation about how to use synonyms in Elasticsearch is good but because it's such an advanced topic, even if you read the documentation carefully, you're still left with lots of questions. Let me show you some things I've learned about how to use synonyms in Python with elasticsearch-dsl.

What's the nature of your documents?

I'm originally from Sweden but moved to London, UK in 1999 and started blogging a few years after. So I wrote most of my English with British English spelling. E.g. "centre" instead of "center". Later I moved to California in the US and slowly started to change my own English over to American English. I kept blogging but now I would prefer to write "center" instead of "centre".

Another example... Certain technical words or namings are tricky. For example, is it "go" or is it "golang"? Is it "React" or is it "ReactJS"? Is it "PostgreSQL" or "Postgres". I never know. Not only is it sometimes hard to know which is right because people use them differently, but also sometimes "brands" like that change over time since inception, the creator might have preferred something but the masses of people call it something else.

So with all that in mind, not only has the nature of my documents (my blog post texts) changed in terminology over the years. My visitors are also coming both from British English and American English. Or, suppose that I knew the perfect way to phrase that relational database that starts with "Postg...". Even if my text is always spelled one particular way, perfectly, my visitors will most likely refer to it as "postgres" sometimes and "postgresql" sometimes.

The simple solution, match all!

Create a custom analyzer

Let's jump straight into the code. People who have used elasticsearch_dsl should be familiar with most of this:

from elasticsearch_dsl import (
    DocType,
    Text,
    Index,
    analyzer,
    Keyword,
    token_filter,
)
from django.conf import settings


index = Index(settings.ES_INDEX)
index.settings(**settings.ES_INDEX_SETTINGS)


synonym_tokenfilter = token_filter(
    'synonym_tokenfilter',
    'synonym',
    synonyms=[
        'reactjs, react',  # <-- important
    ],
)

text_analyzer = analyzer(
    'text_analyzer',
    tokenizer='standard',
    filter=[
        # The ORDER is important here.
        'standard',
        'lowercase',
        'stop',
        synonym_tokenfilter,
        # Note! 'snowball' comes after 'synonym_tokenfilter'
        'snowball',
    ],
    char_filter=['html_strip']
)

class BlogItemDoc(DocType):
    oid = Keyword(required=True)
    title = Text(
        required=True, 
        analyzer=text_analyzer
    )
    text = Text(analyzer=text_analyzer)

index.doc_type(BlogItemDoc)

This code above is copied from the "real code" but a lot of distracting things that aren't important to the point, have been removed.

The magic sauce here is that you create a token_filter and you can call it whatever you want. I called mine synonym_tokenfilter and that's also what the instance variable is called.

Notice the list of synonyms. It's a plain list of strings. Specifically, it's a list of 1 string reactjs, react.

Let's see how Elasticsearch analyzes this:
First with the text react.

$ curl -XGET 'http://127.0.0.1:9200/peterbecom/_analyze?analyzer=text_analyzer&text=react&pretty=1'
{
  "tokens" : [
    {
      "token" : "react",
      "start_offset" : 0,
      "end_offset" : 5,
      "type" : "",
      "position" : 0
    },
    {
      "token" : "reactj",
      "start_offset" : 0,
      "end_offset" : 5,
      "type" : "SYNONYM",
      "position" : 0
    }
  ]
}

Note that the analyzer snowball, converted reactjs to reactj which is wrong in a sense, because there's not plural "reacts", but it ultimately doesn't matter much. At least not in this particular case.

Secondly, analyze it with the text reactjs:

$ curl -XGET 'http://127.0.0.1:9200/peterbecom/_analyze?analyzer=text_analyzer&text=reactjs&pretty=1'
{
  "tokens" : [
    {
      "token" : "reactj",
      "start_offset" : 0,
      "end_offset" : 7,
      "type" : "",
      "position" : 0
    },
    {
      "token" : "react",
      "start_offset" : 0,
      "end_offset" : 7,
      "type" : "SYNONYM",
      "position" : 0
    }
  ]
}

Same tokens! Just different order.

Test it for reals

Now, the real proof is in actually doing a search on this. Look at these two screenshots:

Search for 'react'

Search for 'reactjs'

It worked! Different ways of phrasing your search but ultimately found all the documents that matched independent of different people or different authors might prefer to spell it.

Try it for yourself:

What it looked like before

Check out these two screenshots of how it would look like before, when synonyms for postgres and postgresql had not been set up yet:

Searching for 'postgresql'

Searching for 'postgres'

One immediate thought I have is what a mess I've been in blogging about that database. Clearly I struggled to pick one way to spell it consistently.

And here's what it would look like once that synonym has been set up:

Synonym set up for 'postgres' and 'postgresql'

"go" versus "golang"

Go is a programming language. That term, too, struggles with a name ambiguity. Granted, I rarely hear people say "golang", but it's definitely a written word that turns up a lot.

The problem with setting up a synonym for go == golang is that "go" is common English word. It's also the stem of the word "going" and such. So if you set up a synonym, like I did for react and reactjs above, this is what happens:

Search for 'golang'

This is now the exact search results as if I had searched for go. But look what it matched! It matched "Go" (good) but also "Going real simple..." (bad) and "...I should go" (bad).

If someone searches for the simple term "go" they probably intend to search for the Go programming language. All that snowball stemming is critical for a bunch of other non-computer-term searches so we can't remove the stemming.

The solution is to use what's called "Simple Contraction". And it looks like this:

all_synonyms = [
    'go => golang',
    'react => reactjs',
    'postgres => postgresql',
]

That basically means that a search for go is a search for golang. And a document that uses the word go (alone) is indexed as golang.

What happens is that the word go gets converted to golang which doesn't get stemming converted down to any other forms.

However, this is no silver bullet. Any search for the term go is ultimately a search for the word golang and the regular English word go. So the benefit of all of this was that we got rid of search results matching on going and gone.

What you have to decide...

The case for go is similar to the case for react. Both of these words are nouns but they're also verbs.

Should people find "reacting to events" when they search for "react"? If so, use react, reactjs in the synonyms list.

Should people only find documents related to noun "React" when they search for "event handing in react"? If so, use react => reactjs in the synonyms list.

It's up to you and your documents and what your users tend to search for.

Bonus! For American vs British English

AVKO.org publishes a list of all British to American English synonyms. You can download the whole list here. Unfortunately I can't find a license for this file but the compiled synonyms file is part of this repo which is licensed under MIT.

I download this list and keep it in the repo. Then when setting up the analyzer and token filters I load it in like this:

synonyms_root = os.path.join(
    settings.BASE_DIR, 'peterbecom/es-synonyms'
)
american_british_syns_fn = os.path.join(
    synonyms_root, 'be-ae.synonyms'
)

with open(american_british_syns_fn) as f:
    for line in f:
        if (
            '=>' not in line or 
             line.strip().startswith('#')
         ):
            continue
        all_synonyms.append(line.strip())

Now I can finally enjoy not having to worry about the fact that sometimes I spell it "license" and sometimes I spell it "licence". It's all the same now. Brits and Americans, rejoice on common ground!

Bonus! For terrible spellers

Although I don't have a big problem with this on my techy blog but you can use the Simple Contraction technique to list unambiguously bad spelling. Add dont => don't to the list of synonyms and a search for dont is a search for don't.

Last but not least, the official Elasticsearch documentation is the place to go. This blog post hopefully phrases it in more approachable terms. Especially for Python peeps.

Simple or fancy UPSERT in PostgreSQL with Django

11 October 2017 6 comments   PostgreSQL, Django, Web development, Python


As of PostgreSQL 9.5 we have UPSERT support. Technically, it's ON CONFLICT, but it's basically a way to execute an UPDATE statement in case the INSERT triggers a conflict on some column value. By the way, here's a great blog post that demonstrates how to use ON CONFLICT.

In this Django app I have a model that has a field called hash which has a unique=True index on it. What I want to do is either insert a row, or if the hash is already in there, it should increment the count and the modified_at timestamp instead.

The Code(s)

Here's the basic version in "pure Django ORM":

if MissingSymbol.objects.filter(hash=hash_).exists():
    MissingSymbol.objects.filter(hash=hash_).update(
        count=F('count') + 1,
        modified_at=timezone.now()
    )
else:
    MissingSymbol.objects.create(
        hash=hash_,
        symbol=symbol,
        debugid=debugid,
        filename=filename,
        code_file=code_file or None,
        code_id=code_id or None,
    )

Here's that same code rewritten in "pure SQL":

from django.db import connection


with connection.cursor() as cursor:
    cursor.execute("""
        INSERT INTO download_missingsymbol (
            hash, symbol, debugid, filename, code_file, code_id,
            count, created_at, modified_at
        ) VALUES (
            %s, %s, %s, %s, %s, %s,
            1, CLOCK_TIMESTAMP(), CLOCK_TIMESTAMP()
          )
        ON CONFLICT (hash)
        DO UPDATE SET
            count = download_missingsymbol.count + 1,
            modified_at = CLOCK_TIMESTAMP()
        WHERE download_missingsymbol.hash = %s
        """, [
            hash_, symbol, debugid, filename,
            code_file or None, code_id or None,
            hash_
        ]
    )

Both work.

Note the use of CLOCK_TIMESTAMP() instead of NOW(). Since Django wraps all writes in transactions if you use NOW() it will be evaluated to the same value for the whole transaction, thus making unit testing really hard.

But which is fastest?

The Results

First of all, this hard to test locally because my Postgres is running locally in Docker so the network latency in talking to a network Postgres means that the latency is less and having to do two different executions would cost more if the network latency is more.

I ran a simple benchmark where it randomly picked one of the two code blocks (above) depending on a 50% chance.
The results are:

METHOD     MEAN       MEDIAN
SQL        6.99ms     6.61ms
ORM        10.28ms    9.86ms

So doing it with a block of raw SQL instead is 1.5 times faster. But this difference would surely grow when the network latency is higher.

Discussion

There's an alternative and that's to use django-postgres-extra but I'm personally hesitant. The above little raw SQL hack is the only thing I need and adding more libraries makes far-future maintenance harder.

Beyond the time optimization of being able to send only 1 SQL instruction to PostgreSQL, the biggest benefit is avoiding concurrency race conditions. From the documentation:

"ON CONFLICT DO UPDATE guarantees an atomic INSERT or UPDATE outcome; provided there is no independent error, one of those two outcomes is guaranteed, even under high concurrency. This is also known as UPSERT — "UPDATE or INSERT"."

I'm going to keep this little hack. It's not beautiful but it works and saves time and gives me more comfort around race conditions.

crontabber now supports locking, both high- and low-level

04 March 2017 0 comments   PostgreSQL, Mozilla, Python

https://github.com/mozilla/crontabber#how-locking-works


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 distinct terminals sending an UPDATE to psql. One will error.
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.

Optimization of QuerySet.get() with or without select_related

03 November 2016 1 comment   Python, Django, PostgreSQL


If you know you're going to look up a related Django ORM object from another one, Django automatically takes care of that for you.

To illustrate, imaging a mapping that looks like this:

class Artist(models.Models):
    name = models.CharField(max_length=200)
    ...

class Song(models.Models):
    artist = models.ForeignKey(Artist)
    ...

And with that in mind, suppose you do this:

>>> Song.objects.get(id=1234567).artist.name
'Frank Zappa'

Internally, what Django does is that it looks the Song object first, then it does a look up automatically on the Artist. In PostgreSQL it looks something like this:

SELECT "main_song"."id", "main_song"."artist_id", ... FROM "main_song" WHERE "main_song"."id" = 1234567
SELECT "main_artist"."id", "main_artist"."name", ... FROM "main_artist" WHERE "main_artist"."id" = 111

Pretty clear. Right.

Now if you know you're going to need to look up that related field you can ask Django to make a join before the lookup even happens. It looks like this:

>>> Song.objects.select_related('artist').get(id=1234567).artist.name
'Frank Zappa'

And the SQL needed looks like this:

SELECT "main_song"."id", ... , "main_artist"."name", ... 
FROM "main_song" INNER JOIN "main_artist" ON ("main_song"."artist_id" = "main_artist"."id") WHERE "main_song"."id" = 1234567

The question is; which is fastest?

Well, there's only one way to find out and that is to measure with some relatistic data.

Here's the benchmarking code:

def f1(id):
    try:
        return Song.objects.get(id=id).artist.name
    except Song.DoesNotExist:
        pass

def f2(id):
    try:
        return Song.objects.select_related('artist').get(id=id).artist.name
    except Song.DoesNotExist:
        pass

def _stats(r):
    #returns the median, average and standard deviation of a sequence
    tot = sum(r)
    avg = tot/len(r)
    sdsq = sum([(i-avg)**2 for i in r])
    s = list(r)
    s.sort()
    return s[len(s)//2], avg, (sdsq/(len(r)-1 or 1))**.5

times = defaultdict(list)
functions = [f1, f2]
for id in range(100000, 103000):
    for f in functions:
        t0 = time.time()
        r = f(id)
        t1 = time.time()
        if r:
            times[f.__name__].append(t1-t0)
    # Shuffle the order so that one doesn't benefit more
    # from deep internal optimizations/caching in Postgre.
    random.shuffle(functions)

for k, values in times.items():
    print(k, [round(x * 1000, 2) for x in _stats(values)])

For the record, here are the parameters of this little benchmark:

The Result

Function Median Average Std Dev
f1 3.19ms 9.17ms 19.61ms
f2 2.28ms 6.28ms 15.30ms

The Conclusion

If you use the median, using select_related is 30% faster and if you use the average, using select_related is 46% faster.

So, if you know you're going to need to do that lookup put in .select_related(relation) before every .get(id=...) in your Django code.

Deep down in PostgreSQL, the inner join is ultimately two ID-by-index lookups. And that's what the first method is too. It's likely that the reason the inner join approach is faster is simply because there's less connection overheads.

Lastly, YOUR MILEAGE WILL VARY. Every benchmark is flawed but this quite realistic because it's not trying to be optimized in either way.

Select all relations in PostgreSQL

10 December 2015 1 comment   PostgreSQL


tl;dr

Start psql with -E or --echo-hidden

I wanted to find out EVERYTHING that's related to a specific topic. Tables, views, stored procedures etc.
One way of doing that is to go into psql and type \d and/or \df and look through that list. But that's unpractical if it gets large and I might want to get it out stdout instead so I can grep and grep -v.

There are lots of Stackoverflow questions about how to SQL select all tables but I want it all. The solution is to start psql with -E or --echo-hidden. When you do that, it prints out what SQL it used to generate the output for you there. You can then copy that and do whatever you want to do with it. For example:

peterbecom=# \d
********* QUERY **********
SELECT n.nspname as "Schema",
  c.relname as "Name",
  CASE c.relkind WHEN 'r' THEN 'table' WHEN 'v' THEN 'view' WHEN 'm' THEN 'materialized view' WHEN 'i' THEN 'index' WHEN 'S' THEN 'sequence' WHEN 's' THEN 'special' WHEN 'f' THEN 'foreign table' END as "Type",
  pg_catalog.pg_get_userbyid(c.relowner) as "Owner"
FROM pg_catalog.pg_class c
     LEFT JOIN pg_catalog.pg_namespace n ON n.oid = c.relnamespace
WHERE c.relkind IN ('r','v','m','S','f','')
      AND n.nspname <> 'pg_catalog'
      AND n.nspname <> 'information_schema'
      AND n.nspname !~ '^pg_toast'
  AND pg_catalog.pg_table_is_visible(c.oid)
ORDER BY 1,2;
**************************

With this I was able to come up with this SQL select to get all tables, views, sequences and functions.

SELECT
  c.relname as "Name",
  CASE c.relkind WHEN 'r' THEN 'table'
  WHEN 'v' THEN 'view'
  WHEN 'm' THEN 'materialized view'
  WHEN 'i' THEN 'index'
  WHEN 'S' THEN 'sequence'
  WHEN 's' THEN 'special'
  WHEN 'f' THEN 'foreign table' END as "Type"
FROM pg_catalog.pg_class c
     LEFT JOIN pg_catalog.pg_namespace n ON n.oid = c.relnamespace
WHERE c.relkind IN ('r','v','m','S','f','')
      AND n.nspname <> 'pg_catalog'
      AND n.nspname <> 'information_schema'
      AND n.nspname !~ '^pg_toast'
  AND pg_catalog.pg_table_is_visible(c.oid);


SELECT
  p.proname as "Name",
  'function'
FROM pg_catalog.pg_proc p
     LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace
WHERE pg_catalog.pg_function_is_visible(p.oid)
      AND n.nspname <> 'pg_catalog'
      AND n.nspname <> 'information_schema';

A usecase of this is that I put those two SQL selects in a file and now I can grep:

$ psql mydatabase < everything.sql | grep -i crap

Weight of your PostgreSQL tables "lumped together"

31 October 2015 0 comments   PostgreSQL


We have lots of tables that weigh a lot. Some of the tables are partitions so they're called "mytable_20150901" and "mytable_20151001" etc.

To find out how much each table weighs you can use this query:

select table_name, pg_relation_size(table_name), pg_size_pretty(pg_relation_size(table_name))
from information_schema.tables
where table_schema = 'public'
order by 2 desc limit 10;

It'll give you an output like this:

        table_name        | pg_relation_size | pg_size_pretty
--------------------------+------------------+----------------
 raw_adi_logs             |      14724538368 | 14 GB
 raw_adi                  |      14691426304 | 14 GB
 tcbs                     |       7173865472 | 6842 MB
 exploitability_reports   |       6512738304 | 6211 MB
 reports_duplicates       |       4428742656 | 4224 MB
 addresses                |       4120412160 | 3930 MB
 missing_symbols_20150601 |       3264897024 | 3114 MB
 missing_symbols_20150608 |       3170762752 | 3024 MB
 missing_symbols_20150622 |       3039731712 | 2899 MB
 missing_symbols_20150615 |       2967281664 | 2830 MB
(10 rows)

But as you can see in this example, it might be interesting to know what the sum is of all the missing_symbols_* partitions.

Without further ado, here's how you do that:

select table_name, total, pg_size_pretty(total)
from (
  select trim(trailing '_0123456789' from table_name) as table_name, 
  sum(pg_relation_size(table_name)) as total
  from information_schema.tables
  where table_schema = 'public'
  group by 1
) as agg
order by 2 desc limit 10;

Then you'll get possibly very different results:

        table_name        |    total     | pg_size_pretty
--------------------------+--------------+----------------
 reports_user_info        | 157111115776 | 146 GB
 reports_clean            | 106995695616 | 100 GB
 reports                  | 100983242752 | 94 GB
 missing_symbols          |  42231529472 | 39 GB
 raw_adi_logs             |  14724538368 | 14 GB
 raw_adi                  |  14691426304 | 14 GB
 extensions               |  12237242368 | 11 GB
 tcbs                     |   7173865472 | 6842 MB
 exploitability_reports   |   6512738304 | 6211 MB
 signature_summary_uptime |   6027468800 | 5748 MB
(10 rows)

You can read more about the trim() function here.