Performance testing Adjancency List recursive queries

Yesterday, I wrote up some ideas about doing recursive queries on Adjacency Lists using Postgres. Today, I wrote up some code that allows me to run some tests on larger data sets. It’s worth noting that this is still somewhat “toy” data, but I did see comparable results with a real query.

Firstly, our data structure:

CREATE TABLE node (
  node_id SERIAL PRIMARY KEY,
  parent_id INTEGER,
  FOREIGN KEY (parent_id) REFERENCES node(node_id)
);

Now, we want to be able to populate it with test data. This function will allow you to populate any number of records, with a 10% chance that any given record will be a root (have no parent). If it has a parent, it will be randomly selected from all existing rows. This means that earlier rows have a much higher chance of being a parent, and the first row is overwhemingly likely to have the most descendants (as it has a 90% chance that row 2 will have it as a parent, and therefore any descendants of that will also be descendants of row 1…)

CREATE OR REPLACE FUNCTION populate_nodes(count integer) RETURNS void AS $$
BEGIN
  FOR i IN 2..count LOOP
    IF ((SELECT count(*) FROM node) = 0) or (random() < 0.1) THEN
      INSERT INTO node (parent_id) SELECT NULL;
    ELSE
      INSERT INTO node (parent_id) SELECT node_id FROM node OFFSET random() * (SELECT count(*) FROM node) LIMIT 1;
    END IF;
  END LOOP;
END;
$$ LANGUAGE plpgsql;

-- Let's stick 10k records in there
SELECT populate_nodes(10000);

For now, we want to find all descendants of node 1.

I can think of eleven ways we could write this query:

  1. INNER JOIN with a RECURSIVE VIEW
  2. Implicit CROSS JOIN with RECURSIVE VIEW, filtered using a WHERE clause
  3. Sub-query with a RECURSIVE VIEW
  4. INNER JOIN with a MATERIALIZED VIEW based on the RECURSIVE VIEW
  5. Implicit CROSS JOIN (filtered) with MATERIALIZED VIEW based on RECURSIVE VIEW
  6. Sub-query with MATERIALIZED VIEW based on RECURSIVE VIEW
  7. RECURSIVE CTE, using an INNER JOIN
  8. RECURSIVE CTE, using an implicit CROSS JOIN (filtered)
  9. INNER JOIN with RECURSIVE CTE
  10. Implicit CROSS JOIN (filtered) with RECURSIVE CTE
  11. Subquery that is a RECURSIVE CTE

(Whilst some of these seem similar, we’ll see below how they differ).

In all cases, the actual query used for the tree will be:

SELECT node_id, ARRAY[]::integer[], FALSE FROM node WHERE parent_id IS NULL
UNION ALL
SELECT n.node_id, t.ancestors || n.parent_id, n.parent_id = ANY(t.ancestors)
FROM node n, node_tree t WHERE n.parent_id = t.node_id AND NOT cycle

It is extremely likely that the MATERIALIZED VIEW versions will be fastest: it is worth noting that in a very write-heavy environment (where you still need 100% up-to-date data), there would be an extra cost with the REFRESH MATERIALIZED VIEW.

As for which other ones will be fast (or fast enough), I would expect the CTE and VIEW versions to be roughly equivalent, as they appear to do the same amount of work. I’m not sure if the last three will perform as well as the others, as it seems that a “root” CTE would perform better than one later down the track.

So, let’s get underway. I wasn’t able to easily use the benchmark function I wanted to use, so I repeated each query five times and took the average.

We need our views:

CREATE RECURSIVE VIEW node_tree (node_id, ancestors, cycle) AS (
  SELECT node_id, ARRAY[]::integer[], FALSE FROM node WHERE parent_id IS NULL
  UNION ALL
  SELECT n.node_id, t.ancestors || n.parent_id, n.parent_id = ANY(t.ancestors)
  FROM node n, node_tree t WHERE n.parent_id = t.node_id AND NOT cycle
);

CREATE MATERIALIZED VIEW node_tree_mat AS (SELECT * FROM node_tree);

Results

So, some results. I’ll show the query, and then the timing (from EXPLAIN ANALYZE).

#1

SELECT * FROM node INNER JOIN node_tree USING (node_id) WHERE 1 = ANY(ancestors);

Average Time: 54.9ms (stddev 1.99)

#2

SELECT * FROM node n, node_tree t WHERE n.node_id = t.node_id AND 1 = ANY(ancestors);

Average Time: 57.2ms (stddev 2.98)

#3

SELECT * FROM node WHERE node_id IN
  (SELECT node_id FROM node_tree WHERE 1 = ANY(ancestors));

Average Time: 58.5ms (stddev 3.67)

#4

SELECT * FROM node INNER JOIN node_tree_mat USING (node_id) WHERE 1 = ANY(ancestors);

Average Time: 12.2ms (stddev 0.80)

#5

SELECT * FROM node n, node_tree_mat t WHERE n.node_id = t.node_id AND 1 = ANY(ancestors);

Average Time: 11.7ms (stddev 0.90)

This is the fastest query, but not significantly more so than #4.

#6

SELECT * FROM node WHERE node_id IN
  (SELECT node_id FROM node_tree_mat WHERE 1 = ANY(ancestors));

Average Time: 24.0ms (stddev 0.41)

Interestingly, this is much slower than using a JOIN.

#7

WITH RECURSIVE node_tree_cte(node_id, ancestors, cycle) AS (
    SELECT node_id, ARRAY[]::integer[], FALSE FROM node WHERE parent_id IS NULL
  UNION ALL
    SELECT n.node_id, t.ancestors || n.parent_id, n.parent_id = ANY(t.ancestors)
    FROM node n, node_tree t WHERE n.parent_id = t.node_id AND NOT cycle
) SELECT n.* FROM node n INNER JOIN node_tree_cte t USING (node_id)
WHERE 1 = ANY(t.ancestors);

Average Time: 97.0ms (stddev 1.87)

Immediately, we see that using a CTE has a performance hit over using a VIEW. Unexpected.

#8

WITH RECURSIVE node_tree_cte(node_id, ancestors, cycle) AS (
    SELECT node_id, ARRAY[]::integer[], FALSE FROM node WHERE parent_id IS NULL
  UNION ALL
    SELECT n.node_id, t.ancestors || n.parent_id, n.parent_id = ANY(t.ancestors)
    FROM node n, node_tree t WHERE n.parent_id = t.node_id AND NOT cycle
) SELECT n.* FROM node n, node_tree_cte t
WHERE n.node_id = t.node_id AND 1 = ANY(t.ancestors);

Average Time: 96.1ms (stddev 1.16)

#9

SELECT * FROM node INNER JOIN (
  WITH RECURSIVE node_tree_cte(node_id, ancestors, cycle) AS (
    SELECT node_id, ARRAY[]::integer[], FALSE FROM node WHERE parent_id IS NULL
    UNION ALL
    SELECT n.node_id, t.ancestors || n.parent_id, n.parent_id = ANY(t.ancestors)
    FROM node n, node_tree t WHERE n.parent_id = t.node_id AND NOT cycle
  ) SELECT node_id FROM node_tree_cte WHERE 1 = ANY(ancestors)
) node_tree_cte USING (node_id);

Average Time: 114.3ms (stddev 4.64)

This is the slowest (but again, only just, and not significantly more than #10).

#10

SELECT * FROM node, (
  WITH RECURSIVE node_tree_cte(node_id, ancestors, cycle) AS (
    SELECT node_id, ARRAY[]::integer[], FALSE FROM node WHERE parent_id IS NULL
    UNION ALL
    SELECT n.node_id, t.ancestors || n.parent_id, n.parent_id = ANY(t.ancestors)
    FROM node n, node_tree t WHERE n.parent_id = t.node_id AND NOT cycle
  ) SELECT node_id FROM node_tree_cte WHERE 1 = ANY(ancestors)
) node_tree_cte WHERE node.node_id = node_tree_cte.node_id;

Average Time: 110.0ms (stddev 1.05)

#11

SELECT * FROM node WHERE node_id IN (
  WITH RECURSIVE node_tree_cte(node_id, ancestors, cycle) AS (
    SELECT node_id, ARRAY[]::integer[], FALSE FROM node WHERE parent_id IS NULL
    UNION ALL
    SELECT n.node_id, t.ancestors || n.parent_id, n.parent_id = ANY(t.ancestors)
    FROM node n, node_tree t WHERE n.parent_id = t.node_id AND NOT cycle
  ) SELECT node_id FROM node_tree_cte WHERE 1 = ANY(ancestors)
);

Average Time: 96.1ms (stddev 5.50)

Discussion

So, it appears that Common Table Expressions are nearly twice as slow as using a RECURSIVE VIEW. I didn’t expect that at all, as I thought they were equivalent. Unsurprisingly, MATERIALIZED VIEW is much faster.

This has some implications for the stuff I was working on: I was using a query of the #11 form (a sub-query that is a WITH RECURSIVE statement), which, as it turns out is just as fast as doing a “root” CTE. However, it’s still far slower than doing a JOIN with a VIEW.

The problem I have now is that there is no way to have the addition of a Field to a Model in django to cause an extra migration operation to be added. One solution would be to manually add a RunSQL operation, but that is messy. I’ll also have to investigate costs of REFRESH MATERIALIZED VIEW.

Generating dummy ABNs in Python

I had the need to generate a fake ABN the other day.

Here’s a python function to do it:

#! /usr/bin/python
import sys
import random

weighting = [10, 1, 3, 5, 7, 9, 11, 13, 15, 17, 19]

def validate(abn):
    """
    Validate that the provided number is indeed an ABN.
    """
    values = map(int, list(abn))
    values[0] -= 1
    total = sum([x * w for (x, w) in zip(values, weighting)])
    return total % 89 == 0

def abn():
    """
    Generate a random ABN
    """
    value = ''.join([str(int(random.random() * 10)) for i in range(9)])
    temp = list('00%s' % value)
    total = sum([w * x for (w,x) in zip(weighting, map(int, temp))])
    remainder = total % 89
    prefix = 10 + (89 - remainder)
    abn = '%s%s' % (prefix, value)
    assert validate(abn), '%s is not a valid ABN' % abn
    return abn

sys.stdout.write(abn())

For extra goodness, assign it to :abn in TextExpander.

It’s just a shame Xero made their ABN field correctly, and it prevents you typing non-digits into it.

Long Live Adjacency Lists

I recently wrote about the excellent book SQL Antipatterns, and in it briefly discussed the tree structures. I’ve been thinking about trees in Postgres a fair bit lately, and a discussion on #django gave me further incentive to revisit this topic.

The book discusses four methods of storing a tree in a database.

Adjacency Lists, apart from the inability to grab a full or partial tree easily, are the simplest to understand. The child object stores a reference to it’s parent. Because this is a foreign key, then it always maintains referential integrity. Fetching a parent is simple, as is fetching all children, or siblings. It’s only when you need to fetch an arbitrary depth that things become problematic, unless you use a recursive query. More on that later.

Postgres has an extension called ltree, which provides an implementation of a Path Enumeration, but one thing that really bothers me about this type of structure is the lack of referential integrity. In practice, I’m not sure what having this ltree structure would give you over simply storing the keys in an ARRAY type. Indeed, if Postgres ever gets Foreign Key constraints for ARRAY elements (which there is a patch floating around for), this becomes even more compelling. It also seems to me that restructuring a tree becomes a bit more challenging in a Path Enumeration than an Adjacency List.

Nested Sets are also interesting, and maintain FK integrity, but require potentially rewriting lots of data when any change is made to the tree. They aren’t that appealing to me: perhaps I fail to see any big advantages of this structure.

Finally, Closure Tables are perhaps the most interesting. This stores all ancestor-descendant relationships, rather than just parent-child, which again requires more work when adding or removing. Again, Referential Integrity is preserved, but it seems like there is lots of work to maintain them.

From all of these, there are some significant advantages, in my mind, to using a simple Adjacency List.

  1. Adding a new row never requires you to alter any other rows in the database.
  2. Moving a subtree to a different location only requires a change to one now in the database.
  3. It’s never possible to end up with Referential Integrity errors: the database will prevent you from deleting a parent row whilst it still has children (or, you may set it to CASCADE or SET NULL the children automatically).
  4. It’s conceptually very simple. Everyone understands the parent-child relationship (and all of the relationships that follow, like grand-parents). It’s a similar mental model to how we think about our own families, except we don’t have exactly one parent.

There is really only two things that are hard to do:

  1. Given a node, select all descendants of that node.
  2. Given a node, select all ancestors of that node.

But, as we shall see shortly, it is possible to do these in Postgres using some nice recursive features.

There is another advantage to using an Adjacency List, this time from the perspective of Django. We can do it without needing to install a new package, or subclass or mix-in a new Model:

class Node(models.Model):
    node_id = models.AutoField(primary_key=True)
    parent = models.ForeignKey('self', null=True, blank=True, related_name='children')

That’s it.

Now, using Postgres, it’s possible to build a recursive VIEW that contains the whole tree:

CREATE RECURSIVE VIEW tree (node_id, ancestors) AS (
    SELECT node_id, '{}'::integer[]
    FROM nodes WHERE parent_id IS NULL
  UNION ALL
    SELECT n.node_id, t.ancestors || n.parent_id
    FROM nodes n, tree t
    WHERE n.parent_id = t.node_id
);

We can then query this (replacing %s with the parent node id):

SELECT node_id
FROM nodes INNER JOIN tree USING (node_id)
WHERE %s = ANY(ancestors);

Or, if you want to select for multiple parents:

SELECT node_id
FROM nodes INNER JOIN tree USING (node_id)
WHERE [%s, %s] && ancestors;

This actually performs relatively well, and, if it doesn’t do well enough, we could create a MATERIALIZED VIEW based on the recursive view, and query that instead (refreshing it whenever we need to, perhaps using a trigger).

CREATE MATERIALIZED VIEW tree_m AS (SELECT * FROM tree);

CREATE FUNCTION refresh_tree_m() RETURNS trigger AS $$
  BEGIN
  REFRESH MATERIALIZED VIEW tree_m;
  END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER trig_refresh_tree_m AFTER TRUNCATE OR INSERT OR UPDATE OR DELETE
ON nodes FOR EACH STATEMENT
EXECUTE PROCEDURE refresh_tree_m();

This view is still not perfect though. We can improve it to allow us to limit depth of ancestry:

CREATE RECURSIVE VIEW tree (node_id, ancestors, depth) AS (
    SELECT node_id, '{}'::integer[], 0
    FROM nodes WHERE parent_id IS NULL
  UNION ALL
    SELECT n.node_id, t.ancestors || n.parent_id, t.depth + 1
    FROM nodes n, tree t
    WHERE n.parent_id = t.node_id
);

SELECT node_id FROM nodes INNER JOIN tree USING (node_id)
WHERE %s = ANY(ancestors) AND depth < %s;

This is pretty good now, but if we have cycles in our tree (yes, this makes it technically no longer a tree, but a graph, of which a tree is a restricted kind), this query will run forever. There’s a pretty neat trick to prevent cycles:

CREATE RECURSIVE VIEW tree (node_id, ancestors, depth, cycle) AS (
    SELECT node_id, '{}'::integer[], 0, FALSE
    FROM nodes WHERE parent_id IS NULL
  UNION ALL
    SELECT
      n.node_id, t.ancestors || n.parent_id, t.depth + 1,
      n.parent_id = ANY(t.ancestors)
    FROM nodes n, tree t
    WHERE n.parent_id = t.node_id
    AND NOT t.cycle
);

You don’t need to use the cycle column outside of the view.

The query used for the view can be repurposed into a Common Table Expression, which is basically a way of defining a view that only exists for the query we are executing (but will itself only be executed once, even if it’s referred to lots of times):

WITH RECURSIVE tree (node_id, ancestors, depth, cycle) AS (
    SELECT node_id, '{}'::integer[], 0, FALSE
    FROM nodes WHERE parent_id IS NULL
  UNION ALL
    SELECT
      n.node_id, t.ancestors || n.parent_id, t.depth + 1,
      n.parent_id = ANY(t.ancestors)
    FROM nodes n, tree t
    WHERE n.parent_id = t.node_id
    AND NOT t.cycle
) SELECT n.* FROM nodes n INNER JOIN tree USING (node_id)
WHERE %s = ANY(ancestors);

You can see that this syntax basically defines the view before running the real query.


Looking at it from the perspective of Django, we would like to be able to spell a query something like:

Node.objects.filter(parent__recursive=node)
Node.objects.filter(parent__recursive__in=nodes)
Node.objects.filter(children__recursive__contains=node)

The problem we have with using the CTE immediately above is that we don’t have access to the full query at the time we are dealing with the filter. We could define the view prior to running the query (perhaps in a migration), but this means it’s more than just a simple field: although with the new migrations framework, we could make it so that makemigrations automatically adds a migration operation to create the recursive view.

The other solution is to still use a recursive CTE, but use it as a subquery. I’m still investigating if this will have poor performance characteristics.

Here is an implementation of doing just that:

from django.db import models

SQL = """
WITH RECURSIVE "tree" ("{pk}", "related", "cycle") AS (
    SELECT "{pk}", ARRAY[]::integer[], FALSE
    FROM "{table}" WHERE "{fk}" IS NULL
  UNION ALL
    SELECT a."{pk}", b."related" || a."{fk}", a."{fk}" = ANY(b."related")
    FROM "tree" b, "{table}" a
    WHERE a."{fk}" = b."{pk}" AND NOT b."cycle"
) {query}
"""


class RecursiveRelation(models.ForeignKey):
    def __init__(self, *args, **kwargs):
        super(RecursiveRelation, self).__init__('self', *args, **kwargs)

    def get_lookup_constraint(self, constraint_class, alias, targets, sources, lookups,
                              raw_value):
        if lookups[0] == 'recursive':
            # With a recursive query, we want to build up a subquery that creates
            # the simplest possible tree we can deal with.
            data = {
                'fk': self.get_attname(),
                'pk': self.related_fields[0][1].get_attname(),
                'table': self.model._meta.db_table
            }
            if lookups[-1] == 'in':
                if targets[0] == self:
                    raw_value = ForeignKeyRecursiveInLookup(raw_value, **data)
                else:
                    raw_value = ForeignKeyRecursiveReverseInLookup(raw_value, **data)
            else:
                if targets[0] == self:
                    raw_value = ForeignKeyRecursiveLookup(raw_value, **data)
                else:
                    raw_value = ForeignKeyRecursiveReverseLookup(raw_value, **data)

            # Rewrite some variables so we get correct behaviour.

            # This makes the query based on the original table, not the joined version,
            # which was skipping a level of relation. It still joins the table, however,
            # which can't be great for performance
            alias = self.model._meta.db_table
            # This sets the correct lookup type, removing the recursive bit.
            lookups = lookups[1:] or ['exact']

        return super(RecursiveRelation, self).get_lookup_constraint(
            constraint_class, alias, targets, sources, lookups, raw_value
        )


class ForeignKeyRecursiveLookup(object):
    query = 'SELECT "{pk}" FROM "tree" WHERE %s = ANY("related")'

    def __init__(self, value, **kwargs):
        self.value = value
        self.data = kwargs

    def get_compiler(self, *args, **kwargs):
        return self

    def as_subquery_condition(self, alias, columns, qn):
        sql = SQL.format(
            query=self.query.format(**self.data),
            **self.data
        )
        return '%s.%s IN (%s)' % (qn(alias), qn(self.data['pk']), sql), [self.value]


class ForeignKeyRecursiveInLookup(ForeignKeyRecursiveLookup):
    query = 'SELECT "{pk}" FROM "tree" WHERE %s && "related"'


class ForeignKeyRecursiveReverseLookup(ForeignKeyRecursiveLookup):
    query = 'SELECT unnest("related") FROM "tree" WHERE "{pk}" = %s'


class ForeignKeyRecursiveReverseInLookup(ForeignKeyRecursiveLookup):
    query = 'SELECT unnest("related") FROM "tree" WHERE "{pk}" IN %s'

If we were to use an existing view (created using a migration), then the structure would be largely the same: simply the SQL constant would be simpler:

SQL = 'SELECT {pk} FROM "{table}_{fk}_tree" WHERE {where}'

But then we would need some sort of name mangling for the view: I’ve suggested <tablename>_<fk-name>-tree.

I went into this exercise thinking it would be simple: just write a Lookup (or Transform), but it seems that Foreign Keys in django have a fair bit of special casing. There’s also a bit of lax code around the names of lookups: I may polish it up at some stage.

For now, though, you use it as:

class Node(models.Model):
    node_id = models.AutoField(primary_key=True)
    parent = RecursiveRelation(null=True, blank=True, related_name='children')

Postgres VIEW meet Django Model

Postgres VIEWs are a nice way to store a subset of a table in a way that can itself be queried, or perhaps slightly or radically changing the shape of your table. It has a fairly simple syntax:

CREATE VIEW "foo" AS
SELECT "bar", "baz", "qux"
FROM "corge"
WHERE "grault" IS NULL;

You may use any valid SELECT query as the source of a VIEW, including one that contains UNION or UNION ALL. You can use this form to create a view that takes two similarly formatted tables and combines them into one logical table. Note that for a UNION to work, the columns (and column types) must be identical between the two parts of the query. A UNION will do extra work to ensure all rows are unique: UNION ALL may perform better, especially if you know your rows will be unique (or you need duplicates).

By default, a Postgres VIEW is dynamic, and read-only. With the use of the CREATE MATERIALIZED VIEW form, it’s possible to have a cached copy stored on disk, which requires an UPDATE MATERIALIZED VIEW "viewname" in order to cause an update.

It’s also possible to create a writeable VIEW, but I’m not going to discuss those now.


There is a feature of Django that makes in really simple to use a VIEW as the read-only source of a Django Model subclass: managed = False.

Given the VIEW defined above, we can write a Model that will allow us to query it:

from django.db import models

class Foo(models.Model):
    bar = models.CharField()
    baz = models.CharField()
    qux = models.CharField()

    class Meta:
      managed = False

Psycopg2 also has the ability to automatically convert values as it fetches them, so you don’t even really need to set the fields as the correct type: but you will probably want to where possible, as an aid to code readability.

In my case, I was returning a two-dimensional ARRAY of TIMESTAMPTZ, but didn’t want to have to include the full code for an ArrayField. So, I just defined it as a CharField, and psycopg2 just gave me the type of object I actually wanted anyway.

There is one little catch, and the code above will not quite work.

Django requires a primary key, even though in this case it makes no sense. You could define any field as a primary key, include a relevant key field from the parent model, or even a dummy value that is the same on every row. Relying on the same psycopg2 trick as above, you could use <tablename>-<id> so as to ensure uniqueness, even though that is not normally a valid value for a Django AutoField.

You probably need to be a little careful here, as if you are doing comparisons, Django will test __class__ and pk for testing equality, so you could hurt yourself if you aren’t careful.

You may also want to prevent write access at the Django level. Overriding save() and delete() on the Model class would be a good start, as well as writing a custom Manager/QuerySet that does the same. You could raise an exception that makes sense, like NotImplemented, or just leave it as a database error.

Running more

Back in April, my second son was born. Prior to that, I was reliably running 2-4 hours per week, and clocking up 25-40km. Then, in addition to being really busy (as one is with a newborn), I had a patch of illness, which was followed a couple of months later by another bout. That second one stopped me exercising for pretty much all of July, and nearly half of August.

I’ve recently started to ramp up my running again: the one race I have ever competed in is in mid-September, and whilst I’m not in as good shape as I was when I did my PR, I’m hoping to do at least as well as I did last year. It looks better so far: last year I was only managing to run on Monday nights, in addition to Touch Football on Thursday (and Mel’s arse-kicking on Wednesdays). I’ve squeezed in a few more runs so far, and, whilst I’m still running a bit slow (hardly skipped below 5:00/km), I’m feeling fairly comfortable.

In reality, I’ll be happy if I come in under an hour in the race. If I manage to break 55:00, or even better, I’ll be stoked. My best time (48:52) is well beyond me, but maybe next year…

Avoiding SQL Antipatterns using Django (and Postgres)

The book SQL Antipatterns is one of my favourite books. I took the opportunity to reread it on a trip to Xerocon in Sydney, and as usual it enlightened me to thing I am probably doing in my database interactions.

So, I’m going to look at these Antipatterns, and discuss how you can avoid them when using Django. This post is intended to be read with each chapter of the book. I’ve used the section headings, but instead of the chapter headings, I’ve used the Antipattern headings. They are still in the same order, though.

It seems the printed version of this book is on sale now: I’m tempted to buy a few extra copies for gifts. Ahem, cow-orkers.

Logical Database Design Antipatterns

Format Comma-Separated Lists

This one is pretty simple: use a relation instead of a Comma Separated field. In the cases described in the book, a ManyToManyField is in fact simpler than a Comma Separated field. Django gets a gold star here, both in ease of use, but also in documentation about relations.

However, there may be times when a relation is overkill, and a real array is better. For instance, when storing data related to which days of the week are affected by a certain condition, it may make sense to store it in this way.

But we can do better than a simple Comma Separated field. Storing the data in a Postgres Array means we can rely on the database to validate the data, and allows searching. Similarly, we could store it in JSON, too.

I’ve maintained a JSONField for Django, although it’s not easily queryable. However, an ArrayField is coming in Django 1.8. There are alternatives already available if you need to use one now. I’ve got a project to mostly backport the django.contrib.postgres features to 1.7: django-postgres.

Things like JSON, Array and Hstore are a better solution than storing other-delimitered values in a straight text column too. With Django 1.7, it became possible to have lookups, which can leverage the DBMS' ability to query these datatypes.

Always Depend on One’s Parent

Read chapter online.

Straight into a trickier one! And, Django’s documentation points out how to create his type of relation, but does not call out the possible issues. This book is worth it for this section alone.

So, how do we deal with trees in Django?

We can use django-mptt. This gives us (from what I can see) the “Nested Sets” pattern outlined in the book, but under the name “Modified Preorder Tree Traversal”.

I’m quite interested in the idea of using a Closure Table, and there are a couple of projects with quite different approaches to this:

  • django-ctt: uses a Model class you inherit from.
  • django-ct: better documented, but uses an unusual pattern of a pseudo-manager-thing.

Knowing me, I’m probably going to spend some time building a not-complete implementation at some point.

Update: Whilst I haven’t built an implementation of a Closure Table, I did implement recursive queries for an Adjacency List.

One Size Fits All

Using a field id for all tables by default is probably one of the biggest mistakes I think Django makes. And, as we shall see, we can’t yet avoid them, for at least a subset of situations.

Indeed, Django can use any single column for the primary key, and doesn’t require the use of a key column of name id. So, in my mind, it would have been better to use the <tablename>_id, as suggested in the book. Especially since you may also access the primary key attribute using the pk shortcut.

class Foo(models.Model):
    foo_id = models.AutoField(primary_key=True)

However, it’s not currently possible to do composite primary keys (but may be soon), which makes doing the best thing for a plain ManyToManyField possible: indeed, you don’t control that table anyway, and if you remove the id column (and create a proper primary key), things don’t work. In practice, you can just ignore this issue, since you (mostly) don’t deal with this table, or the objects from it.

So, assuming we are changing the id column into the name suggested in the book, what does that give us?

Nothing, until we actually need to write raw SQL code, and specifically code that joins multiple tables.

Then, we are able to use a slightly less verbose way of defining the join, and not worry about duplicate columns named id:

SELECT * FROM foo_foo JOIN foo_bar USING (foo_id);

I’m still not sure if it’s actually worthwhile doing this or not. I’m going to start doing it, just to see whether there are any drawbacks (already found one in some of my own code, that hard-coded an id field), or any great benefits.

Leave out the Constraints

Within Django, it’s more work to create relations without the relevant constraints, and it’s not possible to create a table without a primary key, so we can just pass this one by with a big:

smile and wave, boys

Use a Generic Attribute Table

Again, it’s possible to create this type of a monstrosity in Django, but not easy. A better solution, if your table’s requirements change is to use migrations (included in Django 1.7), or a more flexible store, like JSON or Hstore. This also has the added advantage of being a column, rather than a related table, which means you can fetch it in one go, simply. Similarly, with Postgres 9.3, you can do all sorts of querying, and even more in 9.4.

Document or key stores are no substitute for proper attributes, but they do have their uses.

The other solution is to use Model inheritance, which Django does well. You can choose either abstract or concrete table inheritance, and with something like django-model-utils, even get some nice features like fetching only the subtypes when fetching a queryset of superclass models.

Use Dual-Purpose Foreign Key

Unfortunately, Django comes with a built-in way to do this: so-called Generic Relations.

Using this, it’s possible to have an association from a given model instance to any other object of any other model class.

“You may find that this antipattern is unavoidable if you use an object-relational programming framework […]. Such a framework may mitigate the risks introduced by Polymorphic Associations by encapsulating application logic to maintain referential integrity. If you choose a mature and reputable framework, then you have some confidence that its designers have written the code to implement the association without error.”

I guess we’ll just have to rely on the fact Django is a mature and reputable framework.

In all reality, I’ve used this type of relation once: for notifications that need to be able to refer to any given object. It’s also possible to use, say, a tagging app that had generic relations. But, I’m struggling to think of too many situations where it would be better than a proper relation.

I’ve also come across it in django-reversion, and running queries against objects from it is a pain in the arse.

Create Multiple Columns

Interestingly, the example for this Antipattern is the example I just used above: tags. And, this type of situation should be done in a better way: a proper relation, or perhaps an Array type. It all depends how good your database is at querying arrays. django.contrib.postgres makes this rather easy:

class Post(models.Model):
    name = models.CharField(...)
    tags = ArrayField(models.CharField(...), blank=True)

Post.objects.filter(tags__contains=['foo'])

What may not be so easy is getting all of the tags in use. This may be possible: I just haven’t thought of a way to do this yet. A nice syntax might be:

Post.objects.aggregate(All('tags'))

The SQL you might be able to use to get this could look like:

SELECT
  array_agg(distinct t) AS tags
FROM (
  SELECT unnest(tags) FROM posts
) t;

I’m not sure if there’s a better way to get this data.

Clone Tables or Columns

I can’t actually see that doing this in Django would be easy, or likely. It’s gotten me interested in some method of seamlessly doing Horizontal Partitioning as a method of archiving old data, and perhaps moving it to a different database. Specifically, moving old audit data into a separate store may become necessary at some point.

Partitioning using a multi-tenancy approach using Postgres' schemata is another of my interests, and I’ve been working on a django-specific way to do this: django-boardinghouse. Note, this is a partial-segmentation approach, where some tables are shared, but others are per-schema.

Physical Database Design Antipatterns

Use FLOAT Data Type

Just don’t.

There’s a DecimalField, and no reason not to use it.

Specify Values in the Column Definition

The example the book uses is to define check constraints on a given question. Django’s approach is a bit different: the valid choices are defined in the column definition, but can be changed in code at any time. Any existing values that are no longer valid are fine, but any attempt to save an object will require it to have one of the newly valid choices.

This is both better and worse than the problem described in the book. There’s no way (short of a migration) to change the existing data, but maybe that’s actually just better.

Again, the best solution is just to use a related field, but in some cases this is indeed overkill: specifically if values are unlikely to change.

Assume You Must Use Files

I’m still 50-50 on this one. Basically, storing binary files in your database (a) makes the database much bigger, which means it takes longer to back it up (and restore it), and (b) means that it’s harder to do things like use the web server, rather than the application server, to serve static files (even those user-supplied, that must be authenticated).

The main disadvantage, of not having backups, is purely an operations issue.

The secondary disadvantage: the lack of transactionality is also easily solved: don’t delete files (unless necessary), and don’t overwrite them. If you really must, then use a Postres NOTIFY delete-file <filepath> or similar, and have a listener that handles that.

The other disadvantage, about SQL privilidges is mostly moot under Django anyway, as you are always running as the one database user.

Using Indexes Without a Plan

Indexes are fairly tangiential to an ORM: I’m going to pass over this one without too much comment. I’ve been doing a fair bit of index-level optimisations on my production database lately, in an effort to improve performance. Mostly, it’s better to optimise the query, as the likely targets for indexes probably already have them.

Query Antipatterns

Use Null as an Ordinary Value, or Vice Versa.

Python has it’s own None type/value, and using it in queries basically converts it into NULL. Django is a little annoying how at times it stores empty strings instead of NULL in string fields. I was playing around with making these into proper NULLs, but it seemed to create other problems.

At least there is no established pattern to use other values instead of NULL.

Reference Non-grouped Columns

Since I’m dealing with Postgres, I understand this one is not much of an issue. Your query will fail if you build it wrong. Which should be the way databases work.

Sort Data Randomly

Read this chapter online.

The problem of how to fetch a single random instance from a Model comes up every now and then on IRC, indeed, it did again last weekend. Unsurprisingly, I provided a link to this chapter.

One solution that is presented in the book is to select a single row, using a random offset:

import random
# Note: the initial version of this would fail since queryset.count()
# is the number of elements, randint(a, b) includes the value 'b',
# and queryset[b] would be out of range.
index = random.randint(0, queryset.count() - 1)
instance = queryset.all()[index]

This, converts to the query:

SELECT * FROM "table" LIMIT 1 OFFSET %s;

However, without an ordering, I believe this will still do a complete table seek. Instead, you want to order on a column with an index. Like the primary key:

instance = queryset.order_by('pk')[index]

It does take two queries, but sometimes two queries is better than one. Obviously, if your table was always going to be small, it may be better to do the random ordering:

instance = queryset.order_by('?')[0]

Pattern Matching Predicates

I’m sorry to say Django makes it far too easy to do this:

queryset.filter(foo__contains='bar')

Becomes something like:

SELECT * FROM "table" WHERE "table"."foo" LIKE '%bar%';

In many cases, this will be fine, but as you can imagine, you may get surprising matches, or performance may really suck.

Using Postgres’s full-text search is relatively simple: you can quite easily make a custom field that handles this, and with Django 1.7 or later, you can even create your own lookups:

from django.db import models


class TSVectorField(models.Field):
    def db_type(self, connection):
        return 'tsvector'


class TSVectorMatches(models.lookups.BuiltinLookup):
    lookup_name = 'matches'
    def process_lhs(self, qn, connection, lhs=None):
        lhs = lhs or self.lhs
        return qn.compile(lhs)

    def get_rgs_op(self, connection, rhs):
        return '@@ to_tsquery(%s)' % rhs

TSVectorField.register_lookup(TSVectorMatches)

Then, you are able, on a correctly defined field, able to do:

queryset.filter(foo__matches='bar')

Which roughly translates to:

SELECT * FROM "table" WHERE (foo @@ to_tsquery('bar'));

It’s actually a little more complicated than that, but I have a working prototype at https://bitbucket.org/schinckel/django-postgres/. There is a field class, but also an example within the search sub-app.

Clearly, you’ll want to be creating the right indexes.

Solve a Complex Problem in One Step

By their very nature, ORMs tend to make this a little less easy to do. Because you don’t normally write custom code, this scenario is less common than you might see in a normal SQL access.

However, with Django, it is possible to write over-complicated queries, but also to use things like .raw(), and .extra() to write “Spaghetti Queries”.

However, it is worth noting that with judicious use of these features, you can indeed write queries that perform exceptionally well, indeed, far better than the ORM is able to generate for you. It’s also worth noting that you can write really, really bad queries that take a very long time, just using the ORM (without even doing things like N+1 queries for related objects).

Indeed, the “how to recognize” section of this chapter shows the biggest red flag I have noticed lately: “Just stick another DISTINCT in there”.

I’ve seen, first-hand how a .distinct() can cause a query to take a very long period of time. Removing the need for a distinct by removing the join, and instead using subqueries, caused a query that was taking around 17 seconds with a given data set to suddenly take less than 200ms.

That alone has forced me to reconsider each and every time I use .distinct() in my code (and probably explains why our code that runs queries against django-reversion) performs so horribly.

A Shortcut That Gets You Lost

I’ve used, in my SQL snippets in this post, the shortcut that is mentioned here: SELECT * FROM .... Luckily, Django doesn’t use this shortcut, and instead lists out every column it expects to see.

This has a really nice side-effect: if your database tables have not been migrated to add that new column, then whenever you try to run any queries against that table, you will have an error. Which is much more likely to happen immediately, rather than at 3am when that column is first actually used.

Application Development Antipatterns

Store Password in Plain Text

There is no, I repeat, no reason you should ever be doing this. It’s a cardinal sin, and Django has a great authentication and authorisation framework, that you can extend however you need it.

As noted in the legitimate uses section: if you are accessing a third-party system, you may need to store the password in a readable format. In this case, something like Oauth, if available, may make things a little safer.

Execute Unverified Input As Code

Read this chapter online.

Most of the risks of SQL Injection are mitigated when you use an ORM like Django’s. Of course, if you write .raw() or .extra() queries that don’t properly escape user-provided data, then you may still be at risk. .extra() in particular has arguments that allow you to pass an iterable of parameters, which will then be correctly escaped as they are added to the query.

Filling in the Corners

Educate your manager if (s)he thinks it’s a bad thing to have non-contiguous primary keys. Transaction rollbacks, deleted objects: there’s all sorts of reasons why there may be gaps.

Making Bricks Without Straw

It goes without saying that you should have error handling within your python code.

Make SQL a Second-Class Citizen

This is kind-of the point of an ORM: to remove from you the need to deal with creating complex queries in raw SQL.

Your Django models are the documentation of your table structure, or documentation can be generated from them. Your migrations files show the changes that have been made over time. Naturally, both of these will be stored in your Source Code Management system.

Clearly, as soon as you are doing anything in raw SQL, then you should follow the practices you do with the rest of your code.

Testing in-database is something I am a little bit interested in. As I move more code into the database (often for performance reasons, sometimes because it’s just fun), it would be nice to have tests for these functions. I have a long list of things in my Reading List about Postgres Unit Testing. Perhaps I’ll get around to them at some point. Integrating these with the Django test runner would be really neat.

The Model Is an Active Record

Django’s use of the Active Record is slightly different to Rails. In Rails, the column types in the database control what attributes are on the model, but in Django, the python object is the master. I think this is more meaningful, because it means that everything you need to know about an object is in the model definition: you don’t need to follow the migrations to see what attributes you have.

I do like the concept of a Domain Model: it’s an approach I’ve lightly tried in the past. Perhaps it is an avenue I’ll push down further at some point. In some ways, Django’s Form classes allow you to encapsulate this, but mostly business logic still lives on our Model classes.

Summary

So, how did Django do?

Pretty good, I’d say. The ones that were less successful either don’t really matter most of the time (primary key column is always called id, choices defined in the model), or you don’t really need to use them (Generic Relations, searching using LIKE %foo%, using raw SQL).

We do fall down a bit with files stored in the database, and fat models, but I would argue that those patterns work just fine, at least for me right now.

Liquid Templates and Django Templates

Note to self: when I get the error:

/Library/Ruby/Gems/1.8/gems/jekyll-0.11.2/bin/../lib/jekyll/convertible.rb:81:in `do_layout': undefined method `name' for #<Jekyll::Post:0x10f842e88> (NoMethodError)
   from /Library/Ruby/Gems/1.8/gems/jekyll-0.11.2/bin/../lib/jekyll/post.rb:189:in `render'
   from /Library/Ruby/Gems/1.8/gems/jekyll-0.11.2/bin/../lib/jekyll/site.rb:193:in `render'
   from /Library/Ruby/Gems/1.8/gems/jekyll-0.11.2/bin/../lib/jekyll/site.rb:192:in `each'
   from /Library/Ruby/Gems/1.8/gems/jekyll-0.11.2/bin/../lib/jekyll/site.rb:192:in `render'
   from /Library/Ruby/Gems/1.8/gems/jekyll-0.11.2/bin/../lib/jekyll/site.rb:40:in `_draft_process'
   from /Users/matt/Dropbox/Blog/_plugins/drafts.rb:15:in `process'
   from /Library/Ruby/Gems/1.8/gems/jekyll-0.11.2/bin/jekyll:250
   from /usr/local/bin/jekyll:19:in `load'
   from /usr/local/bin/jekyll:19

It’s probably because I have used django template tags in the {% (end)highlight %} blocks, and omitted the {% (end)raw %} stuff.

Leveraging HTML and Django Forms: Pagination of Filtered Results

Django’s forms are fantastic for parsing user input, but I’ve come up with a nice way to use them, in conjunction with HTML forms, for pagination, using the inbuilt Django pagination features.

It all stems from the fact that I’ve begun using forms quite heavily for GET purposes, rather than just for POST. Basically, anytime you have a URL that may have some parts of the query string that may need to be built, it’s simpler to use a form element, than to manually build up the url in your template.

Thus, where you may have something like:

<a href="{% url 'foo' %}?page={{ page }}">

It may be better do do something more like:

<form action="{% url 'foo' %}">
  <input type="hidden" name="page" value="{{ page }}">
</form>

Indeed, you can even use named buttons for submission, which will refer to the page. That is the key to the process outlined below.


Django comes with lots of “batteries”, including form handling and pagination. The Class Based Views (CBV) that deal with collections of objects will include pagination, although it is possible to use this pagination in your own views. For simplicity, we’ll stick with a simple ListView.

Let’s begin with that simple view: in our views.py:

from django.views.generic import ListView, DetailView

from .models import Person

person_list = ListView.as_view(
    queryset=Person.objects.all(),
    template_name='person/list.html',
    paginate_by=10
)

person_detail = DetailView.as_view()

Essentially, that’s all you need to do. You could use implied template names, but I almost never do this. The takeaway from this block is that we are stating the queryset that our ListView will use as the base, the template it should render, and the number of items per page.

I’ve stubbed out the person_detail view, just so we can refer to it in our urlconf, and then in turn in the template.

I like to do this in views.py, rather than in my urlconf directly. In current versions of django, you then don’t need to import the views, you can just use the dotted path, but this is going away in the future.

In our urls.py, we have something like:

from django.conf.urls import patterns, url

urlpatterns = patterns('person.views',
    url(r'^people/$', 'person_list', name='person_list'),
    url(r'^people/(?P<pk>\d+)/', 'person_detail', name='person_detail'),
)

Then, in our template, we can render it as (ignoring the majority of the page):

<ul class="people">
  {% for object in object_list %}
    <li>
      <a href="{% url 'person_detail' pk=object.pk %}">
        {{ object }}
      </a>
    </li>
  {% endfor %}
</ul>

But this doesn’t give us our pagination. It will only show the first ten results, with no way to access the others. All we need to do to access the others is to append ?page=X, but, as we will see, there is another way.

Typically, your pagination block might look something like:

<ul class="pagination">
  <li>
    {% if page_obj.has_previous %}
      <a href="?page={{ page_obj.previous_page_number }}">
        prev
      </a>
    {% else %}
      <span>prev</span>
    {% endif %}
  </li>

  {% for page_number in paginator.page_range %}
    {% if page_number = page_obj.number %}
      <li class="active">
        <span>{{ page_number }}</span>
      </li>
    {% else %}
      <li>
        <a href="?page={{ page_number }}">
          {{ page_number }}
        </a>
      </li>
    {% endif %}
  {% endfor %}


  <li>
    {% if page_obj.has_next %}
      <a href="?page={{ page_obj.next_page_number }}">
        next
      </a>
    {% else %}
      <span>next</span>
    {% endif %}
  </li>
</ul>

Depending upon your CSS framework, if you use one, there may already be some pre-prepared styles to help you out with this.

This is all well and good, until you want paginated search results. Then, you can no longer rely on being able to rely on using ?page=N, as this would remove any search terms you were already using. Also, if you were using ajax to fetch and display stuff, you may need to use the whole URL, rather than just the query string.

Instead, we can use a Django form for searching, and just add in the pagination bits.

We will build a page that displays an optionally filtered list of people.

Our Person model will be deliberately simple:

from django.db import models

class Person(models.Model):
    name = models.CharField(max_length=256)

Likewise, our form will be simple. All we need to do is return something we can pass to a QuerySet.filter() method.

from django import forms

class PersonSearchForm(forms.Form):
    query = forms.CharField(label=_('Filter'), required=False)

    def filter_by(self):
      return {
        'name__icontains': self.cleaned_data['query']
      }

Finally, we will need to subclass a ListView. We’ll mixin from FormMixin, so we get the form-handling capabilities:

from django.views.generic.edit import FormMixin
from django.views.generic import ListView

class FilteredListView(ListView):
    def get_form_kwargs(self):
        return {
          'initial': self.get_initial(),
          'prefix': self.get_prefix(),
          'data': self.request.GET or None
        }

    def get(self, request, *args, **kwargs):
        self.object_list = self.get_queryset()

        form = self.get_form(self.get_form_class())

        if form.is_valid():
            self.object_list = self.object_list.filter(**form.filter_by())

        context = self.get_context_data(form=form)
        return self.render_to_response(context)

There’s a little bit to comment on there: we override the get_form_kwargs so we pull our form’s data from request.GET, instead of the default.

We also override get, so we filter results if the form validates (which it will if there was data provided).

Everything else is just standard.

We will want to actually use this view:

people_list = FilteredListView.as_view(
    form_class=PersonSearchForm,
    template_name='person/list.html',
    queryset=Person.objects.all(),
    paginate_by=10
)

Now we need to render this.

<form id="person-list" action="{% url 'person_list' %}">
  <input name="query" value="{{ form.query.value }}" type="search">
  <button type="submit" name="page" value="1">Search</button>

  {% include 'pagination.html' %}
</form>

<div class="results">
  {% include 'person/list-results.html' %}
</div>

You’ll see I’ve split the template up a bit, and put the pagination inside the form itself. Let’s have a look at those other templates.

You may also notice that the search button will result in page=1 being used. This is deliberate.

Our person/list-results.html is just the same as what our person/list.html looked like before:

<ul class="people">
  {% for object in object_list %}
    <li>
      <a href="{% url 'person_detail' pk=object.pk %}">
        {{ object }}
      </a>
    </li>
  {% endfor %}
</ul>

Our pagination.html is very similar to how our other template above looked too, but using <button> elements instead of <a>, and we will disable those that should not be clickable:

<ul class="pagination">
  <li>
    <button
      name="page"
      value="{{ page_obj.previous_page_number }}"
      type="submit"
      {% if not page_obj.has_previous %}
        disabled="disabled"
      {% endif %}>
      prev
    </button>
  </li>

  {% for page_number in paginator.page_range %}
    <li class="{% if page_number = page_obj.number %}active{% endif %}">
      <button
        name="page"
        value="{{ page_number }}"
        type="submit"
        {% if page_number = page_obj.number %}
          disabled="disabled"
        {% endif %}>
        {{ page_number }}
      </button>
    </li>
  {% endfor %}

  <li>
    <button
      name="page"
      value="{{ page_obj.next_page_number }}"
      type="submit"
      {% if not page_obj.has_next %}
        disabled="disabled"
      {% endif %}>
      next
    </button>
  </li>
</ul>

We are getting close now. This will be enough to have clicking on the next/previous or page number buttons resubmitting our search form, resulting in the page reloading with the correct results.

But we can do a bit better. We can easily load the results using AJAX, and just insert them into the page.

We just need one additional method on our View class:

class FilteredListView(ListView):
    # ...

    def get_template_names(self):
        if self.request.is_ajax():
            return [self.ajax_template_name]
        return [self.template_name]

    # ...

and one addition to our view declaration:

people_list = FilteredListView.as_view(
    form_class=PersonSearchForm,
    template_name='person/list.html',
    ajax_template_name='person/list-results.html',
    queryset=Person.objects.all(),
    paginate_by=10,
)

I’ll use jQuery, is it makes for easier to follow code:

// Submit handler for our form: submit it using AJAX instead.
$('#person-list').on('submit', function(evt) {
  evt.preventDefault();

  var form = evt.target;

  $.ajax({
    url: form.action,
    data: $(form).serialize(),
    success: function(data) {
      $('#results').html(data)
    }
  });
});

// Because we are using buttons, which ajax submit will not send,
// we need to add a hidden field with the relevant page number
// when we send our request.
$('#person-list').on('click', '[name=page]', function(evt) {
  var $button = $(evt.target).closest('button');
  var $form = $button.closest('form');

  if (!$form.find('[type=hidden][name=page]')) {
    $form.append('<input type="hidden" name="page">');
  }

  $form.find('[type=hidden][name=page]').val($button.val());

  $form.submit();
});

That should do nicely.


There is one more thing that we need to think about. If we leave the next/prev buttons, then we need to handle multiple clicks on those buttons, which fetch the subsequent page, and possibly cancel the existing AJAX request.

I do have a solution for this, too, although it complicates things a fair bit. First, we need to add some attributes to the next/prev buttons:

<ul class="pagination">
  <li>
    <button
      name="page"
      value="{{ page_obj.previous_page_number }}"
      type="submit"
      data-increment="-1"
      data-stop-at="1"
      {% if not page_obj.has_previous %}
        disabled="disabled"
      {% endif %}>
      prev
    </button>
  </li>

  {% for page_number in paginator.page_range %}
    <li class="{% if page_number = page_obj.number %}active{% endif %}">
      <button
        name="page"
        value="{{ page_number }}"
        type="submit"
        {% if page_number = page_obj.number %}
          disabled="disabled"
        {% endif %}>
        {{ page_number }}
      </button>
    </li>
  {% endfor %}

  <li>
    <button
      name="page"
      value="{{ page_obj.next_page_number }}"
      type="submit"
      data-increment="1"
      data-stop-at="{{ paginator.num_pages }}"
      {% if not page_obj.has_next %}
        disabled="disabled"
      {% endif %}>
      next
    </button>
  </li>
</ul>

And our click handler changes a bit too:

$('#person-list').on('click', 'button[name=page]', function() {
  var page = parseInt(this.value, 10);
  var $form = $(this).closest('form');
  // Only update the value of the hidden form.
  if (!$form.find('[name=page][type=hidden]')) {
    $form.insert('<input name=page type=hidden>');
  }
  $form.find('[name=page][type=hidden]').val(page);
  // Increment any prev/next buttons values by their increment amount,
  // and set the disabled flag on any that have reached their stop-at
  $form.find('[data-increment]').each(function() {
    this.value = parseInt(this.dataset.increment, 10) + page;
    // We want to disable the button if we get to the 'stop-at' value,
    // but this needs to happen after any submit events have occurred.
    if (this.dataset.stopAt) {
      setTimeout(function() {
        this.disabled = (this.value == this.dataset.stopAt);
      }.bind(this), 0);
    }
  });

  $form.submit();
});

Finally, I also have it so that the results are also rendered as inside of the form, as it’s simpler to re-render the pagination each time, rather than muck around with disabling/enabling various buttons.

...

    Tags:

More Cleartext Passwords

Update: I’ve since heard back from my health insurer. They are working to mitigate the risks outlined in the complaint below.

As such, and as an indicator of good faith on my behalf, I’ve removed links and mentions of them. Should they not follow through, this post will be restored, and more venom added!


More plain-text-shenanigans.

I complained some time ago to my health insurer:

I’m writing to complain again about your security practices.

You clearly store passwords in plain text: evidenced by the fact that the password reset process sends me my actual password, instead of doing a password reset, like every responsible company does.

Given the recent spate of security breaches, you really should consider improving this.

I received the following reply:

Dear Mr Schinckel,

Thank you for your email. Your feedback regarding the security of our Online Member Services has been noted.

Please rest assured that as a fund, we ensure that we adhere to the Privacy Act. The security of our members and their information is paramount to us, and systems operate in accordance with relevant legislation and guidelines. Our systems and processes are regularly reviewed and audited to ensure full adherence.

I didn’t do anything about it, but the email I received today made me think about it again.

So, here is the content of my complaint to them.


Consider this a formal complaint.

It also occurs to me that what you are claiming in your privacy policy is false.

[link to privacy policy, and content of privacy policy removed]

You clearly have not taken “reasonable steps to protect all personal information … from unauthorised access”. A simple, very reasonable step would be to store only hashes of passwords, generated using a per-password salt, instead of storing passwords in clear text.

Your storage of passwords in clear text, and then the further sending of those over email, which is inherently not encrypted, means that any administrator of any mail server between your server and the recipient would be able to view the username and password of a member, and masquerade as them.

Even if you encrypted passwords in a manner that means you were able to recover them, in order to send them over email, means that you are still open to attackers appropriating passwords, either in transit, or after gaining access to your systems. This includes having an encrypted database, or encrypted columns within the database.

You clearly have not ensured “that appropriate technical and organisational security measures, consistent with standard industry practice, are in place to attempt to safeguard the security…”, as what your password recovery feature indicates, you are storing passwords in clear text. As a professional Web Developer, I can assure you this is not in line with best practices with regard to software development.

Your claim of non-responsibility “for any breach of security caused by third parties” is laughable, given that any attacker that gains access to your member database automatically has access to all details of all members, and can store passwords, and access those member’s details at any time in the future unless they change their passwords.

You state that “[redacted] does not use any form of encryption … to protect information a member sends from their computer … and emails”. Did it not occur to you that the same is true of the reverse? That information you send over email is likewise not encrypted?

Claiming that your systems operate “in accordance with relevant legislation” is a cop-out. All it does is show that the legislation is out of date.

Please take my complaints seriously. Please fix your security.

Matt.