Simon Willison’s Weblog

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Exploring the UK Register of Members Interests with SQL and Datasette 19 hours ago

Ever wondered which UK Members of Parliament get gifted the most helicopter rides? How about which MPs have been given Christmas hampers by the Sultan of Brunei? (David Cameron, William Hague and Michael Howard apparently). Here’s how to dig through the Register of Members Interests using SQL and Datasette.

Gifts from the Sultan

mySociety have been building incredible civic participation applications like TheyWorkForYou and FixMyStreet for nearly 15 years now, and have accumulated all kinds of interesting data along the way.

They recently launched their own data portal at listing all of the information they have available. While exploring it I stumbled across their copy of the UK Register of Members Interests. Every UK Member of Parliament has to register their conflicts of interest and income sources, and mySociety have an ongoing project to parse that data into a more useful format.

It won’t surprise you to hear that I couldn’t resist turning their XML files into a SQLite database.

The result is—a Datasette instance running against a SQLite database containing over 1.3 million line-items registered by 1,419 MPs over the course of 18 years.

Some fun queries

A few of my favourites so far:

Understanding the data model

Most of the action takes place in the items table, where each item is a line-item from an MP’s filing. You can search that table by keyword (see helicopter example above) or apply filters to it using the standard Datasette interface. You can also execute your own SQL directly against the database.

Each item is filed against a category. There appears to have been quite a bit of churn in the way that the categories are defined over the years, plus the data is pretty untidy—there are no less than 10 ways of spelling “Remunerated employment, office, profession etc.” for example!


There are also a LOT of duplicate items in the set—it appears that MPs frequently list the same item (a rental property for example) every time they fill out the register. SQL DISTINCT clauses can help filter through these, as seen in some of the above examples.

The data also has the concepts of both members and people. As far as I can tell people are distinct, but members may contain duplicates—presumably to represent MPs who have served more than one term in office. It looks like the member field stopped being populated in March 2015 so analysis is best performed against the people table.

Once concept I have introduced myself is the record_id. In the XML documents the items are often grouped together into a related collection, like this:

<regmem personid=""
    memberid="" membername="Diane Abbott" date="2014-07-14">
    <category type="2" name="Remunerated employment, office, profession etc">
        <item>Payments from MRL Public Sector Consultants, Pepple House, 8 Broad Street, Great Cambourne, Cambridge CB23 6HJ:</item>
        <item>26 November 2013, I received a fee of £1,000 for speaking at the 1st African Legislative Summit, National Assembly, Abuja, Nigeria.  Hours: 8 hrs. The cost of my flights, transfers and hotel accommodation in Abuja were also met; estimated value £5,000. <em>(Registered 3 December 2013)</em></item>
        <item>23 July 2013, I received a fee of £5,000 for appearing as a contestant on ITV&#8217;s &#8216;The Chase Celebrity &#8211; Series 3&#8217; television programme.  Address of payer:  ITV Studios Ltd, London Television Centre, Upper Ground, London SE1 9Lt.  Hours: 12 hrs.   <em>(Registered 23 July 2013)</em></item>

While these items are presented as separate line items, their grouping carries meaning: the first line item here acts as a kind of heading to help provide context to the other items.

To model this in the simplest way possible, I’ve attempted to preserve the order of these groups using a pair of additional columns: the record_id and the sort_order. I construct the record_id using a collection of other fields—the idea is for it to be sortable, and for each line-item in the same grouping to have the same record_id:

record_id = "{date}-{category_id}-{person_id}-{record}".format(

The resulting record_id might look like this: 2018-04-16-70b64e89-24878-0

To recreate that particular sequence of line-items, you can search for all items matching that record_id and then sort them by their sort_order. Here’s that record from Diane Abbott shown with its surrounding context.

A single record

How I built it

The short version: I downloaded all of the XML files and wrote a Python script which parsed them using ElementTree and inserted them into a SQLite database. I’ve put the code on GitHub.

A couple of fun tricks: firstly, I borrowed some code from csvs-to-sqlite to create the full-text search index and enable searching:

def create_and_populate_fts(conn):
    create_sql = """
        CREATE VIRTUAL TABLE "items_fts"
        USING {fts_version} (item, person_name, content="items")
        INSERT INTO "items_fts" (rowid, item, person_name)
        SELECT items.rowid, items.item,
        FROM items LEFT JOIN people ON items.person_id =

The best_fts_version() function implements basic feature detection against SQLite by trying operations in an in-memory database.

Secondly, I ended up writing my own tiny utility function for inserting records into SQLite. SQLite has useful INSERT OR REPLACE INTO syntax which allows you to insert a record and will automatically update an existing record if there is a match on the primary key. This meant I could write this utility function and use it for all of my data inserts:

def insert_or_replace(conn, table, record):
    pairs = record.items()
    columns = [p[0] for p in pairs]
    params = [p[1] for p in pairs]
    sql = "INSERT OR REPLACE INTO {table} ({column_list}) VALUES ({value_list});".format(
        column_list=", ".join(columns),
        value_list=", ".join(["?" for p in params]),
    conn.execute(sql, params)

# ...

        "id": person_id,
        "name": regmem_el.attrib["membername"],

What can you find?

I’ve really only scratched the surface of what’s in here with my initial queries. What can you find? Send me Datasette query links on Twitter with your discoveries!

Datasette plugins, and building a clustered map visualization five days ago

Datasette now supports plugins!

Last Saturday I asked Twitter for examples of Python projects with successful plugin ecosystems. pytest was the clear winner: the pytest plugin compatibility table (an ingenious innovation that I would love to eventually copy for Datasette) lists 457 plugins, and even the core pytest system itself is built as a collection of default plugins that can be replaced or over-ridden.

Best of all: pytest’s plugin mechanism is available as a separate package: pluggy. And pluggy was exactly what I needed for Datasette.

You can follow the ongoing development of the feature in issue #14. This morning I released Datasette 0.20 with support for a number of different plugin hooks: plugins can add custom template tags and SQL functions, and can also bundle their own static assets, JavaScript, CSS and templates. The hooks are described in some detail in the Datasette Plugins documentation.


I also released my first plugin: datasette-cluster-map. Once installed, it looks out for database tables that have a latitude and longitude column. When it finds them, it draws all of the points on an interactive map using Leaflet and Leaflet.markercluster.

Let’s try it out on some polar bears!

Polar Bears on a cluster map

The USGS Alaska Science Center have released a delightful set of data entitled Sensor and Location data from Ear Tag PTTs Deployed on Polar Bears in the Southern Beaufort Sea 2009 to 2011. It’s a collection of CSV files, which means it’s trivial to convert it to SQLite using my csvs-to-sqlite tool.

Having created the SQLite database, we can deploy it to a hosting account on Zeit Now alongside the new plugin like this:

# Make sure we have the latest datasette
pip3 install datasette --upgrade
# Deploy polar-bears.db to now with an increased default page_size
datasette publish now \
    --install=datasette-cluster-map \
    --extra-options "--page_size=500" \

The --install option is new in Datasette 0.20 (it works for datasette publish heroku as well)—it tells the publishing provider to pip install the specified package. You can use it more than once to install multiple plugins, and it accepts a path to a zip file in addition to the name of a PyPI package.

Explore the full demo at

Visualize any query on a map

Since the plugin inserts itself at the top of any Datasette table view with latitude and longitude columns, there are all sorts of neat tricks you can do with it.

I also loaded the San Francisco tree list (thanks, Department of Public Works) into the demo. Impressively, you can click “load all” on this page and Leaflet.markercluster will load in all 189,144 points and display them on the same map… and it works fine on my laptop and my phone. Computers in 2018 are pretty good!

But since it’s a Datasette table, we can filter it. Here’s a map of every New Zealand Xmas Tree in San Francisco (8,683 points). Here’s every tree where the Caretaker is Friends of the Urban Forest. Here’s every palm tree planted in 1990:

Palm trees planted in 1990

Update: This is an incorrect example: there are 21 matches on “palm avenue” because the FTS search index covers the address field—they’re not actually palm trees. Here’s a corrected query for palm trees planted in 1990.

The plugin currently only works against columns called latitude and longitude… but if your columns are called something else, don’t worry: you can craft a custom SQL query that aliases your columns and everything will work as intended. Here’s an example against some more polar bear data:

select *, "Capture Latitude" as latitude, "Capture Longitude" as longitude
from [USGS_WC_eartag_deployments_2009-2011]

Writing your own plugins

I’m really excited to see what people invent. If you want to have a go, your first stop should be the Plugins documentation. If you want an example of a simple plugin (including the all-important mechanism for packaging it up using take a look at datasette-cluster-map on GitHub.

And if you have any thoughts, ideas or suggestions on how the plugin mechanism can be further employed please join the conversation on issue #14. I’ve literally just got started with Datasette’s plugin hooks, and I’m very keen to hear about things people want to build that aren’t yet supported.

Building a combined stream of recent additions using the Django ORM one month ago

I’m a big believer in the importance of a “recent additions” feed. Any time you’re building an application that involves users adding and editing records it’s useful to have a page somewhere that shows the most recent objects that have been created across multiple different types of data.

I’ve used a number of techniques to build these in the past—from an extra database table (e.g. the Django Admin’s LogEntry model) to a Solr or Elasticsearch index that exists just to serve recent additions.

For a recent small project I found myself needing a recent additions feed and realized that there’s a new, simple way to build one thanks to the QuerySet.union() method introduced in Django 1.11 back in April 2017.

Consider a number of different ORM models that can be added by users, each with a created timestamp field.

Prior to QuerySet.union(), building a combined recent additions feed across multiple models was difficult: it’s easy to show recent additions for a single model, but how can we intersperse and paginate additions made to models stored across more than one table?

Using .union() to combine records from different models

Consider the following three models:

class Project(models.Model):
    name = models.CharField(max_length=128)
    description = models.TextField()
    created = models.DateTimeField(auto_now_add=True)

class Image(models.Model):
    project = models.ForeignKey(
        Project, related_name='images', on_delete=models.CASCADE
    image = models.ImageField()
    created = models.DateTimeField(auto_now_add=True)

class Comment(models.Model):
    project = models.ForeignKey(
        Project, related_name='comments', on_delete=models.CASCADE
    comment = models.TextField()
    created = models.DateTimeField(auto_now_add=True)

Let’s build a single QuerySet that returns objects from all three models ordered by their created dates, most recent first.

Using .values() we can reduce these different models to a common subset of fields, which we can then .union() together like so:

recent = Project.objects.values(
    'pk', 'created'
    Image.objects.values('pk', 'created'),
    Comment.objects.values('pk', 'created'),

Now if we print out list(recent) it will look something like this:

[{'created': datetime.datetime(2018, 3, 24, 1, 27, 23, 625195, tzinfo=<UTC>),
  'pk': 28},
 {'created': datetime.datetime(2018, 3, 24, 15, 51, 29, 116511, tzinfo=<UTC>),
  'pk': 15},
 {'created': datetime.datetime(2018, 3, 23, 20, 14, 3, 31648, tzinfo=<UTC>),
  'pk': 5},
 {'created': datetime.datetime(2018, 3, 23, 18, 57, 36, 585376, tzinfo=<UTC>),
  'pk': 11}]

We’ve successfully combined recent additions from three different tables! Here’s what the SQL for that looks like:

>>> from django.db import connection
>>> print(connection.queries[-1]['sql'])
(SELECT "myapp_project"."id", "myapp_project"."created" FROM "myapp_project")
 UNION (SELECT "myapp_image"."id", "myapp_image"."created" FROM "myapp_image")
 UNION (SELECT "myapp_comment"."id", "myapp_comment"."created" FROM "myapp_comment")

There’s just one problem: we got back a bunch of pk and created records, but we don’t know which model each of those rows represents.

Using .annotate() to add a type constant to the rows

We can fix this by using Django’s annotate() method combined with a Value() object to attach a constant string to each record specifying the type of the row it represents. Here’s how to do that for a single model:

>>> from django.db.models import Value, CharField
>>> list(Image.objects.annotate(
...     type=Value('image', output_field=CharField()
... )).values('pk','type', 'created')[:2])
[{'created': datetime.datetime(2018, 3, 22, 17, 16, 33, 964900, tzinfo=<UTC>),
  'pk': 3,
  'type': 'image'},
 {'created': datetime.datetime(2018, 3, 22, 17, 49, 47, 527907, tzinfo=<UTC>),
  'pk': 4,
  'type': 'image'}]

We’ve added the key/value pair 'type': 'image' to every record returned from the querystring. Now let’s do that to all three of our models and combine the results using .union():

recent = Project.objects.annotate(
    type=Value('project', output_field=CharField())
    'pk', 'created', 'type'
        type=Value('image', output_field=CharField())
    ).values('pk', 'created', 'type'),
        type=Value('comment', output_field=CharField())
    ).values('pk', 'created', 'type'),

If we run list(recent) we get this:

[{'created': datetime.datetime(2018, 3, 24, 15, 51, 29, 116511, tzinfo=<UTC>),
  'pk': 15,
  'type': 'comment'},
 {'created': datetime.datetime(2018, 3, 24, 15, 50, 3, 901320, tzinfo=<UTC>),
  'pk': 29,
  'type': 'image'},
 {'created': datetime.datetime(2018, 3, 24, 15, 46, 35, 42123, tzinfo=<UTC>),
  'pk': 15,
  'type': 'project'},
 {'created': datetime.datetime(2018, 3, 24, 7, 53, 15, 222029, tzinfo=<UTC>),
  'pk': 14,
  'type': 'comment'}]

This is looking pretty good! We’ve successfully run a single SQL UNION query across three different tables and returned the combined results in reverse chronological order. Thanks to the type column we know which model each record corresponds to.

Inflating the full referenced objects

Now we need to inflate those primary key references a full ORM object from each corresponding table.

The most efficient way to do this is to collect together the IDs for each type and then run a single SQL query per type to load the full objects.

Here’s code that does exactly that: it first collects the list of primary keys that need to be loaded for each type, then executes an efficient SQL IN query against each type to fetch the underlying objects:

records = list(recent)

type_to_queryset = {
    'image': Image.objects.all(),
    'comment': Comment.objects.all(),
    'project': Project.objects.all(),

# Collect the pks we need to load for each type:
to_load = {}
for record in records:
    to_load.setdefault(record['type'], []).append(record['pk'])

# Fetch them 
fetched = {}
for type, pks in to_load.items():
    for object in type_to_queryset[type].filter(pk__in=pks):
        fetched[(type,] = object

# Annotate 'records' with loaded objects
for record in records:
    key = (record['type'], record['pk'])
    record['object'] = fetched[key]

After running the above code, records looks like this:

[{'created': datetime.datetime(2018, 3, 24, 15, 51, 29, 116511, tzinfo=<UTC>),
  'object': <Comment: a comment>,
  'pk': 15,
  'type': 'comment'},
 {'created': datetime.datetime(2018, 3, 24, 15, 50, 3, 901320, tzinfo=<UTC>),
  'object': <Image: Image object (29)>,
  'pk': 29,
  'type': 'image'},
 {'created': datetime.datetime(2018, 3, 24, 15, 46, 35, 42123, tzinfo=<UTC>),
  'object': <Project: Recent changes demo>,
  'pk': 15,
  'type': 'project'},
 {'created': datetime.datetime(2018, 3, 24, 7, 53, 15, 222029, tzinfo=<UTC>),
  'object': <Comment: Here is another comment>,
  'pk': 14,
  'type': 'comment'}]

We can now feed this to a template and use it to render our recent additions page.

Wrapping it in a re-usable function

Here’s a function that implements the above in a re-usable way:

def combined_recent(limit, **kwargs):
    datetime_field = kwargs.pop('datetime_field', 'created')
    querysets = []
    for key, queryset in kwargs.items():
                    key, output_field=CharField()
            ).values('pk', 'recent_changes_type', datetime_field)
    union_qs = querysets[0].union(*querysets[1:])
    records = []
    for row in union_qs.order_by('-{}'.format(datetime_field))[:limit]:
            'type': row['recent_changes_type'],
            'when': row[datetime_field],
            'pk': row['pk']
    # Now we bulk-load each object type in turn
    to_load = {}
    for record in records:
        to_load.setdefault(record['type'], []).append(record['pk'])
    fetched = {}
    for key, pks in to_load.items():
        for item in kwargs[key].filter(pk__in=pks):
            fetched[(key,] = item
    # Annotate 'records' with loaded objects
    for record in records:
        record['object'] = fetched[(record['type'], record['pk'])]
    return records

This is also available as a gist.

I can now use that function to combine arbitrary querysets (provided they share a created datestamp field) like so:

recent = combined_recent(

This will return the most recent 20 records across all three types, with the results looking like this:

[{'when': datetime.datetime(2018, 3, 24, 15, 51, 29, 116511, tzinfo=<UTC>),
  'object': <Comment: a comment>,
  'pk': 15,
  'type': 'comment'},
 {'when': datetime.datetime(2018, 3, 24, 15, 50, 3, 901320, tzinfo=<UTC>),
  'object': <Image: Image object (29)>,
  'pk': 29,
  'type': 'image'},
 {'when': datetime.datetime(2018, 3, 24, 15, 46, 35, 42123, tzinfo=<UTC>),
  'object': <Project: Recent changes demo>,
  'pk': 15,
  'type': 'project'},
 {'when': datetime.datetime(2018, 3, 24, 7, 53, 15, 222029, tzinfo=<UTC>),
  'object': <Comment: Here is another comment>,
  'pk': 14,
  'type': 'comment'}]

Efficient object loading with select/prefetch_related

If you’re going to render these objects on a page, it’s pretty likely you’ll need to load additional data about them. My example models above are deliberately simplified, but in any serious Django project it’s likely they will have additional references to other tables.

We can apply Django’s magic select_related() and prefetch_related() methods directly to the querysets we pass to the function, like so:

recent = combined_recent(

Django’s query optimizer is smart enough to ignore those calls entirely when building the initial union queries, so even with the above extras the initial union query will still look like this:

(SELECT "myapp_project"."id", "myapp_project"."created", 'project' AS "recent_changes_type" FROM "myapp_project")
 UNION (SELECT "myapp_image"."id", "myapp_image"."created", 'image' AS "recent_changes_type" FROM "myapp_image")
 UNION (SELECT "myapp_comment"."id", "myapp_comment"."created", 'comment' AS "recent_changes_type" FROM "myapp_comment")

The select_related() and prefetch_related() clauses will then be incorporated into the subsequent SQL queries that are used to efficiently inflate the full objects from the database.

Taking it further

There are a bunch of interesting extensions that can be made to this pattern.

Want pagination? The initial unioned queryset can be paginated using offset/limit by slicing the queryset, or using the Django Paginator class.

Want more efficient pagination (since offset/limit tends to get slow after the first few thousand rows)? We’re ordering by created already which means it’s not difficult to build efficient range-based pagination, requesting all records where the created date is less than the earliest date seen on the previous page.

Since everything is based on regular Django querysets, it’s possible to build all kinds of variants of the recent additions feed. So far we’ve just built one showing all changes across an entire application, but it’s not hard to apply additional filters to only show changes made by a specific user, or changes made relating to a specific foreign key relationship. If you can represent it as a collection of querysets that each expose a created column you can combine them into a single feed.

You don’t even need to use records that share a created column: if you have objects with columns of differing names you can use an annotation to alias those columns, like so:

recent = combined_recent(

I haven’t extensively load-tested this pattern, but I expect it will work fine for databases with tens-of-thousands of records but may start running into trouble if you have millions of records (though an index on the created column should help a lot). If you need a recent additions feed on something larger scale than that you should probably look at a separate logging table or an external index in something like Elasticsearch instead.

For another interesting thing you can do with .union() check out my article on Implementing faceted search with Django and PostgreSQL.

Datasette Demo (video) from the SF Python Meetup two months ago

I gave a short talk about Datasette last month at the SF Python Meetup Holiday Party. They’ve just posted the video, so here it is:

I showed how I built San Francisco Tree Search using Datasette, csvs-to-sqlite and data from the San Francisco Department of Public Works.



  • System-Versioned Tables in MariaDB (via) Fascinating new feature from the SQL:2011 standard that’s getting its first working implementation in MariaDB 10.3.4. “ALTER TABLE products ADD SYSTEM VERSIONING;” causes the database to store every change made to that table—then you can run “SELECT * FROM products FOR SYSTEM_TIME AS OF TIMESTAMP @t1;” to query the data as of a specific point in time. I’ve tried all manner of horrible mechanisms for achieving this in the past, having it baked into the database itself would be fantastic. #
  • China had about 99 percent of the 385,000 electric buses on the roads worldwide in 2017, accounting for 17 percent of the country’s entire fleet. Every five weeks, Chinese cities add 9,500 of the zero-emissions transporters—the equivalent of London’s entire working fleet

    Jeremy Hodges #

  • Black Onlline Demo (via) Black is “the uncompromising Python code formatter” by Łukasz Langa—it reformats Python code to a very carefully thought out styleguide, and provides almost no options for how code should be formatted. It’s reminiscent of gofmt. José Padilla built a handy online tool for trying it out in your browser. #
  • JSON Escape Text. I built a tiny tool for turning text into an escaped JSON string—I needed it to help create descriptions and canned SQL queries for adding to Datasette’s metadata.json files. #

24th April 2018

  • The current homepage clocks in at 1.9MB of CSS (156KB compressed). After re-building a fully-functional version of the homepage with CSS Blocks, we were able to serve the same page with just 38KB of CSS. To be clear: that’s the uncompressed size. After compression, that CSS file weighed in at less than 9KB!

    Chris Eppstein #

  • dateparser: python parser for human readable dates (via) I’ve used dateutil.parser for this in the past, but dateparser is a major upgrade: it knows how to parse dates in 200 different language locales, can interpret different timezone representations and handles relative dates (“3 months, 1 week and 1 day ago”) as well. #
  • csvs-to-sqlite 0.8. I released a new version of my csvs-to-sqlite tool this morning with a bunch of handy new features. It can now rename columns and define their types, add the CSV filenames as an additional column, add create indexes on columns and parse dates and datetimes into SQLite-friendly ISO formatted values. #

23rd April 2018

  • react-jsonschema-form. Exciting library from the Mozilla Services team: given a JSON Schema definition, react-jsonschema-form can produce a cleanly designed React-powered form for adding and editing data that matches that schema. Includes support for adding multiple items in a nested array, re-ordering them, custom form widgets and more. #
  • Why it took a long time to build that tiny link preview on Wikipedia (via) Wikipedia now shows a little preview card on internal links with an image and summary paragraph of the linked page. As a Wikpedia user I absolutely love this feature—and as an engineer and product designer, it’s fascinating to hear the challenges they overcame to ship it. Of particular interest: actually generating a useful summary of a page, while stripping out the cruft that often accumulates at the beginning of their text. It’s also an impressive scaling challenge: the API they use for this feature is now handling more than 500,000 requests per minute. #
  • Migrations are both essential and frustratingly frequent as your codebase ages and your business grows: most tools and processes only support about one order of magnitude of growth before becoming ineffective, so rapid growth makes them a way of life. [...] As a result you switch tools a lot, and your ability to migrate to new software can easily become the defining constraint for your overall velocity. [...] Migrations matter because they are usually the only available avenue to make meaningful progress on technical debt.

    Will Larson #

20th April 2018

19th April 2018

17th April 2018

  • Text Embedding Models Contain Bias. Here's Why That Matters (via) Excellent discussion from the Google AI team of the enormous challenge of building machine learning models without accidentally encoding harmful bias in a way that cannot be easily detected. #
  • Suppose a runaway success novel/tv/film franchise has “Bob” as the evil bad guy. Reams of fanfictions are written with “Bob” doing horrible things. People endlessly talk about how bad “Bob” is on twitter. Even the New York times writes about Bob latest depredations, when he plays off current events. Your name is Bob. Suddenly all the AIs in the world associate your name with evil, death, killing, lying, stealing, fraud, and incest. AIs silently, slightly ding your essays, loan applications, uber driver applications, and everything you write online. And no one believes it’s really happening. Or the powers that be think it’s just a little accidental damage because the AI overall is still, overall doing a great job of sentiment analysis and fraud detection.

    Daniel Von Fange #

  • A rating system for open data proposed by Tim Berners-Lee, founder of the World Wide Web. To score the maximum five stars, data must (1) be available on the Web under an open licence, (2) be in the form of structured data, (3) be in a non-proprietary file format, (4) use URIs as its identifiers (see also RDF), (5) include links to other data sources (see linked data). To score 3 stars, it must satisfy all of (1)-(3), etc.

    Five stars of open data #

  • Datasette 0.19: Plugins Documentation (via) I’ve released the first preview of Datasette’s new plugin support, which uses the pluggy package originally developed for py.test. So far the only two plugin hooks are for SQLite connection creation (allowing custom SQL functions to be registered) and Jinja2 template environment initialization (for custom template tags), but this release is mainly about exercising the plugin registration mechanism and starting to gather feedback. Lots more to come. #

15th April 2018

  • The way I would talk about myself as a senior engineer is that I’d say “I know how I would solve the problem” and because I know how I would solve it I could also teach someone else to do it. And my theory is that the next level is that I can say about myself “I know how others would solve the problem”. Let’s make that a bit more concrete. You make that sentence: “I can anticipate how the API choices that I’m making, or the abstractions that I’m introducing into a project, how they impact how other people would solve a problem.”

    Malte Ubl #

14th April 2018

  • Datasette 0.18: units (via) This release features the first Datasette feature that was entirely designed and implemented by someone else (yay open source)—Russ Garrett wanted unit support (Hz, ft etc) for his Wireless Telegraphy Register project. It’s a really neat implementation: you can tell Datasette what units are in use for a particular database column and it will display the correct SI symbols on the page. Specifying units also enables unit-aware filtering: if Datasette knows that a column is measured in meters you can now query it for all rows that are less than 50 feet for example. #

12th April 2018

  • What do you mean "average"? (via) Lovely example of an interactive explorable demonstrating mode/mean/median, built as an Observable notebook using D3. #
  • Wireless Telegraphy Register (via) Russ Garrett used Datasette to build a browsable interface to the UK’s register of business radio licenses, using data from Ofcom. #
  • Mozilla Telemetry: In-depth Data Pipeline (via) Detailed behind-the-scenes look at an extremely sophisticated big data telemetry processing system built using open source tools. Some of this is unsurprising (S3 for storage, Spark and Kafka for streams) but the details are fascinating. They use a custom nginx module for the ingestion endpoint and have a “tee” server written in Lua and OpenResty which lets them route some traffic to alternative backend. #
  • The Academic Vanity Honeypot phishing scheme. Twitter thread describing a nasty phishing attack where an academic receives an email from a respected peer congratulating them on a recent article and suggesting further reading. The further reading link is a phishing site that emulates the victim’s institution’s login page. #

11th April 2018

  • Visualizing disk IO activity using log-scale banded graphs (via) This is a neat data visualization trick: to display rates of disk I/O, it splits the rate into a GB, MB and KB section on a stacked chart. This means that if you are getting jitter in the order of KBs even while running at 400+MB/second you can see the jitter in the KB section. #