Fancy SQL Monday: format() instead of quote_*()

In the comments, Isaac pointed out that using format() dramatically increases the readability of SQL. I liked the look of his query, so I dug a little deeper.

As of version 9.1 (first released in 2010), a new function is listed in Postgres’ built-in string function documentation:

format(formatstr text [, str “any” [, …] ]): Format a string. This function is similar to the C function sprintf; but only the following conversion specifications are recognized: %s interpolates the corresponding argument as a string; %I escapes its argument as an SQL identifier; %L escapes its argument as an SQL literal; %% outputs a literal %. A conversion can reference an explicit parameter position by preceding the conversion specifier with n$, where n is the argument position.

We also have examples linked in the definition for various quoting strategies for dynamic SQL.

This is an example where the Postgres documentation probably should have reversed the order what is mentioned.

It turns out that format() makes it much easier to avoid using the quote_*() functions. The code looks a lot more like a python """ string (you can have arbitrary whitespace in there!), with flexible options for usage. The only feature missing is named parameters.

My application requires Postgres 9.2 at this point (for JSON datatype), so my plan is to refactor a few functions using format() instead of quote_ident() in particular.

Are there situations where you’d prefer to use quote_*() other than for backward compatibility? It seems as though format() is far safer, particularly for the quoting and nullable problems mentioned on the Quote Literal Example documentation.

Catalog SQL Friday: using DO when we’ve got tables as variables

Just a quick note about modifying constraints:

There’s no such thing as ALTER CONSTRAINT. So, if you want to safely change a CHECK constraint, like on a partition, you need to DROP and ADD it in a single transaction.

Below is a snippet for finding partitions, their CHECK constraints based on a WHERE clause. Then we DROP the existing constraint and add back the correct constraint. It doesn’t take much sleuthing to figure out what the problem was. :)

 DO $$
  DECLARE myrecord record;
  DECLARE theweek text;
  BEGIN
    FOR myrecord IN SELECT relname, conname from pg_constraint
      JOIN pg_class ON pg_constraint.conrelid = pg_class.oid
      WHERE consrc ~ 'without' and split_part(relname, '_201', 1)
      IN (select table_name from report_partition_info
      WHERE partition_column = 'date_processed') LIMIT 1
    LOOP
       EXECUTE 'ALTER TABLE ' || quote_ident(myrecord.relname)
       || ' DROP CONSTRAINT IF EXISTS '
       || quote_ident(myrecord.conname) || ';';</p>

   theweek = substring(myrecord.relname from '........$');

   EXECUTE 'ALTER TABLE ' || quote_ident(myrecord.relname)
   || ' ADD CONSTRAINT ' || quote_ident(myrecord.conname)
   || ' CHECK ((date_processed >= timestamptz('
   || quote_literal(to_char(date(theweek), 'YYYY-MM-DD')) || '))'
   || ' AND (date_processed < timestamptz('
   || quote_literal(to_char(date(theweek) + 7, 'YYYY-MM-DD'))
   || ')));';

   RAISE NOTICE 'DONE: %', myrecord.relname;
END LOOP;

END$$; 

Here’s the gist version for easier reading.

The couple things I learned in this process was a nice feature in substring() allowing me to return the date portion of my partition names easily, and split_part() which allowed me to return the parent table name and compare it to my list of partitionable tables for the specific partition column. I recently added support for partitioning on a different column for certain tables, so I have to differentiate for this fix. The string function docs are pretty great.

I didn’t do any optimization of this — just got it working and am now testing it in our stage environment. The final script is going to perform the changes on a month’s worth of partitions at a time to help reduce the chance of deadlocking.

If you have thoughts on how I could have done this more efficiently, let me know in the comments!

Why I gave $1024 to the Ada Initiative

AdaCamp participants

I’m one of the founding board members and advisors to the Ada Initiative, a 501(c)3 charitable non-profit that supports women in open technology and culture. This year, I am supporting the organization through a donation of $1024.

My reasons for supporting the Ada Initiative, advocating for their work, and now donating are many. I started out working with open source software in 1996, as a sysadmin setting up backups for a small research lab at the University of Oregon. I’d installed Linux from floppies in 1994, and surrounded myself with other open source advocates, sysadmins, hackers and finally, computer science students. Open source software has defined my career, and most of what I’ve chosen to do with my life.

In those few years in college, I met only two women who were interested in open source software – one that I brought with me from Chemistry into the Computer Science program and a woman who worked in Academic Services. I met no other professors, fellow students or friends online to collaborate or talk with.

All my mentors and friends were men. And, to be honest, I never thought much about that. Growing up, I tended to hang out with boys or spend a lot of time alone. As I started to think about computers and programming as fundamentally changing society, as tools that enabled and could dramatically change lives, I felt discomfort that only men seemed to know the things I knew.

A critically important aspect of being involved in the work that the Ada Initiative does is simply introducing women to each other in our communities. I’ve attended all three AdaCamps – in Melbourne, AU, Washington DC and San Francisco. Each gathering was larger than the last, and I’ve had important conversations at each that have changed my thinking and my activism in significant ways.

My most striking realization is how little work is done to educate adult women about computer science, about open source and open culture. Most outreach efforts focus on children, with the implication that adults are some how “not worth the effort”, “beyond help”, or that adults will simply find the things that interest them on their own.

My experience with the Ada Initiative, and with PyLadies contradicts everything that I had assumed, and everything that some advocates for early CS education for girls have implied — that the only way to change diversity is to start with a new generation of kids.

Adult women want the same skills that anyone interested in computers want. But there are important social and economic barriers to pursuing those goals.

The grassroots movement to educate adult women in writing software is inspiring. It reinforces my belief that it is never too late to learn these skills, and the women who seek out these classes inspire me with their dedication and fearlessness. Because of what I’ve learned and experienced, I’m now taking a break from the Ada Initiative advisors board to focus on my work with PyLadies.

Many people share my discomfort with a world whose code is only written by men. We’re making important changes in the way women view open technology and culture. We’re showing women that they definitely can learn and master these skills, that there are huge benefits to doing so and that women fundamentally belong in our communities. That work, I believe, also leads to the kind of intersectional awakening about diversity that all community builders should have.

The work of the Ada Initiative is difficult, wonderful and making a huge difference in my life. Please join me in supporting the Ada Initiative in 2013.

Using JSON data type in production with Socorro

Back in June, we started using the JSON datatype in production for Mozilla’s Socorro. Our implementation analyzes crashes from Firefox and other Mozilla products, configured with a HBase backend and Postgres version 9.2 processing and serving reports to middleware and a Django front-end.

The guts of the application that stores raw data into Postgres is implemented in a crashstorage class, and an example of how we use this field in report generation is in a stored procedure for rolling up our “Top Crashers By Signature” reports.

The idea is to get the metadata we store for each crash into Postgres in its raw JSON form available to SQL queries. We are currently still storing core crash report data in a normalized table (extracted from the original JSON). We are considering re-writing everything to just use a JSON column at the core. There may be some significant negative performance impacts, so more testing is needed before we move forward.

Previously, we only stored metadata in HBase. While it is convenient to have a year’s worth of data in HBase for running arbitrary queries against, once we settle on reports, it is far faster and more convenient to be able to use Postgres with JSON functions and SQL to rollup aggregates. I’m sure this isn’t true for everyone. We’re working with large, but manageable data sets – around 30 GB per week of JSON, and only 2-3 weeks of data at a time.

The advantage to having the JSON in Postgres is that we no longer are adding columns to our base tables. It’s not terribly difficult to add columns, but it requires some special work to make the database schema migrations as low-lock as possible on tables with weekly partitions. Cutting out the DBA as an intermediary before developers can deploy new features and reports is a huge win for automation and sustainability of the application.

My preference is that developers never require a DBA to be present when deploying an app — even when there are schema changes. There are lots of features in Postgres that significantly reduce the need for DBA involvement — streaming replication, pg_basebackup, zero downtime column ADD, DROP and ALTER, transactional DDL and CREATE INDEX CONCURRENTLY to name a few. We’re not quite there for Socorro, but my plan is to get there.

The ideal role of the Postgres DBA is more to write database code, monitor and improve performance. That kind of role feels a lot more like a partner to developers, rather than a separate, mysterious entity. In much the same way that DevOps and configuration management enable great cooperation between IT and developers, many of Postgres’ maintenance features enable greater cooperation and less friction between DBAs and developers deploying new features.

There are still a few configuration and initialization issues to work through before Postgres can be seen as completely past it’s reputation as a difficult to deploy database. Some of that can be solved with more widely used configuration recipes for tools like Puppet and Chef. My colleagues in IT have been working on a Puppet module that they plan to release that automatically sets up a master database and as many replicas as you’d like to configure.

My hope is that people more widely share their automation stories and code!

In anycase, the JSON datatype is solving an important problem for our team, and significantly reducing the demand for schema changes. I’d love to hear more reports from users trying out JSON the field.

Fancy SQL Monday: Why use OVER() instead of a CROSS JOIN?

EDIT: I had to add this, because it was cracking me up:

FOR LIKE EVERY REASON EVER

I’m reimplementing some expensive database queries, moving them from our middleware into materialized view tables. We make pretty extensive use of Common Table Expressions (CTEs). And we generate many reporting queries that calculate averages and percentages of a total. One way this could be done is with a CROSS JOIN, which is a cartesian product of two tables, adding the total and then the percentage calculation to our original table that produces counts of events. For information about JOIN types supported by PostgreSQL, see SELECT…FROM documentation.

Or we could use OVER(), one of several Window Functions supported by Postgres. From the Postgres documentation, Window Functions are:

A window function performs a calculation across a set of table rows that are somehow related to the current row. This is comparable to the type of calculation that can be done with an aggregate function. But unlike regular aggregate functions, use of a window function does not cause rows to become grouped into a single output row — the rows retain their separate identities. Behind the scenes, the window function is able to access more than just the current row of the query result.

A few other folks have written about Window Functions: Postgres Guide to Window Functions, Window Functions: Postgres’s best kept secret, SQL Window Functions (examples in Ruby). “All built-in and user-defined aggregate functions — such as count, max, bit_or, or xmlagg” — are available for computation as a window function, quoting Open Logic, who phrased it well.

If you read about OVER(), you’ll see that all the examples specify a PARTITION clause. But you don’t have to specify a PARTITION to take advantage of the feature.

We had a query containing two CTEs and a final SELECT that produced the matview data. Here’s an example of that query:

WITH crashes as (
  SELECT
    product_name as category
    , version_string
    , SUM(report_count) as report_count
  FROM signature_summary_products
  JOIN signatures USING (signature_id)
  WHERE signatures.signature = 'libflashplayer.so@0x1f2a14'
  AND report_date >= now()::date - '15 day'::interval
  AND report_date < now()::date 
  GROUP BY product_name, version_string
),
totals as (
  SELECT
    category
    , version_string
    , report_count
    , SUM(report_count) OVER () as total_count
  FROM crashes
)
SELECT category
  , version_string
  , report_count
  , round((report_count * 100::numeric)/total_count,3)::TEXT
  as percentage
FROM totals
ORDER BY report_count DESC;

The part under consideration is:

    , SUM(report_count) OVER () as total_count

So, you see that OVER() has no PARTITION defined, meaning that the SUM will be calculated over the entire result.

Here is that same query, implemented using SUM() and a CROSS JOIN:

WITH crashes as (
  SELECT
    product_name as category
    , version_string
     , SUM(report_count) as report_count
  FROM signature_summary_products
  JOIN signatures USING (signature_id)
  WHERE signatures.signature = 'libflashplayer.so@0x1f2a14'
    AND report_date >= now()::date - '15 day'::interval
    AND report_date < now()::date 
  GROUP BY product_name, version_string
),
totals as (
  SELECT
    SUM(report_count) AS total_count
  FROM crashes
)
SELECT category
  , version_string
  , report_count
  , round((report_count * 100::numeric)/total_count,3)::TEXT
as percentage
FROM crashes CROSS JOIN totals
ORDER BY report_count DESC;

What’s the difference to Postgres between that and a SUM() plus a CROSS JOIN?

Here’s the EXPLAIN output from this query, pared down to the relevant section:

   CTE totals
     ->  WindowAgg  (cost=0.00..0.03 rows=1 width=72) (actual time=0.112..0.114 rows=3 loops=1)
           ->  CTE Scan on crashes  (cost=0.00..0.02 rows=1 width=72) (actual time=0.097..0.100 rows=3 loops=1)
   ->  CTE Scan on totals  (cost=0.00..0.04 rows=1 width=104) (actual time=0.121..0.129 rows=3 loops=1)

The important bit to have a look at is WindowAgg right after CTE totals.

Now compare to the EXPLAIN output from a SUM() plus a CROSS JOIN query:

   CTE totals
     ->  Aggregate  (cost=0.02..0.03 rows=1 width=8) (actual time=0.015..0.015 rows=1 loops=1)
           ->  CTE Scan on crashes  (cost=0.00..0.02 rows=1 width=8) (actual time=0.001..0.004 rows=3 loops=1)
   ->  Nested Loop  (cost=0.00..0.07 rows=1 width=104) (actual time=0.191..0.205 rows=3 loops=1)
         ->  CTE Scan on crashes  (cost=0.00..0.02 rows=1 width=72) (actual time=0.162..0.164 rows=3 loops=1)
         ->  CTE Scan on totals  (cost=0.00..0.02 rows=1 width=32) (actual time=0.006..0.006 rows=1 loops=3)

You can see here that we now have an Aggregate (for the SUM()) plus a Nested Loop (for the CROSS JOIN). This example query only has three results returned, but our more typical queries involve 10k or more rows returned.

Avoiding the Aggregate and Nested Loop will save us lots of memory and processing time on every run of a very expensive query.

Speaking at the Cash Music Summit today

I’m headed off to the Cash Music Summit in Portland, OR today.

Here’s the blurb I wrote out for Jesse’s zine:

What’s open source got to do with it?

Free software sounds like a 70s era free-love pipe dream. The idea and a copyright hack to enable it were born in 1984, brought to term by an academic who just wanted to fix a problem he had with his printer.

Free and open source software underpin Cash Music’s platform. Why should that matter to you? Software developers prefer working with code they can freely read, understand and modify. But the benefits to end users are not always as clear.

There’s a method to the madness that is giving away something many believe is worth more if kept secret. It’s not just about sharing, but also accountability, choice and freedom. And, it’s about creating communities we love contributing to, with the kind of people we love to collaborate with.

I’m not sure exactly which story I’m going to tell today – I’ve got 10 minutes though and a pretty sweet picture of Tina Turner.

If you’ve not seen Cash Music or learned about it’s mission, check out this NYTimes article about their work.