In the previous article of the series Modeling for Concurrency, we saw how to model your application for highly concurrent activity. It was a follow-up to the article entitled PostgreSQL Concurrency: Isolation and Locking, which was a primer on PostgreSQL isolation and locking properties and behaviors.

Today’s article takes us a step further and builds on what we did in the previous articles in our series. After having had all the characters from Shakespeare’s A Midsummer Night’s Dream tweet their own lines in our database in PostgreSQL Concurrency: Data Modification Language, and having had them like and retweet a lot in PostgreSQL Concurrency: Isolation and Locking, we saw how to manage concurrent retweets in an efficient way in Computing and Caching.

What we did implement in the previous article is a cache system, all with its necessary cache invalidation policy. Sometimes though, the processing of an event needs to happen within the same transaction where the event is registered in your system. PostgreSQL makes it possible to maintain a summary table transactionally thanks to its trigger support. Today, we’re going to dive in how to maintain a summary table with triggers, and its impact on concurrency.

Triggers

When a cache refresh policy of minutes isn’t advisable, a common approach is to implement event-based processing. Most SQL systems, including PostgreSQL, implement an event-based facility called a trigger.

A trigger allows registering a procedure to be executed at a specified timing when an event is produced. The timing can be before, after or instead of, and the event can be insert, update, delete or truncate. As usual, the PostgreSQL documentation covers the topic in full details and is available online, in our case now at the manual page for the command CREATE TRIGGER.

Many triggers in PostgreSQL are written in the PL/pgSQL — SQL Procedural Language, so we also need to read the PLpgSQL trigger procedures documentation for completeness.

Note that with PostgreSQL, it is possible to write procedures and triggers in other programming languages. Default PostgreSQL builds include support for PL/Tcl, PL/Perl, PL/Python and of course C-language functions.

PostgreSQL extensions for other programming languages are available too, maintained separately from the PostgreSQL core. You can find PL/Java, PL/v8 for Javascript powered by the V8 engine, or PL/XSLT as we saw in PostgreSQL Data Types: XML. For even more programming language support, see the PL Matrix in the PostgreSQL wiki.

Unfortunately, it is not possible to write triggers in plain SQL language, so we have to write stored procedures to benefit from the PostgreSQL trigger capabilities.


This article is extracted from my book Mastering PostgreSQL in Application Development, which teaches SQL to developers so that they may replace thousands of lines of code with very simple queries. The book has a full chapter about Data Manipulation and Concurrency Control in PostgreSQL, including caching with materialized views, check it out!


Transactional Event Driven Processing

PostgreSQL triggers call a registered procedure each time one of the supported events is committed. The execution of the procedure is always taken as a part of the transaction, so if your procedure fails at runtime then the transaction is aborted.

A classic example of an event driven processing with a trigger in our context is to update the counters of rts and favs each time there’s a related insert in the tweet.activity table.

begin;

create table twcache.daily_counters
 (
   day     date not null primary key,
   rts     bigint,
   de_rts  bigint,
   favs    bigint,
   de_favs bigint
 );

create or replace function twcache.tg_update_daily_counters ()
 returns trigger
 language plpgsql
as $$
declare
begin
      update twcache.daily_counters
         set rts = case when NEW.action = 'rt'
                        then rts + 1
                        else rts
                    end,
             de_rts = case when NEW.action = 'de-rt'
                        then de_rts + 1
                        else de_rts
                    end,
             favs = case when NEW.action = 'fav'
                         then favs + 1
                         else favs
                     end,
             de_favs = case when NEW.action = 'de-fav'
                         then de_favs + 1
                         else de_favs
                     end
       where daily_counters.day = current_date;

  if NOT FOUND
  then
      insert into twcache.daily_counters(day, rts, de_rts, favs, de_favs)
           select current_date,
                  case when NEW.action = 'rt'
                       then 1 else 0
                    end,
                  case when NEW.action = 'de-rt'
                       then 1 else 0
                   end,
                  case when NEW.action = 'fav'
                       then 1 else 0
                   end,
                  case when NEW.action = 'de-fav'
                       then 1 else 0
                   end;
  end if;

  RETURN NULL;
end;
$$;

CREATE TRIGGER update_daily_counters
         AFTER INSERT
            ON tweet.activity
      FOR EACH ROW
       EXECUTE PROCEDURE twcache.tg_update_daily_counters();

insert into tweet.activity(messageid, action)
     values (7, 'rt'),
            (7, 'fav'),
            (7, 'de-fav'),
            (8, 'rt'),
            (8, 'rt'),
            (8, 'rt'),
            (8, 'de-rt'),
            (8, 'rt');

select day, rts, de_rts, favs, de_favs
  from twcache.daily_counters;

rollback;

Again, we don’t really want to have that trigger in our setup, so the transaction ends with a ROLLBACK. It’s also a good way to try in-progress development in psql in an interactive fashion, and fix all the bugs and syntax errors until it all works.

Without this trick, then parts of the script pass and others fail, and you then have to copy and paste your way around until it’s all okay, but then you’re never sure that the whole script will pass from the start again, because the conditions in which you want to apply have been altered on the partially successful runs.

Here’s the result of running our trigger test script:

BEGIN
CREATE TABLE
CREATE FUNCTION
CREATE TRIGGER
INSERT 0 8
    day     │ rts │ de_rts │ favs │ de_favs 
════════════╪═════╪════════╪══════╪═════════
 2017-09-21 │   5 │      1 │    1 │       1
(1 row)

ROLLBACK

The thing is, each time there’s a tweet.activity inserted this trigger will transform the insert into an update against a single row, and the same target row for a whole day.

This implementation is totally killing any ambitions we might have had about concurrency and scalability properties of our model, in a single trigger. Yet it’s easy to write such a trigger, so it’s seen a lot in the wild.

Trigger and Counters Anti-Pattern

You might also notice that this triggers is very wrong in its behavior, as coded. The implementation of the insert or update — a.k.a. upsert — is coded in a way to leave the door open to concurrency issues. To understand those issues, we need to consider what happens when we start a new day:

  1. The first transaction of the day attempts to update the daily counters table for this day, but finds no records because it’s the first one.

  2. The first transaction of the day then inserts the first value for the day with ones and zeroes for the counters.

  3. The second transaction of the day then executes the update to the daily counter, finds the existing row, and skips the insert part of the trigger.

That’s the happy scenario where no problem occurs. Now, in the real life, here’s what will sometimes happen. It’s not always, mind you, but not never either. Concurrency bugs — they like to hide in plain sight.

  1. The first transaction of the day attempts to update the daily counters table for this day but finds no records because it’s the first one.

  2. The second transaction of the day attempts to update the daily counters table for this day, but finds no records, because the first one isn’t there yet.

  3. The second transaction of the day now proceeds to insert the first value for the day, because the job wasn’t done yet.

  4. The first transaction of the day then inserts the first value… and fails with a primary key conflict error because that insert has already been done. Sorry about that!

There are several ways to address this issue, and the classic one is documented at A PL/pgSQL Trigger Procedure For Maintaining A Summary Table example in the PostgreSQL documentation.

The solution there is to loop over attempts at update then insert until one of those works, ignoring the UNIQUE_VIOLATION exceptions in the process. That allows implementing a fall back when another transaction did insert a value concurrently, i.e. in the middle of the NOT FOUND test and the consequent insert.

Starting in PostgreSQL 9.5 with support for the on conflict clause of the insert into command, there’s a much better way to address this problem.

Fixing the Behavior

While it’s easy to maintain a cache in an event driven fashion thanks to PostgreSQL and its trigger support, turning an insert into an update with contention on a single row is never a good idea. It’s even a classic anti-pattern.

Here’s a modern way to fix the problem with the previous trigger implementation, this time applied to a per-message counter of retweet and favorite actions:

begin;

create table twcache.counters
 (
   messageid  bigint not null references tweet.message(messageid),
   rts        bigint,
   favs       bigint,

   unique(messageid)
 );

create or replace function twcache.tg_update_counters ()
 returns trigger
 language plpgsql
as $$
declare
begin
   insert into twcache.counters(messageid, rts, favs)
        select NEW.messageid,
               case when NEW.action = 'rt' then 1 else 0 end,
               case when NEW.action = 'fav' then 1 else 0 end
   on conflict (messageid)
     do update
           set rts = case when NEW.action = 'rt'
                          then counters.rts + 1

                          when NEW.action = 'de-rt'
                          then counters.rts - 1

                          else counters.rts
                      end,

               favs = case when NEW.action = 'fav'
                           then counters.favs + 1

                           when NEW.action = 'de-fav'
                           then counters.favs - 1

                           else counters.favs
                       end
         where counters.messageid = NEW.messageid;

  RETURN NULL;
end;
$$;

CREATE TRIGGER update_counters
         AFTER INSERT
            ON tweet.activity
      FOR EACH ROW
       EXECUTE PROCEDURE twcache.tg_update_counters();

insert into tweet.activity(messageid, action)
     values (7, 'rt'),
            (7, 'fav'),
            (7, 'de-fav'),
            (8, 'rt'),
            (8, 'rt'),
            (8, 'rt'),
            (8, 'de-rt'),
            (8, 'rt');

select messageid, rts, favs
  from twcache.counters;

rollback;

And here’s the result of running that file in psql, either from the command line with psql -f or with the interactive \i <path/to/file.sql command:

BEGIN
CREATE TABLE
CREATE FUNCTION
CREATE TRIGGER
INSERT 0 8
 messageid │ rts │ favs 
═══════════╪═════╪══════
         7 │   1 │    0
         8 │   3 │    0
(2 rows)

ROLLBACK

You might have noticed that the file ends with a ROLLBACK statement. That’s because we don’t really want to install such a trigger, it’s meant as an example only.

The reason why we don’t actually want to install it is that it would cancel all our previous efforts to model for tweet activity scalability by transforming every insert into tweet.activity into an update twcache.counters on the same messageid. We looked into that exact thing in the previous article Computing and Caching and we saw that it would never scale to our requirements.

Conclusion

PostgreSQL triggers are a powerful tool that allows extra business logic to happen right in the transaction that registers events in your system. Deeper thoughts about this complex topic are available in my article SQL and Business Logic.

When using PostgreSQL triggers, concurrency behavior of your implementation must be analyzed. It’s quite easy to deploy a trigger to your production system and implement simple and complex processing in a transactional way, and it’s incredibly easy to kill your concurrency scaling while doing that, as seen here.

In our next article in the series, we will learn how to implement Event Processing in PostgreSQL with LISTEN and NOTIFY and a complete client example, in Go.