Recently we have covered “count” quite extensively on this blog. We discussed optimizing count(*) and also talked about “max(id) – min(id)” which is of course a bad idea to count data in any relational database (not just in PostgreSQL). Today I want to focus your attention on a different kind of problem and its solution: Suppose you want to grant a user access to a certain piece of data only X times. How can one implement that safely?

SQL: Trying the trivial approach

One might argue that this is easy. Here is some pseudo code:

BEGIN;
SELECT count(*)
FROM   log
WHERE  customer_id = 10
       AND tdate = '2020-04-09'

if count >= limit
   ROLLBACK
else
   INSERT INTO log
   “do some work”
   COMMIT;

Looks easy, doesn’t it? Well, not really.

What happens if two people run this concurrently. Both sessions will receive the same result for count(*) and therefore one session will come to the wrong conclusion. Both transactions will proceed and do the work even if only one is allowed to do it. That is clearly a problem.

To solve the problem one could do …

 LOCK TABLE log IN ACCESS EXCLUSIVE MODE; 

… to eliminate concurrency all together. But, this is clearly a problem because it would block pg_dump or some analytics jobs or even VACUUM. Obviously not an attractive option. Alternatively we could try a SERIALIZABLE transaction but that might lead to many transactions failing due to concurrency issue.

One more problem is that count(*) might be really slow if there are already many entries. In short this approach has many problems:

  • It is buggy
  • It is slow
  • It does not scale
  • Runtime is unpredictable

But how can we do better?

Introducing a “count” table

To solve the problem we can introduce a “count table” but let us start right at the beginning. Let me create a table structure first:

CREATE EXTENSION btree_gist;

CREATE TABLE t_customer
(
    id                    serial PRIMARY KEY,
    customer_email        text NOT NULL UNIQUE,
    login_whatever_else   text
);

CREATE TABLE t_customer_limit
(
    id                serial  PRIMARY KEY,
    customer_id       int     REFERENCES t_customer (id)
                              ON UPDATE CASCADE
                              ON DELETE SET NULL,
    clicks_per_day    int     CHECK (clicks_per_day > 0) NOT NULL,
    trange            tstzrange,
    EXCLUDE USING gist (trange WITH &&, customer_id WITH =)
);

CREATE INDEX idx_limit ON t_customer_limit(customer_id);

First of all I have installed an extension which is needed for one of the tables (more on that later).The customer table contains an id, some login email address and some other fields for whatever purpose. Note that this is mostly a prototype implementation.

Then comes an important table: t_customer_limit. It tells us how many clicks per day which customer can have. PostgreSQL supports “range types”. A period can be stored in a single column. Using range types has a couple of advantages. PostgreSQL will automatically check if the period is valid (end > start and so on). In addition to that the database provides a couple of operators to make handling really easy.

In the next step I have created a table to track all requests thrown at us. Creating such a table has the advantage that you can do a lot of analytics later on:

CREATE TABLE t_log
(
    id                  serial         PRIMARY KEY,
    tstamp              timestamptz    DEFAULT now() NOT NULL,
    customer_id         int            NOT NULL,
    http_referrer       inet,
    http_other_fields   text           NOT NULL,    -- put in more fields
    request_infos       jsonb                       -- what else we know from the request
);

CREATE INDEX idx_log ON t_log (tstamp, customer_id);

If you decide to store whatever there is in the HTTP header you might be able to identify spammers, access patterns and a lot more later on. This data can be really beneficial later on.

But due to the problems outlined before it is pretty tricky and inefficient to limit the number of entries per day for a single user. Therefore I have added a “count” table which has some interesting information:

CREATE TABLE t_log_count
(
    customer_id           int REFERENCES t_customer (id)
                              ON UPDATE CASCADE
                              ON DELETE SET NULL,
    tdate                 date NOT NULL,
    counter_limit         int DEFAULT 0,
    counter_limit_real    int DEFAULT 0,
    UNIQUE (customer_id, tdate)
) WITH (FILLFACTOR=60);

CREATE INDEX idx_log_count ON t_log_count (tdate, customer_id);

For each day we store the counter_limit which is the number of hits served to the client on a given day. This number will never go higher than the limit of the user for the day. Then we got the counter_limit_real which contains the number of requests the user has really sent. If counter_limit_real is higher than counter_limit we know that we had to reject requests due to high consumption. It is also trivial to figure out how many requests we already had to reject on a given day for a user.

Let us add some sample data:

-- sample data
INSERT INTO t_customer (customer_email) VALUES ('hans company');

INSERT INTO t_customer_limit (customer_id, clicks_per_day, trange)
       VALUES (1, 4, '["2020-01-01", "2020-06-30"]');

‘hans_company’ is allowed to make 4 requests per day for the period I have specified.

Now that the data structure is in place I have created a simple SQL function which contains the entire magic we need to make this work efficiently:

CREATE FUNCTION register_call(int)
   RETURNS t_log_count AS
$$
   WITH x AS (SELECT * FROM t_customer_limit WHERE now() <@ trange AND customer_id = $1),
        y AS (INSERT INTO t_log (customer_id, http_other_fields) VALUES ($1, 'whatever info') )
   INSERT INTO t_log_count (customer_id, tdate, counter_limit, counter_limit_real)
        VALUES ($1, now()::date, 1, 1)
        ON CONFLICT (customer_id, tdate)
        DO UPDATE 
           SET counter_limit_real = t_log_count.counter_limit_real + 1,
               counter_limit = CASE WHEN t_log_count.counter_limit < (SELECT clicks_per_day FROM x)
                               THEN t_log_count.counter_limit + 1 ELSE t_log_count.counter_limit END

           WHERE EXCLUDED.customer_id = $1
                 AND EXCLUDED.tdate = now()::date
   RETURNING *;
$$ LANGUAGE 'sql';

This might look a bit nasty at first glance but let us dissect the query bit by bit. The first CTE (x) tells today’s limits for a specific user. We will need this information later on. “x” is not needed explicitly because we can also use a simple subselect. But if you want to sophisticate my prototype it might come in handy if “x” can be reused in the query multiple times.

Then the query inserts a row into t_log. This should be a pretty straight forward operation. Then we attempt to insert into t_log_count. If there is no entry for the customer for the day yet the insert will go through. The important part is that if two INSERT statements happen at the same time PostgreSQL will report a key violation and we cannot end up with a duplicate entry under and circumstances.

But what happens if the key is violated? In this case the ON CONFLICT clause we show its worth: If the INSERT goes wrong we want to perform an UPDATE. If we have reached the limit we only increment counter_limit_real – otherwise we increment both values. Then the updated row is returned. If counter_limit_real and counter_limit are identical we can allow the request – otherwise the user has to be rejected.

The count table has some nice attributes: When it is updated only ONE row will be locked at a time. That means that no operations on t_log will be blocked for reporting, VACUUM and so on. The row lock on the second table will perfectly synchronize things and there is no chance of serving requests which are not allowed. Minimal locking, maximum performance while maintaining flexibility.

Of course this is only a prototype which leaves some room for improvements (such as partitioning and so on). From a looking and efficiency point of view this is promising.

A simple test on my (relative old) iMac has shown that I can run around 20.000 requests in around 10 seconds (single connection). This is more than enough for most applications assuming that there will be many clients and a lot of parallelism.

What you have to keep in mind is bloat. Keep an eye on the updated table at all time and make sure that it does not grow out of proportion.

concurrent counting

Finally …

Avoiding bloat is important. Check out our post about VACUUM and PostgreSQL database bloat here. You will learn all you need to know to ensure your database is small and efficient.