Re: Compressing large column by moving it to a unique table

[Adrian Garcia Badaracco <adrian@xxxxxxxxxxxx>]

I'm using PostgreSQL 15.5.

Here's a self-contained example. I included links to public pgMustard query plans.

Gist link: https://gist.github.com/adriangb/05a01cca99a438de2a286945903c44f4

Also copied below for archiving:

```sql
CREATE OR REPLACE FUNCTION random_bytes(length integer)
RETURNS bytea AS $$
DECLARE
  bytes bytea := '';
  i integer := 0;
BEGIN
  -- generate 1024 bytes at a time using gen_random_bytes(1024)
  WHILE i < length LOOP
    bytes := bytes || gen_random_bytes(least(1024, length - i));
    i := i + 1024;
  END LOOP;

  RETURN bytes;
END;
$$ LANGUAGE plpgsql;

DROP TABLE IF EXISTS original, original_hashed, dictionary_table CASCADE;
DROP VIEW IF EXISTS joined_view;

CREATE TABLE original AS (
  WITH large_random_bytes AS (
    SELECT n AS id, random_bytes(4096 + n) AS attributes
    FROM generate_series(1, 1000) n
  )
  SELECT
    -- An incrementing timestamp
    '2024-01-01 00:00:00.00+00'::timestamptz + (n || ' seconds')::interval AS start_timestamp,
    -- Another relatively small column, let's just make it a random string including `n` to make it unique
    'random_string_' || (n % 100) AS event_name,
    -- The attributes column
    lrb.attributes AS attributes
    FROM generate_series(0, 1000000) n
    JOIN large_random_bytes lrb ON n % 1000 = lrb.id
);
CREATE INDEX ON original(start_timestamp);

CREATE TABLE dictionary_table AS (
  SELECT DISTINCT time_bucket('1 day', start_timestamp) AS start_timestamp_range, attributes, md5(attributes) AS hash
  FROM original
);
CREATE INDEX ON dictionary_table (start_timestamp_range, hash);

CREATE TABLE original_hashed AS (
  SELECT
    start_timestamp,
    event_name,
    md5(attributes) AS hash
  FROM original
);
CREATE INDEX ON original_hashed (start_timestamp, time_bucket('1 day', start_timestamp), hash);


CREATE VIEW joined_view AS (
  SELECT
    original_hashed.start_timestamp,
    original_hashed.event_name,
    dictionary_table.attributes
  FROM original_hashed
  LEFT JOIN dictionary_table ON (
    time_bucket('1 day', original_hashed.start_timestamp) = dictionary_table.start_timestamp_range
    AND
    original_hashed.hash = dictionary_table.hash
  )
);

-- Select all data
explain (analyze, buffers, verbose, settings, format json)
SELECT *
FROM joined_view
WHERE (
  start_timestamp > ('2024-01-01 00:00:00.00+00'::timestamptz + interval '1 day')
  AND
  start_timestamp < ('2024-01-01 00:00:00.00+00'::timestamptz + interval '2 day')
);
-- https://app.pgmustard.com/#/explore/f8b3b7d5-c236-4326-9a67-20fa4cdff4bc

explain (analyze, buffers, verbose, settings, format json)
SELECT *
FROM original
WHERE (
  start_timestamp > ('2024-01-01 00:00:00.00+00'::timestamptz + interval '1 day')
  AND
  start_timestamp < ('2024-01-01 00:00:00.00+00'::timestamptz + interval '2 day')
);
-- https://app.pgmustard.com/#/explore/bcad3767-b8aa-4a37-acc7-7667266f29be

-- A relatively aggregation selective query
explain (analyze, buffers, verbose, settings, format json)
SELECT count(*)
FROM joined_view
WHERE (
  get_byte(attributes, 4) < 100
  AND
  start_timestamp > ('2024-01-01 00:00:00.00+00'::timestamptz + interval '1 day')
  AND
  start_timestamp < ('2024-01-01 00:00:00.00+00'::timestamptz + interval '2 day')
);
-- https://app.pgmustard.com/#/explore/c5b68839-4c66-4b14-8de5-757bfc22fdb3

explain (analyze, buffers, verbose, settings, format json)
SELECT count(*)
FROM original
WHERE (
  get_byte(attributes, 4) < 100
  AND
  start_timestamp > ('2024-01-01 00:00:00.00+00'::timestamptz + interval '1 day')
  AND
  start_timestamp < ('2024-01-01 00:00:00.00+00'::timestamptz + interval '2 day')
);
-- https://app.pgmustard.com/#/explore/831d9c4c-e911-4caf-a6c3-eee5343e44d8

-- Select only some columns
explain (analyze, buffers, verbose, settings, format json)
SELECT start_timestamp
FROM joined_view
WHERE (
  start_timestamp > ('2024-01-01 00:00:00.00+00'::timestamptz + interval '1 day')
  AND
  start_timestamp < ('2024-01-01 00:00:00.00+00'::timestamptz + interval '2 day')
);
-- https://app.pgmustard.com/#/explore/5f617bfa-7816-4f8d-9fd7-24388c08978c

explain (analyze, buffers, verbose, settings, format json)
SELECT start_timestamp
FROM original
WHERE (
  start_timestamp > ('2024-01-01 00:00:00.00+00'::timestamptz + interval '1 day')
  AND
  start_timestamp < ('2024-01-01 00:00:00.00+00'::timestamptz + interval '2 day')
);
-- https://app.pgmustard.com/#/explore/f05de3e8-ca8d-4404-ad33-69f103a3b85a


-- A relatively selective query
explain (analyze, buffers, verbose, settings, format json)
SELECT *
FROM joined_view
WHERE (
  get_byte(attributes, 4) < 100
  AND
  start_timestamp > ('2024-01-01 00:00:00.00+00'::timestamptz + interval '1 day')
  AND
  start_timestamp < ('2024-01-01 00:00:00.00+00'::timestamptz + interval '2 day')
);
-- https://app.pgmustard.com/#/explore/a75b20bd-07d5-4402-a0d8-22419682307a

explain (analyze, buffers, verbose, settings, format json)
SELECT *
FROM original
WHERE (
  get_byte(attributes, 4) < 100
  AND
  start_timestamp > ('2024-01-01 00:00:00.00+00'::timestamptz + interval '1 day')
  AND
  start_timestamp < ('2024-01-01 00:00:00.00+00'::timestamptz + interval '2 day')
);
-- https://app.pgmustard.com/#/explore/47ef84f0-a96e-4baa-af40-2ec241cbb6e2
```

On Tue, Feb 13, 2024 at 11:04 AM Ron Johnson <ronljohnsonjr@xxxxxxxxx> wrote:

1. Show us the PG version, view definition, the exact query that's slow, and the EXPLAIN (ANALYZE).

2. Presumably there's an index on each table's *_hash column?

On Tue, Feb 13, 2024 at 8:48 AM Adrian Garcia Badaracco <adrian@xxxxxxxxxxxx> wrote:

Thank you for the reply Ron.

Yes there are many fewer (<1%) the number of rows in new_table.

Thanks for making me think of normalization, I hadn’t seen it that way. Although there is no theoretical relationship between the rows in the other columns in the original table and the attributes column, in practice there is a strong correlation, so I guess what I am trying to capture here is taking advantage of that correlation, while not completely depending on it because it can be broken.

In any case, whatever theoretical framework is put around this solution, I am also interested in the practical aspects, in particular that case of selecting a subset of columns from the view that I know doesn’t need the join but the query planner thinks does.

On Tue, Feb 13, 2024 at 3:16 AM Ron Johnson <ronljohnsonjr@xxxxxxxxx> wrote:

On Mon, Feb 12, 2024 at 10:12 PM Adrian Garcia Badaracco <adrian@xxxxxxxxxxxx> wrote:

I am using Timescale so I'll be mentioning some timestamp stuff but I think this is a general postgres question for the most part.

I have a table with some fixed, small columns (id, timestamp, etc) and a large JSONB column (let's call it `attributes`). `attributes` has 1000s of schemas, but given a schema, there's a lot of duplication. Across all rows, more than 99% of the data is duplicated (as measured by `count(attributes)` vs `count(distinct attributes)`.

I can't normalize `attributes` into real columns because it is quite variable (remember 1000s of schemas).

My best idea is to make a table like `(day timestamptz, hash text, attributes jsonb)` and then in my original table replace `attributes` with a reference to `new_table`.

Meaning that there are many fewer rows in new_table?

 

I can then make a view that joins them `select original_table.timestamp, new_table.attributes from original join new_table on (time_bucket('1 day', timestamp) = day AND original.hash = new_table.hash)` or something like that. The idea of time bucketing into 1 day is to balance write and read speed (by relying on timescale to do efficient time partitioning, data retention, etc.).

I recognize this is essentially creating a key-value store in postgres and also janky compression, so I am cautious about it.

If my interpretation (that there are many fewer rows in new_table) is correct, then you've stumbled into the Second Normal Form of database design: https://en.wikipedia.org/wiki/Second_normal_form#Example