Thread: Design strategy for table with many attributes

Or say, what is the maximum number of columns per table we should restrict? Should we break the single transaction into multiple tables like one main table and other addenda tables with the same primary key to join and fetch the results wherever necessary?

Ultimately, the way you should store the data depends on how you will use it. When you retrieve these values, what are
thedifferent ways you’ll be using them? 

Normalised representations are more flexible, and the pragmatic, performance-based consideration is that all the values
ina row are always effectively retrieved together. So if you reasonably often have a query that only accesses the
creationdate and transaction id, then it will be pretty slow if you are also always retrieving 500 other columns you
don’tneed. 

So: you might often pull all the values *other* than the attributes (creation date, creator, job information, whatever)
together.This argues that those values should be in one table, and the attributes in another. 

Will you usually be using *all* of the attributes for a particular transaction together in the same operation? It might
makesense to store them in eg an array in that case. But this significantly reduces the speed of accessing particular
attributesseparately. 

It is likely that you will want to slice things by particular named attribute across many transactions. This argues for
themore normalised form, as does the general principle of doing things in the way that is most general and flexible. 

When considering how the data will be used, please consider not only the developers of your current application(s), but
alsoeg data analysts, managers, future applications etc. 

The less specific you can be about how you want to use the data, the more you should lean toward fully normalising.

Note also that you can store your data in a normalised and maximally flexible form, but also use triggers or batch jobs
togather various permutations of the data for specific purposes. If you really do, say, both have some major part of
yourproject that uses all the attributes on a given transaction together, but you also have other uses, you may want to
storeboth the normalised/attribute table and the “all the values together” version. 

Even if you want to store “all the values together”, it may well be better to use an array, JSON or HStore, rather than
havinga super-wide table. JSON would eg let you enumerate all the column names (for example) and employ Postgres’s
reallynice JSON query features. 


> On Jul 4, 2024, at 12:37, Lok P <loknath.73@gmail.com> wrote:
>
> Hello,
> In one of the applications we are getting transactions in messages/events format and also in files and then they are
gettingparsed and stored into the relational database. The number of attributes/columns each transaction has is ~900+.
Logicallythey are part of one single transaction and should be stored in one table as one single row. There will be
~500millionsuch transactions each day coming into the system. And there will be approx ~10K peak write TPS and 5K read
TPSin target state. This system has a postgres database as a "source of truth" or OLTP store. And then data moves to
snowflakesfor the olap store. 
>
> Initially when the system was designed the number of attributes per transaction was <100 but slowly the business
wantsto keep/persist other attributes too in the current system and the number of columns keep growing. 
>
> However, as worked with some database systems , we get few suggestions from DBA's to not have many columns in a
singletable. For example in oracle they say not to go beyond ~255 columns as then row chaining and row migration type
ofthings are going to hunt us. Also we are afraid concurrent DMLS on the table may cause this as a contention point. So
Iwanted to understand , in such a situation what would be the best design approach we should use irrespective of
databases?Or say, what is the maximum number of columns per table we should restrict? Should we break the single
transactioninto multiple tables like one main table and other addenda tables with the same primary key to join and
fetchthe results wherever necessary? 
>
> Regards
> Lok

On Fri, 5 Jul 2024 at 17:07, Lok P <loknath.73@gmail.com> wrote:
> Also I understand the technical limitation of the max number of columns per table is ~1600. But should you advise to
restrict/stopus to some low number long before reaching that limit , such that we will not face any anomalies when we
growin future. And if we should maintain any specific order in the columns from start to end column in the specific
table?

Something else you may wish to consider, depending on the column types
of your 900+ columns is the possibility that some INSERTs may fail due
to row length while others with shorter variable length values may be
ok.

Here's a quick example with psql:

select 'create table a (' || string_agg('a'||x||' text not null
default $$$$',',') || ')' from generate_series(1,1000)x;
\gexec
insert into a default values;
INSERT 0 1

again but with a larger DEFAULT to make the tuple larger.

select 'create table b (' || string_agg('a'||x||' text not null
default $$hello world$$',',') || ')' from generate_series(1,1000)x;
\gexec
insert into b default values;
ERROR:  row is too big: size 12024, maximum size 8160

There is a paragraph at the bottom of [1] with some warnings about
things relating to this.

The tuple length would be fixed for fixed-length types defined as NOT
NULL. So, if you have that, there should be no such surprises.

David

[1] https://www.postgresql.org/docs/current/limits.html

But do you also suggest keeping those table pieces related to each other through the same primary key ?

 Won't there be a problem when we load the data like say for example , in normal scenario the data load will be to one table but when we break it to multiple tables it will happen to all the individual pieces, won't that cause additional burden to the data load?

Also I understand the technical limitation of the max number of columns per table is ~1600. But should you advise to restrict/stop us to some low number long before reaching that limit , such that we will not face any anomalies when we grow in future.

 And if we should maintain any specific order in the columns from start to end column in the specific table?

On Fri, 5 Jul 2024 at 19:53, Lok P <loknath.73@gmail.com> wrote:
> As David suggested it breaks if a row exceeds the 8k limit I. E a single page size , will that still holds true if we
havea column with JSON in it?
 

You wouldn't be at risk of the same tuple length problem if you
reduced the column count and stored the additional details in JSON.
Each varlena column is either stored in the tuple inline, or toasted
and stored out of line. Out of line values need an 18-byte pointer to
the toasted data. That pointer contributes to the tuple length.

This isn't me advocating for JSON. I'm just explaining the
limitations. I think I'd only advocate for JSON if the properties you
need to store vary wildly between each tuple.  There's a large
overhead to storing JSON labels, which you'd pay the price of for each
tuple. That sounds like it would scale terribly with the data volumes
you've suggested you'll be processing.

David

On Fri, 5 Jul 2024 at 20:53, Lok P <loknath.73@gmail.com> wrote:
> However out of curiosity, if the roasted/compressed component or column which is JSON itself goes beyond 8k post
compression,will it break then?
 

No. The size limit of a varlena field such as TEXT, JSON or JSONB is
1GB.  See "field size" in [1].

Please also read [2].

David

[1] https://www.postgresql.org/docs/current/limits.html
[2] https://www.postgresql.org/docs/current/storage-toast.html