Обсуждение: How to do faster DML

On Sat, 3 Feb 2024 at 19:29, Greg Sabino Mullane <htamfids@gmail.com> wrote:
...
> Given the size of your table, you probably want to divide that up.
> As long as nothing is changing the original table, you could do:
>
> insert into mytable2 select * from mytable1 order by ctid limit 10_000_000 offset 0;
> insert into mytable2 select * from mytable1 order by ctid limit 10_000_000 offset 10_000_000;
> insert into mytable2 select * from mytable1 order by ctid limit 10_000_000 offset 20_000_000;

Is it me or does this have the faint smell of quadratic behaviour? I
mean, you need to read and discard the first 10M to do offset 10M (
also I believe ctid order is implied in sequential scan, but no so
sure, if not it would need a full table sort on each pass ).

When doing things like this, I normally have some kind of unique index
and do it by selecting with limit above the last read value( stored
when doing it via code, as I normally load first, index later so I
cannot select max() fast on the target ). Or, with some kind of
"sparse" index (meaning, groups much less than the batch size ) and a
little code you can select where index_col > last order by index_col
limit 10M, remember last received index_col and reselect discarding
missing ( or just reselect it when doing your on conflict do nothing,
which also has a fast select max(id) due to the PK, it will work if it
has an index on id column on the original even if not unique ) to
avoid that.

Also, I'm not sure if ctid is ordered and you can select where
ctid>last ordered, if that works it probably is faster for immutable
origins.

Francisco Olarte.

On 2024-02-03 19:25:12 +0530, Lok P wrote:
> Apology. One correction, the query is like below. I. E filter will be on on
> ctid which I believe is equivalent of rowid in oracle and we will not need the
> index on Id column then. 
>
>  But, it still runs long, so thinking any other way to make the duplicate
> removal faster? 
>
> Also wondering , the index creation which took ~2.5hrs+ , would that have been
> made faster any possible way by allowing more db resource through some session
> level db parameter setting?
>
> create table TAB1_New
> as
> SELECT  * from TAB1 A
> where CTID in
>       (select min(CTID) from TAB1
>       group by ID having count(ID)>=1 );

That »having count(ID)>=1« seems redundant to me. Surely every id which
occurs in the table occurs at least once?

Since you want ID to be unique I assume that it is already almost
unique - so only a small fraction of the ids will be duplicates. So I
would start with creating a list of duplicates:

create table tab1_dups as
select id, count(*) from tab1 group by id having count(*) > 1;

This will still take some time because it needs to build a temporary
structure large enough to hold a count for each individual id. But at
least then you'll have a much smaller table to use for further cleanup.

        hp

--
   _  | Peter J. Holzer    | Story must make more sense than reality.
|_|_) |                    |
| |   | hjp@hjp.at         |    -- Charles Stross, "Creative writing
__/   | http://www.hjp.at/ |       challenge!"

On 2024-02-04 02:14:20 +0530, Lok P wrote:
> However , as we have ~5billion rows in the base table and out of that , we were
> expecting almost half i.e. ~2billion would be duplicates.

That's way more than I expected from your original description. And it
of course raises the question whether it's a good idea to just throw
away all that data or if you need to keep that in a normalized way.

> And you said, doing the inserts using the "order by CTID Offset"
> approach must cause one full sequential scan of the whole table for
> loading each chunk/10M of rows and that would take a long time I
> believe.
>
> I am still trying to understand the other approach which you suggested. Not
> able to understand "you can select where index_col > last order by index_col 
> limit 10M," .
> However, to get the max ID value of the last 10M loaded rows in target, do you
> say that having an PK index created on that target table column(ID) will
> help,

Yes. Getting the maximum value from an index is a very fast operation.
You just have to traverse down the right edge of the tree (or you may
even be able to access the right-most leaf page directly).

>  and we can save the max (ID) value subsequently in another table to fetch
> and keep loading from the source table (as ID>Max_ID stored in temp table)?

Another table or a variable in a script (personally, if I need to do
something repeatedly, I usually write a script in the scripting language
I feel most comfortable in (which has been Python for the last 7 or 8
years, Perl before that) which gives you variables, loops, conditionals
and - above all - repeatability.

> OR
> Would it be better to do it in one shot only , but by setting a higher value of
> some parameters like "maintenance_work_mem" or "max_parallel_workers"?

Hard to say. Normally, processing in fewer. bigger chunks is faster. But
RAM is much faster than disk (even with SSDs), so it might be faster to
make work_mem as large as you can and then use a chunk size which just
fits inside work_mem is faster. Of course finding that sweet spot takes
experimentation, hence time, and it may make little sense to experiment
for 20 hours just to save 40 minutes.

        hp

--
   _  | Peter J. Holzer    | Story must make more sense than reality.
|_|_) |                    |
| |   | hjp@hjp.at         |    -- Charles Stross, "Creative writing
__/   | http://www.hjp.at/ |       challenge!"

> On 3 Feb 2024, at 13:20, Lok P <loknath.73@gmail.com> wrote:
>
> Hello All,
> A non partitioned table having ~4.8 billion rows in it and having data size as ~1.4TB , row size as ~313 bytes having
~127columns in it. This has got approx ~1billion+ duplicate rows inserted in it and we want to get the duplicate data
removedfor this table and create a PK/unique constraint back so as to not have the duplicate values in future. We are
strugglingto do the same. 

~4.8 billion rows of which ~1 billion are duplicates… Wait a minute…

Did you verify that your ID column is larger than 32-bits?
Because if that’s a 32 bit integer, the range of values it can hold is about 4.3 billion, after which it wraps around.

With ~4.8 billion rows that would result in about ~0.5 billion repeated ID values, giving you the reported ~1 billion
duplicateID's. 

If that’s the case, your duplicates obviously aren’t really duplicates and you require a different type of solution.


> Teammates suggested doing this using CTAS method, i.e. create a new table with the unique record set and then drop
themain table. Something as below 
>
> create table TAB1_New
> as
> SELECT  * from TAB1 A
> where ID in
>       (select min(ID) from TAB1
>       group by ID having count(ID)>=1 );
>
> But for the above to work faster , they mentioned to have an index created on the column using which the duplicate
checkwill be performed i.e ID column. So, creating the index itself took ~2hrs+ and the index size now shows as ~116GB. 
>
> Create index idx1 on TAB1(ID)

Are your duplicates exact duplicates? Or is there an order of preference among them?
And if so, what really makes those rows unique?

That matters for solutions on how to deduplicate these rows.

Regards,

Alban Hertroys
--
If you can't see the forest for the trees,
cut the trees and you'll find there is no forest.

Lok:

On Sat, 3 Feb 2024 at 21:44, Lok P <loknath.73@gmail.com> wrote:

> On Sun, Feb 4, 2024 at 12:50 AM Francisco Olarte <folarte@peoplecall.com> wrote:

> However , as we have ~5billion rows in the base table and out of that , we were expecting almost half i.e. ~2billion
wouldbe duplicates. And you said, doing the inserts using the "order by CTID Offset" approach must cause one full
sequentialscan of the whole table for loading each chunk/10M of rows and that would take a long time I believe. 

I did not say "MUST CAUSE". In fact I said I believe it would not.

What I am gonna say ( now ) is test it. Make 1k, 10k, 100k, 1M tables
in a scratch database, explain and test your things there w/ & w/o
index etc.. Not all needed, but testing 100k & 1M in 1k batches could
show you missing quadratic behaviour. Explain would show you
unexpected sorts or scans.

> I am still trying to understand the other approach which you suggested. Not able to understand "you can select where
index_col> last order by index_col limit 10M," . 
> However, to get the max ID value of the last 10M loaded rows in target, do you say that having an PK index created on
thattarget table column(ID) will help, and we can save the max (ID) value subsequently in another table to fetch and
keeploading from the source table (as ID>Max_ID stored in temp table)? 

I am a programmer by trade. When faced with problems like these,
unless I find a trivial solution, I tend to make auxiliary programs as
it is much easier for me to make a thousand lines of Perl ( or
python/C/C++ Java ) or a couple hundreds of plpgsql ( if I want to
avoid roundtrips ) than trying to debug complicated SQL only
workflows.

For your kind of problem I would make a program to read the rows and
insert them. As an example, lets say you have a non-unique index on ID
and are going to use the on-conflict-do-nothing route ( I do believe
this would be slow due to the need of having an index on the target
table to support it, but let's assume it is ok ). To do that I may
just do a loop, starting with last_id=-1(some id less than any other
id), selecting a chunk of rows with id>=last_id ordered by id and
inserting them. After doing that I may notice that I do not need the
index if the sort order is right, drop the index and the on-conflict
and just do, for every row, if(id>last_id) insert before storing
last_id=id.

Anyway, not knowing the real table characteristics and current usage
patterns I cannot recomend anything concrete.

> Would it be better to do it in one shot only , but by setting a higher value of some parameters like
"maintenance_work_mem"or "max_parallel_workers"? 

It depends on a lot of unknown ( to us ) things.

Francisco Olarte.

On 2024-02-06 11:25:05 +0530, veem v wrote:
> With respect to the storage part:- In Oracle database we were supposed to keep
> the frequently accessed column first and less frequently accessed columns
> towards last in the table while creating table DDL. This used to help the query
> performance while accessing those columns as it's a row store database. Are
> there any such performance implications exists in postgres? And there the data
> types like varchar used to only occupy the space which the real data/value
> contains. 
>
> But here in postgres, it seems a bit different as it has significant
> overhead in storage, as these seem like being fixed length data types and will
> occupy the space no matter what the read data holds.

Yes. Numbers in Oracle are variable length, so most Oracle tables
wouldn't contain many fixed length columns. In PostgreSQL must numeric
types are fixed length, so you'll have quite a lot of them.

> So curious to know, if in this case will there be any performance
> impact accessing those columns, if such a column which is frequently
> accessed but has been put towards the end of the table because of
> storage space saving?

Yes. PostgreSQL has to skip over each column until it gets to the column
it wants to read. So reading a column at the end of the row will be
slower than reading one at the start. A very simplistic benchmark (100
columns of type text each containing a single character) suggests that
accessing column 100 takes about 4 or 5 times as long as column 1, and
the access times for the coiumns between are pretty linear.

So there's a bit of a tradeoff between minimizing alignment overhead and
arranging columns for fastest access.

        hp

--
   _  | Peter J. Holzer    | Story must make more sense than reality.
|_|_) |                    |
| |   | hjp@hjp.at         |    -- Charles Stross, "Creative writing
__/   | http://www.hjp.at/ |       challenge!"

On Sun, 2024-02-11 at 13:25 +0530, veem v wrote:
> On Sun, 2024-02-11 at 01:25 +0100, Peter J. Holzer wrote:
> > On 2024-02-06 11:25:05 +0530, veem v wrote:
> > > With respect to the storage part:- In Oracle database we were supposed to keep
> > > the frequently accessed column first and less frequently accessed columns
> > > towards last in the table while creating table DDL. This used to help the query
> > > performance while accessing those columns as it's a row store database. Are
> > > there any such performance implications exists in postgres? And there the data
> > > types like varchar used to only occupy the space which the real data/value
> > > contains. 
> > >
> > > But here in postgres, it seems a bit different as it has significant
> > > overhead in storage, as these seem like being fixed length data types and will
> > > occupy the space no matter what the read data holds.
> >
> > Yes. Numbers in Oracle are variable length, so most Oracle tablesbetween
> > wouldn't contain many fixed length columns. In PostgreSQL must numeric
> > types are fixed length, so you'll have quite a lot of them.
>
> So it means , say in other databases like (oracle database), we were careless
> choosing the data length , say for example Varchar2(4000), if the real data which
> is inserted into the table holds a varchar string of length 20 bytes then Oracle
> trimmed it to occupy the 20 bytes length only in the storage. but in postgre here
> we need to be cautious and define the length as what the data attribute can max
> contains , because that amount of fixed space is allocated to every value which
> is inserted into the table for that attribute/data element. Similarly for
> Number/Numeric data type.  Please correct if my understanding is wrong.

Your understanding is wrong.  Oracle and PostgreSQL are not too different about
storing values.  The data type that Oracle calls "number", and that is called
"numeric" in PostgreSQL and in the SQL standard, is stored a variable length data
type in both Oracle and PostgreSQL.

It is just that Oracle has very few data types (I saw them gush about adding
"boolean" as a great new feature in version 23).  So, for example, there are
no integer data types in Oracle, and you have to store them as a variable
length data type.  PostgreSQL has integer data types, which are fixed length
(2, 4 or 8 bytes) and provide much more efficient storage for integers.

"character varying" is also pretty similar in Oracle and PostgreSQL, except
that Oracle calls it "varchar2".

The only fixed-length character data type is "character", but that is always
a bad choice, in Oracle as in PostgreSQL.

About your initial question: in PostgreSQL there is also a certain performance
gain if you store frequently used columns first, since the database has to
skip fewer columns to get to the data.  If the previous columns are fixed
length data types like integers, that is cheaper, because we don't have to
look at the data to know how long they are.

Another thing to consider is padding.  Each fixed-width data type has certain
alignment requirements (imposed by CPU processing) that you can find in
"pg_type.typalign".  This can lead to wasted space in the form of
"padding bytes".  For example, if a "bigint" follows a ASCII single-character
"text" or "varchar" value (which occupies two bytes), there will be six
padding bytes between them to align the "bigint" at a storage address that is
a multiple of eight.

But although both of these considerations (skipping over previous columns and
padding) are relevant for performance, they are often a micro-optimization
that you won't be able to measure, and you shouldn't lose too much sleep
over them.

>
> > So there's a bit of a tradeoff between minimizing alignment overhead and
> > arranging columns for fastest access.

Precisely.

Yours,
Laurenz Albe

On 2024-02-11 13:25:10 +0530, veem v wrote:
> On Sun, 11 Feb 2024 at 05:55, Peter J. Holzer <hjp-pgsql@hjp.at> wrote:
>     Yes. Numbers in Oracle are variable length, so most Oracle tables
>     wouldn't contain many fixed length columns. In PostgreSQL must numeric
>     types are fixed length, so you'll have quite a lot of them.
>
>
>
> So it means , say in other databases like (oracle database), we were careless
> choosing the data length , say for example Varchar2(4000), if the real data
> which is inserted into the table holds a varchar string of length 20 bytes then
> Oracle trimmed it to occupy the 20 bytes length only in the storage. but in
> postgre here we need to be cautious and define the length as what the data
> attribute can max contains , because that amount of fixed space is allocated
> to every value which is inserted into the table for that attribute/data
> element.

No. Varchar is (as the name implies) a variable length data type and
both Oracle and PostgreSQL store only the actual value plus some length
indicator in the database. Indeed, in PostgreSQL you don't need to
specify the maximum length at all.

However, if you specify a column as "integer" in PostgreSQL it will
always occupy 4 bytes, whether you store the value 15 in it or
999999999. In Oracle, there is no "integer" type and the roughly
equivalent number(10,0) is actually a variable length floating point
number. So 15 will occupy only 3 bytes and 999999999 will occupy 7
bytes[1].

> Similarly for Number/Numeric data type.

Number in Oracle and numeric in PostgreSQL are variable length types.
But in PostgreSQL you also have a lot of fixed length numeric types
(from boolean to bigint as well as float4 and float8) and you would
normally prefer those over numeric (unless you really need a decimal or
very long type). So padding is something you would encounter in a
typical PostgreSQL database while it just wouldn't happen in a typical
Oracle database.

But as Laurenz wrote that's a micro optimization which you usually
shouldn't spend much time on. OTOH, if you have hundreds of columns in a
table, maybe it is worthwhile to spend some time on analyzing access
patterns and optimizing the data layout.

        hp

[1] From memory. I may be misremembering the details.

--
   _  | Peter J. Holzer    | Story must make more sense than reality.
|_|_) |                    |
| |   | hjp@hjp.at         |    -- Charles Stross, "Creative writing
__/   | http://www.hjp.at/ |       challenge!"

On Sun, 11 Feb 2024 at 19:02, Peter J. Holzer <hjp-pgsql@hjp.at> wrote:

> Similarly for Number/Numeric data type.

Number in Oracle and numeric in PostgreSQL are variable length types.
But in PostgreSQL you also have a lot of fixed length numeric types
(from boolean to bigint as well as float4 and float8) and you would
normally prefer those over numeric (unless you really need a decimal or
very long type). So padding is something you would encounter in a
typical PostgreSQL database while it just wouldn't happen in a typical
Oracle database.

When you said "you would normally prefer those over numeric " I was thinking the opposite. As you mentioned integer is a fixed length data type and will occupy 4 bytes whether you store 15 or 99999999.But in case of variable length type like Number or numeric , it will resize itself based on the actual data, So is there any downside of going with the variable length data type like Numeric, Varchar type always for defining the data elements?

On 2024-02-11 22:23:58 +0530, veem v wrote:
> On Sun, 11 Feb 2024 at 19:02, Peter J. Holzer <hjp-pgsql@hjp.at> wrote:
>
>     > Similarly for Number/Numeric data type.
>
>     Number in Oracle and numeric in PostgreSQL are variable length types.
>     But in PostgreSQL you also have a lot of fixed length numeric types
>     (from boolean to bigint as well as float4 and float8) and you would
>     normally prefer those over numeric (unless you really need a decimal or
>     very long type). So padding is something you would encounter in a
>     typical PostgreSQL database while it just wouldn't happen in a typical
>     Oracle database.
>
>
>
> When you said "you would normally prefer those over numeric " I was thinking
> the opposite. As you mentioned integer is a fixed length data type and will
> occupy 4 bytes whether you store 15 or 99999999.But in case of variable
> length type like Number or numeric , it will resize itself based on the actual
> data, So is there any downside of going with the variable length data type like
> Numeric, Varchar type always for defining the data elements?

The fixed width types are those that the CPU can directly process:
Integers with 16, 32 and 64 bits, floating point numbers with 32 and 64
bits. The CPU can read and write them with a single memory access, it
can do arithmetic with a single instruction, etc.

Number/Numeric are not native types on any CPU. To read them the CPU
needs several memory accesses (probably one per byte unless you get
really clever) and then it can't do any calculations with them
directly, instead it has run a subroutine which does operations on
little chunks and then puts those chunks together again - basically the
same as you do when you're doing long addition or multiplication on
paper. So that's not very efficient.

Also the space savings aren't that great and probably even negative: In
my example the length of a numeric type with at most 10 digits varied
between 3 and 7 bytes, Only for values between -99 and +99 is this (3
bytes) actually shorter, for other values it's the same length or
longer. So you would only save space if most of your values are in that
±99 range. But not if all of them are, because then you could simply use
a smallint (Range -32768..32767) in PostgreSQL and save another byte.

Finally - and I'm probably biased in this as I learned programming 40
years ago - to me the native types feel more natural than product
specific variable-length decimal types.

        hp

--
   _  | Peter J. Holzer    | Story must make more sense than reality.
|_|_) |                    |
| |   | hjp@hjp.at         |    -- Charles Stross, "Creative writing
__/   | http://www.hjp.at/ |       challenge!"

On 2024-02-11 12:08:47 -0500, Ron Johnson wrote:
> On Sun, Feb 11, 2024 at 11:54 AM veem v <veema0000@gmail.com> wrote:
>     When you said "you would normally prefer those over numeric " I was
>     thinking the opposite. As you mentioned integer is a fixed length data type
>     and will occupy 4 bytes whether you store 15 or 99999999.But in case of
>     variable length type like Number or numeric , it will resize itself based
>     on the actual data, So is there any downside of going with the variable
>     length data type like Numeric,
>
>
> Consider a table with a bunch of NUMERIC fields.  One of those records has
> small values (aka three bytes).  It fits neatly in 2KiB.
>
> And then you update all those NUMERIC fields to big numbers that take 15
> bytes.  Suddenly (or eventually, if you update them at different times), the
> record does not fit in 2KiB, and so must be moved to its own.page.  That causes
> extra IO.

I think that's not much of a concern with PostgreSQL because you can't
update a row in-place anyway because of MVCC. So in any case you're
writing a new row. If you're lucky there is enough free space in the same
page and you can do a HOT update, but that's quite independent on
whether the row changes size.

        hp

--
   _  | Peter J. Holzer    | Story must make more sense than reality.
|_|_) |                    |
| |   | hjp@hjp.at         |    -- Charles Stross, "Creative writing
__/   | http://www.hjp.at/ |       challenge!"

On 2024-02-12 11:28:41 -0500, Greg Sabino Mullane wrote:
> On Mon, Feb 12, 2024 at 1:50 AM veem v <veema0000@gmail.com> wrote:
>
>     So we were thinking, adding many column to a table should be fine in
>     postgres (as here we have a use case in which total number of columns may
>     go till ~500+). But then,  considering the access of columns towards the
>     end of a row is going to add more time as compared to the column which is
>     at the start of the row. As you mentioned, accessing 100th column may add 4
>     to 5 times more as compared to the access of first column. So , is it
>     advisable here to go for similar approach of breaking the table into two ,
>     if the total number of column reaches certain number/threshold for a table?
>
>
> I'm not sure of what Peter was testing exactly to get those 4-5x figures,

Sorry, I should have included my benchmark code (it's short enough - see
below).

What i was actually timing was

    select count(*) from t_postgresql_column_bench where v{i} = 'a'

for various i.

> but I presume that is column access time,

That was the goal. Of course there is always some overhead but I figured
that by counting rows where a column has a constant value the overhead
is minimal or at least constant.

> which would not mean a direct effect on your total query time of 4-5x.

Right. In any real application the column access time is only a part of
the total processing time and probably a small part, so the effect on
total processing time is correspondingly smaller.

        hp

------------------------------------------------------------------------
#!/usr/bin/python3

import random
import time
import psycopg2

n_cols = 100
n_rows = 100000

db = psycopg2.connect("")
csr = db.cursor()

csr.execute("drop table if exists t_postgresql_column_bench")
q = "create table t_postgresql_column_bench ("
q += ", ".join(f"v{i} text" for i in range(n_cols))
q += ")"
csr.execute(q)
q = "insert into t_postgresql_column_bench values("
q += ", ".join("%s" for i in range(n_cols))
q += ")"

for j in range(n_rows):
    v = [ chr(random.randint(96+1, 96+26)) for i in range(n_cols)]
    csr.execute(q, v)
db.commit()

for i in range(n_cols):
    q = f"select count(*) from t_postgresql_column_bench where v{i} = 'a'"
    t0 = time.clock_gettime(time.CLOCK_MONOTONIC)
    csr.execute(q)
    r = csr.fetchall()
    print(r)
    t1 = time.clock_gettime(time.CLOCK_MONOTONIC)
    print(i, t1 - t0)
db.commit()
------------------------------------------------------------------------

--
   _  | Peter J. Holzer    | Story must make more sense than reality.
|_|_) |                    |
| |   | hjp@hjp.at         |    -- Charles Stross, "Creative writing
__/   | http://www.hjp.at/ |       challenge!"

On 2024-02-13 01:53:25 +0530, veem v wrote:
> On Mon, 12 Feb 2024 at 03:40, Peter J. Holzer <hjp-pgsql@hjp.at> wrote:
>
>     The fixed width types are those that the CPU can directly process:
>     Integers with 16, 32 and 64 bits, floating point numbers with 32 and 64
>     bits. The CPU can read and write them with a single memory access, it
>     can do arithmetic with a single instruction, etc.
>
>     Number/Numeric are not native types on any CPU. To read them the CPU
>     needs several memory accesses (probably one per byte unless you get
>     really clever) and then it can't do any calculations with them
>     directly, instead it has run a subroutine which does operations on
>     little chunks and then puts those chunks together again - basically the
>     same as you do when you're doing long addition or multiplication on
>     paper. So that's not very efficient.
>
>
> So it looks like the fixed length data type(like integer, float) should be the
> first choice while choosing the data type of the attributes wherever possible,
> as these are native types. (Like choosing "Integer/float" over "Numeric",
> "Char" over "Varchar" etc). 

Please do not conflate "char(n)" with native machine types like int or
float. These are very different things. A char(n) is string of fixed but
arbitrary length. This is not something a CPU can process in a single
instruction. It has to go over it character by character.

There is almost never a reason to use char(n). Just use varchar(n) or in
the case of PostgreSQL just varchar or text.

> However I do see even in Oracle databases, we have Integer type too,

Not really. INTEGER is just an alias for NUMBER(38) in Oracle (see for
example
https://docs.oracle.com/en/database/oracle/oracle-database/19/sqlqr/Data-Types.html).
It's not the same as an INTEGER in PostgreSQL.

        hp

--
   _  | Peter J. Holzer    | Story must make more sense than reality.
|_|_) |                    |
| |   | hjp@hjp.at         |    -- Charles Stross, "Creative writing
__/   | http://www.hjp.at/ |       challenge!"

On Wed, 14 Feb, 2024, 10:30 am Ron Johnson, <ronljohnsonjr@gmail.com> wrote:

On Tue, Feb 13, 2024 at 4:17 PM veem v <veema0000@gmail.com> wrote:

[sni[] 

One question here, if we have defined one column as a fixed length data type "integer" and slowly we noticed the length of data keeps increasing (in case of a sequence generated PK column which will keep increasing), and we want to alter the column to "bigint" now. In such scenario, will it append/pad the additional spaces to all the existing values which were already stored  with integer type initially in the table. And that would be then an increase to the existing table storage. Please correct me if I'm wrong.

ALTER TABLE foo ALTER COLUMN bar BIGINT; rewrites the whole table.  Do yourself a favor, and start with BIGINT.

Not yet tried, but that's interesting. I was initially thinking as it will be increasing the length, so it would be just a metadata change and finish within seconds.

It depends:

https://www.postgresql.org/docs/current/sql-altertable.html

"Adding a column with a volatile DEFAULT or changing the type of an existing column will require the entire table and its indexes to be rewritten. As an exception, when changing the type of an existing column, if the USING clause does not change the column contents and the old type is either binary coercible to the new type or an unconstrained domain over the new type, a table rewrite is not needed. However, indexes must always be rebuilt unless the system can verify that the new index would be logically equivalent to the existing one. For example, if the collation for a column has been changed, an index rebuild is always required because the new sort order might be different. However, in the absence of a collation change, a column can be changed from text to varchar (or vice versa) without rebuilding the indexes because these data types sort identically. Table and/or index rebuilds may take a significant amount of time for a large table; and will temporarily require as much as double the disk space.

"

create table int_test(int_fld integer);

insert into int_test select * from generate_series(1, 10000, 1);

select ctid, int_fld from int_test ;

ctid   | int_fld
----------+---------
 (0,1)    |       1
 (0,2)    |       2
 (0,3)    |       3
 (0,4)    |       4
 (0,5)    |       5
 (0,6)    |       6
 (0,7)    |       7
 (0,8)    |       8
 (0,9)    |       9
 (0,10)   |      10

alter table int_test alter column int_fld set data type bigint;

select ctid, int_fld from int_test ;

  ctid   | int_fld
----------+---------
 (0,1)    |       1
 (0,2)    |       2
 (0,3)    |       3
 (0,4)    |       4
 (0,5)    |       5
 (0,6)    |       6
 (0,7)    |       7
 (0,8)    |       8
 (0,9)    |       9
 (0,10)   |      10

update int_test set  int_fld = int_fld;

select ctid, int_fld from int_test  order by int_fld;

(63,1)    |       1
 (63,2)    |       2
 (63,3)    |       3
 (63,4)    |       4
 (63,5)    |       5
 (63,6)    |       6
 (63,7)    |       7
 (63,8)    |       8
 (63,9)    |       9
 (63,10)   |      10

Where ctid is:

https://www.postgresql.org/docs/current/ddl-system-columns.html

"

ctid

The physical location of the row version within its table. Note that although the ctid can be used to locate the row version very quickly, a row's ctid will change if it is updated or moved by VACUUM FULL. Therefore ctid is useless as a long-term row identifier. A primary key should be used to identify logical rows.

"

 But as you mentioned, it seems to be the effect of "fixed length data type" which is why it's going to rewrite whole table even we just increases the column length. Hope it won't be the case in variable length data type. 

-- 
Adrian Klaver
adrian.klaver@aklaver.com

float data types rather than numeric. This will give better performance.

On 2/15/24 08:16, Greg Sabino Mullane wrote:
>     So as I also tested the same as you posted, there has been no change
>     in "ctid" , when I altered the column data type from 'int' to
>     'bigint' in the table, so that means full table rewriting
>     won't happen in such a scenario.
> 
> 
> No it was definitely rewritten - do not depend on the ctid to verify 
> that. Take our word for it, or 
> use*pg_relation_filenode('int_test');* before and after, as well as 
> *pg_relation_size('int_test')*;

That is a mixed bag:

test=# select pg_relation_filenode('int_test');
  pg_relation_filenode
----------------------
                 69999
(1 row)

test=# select pg_relation_size('int_test');
  pg_relation_size
------------------
            368640
(1 row)

test=# alter table int_test alter column int_fld set data type bigint;
ALTER TABLE
test=# select pg_relation_filenode('int_test');
  pg_relation_filenode
----------------------
                 70002
(1 row)

test=# select pg_relation_size('int_test');
  pg_relation_size
------------------
            368640

> 
> Cheers,
> Greg
> 

-- 
Adrian Klaver
adrian.klaver@aklaver.com

On 2/15/24 09:00, Greg Sabino Mullane wrote:
> On Thu, Feb 15, 2024 at 11:43 AM Adrian Klaver 
> <adrian.klaver@aklaver.com <mailto:adrian.klaver@aklaver.com>> wrote:
> 
>     That is a mixed bag:
> 
> 
> Ha! Good point. Our contrived example table does suffer from that, so 
> perhaps the test should be:
> 
> create table int_test(c1 int, c2 int);

Alright now I see:

test=# create table int_test(c1 int, c2 int);
CREATE TABLE

test=# select pg_relation_filenode('int_test');
  pg_relation_filenode
----------------------
                 70021
(1 row)


test=# insert into int_test select a, a+1  from generate_series(1, 
10000, 1) as t(a);
INSERT 0 10000

test=# select pg_relation_size('int_test');
  pg_relation_size
------------------
            368640
(1 row)

test=# alter table int_test alter column c2 set data type bigint;
ALTER TABLE

test=# select pg_relation_filenode('int_test');
  pg_relation_filenode
----------------------
                 70024
(1 row)

test=# select pg_relation_size('int_test');
  pg_relation_size
------------------
            450560
(1 row)

> 
> Cheers,
> Greg
> 

-- 
Adrian Klaver
adrian.klaver@aklaver.com

On 2024-02-14 22:55:01 -0700, David G. Johnston wrote:
> On Tuesday, February 13, 2024, veem v <veema0000@gmail.com> wrote:
>
>     float data types rather than numeric. This will give better performance.
>
>
>  Only use an inexact floating-point data type if you truly understand what you
> are getting yourself into.  Quickly getting the wrong answer isn’t tolerable
> solution.

Do NOT assume that a decimal type (even if it can grow to ridiculuous
lengths like PostgreSQL's numeric) is exact in the mathematical sense.
It isn't. It cannot represent almost all real numbers. No pi or e of
course, but also no seemingly simple fractions like 1/3 or 1/7.

Unless you never divide anything, you will need to be aware of the
rounding behaviour, just as you have to with binary floating point
types. And if you use a finite precision you will also have to deal with
rounding on multiplication (and possibly even addition and subtraction,
if you use different precisions).

Almost 40 years ago, our numerical methods professor started his first
lecture with the sentence "You can use a computer for anything - except
computing". He spent the rest of the semester proving himself wrong,
of course, but computing correctly is hard - and choosing a data type
which more closely mimics the way we learn to compute in primary school
doesn't necessarily make it easier. Mostly it just makes it harder to
spot the errors ;-).

        hp

--
   _  | Peter J. Holzer    | Story must make more sense than reality.
|_|_) |                    |
| |   | hjp@hjp.at         |    -- Charles Stross, "Creative writing
__/   | http://www.hjp.at/ |       challenge!"

On 2024-02-15 16:51:56 -0700, David G. Johnston wrote:
> On Thu, Feb 15, 2024 at 4:31 PM Peter J. Holzer <hjp-pgsql@hjp.at> wrote:
>     On 2024-02-14 22:55:01 -0700, David G. Johnston wrote:
>     > On Tuesday, February 13, 2024, veem v <veema0000@gmail.com> wrote:
>     >
>     >     float data types rather than numeric. This will give better
>     >     performance.
>     >
>     >
>     > Only use an inexact floating-point data type if you truly
>     > understand what you are getting yourself into.  Quickly getting
>     > the wrong answer isn’t tolerable solution.
>
>     Do NOT assume that a decimal type (even if it can grow to ridiculuous
>     lengths like PostgreSQL's numeric) is exact in the mathematical sense.
>     It isn't. It cannot represent almost all real numbers
>
>
> That is an unusual definition for exact, I wouldn't have considered the
> requirement to represent all real numbers to be included in it.
>
> What you see with an exact type is what you get, which allows for implementing
> equality, unlike inexact which requires epsilon checking.

You can check binary fp values for equality. If they are equal, they
will compare equal. If they aren't, they won't.

What you can't expect is that the laws of commutativity, associativity,
etc. hold. If you compute a value in two different ways which should be
equivalent mathematically (e.g. a*b/c vs. a/c*b), the result may be
different and an equality test may fail. But that is also the case for
numeric (and of course integer). You might get around that by epsilon
checking, but whether that's the right thing to do depends on your
application.

And most importantly, and gets most people on the "oh noes, binary fp is
inexact" bandwagon is that decimal fractions (1/10, 1/100, ...) are not
exactly representable in binary, just like 1/3, 1/7, 1/11, ... aren't
exactly represntable in decimal. People are used the latter, but not
the former. But mathematically, that doesn't really make a difference.

        hp

--
   _  | Peter J. Holzer    | Story must make more sense than reality.
|_|_) |                    |
| |   | hjp@hjp.at         |    -- Charles Stross, "Creative writing
__/   | http://www.hjp.at/ |       challenge!"

On 2024-02-16 12:10:20 +0530, veem v wrote:
>
> On Fri, 16 Feb 2024 at 06:04, Peter J. Holzer <hjp-pgsql@hjp.at> wrote:
>
>     On 2024-02-15 16:51:56 -0700, David G. Johnston wrote:
>     > On Thu, Feb 15, 2024 at 4:31 PM Peter J. Holzer <hjp-pgsql@hjp.at> wrote:
>     >     On 2024-02-14 22:55:01 -0700, David G. Johnston wrote:
>     >     > On Tuesday, February 13, 2024, veem v <veema0000@gmail.com> wrote:
>     >     >
>     >     >     float data types rather than numeric. This will give better
>     >     >     performance.
>     >     >
>     >     >
>     >     > Only use an inexact floating-point data type if you truly
>     >     > understand what you are getting yourself into.  Quickly getting
>     >     > the wrong answer isn’t tolerable solution.
>     >
>     >     Do NOT assume that a decimal type (even if it can grow to ridiculuous
>     >     lengths like PostgreSQL's numeric) is exact in the mathematical
>     sense.
>     >     It isn't. It cannot represent almost all real numbers
>     >
>     >
>
>
> Thank You.
>
> So it looks like the use cases where we don't need precision or decimal point
> values to be stored in postgres , integer data type is the way to go without a
> doubt.
>
> However in cases of precision is required, as you all mentioned there are
> certain issues(rounding error etc) with "Float" data type and considering a
> normal developers usage point of view, it should be the Numeric type which we
> should use.

You misunderstood. My point was that these rounding errors also happen
with numeric, and if you want to avoid or minimize them you have to
understand what you are doing. For a hilarious example of what happens
if you don't understand that, see
https://en.wikipedia.org/wiki/Vancouver_Stock_Exchange#Rounding_errors_on_its_Index_price

I basically see two reasons to use numeric:

* Your numbers are amounts of money. Accountants are neither mathematicians
  nor engineers, and numeric mimics the way they think. So the results
  will be wrong in the correct way ;-)
* You need lots (more than 15 or 18) digits.

For anything else there is a good chance that float8 or int8 is a better
choice, because those types behave much more consistently.

Of course there are valid reasons to use other types (including numeric)
but the point is that each type has real pros and cons and false
arguments like "numeric is an exact type and float isn't" is not
helpful. That said, "I don't understand binary numbers" might be a valid
reason.

        hp

--
   _  | Peter J. Holzer    | Story must make more sense than reality.
|_|_) |                    |
| |   | hjp@hjp.at         |    -- Charles Stross, "Creative writing
__/   | http://www.hjp.at/ |       challenge!"

On 2024-02-16 01:34:01 +0100, Peter J. Holzer wrote:
> On 2024-02-15 16:51:56 -0700, David G. Johnston wrote:
> > What you see with an exact type is what you get, which allows for implementing
> > equality, unlike inexact which requires epsilon checking.
>
> You can check binary fp values for equality. If they are equal, they
> will compare equal. If they aren't, they won't.
>
> What you can't expect is that the laws of commutativity, associativity,
> etc. hold. If you compute a value in two different ways which should be
> equivalent mathematically (e.g. a*b/c vs. a/c*b), the result may be
> different and an equality test may fail. But that is also the case for
> numeric (and of course integer).

To illustrate that point:

hjp=> create table t_n (a numeric, b numeric, c numeric);
CREATE TABLE

hjp=> insert into t_n values(47, 52, 10);
INSERT 0 1

-- the numbers are not specially chosen. I just invoked
-- select (random()*100)::int;
-- three times, and they were the ones that came up.

hjp=> select *, a / b * c, a * c / b, a / b * c = a * c / b from t_n;
╔════╤════╤════╤════════════════════════╤════════════════════╤══════════╗
║ a  │ b  │ c  │        ?column?        │      ?column?      │ ?column? ║
╟────┼────┼────┼────────────────────────┼────────────────────┼──────────╢
║ 47 │ 52 │ 10 │ 9.03846153846153846150 │ 9.0384615384615385 │ f        ║
╚════╧════╧════╧════════════════════════╧════════════════════╧══════════╝
(1 row)

So with type numeric two expressions which should be equal
mathematically, aren't in fact equal.

Now let's try the same thing with binary floating point:

hjp=> create table t_f (a float8, b float8, c float8);
CREATE TABLE

hjp=> insert into t_f values(47, 52, 10);
INSERT 0 1

hjp=> select *, a / b * c, a * c / b, a / b * c = a * c / b from t_f;
╔════╤════╤════╤═══════════════════╤═══════════════════╤══════════╗
║ a  │ b  │ c  │     ?column?      │     ?column?      │ ?column? ║
╟────┼────┼────┼───────────────────┼───────────────────┼──────────╢
║ 47 │ 52 │ 10 │ 9.038461538461538 │ 9.038461538461538 │ t        ║
╚════╧════╧════╧═══════════════════╧═══════════════════╧══════════╝
(1 row)

Now they are indeed equal. This is *not* guaranteed and I got a bit
lucky here, but the fact that I got lucky on the first try shows that
"float bad, numeric good" is not backed up by reality.

        hp

--
   _  | Peter J. Holzer    | Story must make more sense than reality.
|_|_) |                    |
| |   | hjp@hjp.at         |    -- Charles Stross, "Creative writing
__/   | http://www.hjp.at/ |       challenge!"