That Guy From Delhi: 2024

27 Oct 2024

What's in an empty table?

How much storage does an empty table in Postgres take?

This is a post about Postgres tables that store ... well basically ... Nothing.

The idea for this post came from this tweet that hinted that an empty table on most databases today takes 16Kb of storage. Now admittedly Franck was probably reminiscing the good-old days so this is probably quite out of context, but it did get me thinking, and thus this post.

NB: Here's a video showing this in action ! - See video .

A "regular" empty table in Production

Here's a regular small table that could be found in Production. It has a Primary Key, a text column and a JSONB column. Let's check the table size using the pg_total_relation_size() postgres function (you can read more about that function here).

db1=# create table t(id bigint primary key, b text, c jsonb);

CREATE TABLE

db1=# select pg_total_relation_size('t');

pg_total_relation_size

------------------------

16384

(1 row)

Hmmm, so that tweet did have a point. Given how low-cost memory has become over the decades, it is easy to understand why databases today chose to optimize speed over memory efficiency (more on this later) and so even an empty table in Postgres, does consume 16 kb.

But "where" is the 16kb being used?

db1=# select pg_relation_size('t');

pg_relation_size

------------------

(1 row)

The relation itself isn't consuming any space! That is good (again more on this later) but then where is the space being used then?

db1=# \d t

Table "public.t"

Column | Type | Collation | Nullable | Default

--------+--------+-----------+----------+---------

id | bigint | | not null |

b | text | | |

c | jsonb | | |

Indexes:

"t_pkey" PRIMARY KEY, btree (id)

We see that the table has a Primary Key - and thus an index - t_pkey.

Does the index is consuming 16kb?

db1=# select pg_relation_size('t_pkey');

pg_relation_size

------------------

8192

(1 row)

So the index is using some of it - 8kb - so that is progress - but who's using the other 8kb?

Let's start cutting the table down

Let's start cutting down the columns and see if the disk-usage goes down.

db1=# DROP TABLE t; CREATE TABLE t (id BIGINT PRIMARY KEY, b JSONB); select pg_total_relation_size('t');

DROP TABLE

CREATE TABLE

pg_total_relation_size

------------------------

16384

(1 row)

db1=# DROP TABLE t; CREATE TABLE t (id BIGINT PRIMARY KEY, b TEXT); select pg_total_relation_size('t');

DROP TABLE

CREATE TABLE

pg_total_relation_size

------------------------

16384

(1 row)

Hmmm, that didn't help at all. Dropping either of the TEXT or JSONB column didn't help. Let's look at the expanded version of this table to see if there's any similarity in the two columns. (I've clipped the output to make it easier to read)

db1=# \d+ t

Table "public.t"

--------+--------+-----------+----------+---------+----------+-...

b | text | | | | extended | ...

c | jsonb | | | | extended | ...

Indexes:

"t_pkey" PRIMARY KEY, btree (id)

Access method: heap

Clearly, the two column's are "extended" (you can read more about it here), but basically what happened here is that an extended column type resulted in the creation of a TOAST table (you can read more about TOAST here). Let's find out "how" to find the TOAST table for the table "t", and check if that could be consuming the remaining 8kb?

db1=# select oid, relname, reltoastrelid, reltoastrelid::regclass from pg_class where relname = 't';

oid | relname | reltoastrelid | reltoastrelid

-------+---------+---------------+-------------------------

18300 | t | 18303 | pg_toast.pg_toast_18300

(1 row)

db1=# select pg_relation_size('pg_toast.pg_toast_18300');

pg_relation_size

------------------

(1 row)

Hmmm, it's not the TOAST table. But just like the main table "t", could it be that the TOAST table has supporting relations that are to blame?

db1=# select pg_total_relation_size('pg_toast.pg_toast_18300');

pg_total_relation_size

------------------------

8192

(1 row)

db1=# \d pg_toast.pg_toast_18300

TOAST table "pg_toast.pg_toast_18300"

Column | Type

------------+---------

chunk_id | oid

chunk_seq | integer

chunk_data | bytea

Owning table: "public.t"

Indexes:

"pg_toast_18300_index" PRIMARY KEY, btree (chunk_id, chunk_seq)

db1=# select pg_relation_size('pg_toast.pg_toast_18300_index');

pg_relation_size

------------------

8192

(1 row)

Yes! So we see above, that the TOAST table implicitly has a primary key (of it's own) that uses an index - which has 1 page (8kb) assigned to it.

In a nutshell, the empty table 't' above, consumes 16kb and the storage allocation takes this shape:

Main relation - 0 bytes - 't'
Main relation Index - 8kb - 't_pkey'

Toast relation - 0 bytes - 'pg_toast_18300'
Toast relation Index - 8kb - 'pg_toast_18300_index'

Cut Cut Cut

Okay, lets see if we can reduce the table size further, by dropping both the Extended columns.

db1=# DROP TABLE t; CREATE TABLE t (id BIGINT PRIMARY KEY); select pg_total_relation_size('t');

DROP TABLE

CREATE TABLE

pg_total_relation_size

------------------------

8192

(1 row)

Okay, that makes sense. Now the main table (heap) is still not consuming anything, but the index still does.

Let's reduce further.

db1=# DROP TABLE t; CREATE TABLE t (id BIGINT); select pg_total_relation_size('t');

DROP TABLE

CREATE TABLE

pg_total_relation_size

------------------------

(1 row)

0 bytes!!

Nice! But seriously - 0 bytes?

It kind of makes sense, that since Postgres doesn't yet have anything to store - besides the metadata of the table (and since the metadata is stored in the system catalogs - for e.g. pg_catalog schema) - there isn't anything to store in the main relation (heap) as yet.

Disk Usage - Check filesystem

Hmmm - Nah, what if I don't trust Postgres?

Let's skip Postgres functions and ask the filesystem directly - and see if the table is actually 0 bytes. Here we first find the file path of the table in question using the postgres function pg_relation_filepath() and then ask the file-system for the file-size.

db1=# select pg_relation_filepath('t');

pg_relation_filepath

----------------------

base/17727/18356

(1 row)

db1=# \! ls -la /home/robins/proj/localpg/data/base/17727/18356

-rw------- 1 robins robins 0 Sep 23 10:19 /home/robins/proj/localpg/data/base/17727/18356

So the file corresponding to the table, actually is using 0 bytes. Nice!

Now, when a table is created, some entries are added to the system catalog. Let see if the database size is signficantly more than a blank database?

postgres=# create database db1;

CREATE DATABASE

postgres=# \c db1

You are now connected to database "db1" as user "robins".

db1=# CREATE TABLE t (id BIGINT);

CREATE TABLE

db1=# select pg_database_size('db1');

pg_database_size

------------------

7482515

(1 row)

db1=# create database db2;

CREATE DATABASE

db1=# \c db2

You are now connected to database "db2" as user "robins".

db2=# select pg_database_size('db2');

pg_database_size

------------------

7482515

(1 row)

Good. So this somewhat confirms that a blank database and a database with an "empty" table use the same disk-space.

Postgres is hiding something

Technically though, I'm lying. Well actually Postgres is ~~lying~~ hiding something from the file-system (i.e. every new table does make the database grow logically - a tad little - just that most of the times, the filesystem doesn't get the memo).

Under the cover, the way postgres stores data in a table (catalog table, or any user table), although it consumes a page of disk-space (often 8Kb), logically it may be consuming only a small part of that page. This is very helpful when more rows need to be stored in the table. When more rows come in, Postgres is able to reuse the same (first) page to now logically store more data - although for the filesystem - no extra pages were requested. This is what I was hinting at earlier that today's database use disk space (and thus memory cache) with larger (8kb) chunks and continue to keep using that page (until a new page is needed). Further below, I show a brief example of how all of this works.

But to summarize, it is then unfair to say that the system catalog did not grow at all (when a new table was added) - since some catalogs are guaranteed to have grown (for e.g. metadata of the new table is stored as an extra row in pg_class etc.) within the disk blocks already allocated as in-use for that catalog.

Let's squeeze a little more?

db1=# create table a();

CREATE TABLE

Right off the bat, that might seem completely wrong. Does Postgres allow a table with no Columns?

Yes !! 😎

All databases allow creation of an empty table (obviously), but Postgres allows a new table even if there are no columns! Let's verify this from the Postgres Documentation. Although suttle, we can see that in the syntax section of the CREATE TABLE page, the column_name data_type is enclosed with a square braces [] - which implies that columns are in fact, optional. What's more, this "feature" is a part of Postgres at least for the past 20 years!

Now the utility of this table is arguable (we'll explore that below), but it is now clear that this syntax is legal and works just fine.

Let's see how such a table looks like with psql \d

db1=# \d a

Table "public.a"

Column | Type | Collation | Nullable | Default

--------+------+-----------+----------+---------

That's it! That's the complete output - Since the table has no columns, the output above (rightly) doesn't show anything.

Let's go a little deeper

The obvious next question is - What on earth could a table like this be used for? That is a perfectly good question, and the answer is probably not much. However if you instead ask whether "Squeeze a little more" mean that a no column table takes less storage space? The answer (depends on input data but) is most probably yes. Let's see how does it help Postgres if it knows that you don't want to store any column in the table.

db1=# create table a();

CREATE TABLE

db1=# \dt+ a

List of relations

--------+------+-------+--------+-------------+---------------+---------+-------------

(1 row)

db1=# insert into a select;

INSERT 0 1

db1=# \dt+ a

List of relations

--------+------+-------+--------+-------------+---------------+------------+-------------

(1 row)

Nothing new here. We see that although an empty (0 column) table consumes 0 byte for storage. And like a regular table, as soon as the first row is inserted, the table uses 1 page - which in my test database (and probably 99.99% of postgres databases world-wide) consumes 8192 bytes. This is expected, but do note that the storage of logical rows in a postgres page, is a little oddly done (and for good reason). There is lllllooooottttt of detail here - but I wouldn't blame you if you'd want to keep that aside for a cold winter morning - when armed with a cup of hot coffee.

For now, we see below that each row that is inserted into the table, consumes 24 bytes - in that 8kb page.

db1=# create extension pageinspect ;

CREATE EXTENSION

db1=# truncate table a;

TRUNCATE TABLE

db1=# insert into a select FROM generate_series(1,3);

INSERT 0 3

db1=# SELECT * FROM heap_page_items(get_raw_page('a', 0));

| t_oid | t_data

----+--------+----------+--------+--------+--------+----------+--------+-------------+------------+--------+--------

+-------+--------

1 | 8168 | 1 | 24 | 85105 | 0 | 0 | (0,1) | 0 | 2048 | 24 |

| | \x

2 | 8144 | 1 | 24 | 85105 | 0 | 0 | (0,2) | 0 | 2048 | 24 |

| | \x

3 | 8120 | 1 | 24 | 85105 | 0 | 0 | (0,3) | 0 | 2048 | 24 |

| | \x

(3 rows)

Still going Deeper - but on a Tangent

So does the above imply that adding more columns to a table would mean Postgres consumes more bytres-per-row? Let's verify:

db1=# drop table t;

DROP TABLE

db1=# create table t(id bigint);

CREATE TABLE

db1=# truncate table t; insert into t select generate_series(1,3); vacuum full t; \dt+ t

TRUNCATE TABLE

INSERT 0 3

VACUUM

List of relations

--------+------+-------+--------+-------------+---------------+------------+-------------

(1 row)

db1=# SELECT * FROM heap_page_items(get_raw_page('t', 0));

| t_oid | t_data

----+--------+----------+--------+--------+--------+----------+--------+-------------+------------+--------+--------

+-------+--------------------

1 | 8160 | 1 | 32 | 85100 | 0 | 0 | (0,1) | 1 | 2816 | 24 |

| | \x0100000000000000

2 | 8128 | 1 | 32 | 85100 | 0 | 0 | (0,2) | 1 | 2816 | 24 |

| | \x0200000000000000

3 | 8096 | 1 | 32 | 85100 | 0 | 0 | (0,3) | 1 | 2816 | 24 |

| | \x0300000000000000

(3 rows)

Here we see that each row is now consuming 32 bytes - which is an extra 8 bytes from earlier. Good chances the only column we've added is the reason for the extra 8 bytes. Let's verify that using the pg_column_size() function (you can read more about it here):

db1=# select pg_column_size(1::bigint);

pg_column_size

----------------

(1 row)

But wait, there's one more twist here:

db1=# INSERT INTO t SELECT;

INSERT 0 1

db1=# SELECT * FROM heap_page_items(get_raw_page('t', 0));

| t_oid | t_data

----+--------+----------+--------+--------+--------+----------+--------+-------------+------------+--------+--------

--+-------+--------------------

1 | 8160 | 1 | 32 | 85111 | 0 | 0 | (0,1) | 1 | 2816 | 24 |

| | \x0100000000000000

2 | 8128 | 1 | 32 | 85111 | 0 | 0 | (0,2) | 1 | 2816 | 24 |

| | \x0200000000000000

3 | 8096 | 1 | 32 | 85111 | 0 | 0 | (0,3) | 1 | 2816 | 24 |

| | \x0300000000000000

4 | 8072 | 1 | 24 | 85113 | 0 | 0 | (0,4) | 1 | 2049 | 24 | 0000000

0 | | \x

(4 rows)

So wait, see row 4. Although the table has a column, just because the column didn't have a value, the row actually consumed only 24 bytes (the minimum)?

Is this scalable? I mean can I have a 5 column table and still Postgres stores a row, but only consume the bare minimum 24 bytes? Let's see:

db1=# drop table h;

DROP TABLE

db1=# create table h(c1 bigint, c2 bigint, c3 bigint, c4 bigint, c5 bigint);

CREATE TABLE

db1=# insert into h select;

INSERT 0 1

db1=# SELECT * FROM heap_page_items(get_raw_page('h', 0));

| t_oid | t_data

----+--------+----------+--------+--------+--------+----------+--------+-------------+------------+--------+--------

--+-------+--------

1 | 8168 | 1 | 24 | 86262 | 0 | 0 | (0,1) | 5 | 2049 | 24 | 0000000

0 | | \x

(1 row)

So yes, that does work and it does scale for "many" columns - but with a minor variation. There's more detail in code, but basically for regular columns the header contains 1 bit per column which expands in 8 byte chunks - and so say for 100 column table (with no data) - Postgres consumes ~40 bytes per row.

db1=# drop table h; create table h(); select 'alter table h add column c' || n || ' bigint;' from generate_series(1,100) e(n); \gexec

ALTER TABLE

db1=# select count(*) from pg_attribute where attrelid = 'h'::regclass;

count

-------

106

(1 row)

db1=# insert into h select; vacuum full h; SELECT * FROM heap_page_items(get_raw_page('h', 0));

INSERT 0 1

VACUUM

t_bits | t_oid | t_data

----+--------+----------+--------+--------+--------+----------+--------+-------------+------------+--------+--------

--------------------------------------------------------------------------------------------------+-------+--------

1 | 8152 | 1 | 40 | 88376 | 0 | 0 | (0,1) | 100 | 2817 | 40 | 0000000

0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 | | \x

(1 row)

Given the task at hand, I'd say that's still decently crisp.

So does a zero column table, squeeze max rows per page?

Yes, and No. So going back to a 0 column table - let's try to fill the whole page with rows and see how many can be stuffed on a single page.

If you're running low on coffee - the page-header is 24 bytes and so back-of-the-envelope math suggests that number of rows possible to squeeze into a page should be - ( 8192 bytes in a page - some bytes for page header & footer ) / 24 bytes per row = ~340 rows.

db1=# truncate table a; insert into a select FROM generate_series(1,340); vacuum full a; \dt+ a

TRUNCATE TABLE

INSERT 0 340

VACUUM

List of relations

--------+------+-------+--------+-------------+---------------+-------+-------------

(1 row)

Something's not right! That size should have stayed 8Kb. Why did Postgres use 16Kb (two pages - instead of one)?

That's because of this tiny bit of trivia that the maximum number of tuples that Postgres can squeeze onto a page, is hard-coded to 291 (for an 8kb page) - which was interesting to know - but to clarify, Heap-Only-Tuples (HOT feature) can effectively force a few more rows on a running database, but we'll go deeper into that some other day.

So let's go back and confirm if that understanding is correct - that Postgres can in fact squeeze at max only 291 rows onto the same page.

db1=# truncate table a; insert into a select FROM generate_series(1,291); vacuum full a; \dt+ a

TRUNCATE TABLE

INSERT 0 291

VACUUM

List of relations

--------+------+-------+--------+-------------+---------------+------------+-------------

(1 row)

db1=# truncate table a; insert into a select FROM generate_series(1,292); vacuum full a; \dt+ a

TRUNCATE TABLE

INSERT 0 292

VACUUM

List of relations

--------+------+-------+--------+-------------+---------------+-------+-------------

(1 row)

Here we see that:

When 291 rows are inserted (blue), the table stays at 1 page (8kb)
Whereas when 292 (291+1) rows are inserted (green), the table expands to 2 pages (16 kb)

Utility

So, that's all fine, but what's this table good for?

Well beyond understanding Postgres :) not much. This table is unhelpful for most database tasks. It can't store values - it wouldn't allow columns / indexes / selective deletes (let's avoid ctid hacks for now) etc.

But if I was forced to conjure an idea, a high-contention ticker app (that only needs to store +1s) via postgres functions - may be (and that's a BIG may be) this table could be used to store +1s - with a back-off algorithm on the application side. Good chances if there's a good DBA - this is done much better in many other ways (simplest of which is at the application end, or as a value in a column etc.) but it'd be better than nothing.

There are few other possible use-cases discussed here - for e.g. if (for some reason) you'd want to add columns to a table in a programmatic fashion after a base table is created - like we did above in the 100 column table test, OR, if you want to reserve a table name (in a multi-user setup) months / years in advance etc.

Finally

Much Ado About Nothing... This was a good exercise where we learnt something new about how Postgres stores table data - when ironically - there's nothing to store :)

Hope you had fun! Comments more than welcome.

8 Jul 2024

On-Prem AI chatbot - Hello World!

In continuation of the recent posts...

Finally got a on-premise chat-bot running! Once downloaded, the linux box is able to spin up / down the interface in a second.

(myvenv) ai@dell:~/proj/ollama$ time ollama run mistral
>>> /bye

real    0m1.019s
user    0m0.017s
sys     0m0.009s

That, on a measly ~$70 Marketplace i5/8GB machine is appreciable (given what all I had read about the NVidia RTX 4090s etc.). Now obviously this doesn't do anything close to 70 tokens per second, but am okay with that.

(myvenv) ai@dell:~/proj/ollama$ sudo dmesg | grep -i bogo
[sudo] password for ai:
[    0.078220] Calibrating delay loop (skipped), value calculated using timer frequency.. 6585.24 BogoMIPS (lpj=3292624)
[    0.102271] smpboot: Total of 4 processors activated (26340.99 BogoMIPS)

Next, I wrote a small little hello-world script to test the bot. Now where's the fun if it were to print a static text!!:

(myvenv) ai@dell:~/t$ cat a.py
from langchain_community.llms import Ollama

llm = Ollama(model="llama3")
result=llm.invoke("Why is 42 the answer to everything? Keep it very brief.")
print (result)

And here's the output, in just ......... 33 seconds :)

(myvenv) ai@dell:~/t$ time python a.py
A popular question! The joke about 42 being the answer to everything originated from Douglas Adams' science fiction series "The Hitchhiker's Guide to the Galaxy." In the book, a supercomputer named Deep Thought takes 7.5 million years to calculate the "Answer to the Ultimate Question of Life, the Universe, and Everything," which is... 42!

real    0m33.299s
user    0m0.568s
sys     0m0.104s
(myvenv) ai@dell:~/t$

And, just for kicks, works across languages / scripts too. Nice!

(myvenv) ai@dell:~/t$ ollama run mistral
>>> भारत की सबसे लंबी नदी कौन सी है?
 भारत की सबसे लंबी नदी गंगा है, जिसका पूरण 3670 किमी होता है। यह एक विश्वमित्र नदी है और बहुप्रकार से कई प्रदेशों के झिल्ले-ढाल में विचलित है।

>>>

Again, am pretty okay with this for now. I'll worry about speed tomorrow, when I have a script that's able to test the limits, and that's not today.

Hello World!

7 Jul 2024

Installing Ollama on an old linux box

Trying out Ollama - Your 10 year old box would do too.

TLDR

Yes, you CAN install an AI engine locally
No, you DON'T need to spend thousands of dollars to get started!
Agreed, that your ai engine wouldn't be snappy, it's still great to get started.

Server

You'd realise that any machine should get you going.

I had recently bought a second-hand desktop box (Dell OptiPlex 3020) from FB Marketplace and repurposed it here.
For specs, it was an Intel i5-4590 CPU @ 3.30GHz with 8GB of RAM and 250 GB of disk, nothing fancy.
It came with an AMD Radeon 8570 (2GB RAM) [4], and the Ollama install process recognized and optimized for the decade old GPU. Super-Nice!
For completeness, the box cost me $70 AUD (~50 USD) in May 2024. In other words, even for a cash-strapped avid learner, there's a very low barrier to entry here.

Install

The install steps were pretty simple [1] but as you may know, the models themselves are huge.

For e.g. look at this [3]:

mistral-7B - 4.1 GB
gemma2-27B - 16 GB
Code Llama - 4.8 GB

Given that, I'd recommend switching to a decent internet connection. If work allows, this may be a good time to go to work instead of WFH on this one. (Since I didn't have that luxury, my trusty but slow 60Mbps ADSL+ meant that I really worked up on my patience this weekend)

The thing that actually tripped me, was that Ollama threaded downloads really scream speed and it ended up clogging my test server (See my earlier blog post that goes into some details [2]).

Run with Nice

With system resources in short-supply, it made good sense, to ensure that once Ollama is installed, it is spun up with least priority.

On an Ubuntu server, I did this by modifying the ExecStart config for Ollama's systemd script.

ai@dell:~$ sudo service ollama status | grep etc
     Loaded: loaded (/etc/systemd/system/ollama.service; enabled; preset: enabled)

ai@dell:~$ cat /etc/systemd/system/ollama.service | grep ExecStart
ExecStart=nice -n 19 /usr/local/bin/ollama serve

So when I do end up asking some fun questions, ollama is always playing "nice" :D

Enjoy ...

Reference:

Install + Quick Start: https://github.com/ollama/ollama/blob/main/README.md#quickstart
Model downloads made my server unresponsive: https://www.thatguyfromdelhi.com/2024/07/ollama-is-missing-rate-limits-on.html
Model sizes are in GBs: https://github.com/ollama/ollama/blob/main/README.md#model-library
Radeon 8570: https://www.techpowerup.com/gpu-specs/amd-radeon-hd-8570.b1325

6 Jul 2024

Ollama is missing --rate-limits on downloads

I am just starting my AI journey, and trying to get Ollama to work on my linux box, was an interesting non-AI experience.

I noticed, that everytime I was trying out something new, my linux box got reliably stuck every single time I pulled a new model. htop helped point out, that each time I did a ollama pull or ollama run, it spun up a ton of threads.

Often things got so bad, that the system became quite unresponsive. Here, you can see "when" I triggered the pull:

Reply from 192.168.85.24: bytes=32 time=7ms TTL=64
Reply from 192.168.85.24: bytes=32 time=7ms TTL=64
Reply from 192.168.85.24: bytes=32 time=7ms TTL=64
Reply from 192.168.85.24: bytes=32 time=8ms TTL=64
Reply from 192.168.85.24: bytes=32 time=65ms TTL=64
Reply from 192.168.85.24: bytes=32 time=286ms TTL=64
Reply from 192.168.85.24: bytes=32 time=286ms TTL=64
Reply from 192.168.85.24: bytes=32 time=304ms TTL=64

A little searching, led me to this on-going Github thread where a feature like --rate-limit were requested for multiple reasons. Some people were unhappy with how a pull clogged their routers, some were unhappy with how it jammed all other downloads / browsing on the machine. I was troubled since my linux box (a not-so-recent but still 6.5k BogoMIPS 4vCPU i5) came to a crawl.

While the --rate-limit feature takes shape, here are two solutions that did work for me :

As soon as I started the fetch (ollama run or ollama pull etc), I used iotop to change the ionice priority to idle. This made the issue go away completely (or at least made the system quite usable). However, it was still frustrating since (unlike top and htop) one had to type the PIDs... and as you may have guessed it already, Ollama creates quite a few when it does such the fetch.

Note that doing something like nice -n 19 did not help here. This was because the ollama processes weren't actually consuming (much) CPU for this task at all!

Then I tried to use ionice, which didn't work either! Note that since Ollama uses threads, the ionice tool didn't work for me. This was because ionice doesn't work with threads within a parent process. So this meant, something like the following did not work for me:

# These did not help!

robins@dell:~$ nice -n 19 ollama run mistral # Did not work!
robins@dell:~$ ionice -c3 ollama run mistral # Did not work either!!

After some trial-and-error, a far simpler solution was to just run a series of commands immediately after triggered a new model fetch. Essentially, it got the parent PID, and then set ionice for each of the child processes for that parent:

pid=`ps -ef | grep "ollama run" | grep -v grep | awk '{print $2}'`
echo $pid
sudo ionice -c3 -p `ps -T -p $pid | awk '{print $2}' | grep -v SPID | tr '\r\n' ' '`

This worked something like this:

robins@dell:~$ pid=`ps -ef | grep "ollama run" | grep -v grep | awk '{print $2}'` && [ ${#pid} -gt 1 ] && ( sudo ionice -c3 -p `ps -T -p $pid | awk '{print $2}' | grep -v SPID | tr '\r\n' ' '` ; echo "done" ) || echo "skip"skip

robins@dell:~$ pid=`ps -ef | grep "ollama run" | grep -v grep | awk '{print $2}'` && [ ${#pid} -gt 1 ] && ( sudo ionice -c3 -p `ps -T -p $pid | awk '{print $2}' | grep -v SPID | tr '\r\n' ' '` ; echo "done" ) || echo "skip"done

After the above, iotop started showing idle in front of each of the ollama processes:

Total DISK READ:         0.00 B/s | Total DISK WRITE:         3.27 M/s
Current DISK READ:       0.00 B/s | Current DISK WRITE:      36.76 K/s
    TID  PRIO  USER     DISK READ DISK WRITE>    COMMAND                                                                                                                                                                                                                      2692712 idle ollama      0.00 B/s  867.62 K/s ollama serve
2705767 idle ollama      0.00 B/s  852.92 K/s ollama serve
2692707 idle ollama      0.00 B/s  849.24 K/s ollama serve
2693740 idle ollama      0.00 B/s  783.07 K/s ollama serve
      1 be/4 root        0.00 B/s    0.00 B/s init splash
      2 be/4 root        0.00 B/s    0.00 B/s [kthreadd]
      3 be/4 root        0.00 B/s    0.00 B/s [pool_workqueue_release]
      4 be/0 root        0.00 B/s    0.00 B/s [kworker/R-rcu_g]
      5 be/0 root        0.00 B/s    0.00 B/s [kworker/R-rcu_p]
      6 be/0 root        0.00 B/s    0.00 B/s [kworker/R-slub_]

While at it, it was funny to note that the fastest way to see whether the unresponsive system is "going to" recover (because of what I just tried) was by keeping a separate ping session to the linux box. On my local network, I knew the system is going to come back to life in the next few seconds, when I noticed that the pings begin ack'ing in 5-8ms instead of ~100+ ms during the logjam.

So yeah, +10 on the --rate-limit or something similar!

Reference:

https://github.com/ollama/ollama/issues/2006

16 Jun 2024

Compiling latest gcc to test more architectures

Off late, I've had two separate needs to compile GCC by hand and although my first foray into compiling gcc from git took patience, stumbling over the basics was interesting to say the least.

The first time I realised that an old GCC version could matter, was this feedback [1] that one of my buildfarm members was running an old (for its arch) gcc version, something that I almost never paid much attention to. The other being that that led me to newer architectures (more on that below) and how this could repeat itself if / when I end up playing with more architectures.

So finally, I can say I have a framework that frequently checks / recompiles gcc and ensures all my local tests are using the latest and the greatest gcc :) . (I am happy with how this has taken shape on my home server, and once I am able to port it to my other machines, don't see why this shouldn't land on github).

Now admittedly, compiling gcc on a nightly basis was already an overkill, but then what the heck - I went and did this hourly basis just because well-why-not. My personal ask was to:

Incrementally learn how compiling gcc unfolds
Have some fun scripting while at it
... but most importantly, see whether I could utilize this experience in other experiments where the idea is to forewarn database developers about upcoming changes.

A little more on point 3 above, I oversee a few machines on the postgres buildfarm and they differ in some aspects:

Different archictures:

aarch64: Gravitons
x86-64: A vanilla off-the-shelf dell workstation
armv7l - Raspberry Pi4

Different GCCs:

8.3.0 (default in pi4)
7.3.1 (default on most ALs)
13.2 (default on Ubuntu)
14.0.1 (naive attempt at compiling whatever cleared make check)
gcc (experimental nightly)

(Internally I also run some different fuzzing workloads but that's besides the point)
(Future plans - add some form of randomizer to test odd combinations of compilation flags, but more on that in an upcoming post)

Now if all goes to plan, I should also add to the mix, 2 new architectures. They wouldn't be the snappiest processors in the market (at least not in the pricing-level I am after), but hey it should be fun to play with!

loongarch64 - (cough) Recent (but sure as butterflies, a promising) entrant - the MIPS64 Loongson has been around for some time now, but my interest has grown off late owing to sporadic reports that its becoming somewhat competitive, which should be interesting to review.
riscv64 - Another interesting arch that should be fun to try out. Again, am not holding my breath that it'd top any charts, but could still end up being interesting nonetheless.

On the GCC front, getting the setup ready and stable, clears the path to now focus upgrading my buildfarm animals one-by-one and basically start focussing beyond this hurdle.

gcseb02 20240615_1700 - git checkout successful.
gcseb02 20240615_1700 - git pull successful.
gcseb02 20240615_1700 - No change in gcc version. Quitting.

gcsa36f 20240615_1800 - git checkout successful.
gcsa36f 20240615_1800 - git pull successful.
gcsa36f 20240615_1800 - gcc has changed - [471fb092601] vs [57af69d56e7]. Recompiling.
gcsa36f 20240615_1800 - make successful
gcsa36f 20240615_1800 - make install successful.
gcsa36f 20240615_1800 - gcc version string has changed from [15.0.0 20240615 (experimental) - 471fb092601] to [15.0.0 20240615 (experimental) - 57af69d56e7]

gcsf66a 20240615_1900 - git checkout successful.
gcsf66a 20240615_1900 - Unable to git pull. Are we connected? Quitting.
gcsf66a 20240615_1900 - git switched back to 57af69d56e7.

gcs629f 20240615_2000 - git checkout successful.
gcs629f 20240615_2000 - git pull successful.
gcs629f 20240615_2000 - gcc has changed - [57af69d56e7] vs [6762d5738b0]. Recompiling.
gcs629f 20240615_2000 - make successful
gcs629f 20240615_2000 - make install successful.
gcs629f 20240615_2000 - gcc version string has changed from [15.0.0 20240615 (experimental) - 57af69d56e7] to [15.0.0 20240615 (experimental) - 6762d5738b0]

.
.

gcsc115 20240616_0400 - git checkout successful.
gcsc115 20240616_0400 - git pull successful.
gcsc115 20240616_0400 - No change in gcc version. Quitting.

Reference

1. https://www.postgresql.org/message-id/a1e56c889422463ebb0a7e4de0b8f596%40amazon.com