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!

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:

1. Install + Quick Start: https://github.com/ollama/ollama/blob/main/README.md#quickstart

2. Model downloads made my server unresponsive: https://www.thatguyfromdelhi.com/2024/07/ollama-is-missing-rate-limits-on.html

3. Model sizes are in GBs: https://github.com/ollama/ollama/blob/main/README.md#model-library

4. Radeon 8570: https://www.techpowerup.com/gpu-specs/amd-radeon-hd-8570.b1325

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 :

1. 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!!

2. 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:

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

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:

1. Incrementally learn how compiling gcc unfolds
2. Have some fun scripting while at it
3. ... 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:

1. Different archictures:
1. aarch64: Gravitons
2. x86-64: A vanilla off-the-shelf dell workstation
3. armv7l - Raspberry Pi4
2. Different GCCs:
1. 8.3.0 (default in pi4)
2. 7.3.1 (default on most ALs)
3. 13.2 (default on Ubuntu)
4. 14.0.1 (naive attempt at compiling whatever cleared make check)
5. gcc (experimental nightly)
3. (Internally I also run some different fuzzing workloads but that's besides the point)
4. (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!

1. 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.
2. 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

Parse RSS feed in bash

This is probably a fun poke at Planet PostgreSQL itself :) !

Recently I did a multi-part series [1][2][3] on a given topic and ended up "Scheduling" successive posts, so that they don't go out altogether.

And an interesting question came up:

What would be a good day/time for the next post?

A little bit of XML parsing later, I got a hint of what would looks like a lean period!

robins@fw:~/dl$ wget -q https://planet.postgresql.org/rss20_short.xml; echo "cat  //pubDate/text()" | xmllint --shell  rss20_short.xml | grep -ve "----" | grep -v "\/" | date +%A -f - | sort | uniq -c | sort -n
      1 Sunday
      3 Monday
      3 Saturday
      4 Friday
      5 Thursday
      6 Wednesday
      8 Tuesday

Now obviously the count for posts on a given weekday kept changing (depending on when I checked) but Sunday was undeniably a lean day. Given that, for some this may mean that Sunday is NOT the best time to post an article, but think I'll leave that to you 😊 !

Happy reading!

Boost Database Security: Restrict Users to Read Replicas

Only Allow Login to Read-Replicas and Standbys

When you're working with large databases in production, it is incredibly common to use read-replicas to improve performance. These read-replicas are a copy of your primary (main) database and let your applications offload read-heavy queries, which in-turn reduces strain on your primary database, effectively making the application faster and snappier.

Sometimes, you may want to restrict specific database users so they can connect ONLY to these read-replicas, and not to the primary database server. This can be tricky to implement, since any permissions configured for this use-case, whether on the user-level, the database level, the schema-level or even the table level would be quickly replicated to the read-replicas and thus would not work as expected.

This guide will show how to configure a database user to only login successfully on a read-replica. The only requirement is to enable the pg_tle extension [3] on your PostgreSQL database. This is simple to do on your Ubuntu based Laptop (see how to do that here [2]) or virtual-machines offered by your favourite cloud-provider. Furthermore, you could apply your login rules using Pl/PgSQL, PL/v8 or even PL/Rust - See here[1].

Why Restrict Access?

There are many good reasons for restricting users to read-replicas:

• Performance: You can dedicate your primary database server to handling write operations (like updating data), ensuring those operations happen as fast as possible.

• Reporting / Analytics: Production environments often have dedicated users for ancillary tasks, such as monitoring, reporting dashboards, read-only tenants etc. Restricting these database users to read-replica helpsreducing extra load on the primary database.

• Security: In some cases, granting direct access to the primary database might be considered a security risk. Further, you may not be able to force login hygeine for all your database users, and then having a lockdown system to reject those database users to login to primary is crucial for application rollout.

Prerequisites

• An existing PostgreSQL database instance with at least one read-replica.
  • You could also try this on your own Postgres database with pg_tle extension. Read here [2] for more on how to install pg_tle on your Ubuntu system.
• Basic understanding of users and permissions within a database.

Steps

1. Identify Target Database and Users: First we need to define how to implement the restriction. i.e. Which users (and database) are to be restricted to login only to read-replica. In the example below, we would restrict the user standby_only_user to only be able to login to Standbys / Read-Replicas on database prod_db.

psql <<SQL
  \c prod_db
  CREATE EXTENSION pg_tle;
SQL 

2. Ensure that shared_preload_libraries is properly set to allow pg_tle. Also make sure that the pgtle.clientauth_db_name is appropriately set to the desired database (here prod_db):

cat <<EOL >> data/postgresql.conf
  shared_preload_libraries='pg_tle'
  pgtle.enable_clientauth=require
  pgtle.clientauth_db_name=prod_db
  pgtle.clientauth_users_to_skip=robins
  pgtle.clientauth_databases_to_skip=''
EOL

3. Secret Sauce:

Next we create the key pg_tle function that restricts the user standby_only_user to login successfully only if this is a standby / read-replica:

SELECT pgtle.install_extension (
  'standbyusercheck',
  '1.0',
  'Allow some users to login only to standby / read-replicas',
$_pgtle_$
  CREATE SCHEMA standbycheck_schema;

  REVOKE ALL ON SCHEMA standbycheck_schema FROM PUBLIC;
  GRANT USAGE ON SCHEMA standbycheck_schema TO PUBLIC;

  CREATE OR REPLACE FUNCTION standbycheck_schema.standbycheck_hook(port pgtle.clientauth_port_subset, status integer)
  RETURNS void AS $$
    DECLARE
      is_standby bool := TRUE;
    BEGIN
      IF port.user_name = 'standby_only_user' THEN
        SELECT pg_is_in_recovery()
          INTO is_standby;
        IF is_standby THEN
          RAISE NOTICE 'User allowed to login';
        ELSE
          RAISE EXCEPTION 'User can only login to Standby / Read-Replicas';
        END IF;
      END IF;
    END
  $$ LANGUAGE plpgsql SECURITY DEFINER;

  GRANT EXECUTE ON FUNCTION standbycheck_schema.standbycheck_hook TO PUBLIC;
  SELECT pgtle.register_feature('standbycheck_schema.standbycheck_hook', 'clientauth');
  REVOKE ALL ON SCHEMA standbycheck_schema FROM PUBLIC;
$_pgtle_$
);

And now that the function is defined,CREATE EXTENSION would install the function and bind it to future login attempts.

CREATE EXTENSION standbyusercheck;
SHOW pgtle.clientauth_db_name;

4. Test Connection:

• Attempting to connect as a privileged user (here robins) to either of primary or read-replica should succeed.

Logging into Replica as robins
 login  | current_database | pg_is_in_recovery
--------+------------------+-------------------
 robins | prod_db          | t
(1 row)

Logging into Primary as robins
 login  | current_database | pg_is_in_recovery
--------+------------------+-------------------
 robins | prod_db          | f
(1 row)

• However, the user standby_only_user should NOT be able to login to the primary.

Logging into Primary as standby_only_user
psql: error: connection to server at "localhost" (127.0.0.1), port 6432 failed: FATAL:  User can only login to Standby / Read-Replicas

• While the user (standby_only_user) should only be able to login to any read-replica.

Logging into Replica as standby_only_user
       login       | current_database | pg_is_in_recovery
-------------------+------------------+-------------------
 standby_only_user | prod_db          | t
(1 row)

Other important aspects of this feature

• You could force clientauth for all logins by setting the parameter pgtle.enable_clientauth = require

• You could configure some users to always be allowed to login to either of Primary / Read-replica in cases of emergency, by adding that user to the pgtle.clientauth_users_to_skip. Ideally you would want your admin database roles to this list.

• Orthogonally, you could configure some databases to always allow users to skip clientauth by setting the pgtle.clientauth_databases_to_skip feature.

• Note, that both clientauth_databases_to_skip and clientauth_databases_to_skip can be utilised together. This is a good way to ensure that some set of database users (and some databases) are exempt from such a login restriction.

• If pgtle.enable_clientauth is set to on or require and if the database mentioned in pgtle.clientauth_db_name is not configured correctly, postgres would complain with the messsage FATAL: pgtle.enable_clientauth is set to require, but pg_tle is not installed or there are no functions registered with the clientauth feature. This is a good engine check, helping us avoid basic misconfigurations.

• If you're anticipating connection storms, you can also increase the workers (that would help enforce the login restriction) by setting the pgtle.clientauth_num_parallel_workers parameter to greater than 1.

Conclusion

By following the above steps, you've now successfully configured your PostgreSQL environment to restrict certain users to only login to the read-replicas. This helps not just optimize your database performance, but also bolster security.

Let me know if you'd like to explore more advanced scenarios or discuss IAM integration for fine-grained access control!

Reference

1. Clientauth Hook Documentation - https://github.com/aws/pg_tle/blob/main/docs/04_hooks.md'
2. Install pg_tle On Ubuntu - https://www.thatguyfromdelhi.com/2024/04/installing-pgtle-on-ubuntu-quick-guide.html
3. Unlock PostgreSQL Super Powers with pg_tle - https://www.thatguyfromdelhi.com/2024/04/unlock-postgresql-superpowers-with-pgtle.html

Installing pg_tle on Ubuntu: A Quick Guide

Compile & Install pg_tle on Postgres

PostgreSQL is a powerful database, but sometimes you want to extend its functionality with custom features, and that's where extensions like pg_tle (Trusted Language Extensions) come into play.

If you're new to pg_tle, here's a quick round-up of why it makes sense for you - See Unlock PostgreSQL Superpowers with pg_tle.

Given the power of pg_tle, you may want to install it locally (on your laptop or an EC2 instance) before deploying to environments with restricted access (such as Production, or PostgreSQL services offered by major cloud providers). This is not only helpful to thoroughly test your code, but also to save on cost given that all development can then happen on-premise.

In this blog post, we'll go through the process of compiling and installing pg_tle for postgres on your Ubuntu system.

Prerequisites

An operating system running Ubuntu (this guide assumes Ubuntu 20.04 or similar).

• A PostgreSQL database server, installed and running.
• Basic familiarity with the command line and postgresql.conf.
• Some development tools (we'll install these as we go).

Steps

Install Build Tools and Dependencies:

Start by updating your Ubuntu package list & install the necessary tools and libraries (This is required, since we would be compiling the pg_tle extension by source).

$ sudo apt update
$ sudo apt install build-essential make git postgresql-server-dev-all 

The postgresql-server-dev-all package version may need to be adjusted to match your specific PostgreSQL version. If this doesn't work for you, you can instead read more about setting up your Ubuntu operating system (albeit in a dated post) here - See Setup Linux for PostgreSQL development [3].

Download pg_tle Source Code:

Get the pg_tle source code from the GitHub project repository:

$ git clone https://github.com/aws/pg_tle.git

Compile pg_tle:

Compile the source code to create the extension files:

$ cd pg_tle
$ make

Although rare, make may fail if it can't find pg_config. As in the example below, a quick hack could be to help by pointing make to the pg_config binary location:

$ make                                     <<===== Fails
Makefile:24: /usr/lib/postgresql/15/lib/pgxs/src/makefiles/pgxs.mk: No such file or directory
make: *** No rule to make target '/usr/lib/postgresql/15/lib/pgxs/src/makefiles/pgxs.mk'.  Stop.

$ PG_CONFIG="`type -P -a pg_config`" make  <<===== Works successfully
gcc -Wall -Wmissing-prototypes ...
.
.
. (compiling starts successfully)

Install pg_tle:

Install the compiled extension into your PostgreSQL database. This command would install the extension related files to the postgres binaries folder, pointed to by PG_CONFIG:

$ sudo make install

Enable pg_tle in Your Database:

Connect to your PostgreSQL database using your preferred tool (e.g., psql) and run the following SQL command:

test_pgtle=# CREATE EXTENSION pg_tle;
CREATE EXTENSION

Verification:

To confirm pg_tle is installed correctly, run this SQL query:

test_pgtle=# SELECT * FROM pg_available_extensions WHERE name = 'pg_tle';
-[ RECORD 1 ]-----+-------------------------------------------
name              | pg_tle
default_version   | 1.4.0
installed_version | 1.4.0
comment           | Trusted Language Extensions for PostgreSQL

You should see a result similar to the above, where installed_version confirms the pg_tle version that's installed successfully.

Conclusion

You've now successfully compiled and installed the pg_tle extension on your Ubuntu system! This opens up the possibility to create and deploy custom extensions to enhance your PostgreSQL database.

pg_tle is a powerful tool that allows you to develop more advanced extensions. You can find more information and examples in the official pg_tle documentation at https://github.com/aws/pg_tle.

If you're intrigued, keep an eye out for a follow-up post where I'll show a simple example of how to use pg_tle extension for a real-world need!

References

1. Unlock PostgreSQL Superpowers with pg_tle - https://www.thatguyfromdelhi.com/2024/04/unlock-postgresql-superpowers-with-pgtle.html
2. Setup Linux for PostgreSQL development - https://www.thatguyfromdelhi.com/2011/12/setup-ubuntu-for-postgresql-development.html