parsemail VS rum

It's a pain in the ass right now. Original I come up with the domain hanami.run because I explained here https://www.youtube.com/watch?v=O1p2crPpFIc I feel like wind blow flowers where Hanami blow out emails.

I run an email forwarding services (https://hanami.run if you want to check it out) and I can share some info:

I found https://hanami.run (soon to be mailwip.com due to name conflict with hanamirb.org) to setup email forwarding and a simple blog platform by "email to post" and webhook.

Use it you can consolidate emails from multiple domains to forward to the same inbox. And you can add webhook/slack notification too.

You can look into mine (https://hanami.run) very fast to sign up and have a few cool features about webhook or smtp.

Also, improvmx.com is a great product as well.

If you like open source, https://maddy.email/ is a single binary deployment that can handle everything even IMAP.

https://mailcow.github.io/mailcow-dockerized-docs/ is a dockerize solution with super detail document as well.

If you want to compete with the big players, you have to solve the most important pain point and work upward from that small use base.

My case: I work on https://hanami.run (will soon move to https://mailwip.com due to hanamirb.org conflict) and email forwarding is very competitive. Big and old players are all over the place because at the end of day, setting up email forwarding isn't hard and many open source project did it, heck you can spin up AWS lambda for incoming email in no time.

The pain point is: email will drop sometime, time to time no matter how good an email forwarding service is because they have to scan spam, have false positive, or because of strict DMARC/SPF rule. And I have no tools available to help me out there. So I focus strongly on my maillog features with many level of privacy:

- no log at all

- docker-compose to spin up everything. It's super nice. Again, the deployment is done with a `rsync` then `docker-compose up -f docker-compose-prod.yml`

Eventually when deployment changes very frequent and need scale/ha I added in Kubernetes. K8S is way easiser to setup than you think and it handle all other suff(load balancer, environment variable etc).

And my deploy now become: `kubectl apply -f`

One trick I used is to use `sed` or `envsubst` to replace the image hash.

For backedup, I again, literally setup cronjob from an external server, `ssh` into database and run `pgdump`.

I also have a nice NFS server to centralize config and sync back to our git repo.

I used this whole setup to operate https://hanami.run an email forwarding service for the first 3 months before I added Kubernetes.

So I want to share a story about user asking for a feature then not using it.

I run an email forwarding services(https://hanami.run) basically you add your domains in and add some records.

We had this one heavy users who has like hundreds of domains. So our UI isn't design for that. Who has hundreds of domains? So they approach and asked us for a way to organize those domains into a hierarchy structure.

All good.

They are paid our highest tier ($30 per month) so we prioritize the requests and work on it.

2 days later that same user downgrade to the lowest plan and delete all of their hundred of domains...

That complicated features remain unused to nowadays...

I used hanami.run and they support that. A catch-all then an explicitly deny rule to disable certain address.

Wix doesn't have built-in email forwarding but you can use any email forwarding service. Look into hanami.run and simply follow their onboarding process to add your MX record. https://hanami.run/docs/configure_dns#mx

the rum index has worked well for us on roughly 1TB of pdfs. written by postgrespro, same folks who wrote core text search and json indexing. not sure why rum not in core. we have no problems.

   https://github.com/postgrespro/rum

If you need ranking, and you have the possibility to install PostgreSQL extensions, then you can consider an extension providing RUM indexes: https://github.com/postgrespro/rum. Otherwise, you'll have to use an "external" FTS engine like ElasticSearch.

>Reduce the memory usage of prepared queries

Yes query plan reuse like every other db, this still blows me away PG replans every time unless you explicitly prepare and that's still per connection.

Better full-text scoring is one for me that's missing in that list, TF/IDF or BM25 please see: https://github.com/postgrespro/rum

for postgres, i highly recommend the rum index over the core fts. rum is written by postgrespro, who also wrote core fts and json indexing in pg.

    https://github.com/postgrespro/rum

My experience with Postgres FTS (did a comparison with Elastic a couple years back), is that filtering works fine and is speedy enough, but ranking crumbles when the resulting set is large.

If you have a large-ish data set with lots of similar data (4M addresses and location names was the test case), Postgres FTS just doesn't perform.

There is no index that helps scoring results. You would have to install an extension like RUM index (https://github.com/postgrespro/rum) to improve this, which may or may not be an option (often not if you use managed databases).

If you want a best of both worlds, one could investigate this extensions (again, often not an option for managed databases): https://github.com/matthewfranglen/postgres-elasticsearch-fd...

Either way, writing something that indexes your postgres database into elastic/opensearch is a one time investment that usually pays off in the long run.

Mandatory mention of the RUM extension (https://github.com/postgrespro/rum) if this caught your eye. Lots of tutorials and conference presentations out there showcasing the advantages in terms of ranking, timestamps...

You might be just fine adding an unindexed tsvector column, since you've already filtered down the results.

The GIN indexes for FTS don't really work in conjunction with other indices, which is why https://github.com/postgrespro/rum exists. Luckily, it sounds like you can use your existing indices to filter and let postgres scan for matches on the tsvector.

We have been bitten by the same behavior. I gave a talk with a friend about this exact topic (diagnosing GIN pending list updates) at PGCon 2019 in Ottawa[1][2].

What you need to know is that the pending list will be merged with the main b-tree during several operations. Only one of them is so extremely critical for your insert performance - that is during actual insert. Both vacuum and autovacuum (including autovacuum analyze but not direct analyze) will merge the pending list. So frequent autovacuums are the first thing you should tune. Merging on insert happens when you exceed the gin_pending_list_limit. In all cases it is also interesting to know which memory parameter is used to rebuild the index as that inpacts how long it will take: work_mem (when triggered on insert), autovacuum_work_mem (when triggered during autovauum) and maintainance_work_mem (triggered by a call to gin_clean_pending_list()) define how much memory can be used for the rebuild.

What you can do is:

- tune the size of the pending list (like you did)

- make sure vacuum runs frequently

- if you have a bulk insert heavy workload (ie. nightly imports), drop the index and create it after inserting rows (not always makes sense business wise, depends on your app)

- disable fastupdate, you pay a higher cost per insert but remove the fluctuctuation when the merge needs to happen

The first thing was done in the article. However I believe the author still relies on the list being merged on insert. If vacuums were tuned agressively along with the limit (vacuums can be tuned per table). Then the list would be merged out of bound of ongoing inserts.

I also had the pleasure of speaking with one main authors of GIN indexes (Oleg Bartunov) during the mentioned PGCon. He gave probably the best solution and informed me to "just use RUM indexes". RUM[3] indexes are like GIN indexes, without the pending list and with faster ranking, faster phrase searches and faster timestamp based ordering. It is however out of the main postgresql release so it might be hard to get it running if you don't control the extensions that are loaded to your Postgres instance.

[1] - wideo https://www.youtube.com/watch?v=Brt41xnMZqo&t=1s

[2] - slides https://www.pgcon.org/2019/schedule/attachments/541_Let's%20...

[3] - https://github.com/postgrespro/rum

I suggest to have a look at https://github.com/postgrespro/rum if you haven’t yet. It solves the issue of slow ranking in PostgreSQL FTS.