septum VS rum

https://github.com/pyjarrett/septum

The hardest part about getting code search right imo is grabbing the right amount of surrounding context, which septum is aimed at solving on a per-file basis.

Another one I'm surprised hasn't been mentioned is stack-graphs (https://github.com/github/stack-graphs), which tries to incrementally resolve symbolic relationships across the whole codebase. It powers github's cross-file precise indexing and conceptually makes a lot of sense, though I've struggled to get the open source version to work

septum - interactive searching for contexts matching and excluding parameters

I do a lot of weird and experimental work in Ada. Some of it works, whereas a lot of it doesn't. While I have done this sort of work in Python, Ruby, Rust, C or C++ in the past, when I do it in Ada, I end up saving time later on since the language forces many "good practices."

I'd appreciate any issues or suggestions you want to report on GitHub to help me improve this.

Ada is another option without a GC. I wrote a search tool for large codebases with it (https://github.com/pyjarrett/septum), and the easy multitasking and pinning to CPUs allows you to easily go wide if the problem you're solving supports it.

There's very little allocation since it supports returning VLAs (like strings) from functions via a secondary stack. Its Alire tool does the toolchain install and provides package management, so trying the language out is super easy. I've done a few bindings to things in C with it, which is ridiculously easy.

I mentioned my project Septum in a HackerNews comment, which caused it to pick up over 200 GitHub stars. That seemed to give Ada some publicity since it's a general purpose tool, so I'll also publish a new up-to-date version (0.0.6) here soon.

I work on code bases with millions of lines, so I wrote a tool called Septum to help me (https://github.com/pyjarrett/septum/). This isn't to replace grep or ripgrep or silver searcher, those are all great tools you should have!

Septum is neighborhood based (context-based) search, so you can find contiguous groups of lines which contain specific things, but exclude other things. It's also interactive so you can add/remove filters as needed. This makes it useful for those cases where terms change based on their context so you can exclude terms related to the contexts you don't want to keep. It reads .septum/config which contains its normal commands to load directories and settings, so you can have different configs per project you're working on.

Here's a short demo video of his Septum tool mentioned in the article: https://asciinema.org/a/459292

I also did a few things: - Wrote an online e-book about Ada - Septum - context-based source code search for multi-million line codebases (I use this nearly every day at work. It's being submitted as my Ada crate of the year. - dir_iterators - library similar to the incredible walkdir. - project_indicators - library for spinners and progress bars. - trendy_terminal - library for cross-platform terminal setup, VT100 support, and GNU readline-like behavior. - trendy_test - library for simple unit testing, which runs tests in parallel. - Ada Ray Tracer - an Ada port of Ray Tracing in One Weekend. - dirs_to_graphviz - Make graphviz files from directory trees. - rst_tables - a tool to draw RST table outlines.

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.