How can I improve NumPy performance without guessing?
A performance change should improve the measured workload without sacrificing correctness or wasting capacity. Which measurements and bottlenecks should be considered first?
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Models, data engineering, retrieval, analytics and databases.
A performance change should improve the measured workload without sacrificing correctness or wasting capacity. Which measurements and bottlenecks should be considered first?
I am using postgresql 14.1, and I re-created my live database using parititons for some tables. since i did that, i could create index when the server wasn't live, but when it's live i can only create the using concurrently but unfortunately when I try to create an index concurrently i get an error. running this: create index concurrently foo on foo_table(co
My current entity looks like this: import { BaseEntity, Column, Entity, PrimaryGeneratedColumn } from 'typeorm'; @Entity() export class Landmark extends BaseEntity { @PrimaryGeneratedColumn('uuid') id: string; @Column() longitude: number @Column() latitude: number } But i wonder if there is a better way to do this, with a special postgres type, that works wi
Motivations I am a running into an issue when trying to proxy PostgreSQL with Traefik over SSL using Let's Encrypt. I did some research but it is not well documented and I would like to confirm my observations and leave a record to everyone who faces this situation. Configuration I use latest versions of PostgreSQL v12 and Traefik v2. I want to build a pure
Recovery needs to recreate the working service and its required data after a machine or process is lost. Which artifacts and state need protection, and how should the restore be checked?
I am using postgresql 15 and I tried running these: grant all privileges on database my_database to my_database_user; grant all privileges on all tables in schema public to my_database_user; grant all privileges on all sequences in schema public to my_database_user; grant all privileges on all functions in schema public to my_database_user; but when I run: p
Configuration must be available to the application without exposing credentials in source control, logs or browser code. What belongs in the runtime and which access controls matter?
I have been having issues with openssl and python@2 with brew, which have explained here (unresolved) . The documented workaround to reinstall Python and openssl was not working, so I decided I would uninstall and reinstall Python. The problem is, when you try to install Python 2 with brew, you receive this message: brew install python@2 Error: No available
I've just upgraded from Fedora 32 to Fedora 33 (which comes with Python 3.9). Since then gcloud command stopped working: [guy@Gandalf32 ~]$ gcloud Error processing line 3 of /home/guy/.local/lib/python3.9/site-packages/XStatic-1.0.2-py3.9-nspkg.pth: Traceback (most recent call last): File "/usr/lib64/python3.9/site.py", line 169, in addpackage exec(line) Fil