Is there a need in Magento 2.4 for Cache warmer extension or Google Page Speed Optimizer?

Magento ver. 2.4.2
4 x 2.50 GHz Cores
8 GB RAM
5 TB Bandwidth
5000 Mbps Network Out

We used to run Magento 1.9.4 but are switching to Magento 2.4.2. Every time I installed Magento 2.x it always seemed very slow on the back-end (we haven’t set up the front end yet to see) even though I have done all the recommended optimisations.

Now I found 2 extensions created by Amasty:

  • Full Page Cache Warmer
  • Google Page Speed Optimizer

They are not the cheapest ones so I was wondering if this would help with Magento 2 or is Magento 2.x designed in such a way where this is no longer needed? Historically (on our Magento 1.9.4) we had a hard time with Caching extensions so we ended up disabling it (I guess we weren’t able to configure them properly).

[TH] Image Optimizer | Nulled Scripts Download

This add-on is designed to allow you to optimize any images that are uploaded on your forum by users while also compressing with best-in-class algorithms.

Image Optimizer will allow you to save storage space and bandwidth with just a few clicks. All-in-all, your website’s load time will be well improved after using this add-on.

General Features:

  • Optimize any image on your forum
  • Save bandwidth and storage space
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sql server – Why using a local temp table (instead of a global temp table or a regular table) influences the Query Optimizer to choose a poor query plan?

This Question brings a situation where the Query Optimizer chooses poorly the seek predicate among the existing predicates of a simple query. After running some tests I got to the conclusion that the poor decision is due to the use of a local temp table instead of a global temp table or a regular table.

db fiddle: Local Temp Table, Global Temp Table, Regular Table.

Index seek info

I couldn’t find any characteristic on the Temporary Tables doc that would explain the different behavior we see when using a local temporary table instead of a global temporary table or a regular table. Is there a logical reason for this or could it be a bug?

performance – Why Does the SQL Server Query Optimizer Not Convert an OUTER JOIN to an INNER JOIN When a Trusted Foreign Key Is Present?

It appears that the SQL Server query optimizer does not translate an OUTER JOIN into an INNER JOIN when the join column in the first table is defined as NOT NULL and has a trusted foreign key constraint to the corresponding column in the second table.

It seems that in this scenario, the OUTER JOIN could be translated into an equivalent INNER JOIN, because each row in the first table is:

  1. guaranteed to have a value in the column (NOT NULL constraint), and
  2. guaranteed to have a matching row in the second table (trusted foreign key constraint).

For example, consider the following tables:

 CREATE TABLE dbo.tbl_fk
 (
    fk_val CHAR(1) NOT NULL PRIMARY KEY CLUSTERED,
    junk VARCHAR(100) NOT NULL
 );
 
 CREATE TABLE dbo.tbl_main
 (
    id INT NOT NULL PRIMARY KEY CLUSTERED IDENTITY(1,1),
    fk_val CHAR(1) NOT NULL FOREIGN KEY REFERENCES dbo.tbl_fk(fk_val)
 );

In the following query, why does the optimizer not convert the LEFT OUTER JOIN to an INNER JOIN in the execution plan?

SELECT m.fk_val, f.junk
FROM dbo.tbl_main AS m
LEFT OUTER JOIN dbo.tbl_fk AS f
    ON m.fk_val = f.fk_val;

By adding a predicate to explicitly remove NULL values from the second table, however, the optimizer converts the LEFT OUTER JOIN into an INNER JOIN in the query plan, as expected:

SELECT m.fk_val, f.junk
FROM dbo.tbl_main AS m
LEFT OUTER JOIN dbo.tbl_fk AS f
    ON m.fk_val = f.fk_val
WHERE f.fk_val IS NOT NULL;

Undocumented trace flags show that a rule is applied to change the OUTER JOIN to an INNER JOIN in the query with the predicate:

SELECT m.fk_val, f.junk
FROM dbo.tbl_main AS m
LEFT OUTER JOIN dbo.tbl_fk AS f
    ON m.fk_val = f.fk_val
WHERE f.fk_val IS NOT NULL
OPTION (QUERYTRACEON 3604, QUERYTRACEON 8621);

***** Rule applied: A LOJ B -> A JN B

These examples were tested using SQL Server 2014 and 2019 using a number of different compatibility levels and also using both the legacy and “new” cardinality estimator. The behavior appeared to be the same in all cases.

Forrest McDaniel pointed out that (as of 2010), Sybase appeared to have such a transformation built into their product (see Example 1): http://dcx.sybase.com/1200/en/dbusage/queryopt-sectb-5356466.html

Mask R-CNN optimizer and learning rate scheduler in Pytorch

In the Mask R-CNN paper the optimizer is described as follows training on MS COCO 2014/2015 dataset for instance segmentation (I believe this is the dataset, correct me if this is wrong)

We train on 8 GPUs (so effective minibatch
size is 16) for 160k iterations, with a learning rate of
0.02 which is decreased by 10 at the 120k iteration. We
use a weight decay of 0.0001 and momentum of 0.9. With
ResNeXt (45), we train with 1 image per GPU and the same
number of iterations, with a starting learning rate of 0.01.

I’m trying to write an optimizer and learning rate scheduler in Pytorch for a similar application, to match this description.

For the optimizer I have:

def get_Mask_RCNN_Optimizer(model, learning_rate=0.02):
    optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=0.0001)
    return optimizer

For the learning rate scheduler I have:

def get_MASK_RCNN_LR_Scheduler(optimizer, step_size):
    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gammma=0.1, verbose=True)
    return scheduler

When the authors say “decreased by 10” do they mean divide by 10? Or do they literally mean subtract by 10, in which case we have a negative learning rate, which seems odd/wrong. Any insights appreciated.

Can you help me make a pin and mash position optimizer and visualizer?

So say I have a 3/16 thick 6×6 grid that needs to be placed at every location where there is a 3×5, 1/4-in thick pin in a zone 3/4 to 1 and 1/4 in away from the back of the pin. You are given mesh dimensions from stock lengths that need to be cut in such a way that the first horizontal rod of the mesh should be some distance away from the end. I want to visualize the final cut and how it would be placed with pin coordinates and frame outline

Best Pc Optimizer Software – Newbies Lounge

Great share, thanks. I’ve been looking for a great PC optimizer because my computer is really old and sometimes doesn’t work properly. I have a windows 10 registry cleaner installed and I clean it constantly, every week or even more often, but still, sometimes it can reboot suddenly or shut off. I’m planning on getting another one soon, but still, I’ll use this old one from time to time, so I need to fix it and keep it optimized. I’ll try this one you recommended, we’ll see how it works for me.

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Does the setting of query optimizer fixes ON activate scalar UDF inlining in a database with compatibility level 140?

Managed Azure SQL database instance. The current compatibility level is set to 140 to match the local SQL Server 2017 test and development server. I just read this article about Scalar UDF Inlining and was curious to see if Query Optimizer Fixes ON would enable this feature. I've read several other articles on the subject, but none of them address this question.

Schedule management optimizer

I had the idea of ​​a program that should help me in my everyday life. We both work and do not live in the same city. I was wondering how to put together a program that, depending on working hours and the number of journeys required between our two houses, is the best way to spend our weekend, e.g. B. in which house we have to be and when we can spend the maximum time together. I know that sounds like a problem you'd find in a textbook, but I really can't imagine how to do it.

Optimization – Does the query optimizer have the right to create temporary (materialized) views if this improves performance?

Does the query optimizer have the right to create temporary (materialized) views that can improve performance? Sorry if this question seems very trivial.

In other words, the query optimizer considers plans that create a temporary table, save the object to disk, and then use it for query execution.