magento2 – Magento 2 : Does redis make any difference on low traffic store

I’ve recently come across redis being installed and configured on single server low traffic stores.

Generally the default response for anyone when reporting their Magento 2 store (which is everyone) is for the web hosts to install and configure redis (among other things). However so far it is my personal opinion that having redis adds an additional barrier when trying to load new content and I see very little performance gain.

Knowing a bit about the technology and how it works it is my understanding is that with a webserver using ssd’s with low traffic the benefit is minimal. I’m wondering if anyone in the community has some benchmarks on low traffic stores with a before and after to confirm my theory.

I do however understand how redis has a positive impact on high traffic stores.

I’m keen to hear other’s experiences. I see questions on configuring redis but I don’t seen that anyone has asked this question on magento 2.

linux – Efficient way of determining whether 2 directories have ANY difference?

I’d like to recursively compare two directory trees that are expected to be identical, but I don’t want a full comparison which would take forever. I’d like to do an efficient comparison so that as soon as 1 difference is detected, the command stops and returns, and tell me which file was different.

What I consider to be a difference:

  • two files have different content (different timestamps doesn’t matter)
  • a file was found in one directory but not the other (at the exact same path, of course)


  • I don’t need to know the actual differences within the file, just the filepath is enough
  • I tried diff 3.7 on Ubuntu 20.04, it doesn’t have a “stop on difference” option that I could see
  • The files are a mix of text and binary

dhcp – What is the difference between a ‘host’ entry and a ‘lease’ entry in dhcpd.leases?

In the managed file dhcpd.leases, our lifecycle management application adds a host { ... } entry when building a virtual-machine from the application. However, already built machines that were migrated into the application and required a lease renewal have the entry lease X.X.X.X { ... client-hostname "h2"; } (note: the lack of domain in the lease entry).

A clearer example of what I’m talking about:

host {
  hardware ethernet 00:11:22:AA:BB:CC;
        supersede server.filename = "pxelinux.0";
        supersede = AA:BB:CC:DD;
        supersede host-name = "";

lease {
  starts 4 2021/01/01 00:00:00;
  ends 6 2021/04/01 00:00:00;
  cltt 4 2021/02/25 00:00:00;
  binding state active;
  next binding state free;
  rewind binding state free;
  hardware ethernet 00:11:22:AA:BB:DD;
  client-hostname "h2"; 

For some additional information: our lifecycle management application also manages DNS. We noticed an entry for in for the host h2 had a lease entry and IP that didn’t match its DNS record. The lease entry was automatically populated when the host requested a new IP. It seems no host record is created upon lease renewal and only appears when the lifecycle management app builds a new host.

What are the differences between the two entries host {...} and lease {...} in dhcpd.leases and what other functions do they affect?

sharepoint online – The difference between a list stored in My Files and stored in my own Teamsite

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Is MySQL more scalable than PostgreSQL due to the difference in how they handle connections?

I’m trying to decide if either MySQL or PostgreSQL would be more suitable for an application that will get hit by potentially thousands of simultaneous requests at a time.

During research, one fact that stands out is that PostgreSQL forks a new process for each connection, whereas MySQL creates a new thread to handle each connection.

  • Does this mean that MySQL is more efficient than PostgreSQL at handling many concurrent connections?

  • How much of an impact does this difference have on how well both systems scale? Is it something that I should worry about to begin with?

google sheets – Calculate time difference within one cell

You can use

=INDEX(SPLIT(F22, " - "),,2) - 
 INDEX(SPLIT(F22, " - "),,1)

enter image description here

This means we split the same cell twice.
Using INDEX we use the second column of the first split minus the first column of the second split.
(Of course you should format the result as duration.)

secp256k1 – What is the difference between secp256k1_ecmult_gen and secp256k1_ecmult?

libsecp256k1 has two implementations, secp256k1_ecmult in ecmult.h and secp256k1_ecmult_gen in ecmult_gen.h, to multiply the points of an elliptic curve.

secp256k1_ecmult_gen supports simple multiplication such as a*G and secp256k1_ecmult supports multiplication involving addition such as a*P + bG. However, secp256k1_ecmult can be used for simple multiplication by setting b=0, actually, it is used as such in eckey_impl.h#secp256k1_eckey_pubkey_tweak_mul.

How should these two functions be used properly? Is there any difference in the performance?

What is a difference in the order of "follow" and "index" in a robots meta tag?

What is the difference between

<meta name="robots" content="follow, index">


<meta name="robots" content="index, follow">

Would either create any issue for indexing or crawling?

What is a Difference Between this 2 meta Robots tag?

<meta name="robots" content="follow, index,


<meta name="robots" content="index, follow,

Have a any any Issue Created in Indexing,Crawling ?

python – Calculate date difference of dataframe groups

I have a dataframe where I need to calculate the length of time (in years) between dates of groups. For example, I want the difference between the first time a NameID group appeared (identified by %_chng=New), and the date in the Date column.

df = pd.DataFrame({'Name': {0: 'Faye', 1: 'Faye', 2: 'Faye', 3: 'Faye', 4: 'Faye', 5: 'Faye', 6: 'Faye', 7: 'Mike', 8: 'Mike', 9: 'Mike', 10: 'Mike', 11: 'Mike', 12: 'Mike', 13: 'Mike', 14: 'Mike'}, 'Date': {0: '2020-12-31', 1: '2020-09-30', 2: '2020-06-30', 3: '2018-09-30', 4: '2018-09-30', 5: '2018-09-30', 6: '2018-06-30', 7: '2020-12-31', 8: '2020-09-30', 9: '2020-09-30', 10: '2020-06-30', 11: '2020-03-30', 12: '2019-12-31', 13: '2019-09-30', 14: '2019-06-30'}, 'ID': {0: 'A', 1: 'A', 2: 'A', 3: 'A', 4: 'A', 5: 'B', 6: 'B', 7: 'A', 8: 'A', 9: 'C', 10: 'C', 11: 'C', 12: 'C', 13: 'C', 14: 'C'}, '%_chng': {0: '0.3', 1: '0.2', 2: 'New', 3: '0.1', 4: 'New', 5: '0.2', 6: 'New', 7: '0.7', 8: 'New', 9: '0.1', 10: '0.2', 11: '0.1', 12: '0.4', 13: '0.3', 14: 'New'}})

    Name        Date ID %_chng
0   Faye  2020-12-31  A    0.3
1   Faye  2020-09-30  A    0.2
2   Faye  2020-06-30  A    New
3   Faye  2018-09-30  A    0.1
4   Faye  2018-09-30  A    New
5   Faye  2018-09-30  B    0.2
6   Faye  2018-06-30  B    New
7   Mike  2020-12-31  A    0.7
8   Mike  2020-09-30  A    New
9   Mike  2020-09-30  C    0.1
10  Mike  2020-06-30  C    0.2
11  Mike  2020-03-30  C    0.1
12  Mike  2019-12-31  C    0.4
13  Mike  2019-09-30  C    0.3
14  Mike  2019-06-30  C    New

So the expected output would look something like:

    Name        Date ID %_chng  date_length
0   Faye  2020-12-31  A    0.3         0.50
1   Faye  2020-09-30  A    0.2         0.25
2   Faye  2020-06-30  A    New         0.00
3   Faye  2018-09-30  A    0.1         0.25
4   Faye  2018-09-30  A    New         0.00
5   Faye  2018-09-30  B    0.2         0.25
6   Faye  2018-06-30  B    New         0.00
7   Mike  2020-12-31  A    0.7         0.25
8   Mike  2020-09-30  A    New         0.00
9   Mike  2020-09-30  C    0.1         1.25
10  Mike  2020-06-30  C    0.2         1.00
11  Mike  2020-03-30  C    0.1         0.75
12  Mike  2019-12-31  C    0.4         0.50
13  Mike  2019-09-30  C    0.3         0.25
14  Mike  2019-06-30  C    New         0.00