Love these quick, yet so informative videos. Please keep them coming. Thank you!

Thanks

Bro, thank you very much for all the knowledge you transmit. Out of curiosity, what is the IDE you use in your videos? Are you using any VSCode extension? Greetings from Argentina!

Always nice

Nice

Huh

no audio?

Thanks. Explained visually nested loops in 90 seconds. Appreciate your method with cards. Thank you.

Can i change the query snap UI... Different to see and to understand... Can u make some changes in video... And make graphic easier

So serious man. I swear they would crucify me if I write sql in lower cases. Thanks

Aaah I don't feel too bad writing sql in lower case like in c and c++

Thanks bro, It's really nice and clear explaination. However, I still have 1 question. Why do we need to run "Hash Function" in the whole table? Why don't we hash 5 cards and do the JOIN?

But doing that you won't achieve the best performance using indexes and keys. Join handles it much better, even if you don't use the other table outputs.

flawless explanations!, you deserve your hair back

Thanks for this 💱

Hello Chris, I have one question regarding order by. Does Oracle Database sort results in ascending order based on the first column of the first query when using set operator Union implicitly(by default)?

This helped me a lot. Thank you for this video

Great video. Thanks. But I didn't understand the reason why the example of joining only five cards in the outer deck shows the disadvantage of hash join (from 8:17 to 8:25). I mean, why can't we simply apply the hash function to those five values? That only costs five operations to construct the hash memory structure?

1.Nested Join - nested for loop - O(N * N) - easiest to implement - time consuming for large dataset - better with either small datasets or index on join attributes 2.Merge Join - sort them first and compare - O(NlogN) + O(NlogN) - Efficient for large datasets - pre-join preparation required (sorting) - scanning of relation happens once while joining - can leverage indexes if available to make it faster 3.Hash Join - using hashing - not good for range queries - creating a hashtable for the query you are searching ( user_id is the key) - used for equi joins - efficient for large datasets - requires additional memory - pre join preparation required -> hash table construction - Hash function should distribute data evenly SQL Engine : Take a look at data , look statistics across all table using cardinality

Very well put 👏 @6:20 - 52*log52 equals 296.4 because the base is 2 not 10.

HI please one more favour. need post on tuning complex query having multiple joins with example to see changes before tuning and after tuning. some of the tuning changes or tips and how they affect the query and make quick access or give response

Wow What an Explanation : )

nice video!

Please go little so your accent not comfortable for asians😢😢

Pure Gold!

I have to create index for a huge table (In Gigabytes) for a new column. But when we tried this, it is bringing down the system completely so we had to drop that. Is there any way we can create this index without downtime.

Excellent presentation, the best SQL course!! I love the creativity in your videos. Thank you so much!!

I like how its explained though I dont get most of it

Refreshing

I am not able to understand the autotrace part where view part individual elapsed time is 728 micro seconds. How this was calculated , Please elaborate. Thankyou.

usefull explanation ! thanks a lot!

Well explained!

Perfect video! You can explain things that everyone can understand it!

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Been doing MSSQL for 20 years and this was by far the best explanation of these joins. Thanks!

Best explanation! Thank you sir!

❤

First time came across such good explanation of these 3 kind of joins. Kudos !!!

Thats the first time i understood how these joins worked... 😅

please remove the sound track from videos