You prolly dont give a shit but does any of you know of a tool to get back into an Instagram account..? I was stupid forgot my login password. I appreciate any help you can give me
@Kristopher Gunnar Thanks for your reply. I got to the site on google and I'm trying it out now. Looks like it's gonna take quite some time so I will get back to you later when my account password hopefully is recovered.
Finally understood what parquet format, thanks. So I have one small doubt, does it mean that footer metadata is nothing but schema details, like underlying table details? Like way to mention table name, column names? etc. I'll also dig from my side, but just meanwhile ....
@databricks - what is the best practice to use or not use nested columns. For Example, I have struct of customer with Age, Gender, Name, etc attributes. Is it better to keep it as struct or separate into its own columns?
Thanks for posting this presentation. Could you clarify something? How does performance improve when you compress pages only to decompress it again to read it? I'm sure I'm not understanding something, but not sure what.
Snappy provides good compression with a low CPU overhead during compression/decompression. The real win in performance comes from reduced I/O cost when reading a column chunk's page. The overall cost (CPU+I/O) is generally lower for reading snappy compressed as compared to uncompressed.
@MGLondon How old are you? I am American (and not from China), and stick to common meats. This is an example of hate/harassment. Are you a high school kid?
How is storing json/xml (not parquet) more efficient than csv? You literally store the "column names" in each "row" in xml/json (at least when stored in a text file) . Also, there is definitely the notion of a "record" in csv.
Example 1. If you wanted to add a new properties to records overtime, you only need to add it to the new records (no need to back date blanks for legacy records for example). So think scale and change at scale.
Example 2. You can leverage hash/dictionary data structures in programming, these can find records at a much better scaling, look up hash functions and big o. Again think scaling related to data access, hashing vs at best search trees.
@@happywednesday6741 i asked how it was more efficient to store it. if i have 500 million "entries" in a text file, I'm definitely storing it in a delimited format or parquet to take advantage of said dictionaries and not json/xml. you can parse either into objects directly from the file, or bulk insert into a db table. the json/xlm formats would be 10x slower to parse/read in based on sheer disk/network i/o alone... if we're talking about efficiency in processing it. no one is going to load csv into memory and start trying to scan row by row for data, it's going to get converted into objects or a db anyways. my concern is when people store json formatted files to disk to be read into objects later. what does that buy you?
