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I wish to move a large set of files from an AWS S3 bucket in one AWS account (source), having systematic filenames following this pattern:

When running the first "almost MWE" code immediately below, which uses conditional panels and a "renderUI" function in the server section, it only runs correctly when I comment out the 3rd line from the bottom, observeEvent(vector.final(periods(),yield_input()),{yield_vector.R <<- unique(vector.final(periods(),yield_input()))}). If I run the code with this line activated, it crashes and I get the error message Error in [: subscript out of bounds which per my research means it is trying to access an array out of its boundary.

I'm looping through a multidimensional array and am left with some values.

This is the complete PHP code.

1. Imagine there is a table with 1M rows with categories 1-100.
2. I need to update rows where f.e. category=10 (there are let's say 150k rows).
3. I will update 120k rows and need to delete 30k rows.

First idea: Currently I am using on beginning update of all rows to 0 and on update change this value to 1. Then delete all rows where category=10 and update=0.

There is problem with performance to update 150k rows to 0 where category=10. Sometimes it takes 30s because there could be 200k rows not only 30k.

Second idea On the beginning, loop all 150k rows to keep id's in array, then fill a new array with updated ids and at the end use array_diff to get the remaining ids to delete.

There is also problem with performance to make sql like "... where id in (...30k ids...)".

Do you guys using something better to solve this work? Thanks.

In the following example, how would one utilize flex classes to make columns no.3 and 4 the same height as columns no.1 and 2? Without Javascript, that is.

More specifically, how would I make the height of all columns change automatically to the height of the column with the biggest content?

I need to create a slider for a game that you can set skills to each player,

The rules are :

1. Each skill starts at 0.
2. The skills cannot total more than 100 points at any time.
3. It should always be possible to assign any 0-100 value to a given skill. Given rule (2), if this gets us over 100 total points, the excess automatically, immediately, removed from the other skills, according to their current values.
4. It's not required to use all 100 points (or any).
5. A skill's value is always an integer.

For example :

• We start with:
  Stamina: 0 | Speed: 0 | Armor: 0 | Strength: 0 | Remaining: 100

• The player adds 50 Speed.
  Stamina: 0 | Speed: 50 | Armor: 0 | Strength: 0 | Remaining: 50

• The player adds 25 Armor.
  Stamina: 0 | Speed: 50 | Armor: 25 | Strength: 0 | Remaining: 25 - 115

• The player now adds 40 Stamina. The excess is automatically reduced from the other skills, weighted by their current values.
  Stamina: 40 | Speed: 40 | Armor: 20 | Strength: 0 | Remaining: 0

• The player then reduces Speed to 10.
  Stamina: 40 | Speed: 30 | Armor: 20 | Strength: 0 | Remaining: 10

• Finally, the player sets Strength to 100.
  Stamina: 0 | Speed: 0 | Armor: 0 | Strength: 100 | Remaining: 0

To do so i've created a function the receives 3 arguments :

1. An array of values of the slider

let arrToCalc = [14,24,55,0]

1. The index number of the skill (0 for Stamina, 1 for Speed ...etc)

let newValueIndex = 2

1. New value for base the calculation on

let newVal = 64.

Im not sure my calculations are accurate so i'm getting partial good results.

when set to

I have four questions. Suppose in spark I have 3 worker nodes. Each worker node has 3 executors and each executor has 3 cores. Each executor has 5 gb memory. (Total 6 executors, 27 cores and 15gb memory). What will happen if:

• I have 30 data partitions. Each partition is of size 6 gb. Optimally, the number of partitions must be equal to number of cores, since each core executes one partition/task (One task per partition). Now in this case, how will each executor-core will process the partition since partition size is greater than the available executor memory? Note: I'm not calling cache() or persist(), it's simply that i'm applying some narrow transformations like map() and filter() on my rdd.

• Will spark automatically try to store the partitions on disk? (I'm not calling cache() or persist() but merely just transformations are happening after an action is called)

• Since I have partitions (30) greater than the number of available cores (27) so at max, my cluster can process 27 partitions, what will happen to the remaining 3 partitions? Will they wait for the occupied cores to get freed?

• If i'm calling persist() whose storage level is set to MEMORY_AND_DISK, then if partition size is greater than memory, it will spill data to the disk? On which disk this data will be stored? The worker node's external HDD?

I have a dataset with several variables like the one below:

I am trying to show all the specialization of doctor in ListView but facing error which I have mentioned below: