gb | Inspired by Apache Benchmark | Performance Testing library

When you do --set-default-version, you're setting the version for future distributions that you install. That doesn't convert or change any current distros you have installed. So for your existing Ubuntu distro that is version 1, you should use the wsl --set-version command to convert it to version 2 or revert back to version 1.

Source: https://docs.microsoft.com/en-us/windows/wsl/install-win10

There is no workaround to reduce the payment. For example, the Basic SKU include at least 10 GB storage, you can't pay less than $0.167 each day for the storage, even if you only use 500MB. Just like you use a part of something, but you can't only buy the part, you need to buy the whole thing.

If I pull the image from other repo. Will be image bei Azure cached? If yes, then what is TTL for my image?

The cache images mean Azure already pulled the images and don't need to pull again. The time to pull your custom image depends on two things. One is if the base image or your custom image in the list of the cached images. Another one is that how many layers your custom image have except the cached image. So Azure suggest you switch to use the cached image as the image or the base image and try to create less layers.

Here are some techniques.

Thanks Hernando - MSFT for helping me. The Accept button in the Licence agreemnet window can be clicked by moving back and forth the hand using W,A,S,D keys.

Previously i thought that i should click using a raycast pointer. It would be nice if i can click using raycast because the current approach is very difficult for any new users.

Additionally, sometimes mouse and keyboard inputs doesn't worked for me. This is because the 'Use Mouse' and 'Use Keyboard' check-boxes under the Simulation Panel is disabled. We have to ensure they are enabled for using mouse and keyboard for simulation inputs.

I answer as I know things on each part, possibly disregarding a few of your assertions:

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 would use 1 Executor, 1 Core. That is the generally accepted paradigm afaik.

• 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. >>> The number of partitions being the same of number of cores is not true. You can service 1000 partitions with 10 cores, processing one at a time. What if you have 100K partition and on-prem? Unlikely you will get 100K Executors. >>> Moving on and leaving Driver-side collect issues to one side: You may not have enough memory for a given operation on an Executor; Spark can spill to files to disk at the expense of speed of processing. However, the partition size should not exceed a maximum size, was beefed up some time ago. Using multi-core Executors failure can occur, i.e. OOM's, also a result of GC-issues, a difficult topic.

• 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) >>> Not if it can avoid it, but when memory is tight, eviction / spilling to disk can and will occur, and in some cases re-computation from source or last checkpoint will occur.

• 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? >>> They will be serviced by a free Executor at a point in time.

• 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? >>> Yes, and it will be spilled to the local file system. I think you can configure for HDFS via a setting, but local disks are faster.

This an insightful blog: https://medium.com/swlh/spark-oom-error-closeup-462c7a01709d

Why don't you simply use a map literal:

The data class you are generating for your JSON response is not correct. Many of the things are objects, but you have assigned it as a List item. Here is the correct data class response based on your JSON. So the JSON response is not being parsed properly.

There is something wrong either with the String you are providing to the parse() method. It seems there is a zero-width null character bug in your String. That is your string has a Unicode u+FEFF Byte order mark character at the beginning and end of the string, which is known as a “Zero-width null character”. It's not whitespace, so trim() will not remove it. For validation just print the length of your String and observing that it would be 2 longer than expected.

How to resolve: Trim these characters. You can write a function to trim before parse it, something like below:

if i understood the problem correctly you can try not to use list structure, edit it like this