hotspots | Identify frequently modified files in a git repository | Code Analyzer library

I'm working on an app where users create certain events in a calendar.

I was thinking on structuring the calendar events data as follows:

As we know from Cassandra's documentation[Link to doc] that partitioner should be such that the data is distributed evenly across multiple nodes to avoid read hotspots. Cassandra offers various partitioning algorithms for that - Murmur3Partitioner, RandomPartitioner, ByteOrderedPartitioner .

Murmur3Partitioner is the default Partitioning Algorithm set by Cassandra. It hashes the partition key and converts into the hash values ranges from -2^63 to +2^63-1. My query here is, we have different data sets which has different partition key. For example, one can set partition key with uuid type data, other can set first name and last name as partitioning key, other can set timestamp as their partitioning key and one can also set city name in partitioning key.

Now assume a data set with city as partitioning key, let's say

Node 1 stores Houston data

Node 2 stores Chicago data

Node 3 stories Phoenix data and so on...

And our data gets more entries of data with Chicago city at one instant of time, then Node 2 will have maximum records of our database and there will be hotspots in that case. In this scenario how will Cassandra manage to evenly distribute data across these nodes?

In the Google Datastore documentation it says to avoid indexing monotonically increasing fields because they can lead to hotspots: link

What about indexing boolean fields? Can those also lead to tablet hotspots since there are only two possible values? Is it safe to index boolean fields? What if they are a part of a composite index?

I'm having a hard time with implementing the »correct« Cloud Firestore document naming.

I have a web storage server with more than 5000 photos. Photos are named 1.jpg -> 5000.jpg. I have decided to add a comment system for each photo.

Example: The app will show a random photo from a server (eg. 123.jpg). Users will be able to comment on that photo and reply to other comments.

The only thing that currently worked is naming the document like this: »photo_1« -> »photo_5000«, but it's recommended not to use:

Do not use monotonically increasing document IDs such as: • Customer1, Customer2, Customer3, ... • Product 1, Product 2, Product 3, ... Such sequential IDs can lead to hotspots that impact latency.

What worked:

• Photos (collection)
• Photo_1 (document)
• Photo_comments (collection)
• UUIDs (document)

// Android

I count some user specific data e.g. received likes and follower in a user document.
Because of the 1 write per second limit I want to move the counters from the user document into a subcollection:

Have the sandbox with working React forms array https://codesandbox.io/s/react-final-form-field-arrays-react-beatiful-dnd-as-drag-drop-forked-uz24z?file=/index.js:5933-6061

Which in result of click on the add hotspots and generate the data tree as

I'm solving a number of instances with my code and I'd need to find the worst hotspots, where "worst" is defined as a hotspot over a wide range of instances. So for every instance I have collected hotspot analysis data in batch mode using amplxe-cl. Now I'd like to aggregate this data, I'd like to analyze them together. Is there any way to do this with vtune?

Update:

This is not an mpi application. There are a number of different datasets (problems, instances, pick your term :-) that need to be processed by my application. Depending on the data in a single instance the application can take very different turns while processing it, thus running the application on different instances can result in different hotspots. The purpose of the aggregation would be, as @ArunJose_Intel guessed, is to find hotspots that are common in all runs, that are present in the processing of all kind of instances.

I can collect hotspot analysis for every instance easily using batch mode and I can inspect them individually, but I'd like to see an aggregate analysis.

Of course, I could just process them in one run one after the other, but that would take several weeks, while I can process them as individual problems in a few hours on a cluster of identical machines.

I am assessing the impact of hotspot single nucleotide polymorphism (SNPs) from a next generation sequencing (NGS) experiment on the protein sequence of a virus. I have the reference DNA sequence and a list of hotspots. I need to first figure out the reading frame of where these hotspots are seen. To do this, I generated a DNAStringSetList with all human codons and want to use a vmatchpattern or matchpattern from the Biostrings package to figure out where the hotspots land in the codon reading frame.

I often struggle with lapply and other apply functions, so I tend to utilize for loops instead. I am trying to improve in this area, so welcome a apply solution should one be available.

Here is the code for the list of codons:

I'm on a laptop with 2 connected keyboards (built-in and USB). I'm obtaining these connected keyboards with libudev and using epoll to poll them for input via the evdev interface: