pools | Connection pooling for Redis and Cassandra | Reactive Programming library

I'm trying to use a separate process to stream data by concurrent futures. However, on the otherside, sometimes the other party stops the datafeed. But as long as I restart this threadable then it would work again. So I design something like this, to be able to keep streaming data without intervention.

Using Terraform I spin up the following resources for my primary using a unique service account for this cluster:

I have a python 3.9 code that uses Pool and ThreadPool from multiprocessing.pool. The intention is to have 2 Pools, each spawning 3 ThreadPools independently. In other words, I expect 2*3 = 6 threads running in parallel.

However, the output of the minimum working example (MWE) code below resulted in only 3 different thread ids.

My question: Why does it behave like this, and how can I reasonably fix that?

In addition, if such a N_POOL * N_THREADPOOL strategy does not look good, advice is welcomed. The actual task is I/O-bound (network download followed by light preprocessing). I am relatively new to parallelism.

According to the documentation:

an example of creating an identity pool would be

gcloud iam workload-identity-pools create my-workload-identity-pool --location="global" --display-name="My workload pool" --description="My workload pool description" --disabled

What does the location global mean? What is the purpose of this argument? It is apparently not a location like us-central1 but a logical location.

Which values are available? I don't find any good documentation on this so i am asking.

Given a connection to the PostgreSQL database for user 'Alice', is there a statement that could be executed to switch to user 'Bob'?

Motivation: Looking to avoid having separate pools for each user (i.e. re-use a connection that was previously used by another user).

I installed ubuntu server VM on Azure there I installed couchbase community edition on now i need to access the couchbase using dotnet SDK but code gives me bucket not found or unreachable error. even i try configuring a public dns and gave it as ip during cluster creation but still its giving the same. even i added public dns to the host file like below 127.0.0.1 public dns The SDK log includes below 2 statements Attempted bootstrapping on endpoint "name.eastus.cloudapp.azure.com" has failed. (e80489ed) A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond.

SDK Doctor Log:

I'm trying to publish a npm package on GAR (Google Artifact Registry) through github using google-github-actions/auth@v0 and google-artifactregistry-auth

For the authentication to google from github here is what I did to use the Federation Workload Identity:

was looking at this V8 design doc where it has a section for Constant Pool Entries

it says

Constant pools are used to store heap objects and small integers that are referenced as constants in generated bytecode. and

... Small integers and the strong referenced oddball type’s have bytecodes to load them directly and do not go into the constant pool.

So I am confused: are small integers pooled or not?

My understanding is that it is not worth it pooling small integers if sizeof(int) < sizeof(int *) - because it is cheaper to just copy the actual integer instead of copying the pointer that points to the integer in the constant pool. Also variables that hold integers can be optimised to be stored directly in CPU registers and skip being allocated in memory first.

Also, are they located on the V8 heap or the stack? My understanding had always been that smis are just be the immediate values allocated on the stack instead of being a pointer + an integer allocated on heap. Also if you take a heap snapshot using chrome devtool you cannot find smis in the heap snapshot - only heap number such as big integers or double like 3.14 are on the heap until I saw this article https://v8.dev/blog/pointer-compression#value-tagging-in-v8

JavaScript values in V8 are represented as objects and allocated on the V8 heap, no matter if they are objects, arrays, numbers or strings. This allows us to represent any value as a pointer to an object.

Now I am just baffled - are smis also allocated on the heap?

I am developing a C++ application, where the program run endlessly, allocating and freeing millions of strings (char*) over time. And RAM usage is a serious consideration in the program. This results in RAM usage getting higher and higher over time. I think the problem is heap fragmentation. And I really need to find a solution.

You can see in the image, after millions of allocation and freeing in the program, the usage is just increasing. And the way I am testing it, I know for a fact that the data it stores is not increasing. I can guess that you will ask, "How are you sure of that?", "How are you sure it's not just a memory leak?", Well.

This test run much longer. I run malloc_trim(0), whenever possible in my program. And it seems, application can finally return the unused memory to the OS, and it goes almost to zero (the actual data size my program has currently). This implies the problem is not a memory leak. But I can't rely on this behavior, the allocation and freeing pattern of my program is random, what if it never releases the memory ?

• I said memory pools are a bad idea for this project in the title. Of course I don't have absolute knowledge. But the strings I am allocating can be anything between 30-4000 bytes. Which makes many optimizations and clever ideas much harder. Memory pools are one of them.
• I am using GCC 11 / G++ 11 as a compiler. If some old versions have bad allocators. I shouldn't have that problem.
• How am I getting memory usage ? Python psutil module. proc.memory_full_info()[0], which gives me RSS.
• Of course, you don't know the details of my program. It is still a valid question, if this is indeed because of heap fragmentation. Well what I can say is, I am keeping a up to date information about how many allocations and frees took place. And I know the element counts of every container in my program. But if you still have some ideas about the causes of the problem, I am open to suggestions.
• I can't just allocate, say 4096 bytes for all the strings so it would become easier to optimize. That's the opposite I am trying to do.

So my question is, what do programmers do(what should I do), in an application where millions of alloc's and free's take place over time, and they are of different sizes so memory pools are hard to use efficiently. I can't change what the program does, I can only change implementation details.

Bounty Edit: When trying to utilize memory pools, isn't it possible to make multiple of them, to the extent that there is a pool for every possible byte count ? For example my strings can be something in between 30-4000 bytes. So couldn't somebody make 4000 - 30 + 1, 3971 memory pools, for each and every possible allocation size of the program. Isn't this applicable ? All pools could start small (no not lose much memory), then enlarge, in a balance between performance and memory. I am not trying to make a use of memory pool's ability to reserve big spaces beforehand. I am just trying to effectively reuse freed space, because of frequent alloc's and free's.

Last edit: It turns out that, the memory growth appearing in the graphs, was actually from a http request queue in my program. I failed to see that hundreds of thousands of tests that I did, bloated this queue (something like webhook). And the reasonable explanation of figure 2 is, I finally get DDOS banned from the server (or can't open a connection anymore for some reason), the queue emptied, and the RAM issue resolved. So anyone reading this question later in the future, consider every possibility. It would have never crossed my mind that it was something like this. Not a memory leak, but an implementation detail. Still I think @Hajo Kirchhoff deserves the bounty, his answer was really enlightening.