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Using ehcache as an inmemory data store - High memory Usage


Using ehcache as an inmemory data store - High memory Usage

By : user2952292
Date : November 19 2020, 12:41 AM
I think the issue was by ths following , Quick list of options:
Split your cache accross different VMs if you can easily shard your data. By having multiple caches in different JVMs you would automatically get lower memory footprint for each, although the overall memory usage will grow due to the multiple JVMs. Consider upgrading Ehcache to have BigMemory Go support, which will allow you to use off-heap memory and thus reduce GC pressure. This is a commercial product requiring a license.
code :


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High memory usage on Core Data delete

High memory usage on Core Data delete


By : user3198158
Date : March 29 2020, 07:55 AM
like below fixes the issue Perhaps the best way of solving this issue that I came across was to re-engineer my data model to create an intermediary object that contained a to-many relationship to all of the objects I was looking to remove. By setting the intermediary object's delete rule to cascade, it was possible to delete the intermediary object and have it remove the remaining objects behind the scenes.
Is it possible to use my data store in Spring Cloud Dataflow (for example, Apache Ignite or another InMemory store) for

Is it possible to use my data store in Spring Cloud Dataflow (for example, Apache Ignite or another InMemory store) for


By : Pot Sriprom
Date : March 29 2020, 07:55 AM
I wish did fix the issue. I don't think it would be a trivial change. The server needs a backend to store it's metadata. By default it actually uses H2 in memory, and it relies on Spring Data JPA abstraction to give users the chance to select their RDBMS.
Storing on a different storage engine, would require not only replacing all the *Repository definitions on several configuration modules, but we do as well some pre population of data. It would become a bit hard to maintain this over time.
fs.createWriteStream doesn't use back-pressure when writing data to a file, causing high memory usage

fs.createWriteStream doesn't use back-pressure when writing data to a file, causing high memory usage


By : user2743691
Date : March 29 2020, 07:55 AM
Any of those help Preliminary observation: you've attempted to get the results you want using multiple approaches. One complication when comparing the approaches you used is that they do not all do the same work. If you run tests on file tree that contains only regular files, that tree does not contain mount points, you can probably compare the approaches fairly, but when you start adding mount points, symbolic links, etc, you may get different memory and time statistics merely due to the fact that one approach excludes files that another approach includes.
I've initially attempted a solution using readdirp, but unfortunately, but that library appears buggy to me. Running it on my system here, I got inconsistent results. One run would output 10Mb of data, another run with the same input parameters would output 22Mb, then I'd get another number, etc. I looked at the code and found that it does not respect the return value of push:
code :
_push(entry) {
    if (this.readable) {
      this.push(entry);
    }
}
const stream = require("stream");
const fs = require("fs");
const { readdir, lstat } = fs.promises;
const path = require("path");

class Walk extends stream.Readable {
  constructor(root, maxDepth = Infinity) {
    super();

    this._maxDepth = maxDepth;

    // These fields allow us to remember where we were when we have to pause our
    // work.

    // The path of the directory to process when we resume processing, and the
    // depth of this directory.
    this._curdir = [root, 1];

    // The directories still to process.
    this._dirs = [this._curdir];

    // The list of files to process when we resume processing.
    this._files = [];

    // The location in `this._files` were to continue processing when we resume.
    this._ix = 0;

    // A flag recording whether or not the fetching of files is currently going
    // on.
    this._started = false;
  }

  async _fetch() {
    // Recall where we were by loading the state in local variables.
    let files = this._files;
    let dirs = this._dirs;
    let [dir, depth] = this._curdir;
    let ix = this._ix;

    while (true) {
      // If we've gone past the end of the files we were processing, then
      // just forget about them. This simplifies the code that follows a bit.
      if (ix >= files.length) {
        ix = 0;
        files = [];
      }

      // Read directories until we have files to process.
      while (!files.length) {
        // We've read everything, end the stream.
        if (dirs.length === 0) {
          // This is how the stream API requires us to indicate the stream has
          // ended.
          this.push(null);

          // We're no longer running.
          this._started = false;
          return;
        }

        // Here, we get the next directory to process and get the list of
        // files in it.
        [dir, depth] = dirs.pop();

        try {
          files = await readdir(dir, { withFileTypes: true });
        }
        catch (ex) {
          // This is a proof-of-concept. In a real application, you should
          // determine what exceptions you want to ignore (e.g. EPERM).
        }
      }

      // Process each file.
      for (; ix < files.length; ++ix) {
        const dirent = files[ix];
        // Don't include in the results those files that are not directories,
        // files or symbolic links.
        if (!(dirent.isFile() || dirent.isDirectory() || dirent.isSymbolicLink())) {
          continue;
        }

        const fullPath = path.join(dir, dirent.name);
        if (dirent.isDirectory() & depth < this._maxDepth) {
          // Keep track that we need to walk this directory.
          dirs.push([fullPath, depth + 1]);
        }

        // Finally, we can put the data into the stream!
        if (!this.push(`${fullPath}\n`)) {
          // If the push returned false, we have to stop pushing results to the
          // stream until _read is called again, so we have to stop.

          // Uncomment this if you want to see when the stream stops.
          // console.log("STOP");

          // Record where we were in our processing.
          this._files = files;
          // The element at ix *has* been processed, so ix + 1.
          this._ix = ix + 1;
          this._curdir = [dir, depth];

          // We're stopping, so indicate that!
          this._started = false;
          return;
        }
      }
    }
  }

  async _read() {
    // Do not start the process that puts data on the stream over and over
    // again.
    if (this._started) {
      return;
    }

    this._started = true; // Yep, we've started.

    // Uncomment this if you want to see when the stream starts.
    // console.log("START");

    await this._fetch();
  }
}

// Change the paths to something that makes sense for you.
stream.pipeline(new Walk("/home/", 5),
                fs.createWriteStream("/tmp/paths3.txt"),
                (err) => console.log("ended with", err));
Memory usage does not decrease when removing data from InMemory Database

Memory usage does not decrease when removing data from InMemory Database


By : user2892897
Date : March 29 2020, 07:55 AM
seems to work fine vishwas-trivedi confirmed that GC does collect the unused memory when forced called. I will close this question. Thank you vishwas-trivedi
https://github.com/aspnet/EntityFrameworkCore/issues/16398
Using Linq to Entity for searching large data cause high memory usage

Using Linq to Entity for searching large data cause high memory usage


By : Nam Hoàng Hoài
Date : March 29 2020, 07:55 AM
I hope this helps you . When using linq there's a concept called materialization, some commands like "ToList" will cause your query to "materialize", which means, data will be fetched from the database into the memory, now take a look at this code
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