if (name.equals("physical id")) {
                                currentID = value;
                            }
                            // Number of cores not including hyper-threading
                            if (name.equals("cpu cores")) {
                                assert currentID.isEmpty() == false;
                                socketToCore.put("currentID", Integer.valueOf(value));

                            if (name.equals("physical id")) {
                                currentID = value;
                            }
                            // Number of cores not including hyper-threading
                            if (name.equals("cpu cores")) {
                                assert currentID.isEmpty() == false;
                                socketToCore.put("currentID", Integer.valueOf(value));

* Ensure to run enough warmup iterations to get the benchmark into a stable state. If you are unsure, don't change the defaults.
* Avoid CPU migrations by pinning your benchmarks to specific CPU cores. On Linux you can use `taskset`.
* Fix the CPU frequency to avoid Turbo Boost from kicking in and skewing your results. On Linux you can use `cpufreq-set` and the
  `performance` CPU governor.

import java.util.function.Function;

/**
 * Benchmarks the overhead of constructing {@link Aggregator}s in many
 * parallel threads. Machines with different numbers of cores will see
 * wildly different results running this from running this with more
 * cores seeing more benefits from preallocation.
 */
@Fork(2)
@Warmup(iterations = 10)
@Measurement(iterations = 5)
@BenchmarkMode(Mode.AverageTime)

When deploying applications you will probably want to have some **replication of processes** to take advantage of **multiple cores** and to be able to handle more requests.

As you saw in the previous chapter about [Deployment Concepts](concepts.md), there are multiple strategies you can use.

streaming compression due to its stability and performance.

This algorithm is specifically optimized for machine generated content.
Write throughput is typically at least 500MB/s per CPU core,
and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

This means that in cases where raw IO is below these numbers
compression will not only reduce disk usage but also help increase system throughput.

            .getResolvedConfiguration()
            .getFiles(reallyThirdParty);
        // don't scan provided dependencies that we already scanned, e.x. don't scan cores dependencies for every plugin
        if (compileOnlyConfiguration != null) {
            jars.removeAll(compileOnlyConfiguration);
        }
        return jars;
    }

    @TaskAction

`max_sleep` to a *lower* value and setting `max_io` to a *higher* value would make heal go faster.

Each node is responsible of healing its local drives; Each drive will have multiple heal workers which is the quarter of the number of CPU cores of the node or the quarter of the configured nr_requests of the drive (https://www.kernel.org/doc/Documentation/block/queue-sysfs.txt). It is also possible to provide a custom number of workers by using this command: `mc admin config set alias/ heal...

![hdfs-configs](https://github.com/minio/minio/blob/master/docs/bigdata/images/image2.png?raw=true "hdfs advanced configs")

Navigate to **Custom core-site** to configure MinIO parameters for `_s3a_` connector

![s3a-config](https://github.com/minio/minio/blob/master/docs/bigdata/images/image5.png?raw=true "custom core-site")

```
sudo pip install yq
alias kv-pairify='yq ".configuration[]" | jq ".[]" | jq -r ".name + \"=\" + .value"'
```

### Multiple Processes - Workers { #multiple-processes-workers }

If you have more clients than what a single process can handle (for example if the virtual machine is not too big) and you have **multiple cores** in the server's CPU, then you could have **multiple processes** running with the same application at the same time, and distribute all the requests among them.