private var coordinatorWakeUpAt = 0L

  /**
   * When we need a new thread to run tasks, we call [Backend.execute]. A few microseconds later we
   * expect a newly-started thread to call [Runnable.run]. We shouldn't request new threads until
   * the already-requested ones are in service, otherwise we might create more threads than we need.
   *

 * href="https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)">graph</a>-structured data.
 *
 * <p>This interface is meant to be used as the type of a parameter to graph algorithms (such as
 * breadth first traversal) that only need a way of accessing the successors of a node in a graph.
 *
 * <h3>Usage</h3>
 *
 * Given an algorithm, for example:
 *
 * <pre>{@code

     * @param columnNumber The one-based index of the column containing the error or {@code -1} if unknown.
     * @param exception The exception that caused this problem, may be {@code null}.
     */
    // mkleint: does this need to be public?
    public DefaultModelProblem(
            String message,
            Severity severity,
            Version version,
            Model source,
            int lineNumber,
            int columnNumber,

# Request Body

When you need to send data from a client (let's say, a browser) to your API, you send it as a **request body**.

A **request** body is data sent by the client to your API. A **response** body is the data your API sends to the client.

Your API almost always has to send a **response** body. But clients don't necessarily need to send **request bodies** all the time, sometimes they only request a path, maybe with some query parameters, but don't send a body.

# OpenAPI

There are several utilities to handle OpenAPI.

For instance, given that TLS is enabled and you need to add trust for MinIO's own CA and for the CA of a Keycloak server, a Kubernetes secret can be created from the certificate files using `kubectl`:

```

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

So, you might still need to use Pydantic models.

But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

///

## Dataclasses in `response_model`

          // it is kind of lame that we need to construct a decoder to access this value.
          // if this is a concern we could add special cases for some known charsets (like utf8)
          // or we could avoid inputstreamreader and use the decoder api directly
          // TODO(lukes): in a real implementation we would need to handle overflow conditions

    // exponent == floor(log2(abs(x)))
    if (exponent < Long.SIZE - 1) {
      return x.longValue();
    } else if (exponent > MAX_EXPONENT) {
      return x.signum() * POSITIVE_INFINITY;
    }

    /*
     * We need the top SIGNIFICAND_BITS + 1 bits, including the "implicit" one bit. To make rounding
     * easier, we pick out the top SIGNIFICAND_BITS + 2 bits, so we have one to help us round up or

	shardSize := int(e.ShardSize())
	var b []byte

	// We should always have enough capacity, but older objects may be bigger
	// we do not need stashbuffer for them.
	if globalBytePoolCap.Load().WidthCap() >= len(readers)*shardSize {
		// Fill buffers
		b = globalBytePoolCap.Load().Get()
		// Seed the buffers.
		for i := range bufs {
			bufs[i] = b[i*shardSize : (i+1)*shardSize]
		}
	}

	return &parallelReader{