//   system scheduling and as such we could either miss our deadline, or unpark() could be delayed
  //   so that it looks like we timed out even though we didn't. For comparison FutureTask respects
  //   completion preferably and AQS is non-deterministic (depends on where in the queue the waiter
  //   is). If we wanted to be strict about it, we could store the unpark() time in the Waiter node

  //   system scheduling and as such we could either miss our deadline, or unpark() could be delayed
  //   so that it looks like we timed out even though we didn't. For comparison FutureTask respects
  //   completion preferably and AQS is non-deterministic (depends on where in the queue the waiter
  //   is). If we wanted to be strict about it, we could store the unpark() time in the Waiter node

         * protect ourselves from this, we park ourselves and tell our interrupter that we did so.
         */
        if (state == PARKED || compareAndSet(state, PARKED)) {
          // Interrupting Cow Says:
          //  ______
          // < Park >
          //  ------
          //        \   ^__^
          //         \  (oo)\_______

```
spark.hadoop.fs.s3a.access.key minio
spark.hadoop.fs.s3a.secret.key minio123
spark.hadoop.fs.s3a.path.style.access true
spark.hadoop.fs.s3a.block.size 512M
spark.hadoop.fs.s3a.buffer.dir ${hadoop.tmp.dir}/s3a
spark.hadoop.fs.s3a.committer.magic.enabled false
spark.hadoop.fs.s3a.committer.name directory
spark.hadoop.fs.s3a.committer.staging.abort.pending.uploads true
spark.hadoop.fs.s3a.committer.staging.conflict-mode append

      </plugin>
      <plugin>
        <artifactId>maven-dependency-plugin</artifactId>
        <executions>
          <execution>
            <id>unpack-failureaccess-sources</id>
            <phase>generate-resources</phase>
            <goals><goal>unpack-dependencies</goal></goals>
            <configuration>
              <includeArtifactIds>failureaccess</includeArtifactIds>
              <classifier>sources</classifier>

      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-dependency-plugin</artifactId>
        <executions>
          <execution>
            <id>unpack-jline-native</id>
            <goals>
              <goal>unpack-dependencies</goal>
            </goals>
            <configuration>
              <includeArtifactIds>jline-native</includeArtifactIds>

    'full_name': None,
}
```

#### Unpacking a `dict` { #unpacking-a-dict }

If we take a `dict` like `user_dict` and pass it to a function (or class) with `**user_dict`, Python will "unpack" it. It will pass the keys and values of the `user_dict` directly as key-value arguments.

So, continuing with the `user_dict` from above, writing:

```Python
UserInDB(**user_dict)
```

MinIO versioning is designed to keep multiple versions of an object in one bucket. For example, you could store `spark.csv` (version `ede336f2`) and `spark.csv` (version `fae684da`) in a single bucket. Versioning protects you from unintended overwrites, deletions, protect objects with retention policies.

		//       .master("local[1]").getOrCreate()
		//
		//     spark.sparkContext.setLogLevel("ERROR")
		//     spark.sparkContext.hadoopConfiguration.set("fs.s3a.endpoint", "http://minio-lb:9000")
		//     spark.sparkContext.hadoopConfiguration.set("fs.s3a.path.style.access", "true")
		//     spark.sparkContext.hadoopConfiguration.set("fs.s3a.access.key", "minioadmin")
		//     spark.sparkContext.hadoopConfiguration.set("fs.s3a.secret.key", "minioadmin")

      readControlFrame()
    }
  }

  /**
   * Reads a message body into across one or more frames. Control frames that occur between
   * fragments will be processed. If the message payload is masked this will unmask as it's being
   * processed.
   */
  @Throws(IOException::class)
  private fun readMessage() {
    while (true) {
      if (closed) throw IOException("closed")

      if (frameLength > 0L) {