long initialActive = bufferManager.getActiveRegions();

            assertTrue(initialAllocated >= 2, "Should have allocated at least 2 regions");
            assertTrue(initialActive >= 2, "Should have at least 2 active regions");

            // Test buffer release and reuse
            bufferManager.releaseSendRegion(sendRegion);
            bufferManager.releaseReceiveRegion(recvRegion);

        assertTrue(bufferManager.getTotalAllocated() > initialAllocated, "Total allocated should increase");
        assertTrue(bufferManager.getActiveRegions() > 0, "Active regions should be positive");

        // Release regions
        bufferManager.releaseSendRegion(region1);
        bufferManager.releaseReceiveRegion(region2);
    }

    @Test

          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "{{server}}",
          "refId": "A"
        }
      ],
      "title": "Avg. Active Workers",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },
      "fieldConfig": {
        "defaults": {

                        </h1>
                    </div>
                    <div class="col-sm-6">
                        <ol class="breadcrumb float-sm-right">
                            <li class="breadcrumb-item active"><la:link href="/admin/searchlog">
                                <la:message key="labels.searchlog_title"/>
                            </la:link></li>
                        </ol>
                    </div>

    <class>
      <name>ActivationProperty</name>
      <version>1.0.0</version>
      <description><![CDATA[
        This is the property specification used to activate a profile. If the value field is empty,
        then the existence of the named property will activate the profile, otherwise it does a case-sensitive
        match against the property value as well.
      ]]></description>
      <fields>
        <field>

# **Disaggregated HDP Spark and Hive with MinIO**

## **1. Cloud-native Architecture**

![cloud-native](https://github.com/minio/minio/blob/master/docs/bigdata/images/image1.png?raw=true "cloud native architecture")

Kubernetes manages stateless Spark and Hive containers elastically on the compute nodes. Spark has native scheduler integration with Kubernetes. Hive, for legacy reasons, uses YARN scheduler on top of Kubernetes.

    }

    /**
     * Get current pool statistics
     * @return pool statistics string
     */
    public String getPoolStatistics() {
        return String.format("Pool statistics: Active=%d, NonPooled=%d, MaxSize=%d, Failures=%d, HealthChecks=%d, Removed=%d",
                connections.size(), nonPooledConnections.size(), maxPoolSize, failCounts.size(), totalHealthChecks.get(),

						<c:when test="${pageNumber == currentPageNumber && pageNumber >= currentPageNumber - 2 && pageNumber <= currentPageNumber + 2}">class="page-item active"</c:when>
						<c:otherwise>class="page-item"</c:otherwise>
					</c:choose>>
					<la:link styleClass="page-link"

//     create a new backup job and select the object storage or a SOBR as a backup target with this setting, the job default
//     setting will be set to this value. This setting will be only applied to newly created jobs (manual changes with Active Full
//     processing possible from the customer side).
//     Optional value, default 1024, allowed values 256,512,1024,4096,8192, value defined in KB size.
//

  // Maps from name of an operation to the Node* in 'graph'.
  std::unordered_map<tensorflow::string, tensorflow::Node*> name_map
      TF_GUARDED_BY(mu);

  // The keys of this map are all the active sessions using this graph. Each
  // value records whether the graph has been mutated since the corresponding
  // session has been run (this is detected in RecordMutation function). If the