VMware vSphere and Azure Data Explorer Integration

Input and output integration overview

The VMware vSphere Telegraf plugin provides a means to collect metrics from VMware vCenter servers, allowing for comprehensive monitoring and management of virtual resources in a vSphere environment.

The Azure Data Explorer plugin allows integration of metrics collection with Azure Data Explorer, enabling users to analyze and query their telemetry data efficiently. With this plugin, users can configure ingestion settings to suit their needs and leverage Azure’s powerful analytical capabilities.

Integration details

VMware vSphere

This plugin connects to VMware vSphere servers to gather a variety of metrics from virtual environments, enabling efficient monitoring and management of virtual resources. It interfaces with the vSphere API to collect statistics regarding clusters, hosts, resource pools, VMs, datastores, and vSAN entities, presenting them in a format suitable for analysis and visualization. The plugin is particularly valuable for administrators who manage VMware-based infrastructures, as it helps to track system performance, resource usage, and operational issues in real-time. By aggregating data from multiple sources, the plugin empowers users with insights that facilitate informed decision-making regarding resource allocation, troubleshooting, and ensuring optimal system performance. Additionally, the support for secret-store integration allows secure handling of sensitive credentials, promoting best practices in security and compliance assessments.

Azure Data Explorer

The Azure Data Explorer plugin allows users to write metrics, logs, and time series data collected from various Telegraf input plugins into Azure Data Explorer, Azure Synapse, and Real-Time Analytics in Fabric. This integration serves as a bridge, allowing applications and services to monitor their performance metrics or logs efficiently. Azure Data Explorer is optimized for analytics over large volumes of diverse data types, making it an excellent choice for real-time analytics and monitoring solutions in cloud environments. The plugin empowers users to configure metrics ingestion based on their requirements, define table schemas dynamically, and set various ingestion methods while retaining flexibility regarding roles and permissions needed for database operations. This supports scalable and secure monitoring setups for modern applications that utilize cloud services.

Configuration

VMware vSphere

[[inputs.vsphere]]
  vcenters = [ "https://vcenter.local/sdk" ]
  username = "[email protected]"
  password = "secret"

  vm_metric_include = [
    "cpu.demand.average",
    "cpu.idle.summation",
    "cpu.latency.average",
    "cpu.readiness.average",
    "cpu.ready.summation",
    "cpu.run.summation",
    "cpu.usagemhz.average",
    "cpu.used.summation",
    "cpu.wait.summation",
    "mem.active.average",
    "mem.granted.average",
    "mem.latency.average",
    "mem.swapin.average",
    "mem.swapinRate.average",
    "mem.swapout.average",
    "mem.swapoutRate.average",
    "mem.usage.average",
    "mem.vmmemctl.average",
    "net.bytesRx.average",
    "net.bytesTx.average",
    "net.droppedRx.summation",
    "net.droppedTx.summation",
    "net.usage.average",
    "power.power.average",
    "virtualDisk.numberReadAveraged.average",
    "virtualDisk.numberWriteAveraged.average",
    "virtualDisk.read.average",
    "virtualDisk.readOIO.latest",
    "virtualDisk.throughput.usage.average",
    "virtualDisk.totalReadLatency.average",
    "virtualDisk.totalWriteLatency.average",
    "virtualDisk.write.average",
    "virtualDisk.writeOIO.latest",
    "sys.uptime.latest",
  ]

  host_metric_include = [
    "cpu.coreUtilization.average",
    "cpu.costop.summation",
    "cpu.demand.average",
    "cpu.idle.summation",
    "cpu.latency.average",
    "cpu.readiness.average",
    "cpu.ready.summation",
    "cpu.swapwait.summation",
    "cpu.usage.average",
    "cpu.usagemhz.average",
    "cpu.used.summation",
    "cpu.utilization.average",
    "cpu.wait.summation",
    "disk.deviceReadLatency.average",
    "disk.deviceWriteLatency.average",
    "disk.kernelReadLatency.average",
    "disk.kernelWriteLatency.average",
    "disk.numberReadAveraged.average",
    "disk.numberWriteAveraged.average",
    "disk.read.average",
    "disk.totalReadLatency.average",
    "disk.totalWriteLatency.average",
    "disk.write.average",
    "mem.active.average",
    "mem.latency.average",
    "mem.state.latest",
    "mem.swapin.average",
    "mem.swapinRate.average",
    "mem.swapout.average",
    "mem.swapoutRate.average",
    "mem.totalCapacity.average",
    "mem.usage.average",
    "mem.vmmemctl.average",
    "net.bytesRx.average",
    "net.bytesTx.average",
    "net.droppedRx.summation",
    "net.droppedTx.summation",
    "net.errorsRx.summation",
    "net.errorsTx.summation",
    "net.usage.average",
    "power.power.average",
    "storageAdapter.numberReadAveraged.average",
    "storageAdapter.numberWriteAveraged.average",
    "storageAdapter.read.average",
    "storageAdapter.write.average",
    "sys.uptime.latest",
  ]

  datacenter_metric_include = [] ## if omitted or empty, all metrics are collected
  datacenter_metric_exclude = [ "*" ] ## Datacenters are not collected by default.

  vsan_metric_include = [] ## if omitted or empty, all metrics are collected
  vsan_metric_exclude = [ "*" ] ## vSAN are not collected by default.

  separator = "_"
  max_query_objects = 256
  max_query_metrics = 256
  collect_concurrency = 1
  discover_concurrency = 1
  object_discovery_interval = "300s"
  timeout = "60s"
  use_int_samples = true
  custom_attribute_include = []
  custom_attribute_exclude = ["*"]
  metric_lookback = 3
  ssl_ca = "/path/to/cafile"
  ssl_cert = "/path/to/certfile"
  ssl_key = "/path/to/keyfile"
  insecure_skip_verify = false
  historical_interval = "5m"
  disconnected_servers_behavior = "error"
  use_system_proxy = true
  http_proxy_url = ""

Azure Data Explorer

[[outputs.azure_data_explorer]]
  ## The URI property of the Azure Data Explorer resource on Azure
  ## ex: endpoint_url = https://myadxresource.australiasoutheast.kusto.windows.net
  endpoint_url = ""

  ## The Azure Data Explorer database that the metrics will be ingested into.
  ## The plugin will NOT generate this database automatically, it's expected that this database already exists before ingestion.
  ## ex: "exampledatabase"
  database = ""

  ## Timeout for Azure Data Explorer operations
  # timeout = "20s"

  ## Type of metrics grouping used when pushing to Azure Data Explorer.
  ## Default is "TablePerMetric" for one table per different metric.
  ## For more information, please check the plugin README.
  # metrics_grouping_type = "TablePerMetric"

  ## Name of the single table to store all the metrics (Only needed if metrics_grouping_type is "SingleTable").
  # table_name = ""

  ## Creates tables and relevant mapping if set to true(default).
  ## Skips table and mapping creation if set to false, this is useful for running Telegraf with the lowest possible permissions i.e. table ingestor role.
  # create_tables = true

  ##  Ingestion method to use.
  ##  Available options are
  ##    - managed  --  streaming ingestion with fallback to batched ingestion or the "queued" method below
  ##    - queued   --  queue up metrics data and process sequentially
  # ingestion_type = "queued"

Input and output integration examples

VMware vSphere

1. Dynamic Resource Allocation: Utilize this plugin to monitor resource usage across a fleet of VMs and automatically adjust resource allocations based on performance metrics. This scenario could involve triggering scaling actions in real time based on CPU and memory usage metrics collected from the vSphere API, ensuring optimal performance and cost-efficiency.

2. Capacity Planning and Forecasting: Leverage the historical metrics gathered from vSphere to conduct capacity planning. Analyzing the trends of CPU, memory, and storage usage over time helps administrators anticipate when additional resources will be needed, avoiding outages and ensuring that the virtual infrastructure can handle growth.

3. Automated Alerting and Incident Response: Integrate this plugin with alerting tools to set up automated notifications based on the metrics gathered. For example, if the CPU usage on a host exceeds a specified threshold, it could trigger alerts and automatically initiate predefined remediation steps, such as migrating VMs to less utilized hosts.

4. Performance Benchmarking Across Clusters: Use the metrics collected to compare the performance of clusters in different vCenters. This benchmarking provides insights into which cluster configurations yield the best resource efficiency and can guide future infrastructure enhancements.

Azure Data Explorer

1. Real-Time Monitoring Dashboard: By integrating metrics from various services into Azure Data Explorer using this plugin, organizations can build comprehensive dashboards that reflect real-time performance metrics. This allows teams to respond proactively to performance issues and optimize system health without delay.

2. Centralized Log Management: Utilize Azure Data Explorer to consolidate logs from multiple applications and services. By utilizing the plugin, organizations can streamline their log analysis processes, making it easier to search, filter, and derive insights from historical data accumulated over time.

3. Data-Driven Alerting Systems: Enhance monitoring capabilities by configuring alerts based on metrics sent via this plugin. Organizations can set thresholds and automate incident responses, significantly reducing downtime and improving the reliability of critical operations.

4. Machine Learning Model Training: By leveraging the data sent to Azure Data Explorer, organizations can perform large-scale analytics and prepare the data for feeding into machine learning models. This plugin enables the structuring of data that can subsequently be used for predictive analytics, leading to enhanced decision-making capabilities.

Feedback

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