Intel PowerStat and Parquet Integration

Input and output integration overview

Monitor power statistics on Intel-based platforms and is compatible with Linux-based operating systems. It helps in understanding and managing power efficiency and CPU performance.

This plugin writes metrics to parquet files, utilizing a schema based on the metrics grouped by name. It supports file rotation and buffered writing for optimal performance.

Integration details

Intel PowerStat

The Intel PowerStat plugin is designed to monitor power statistics specifically on Intel-based platforms running a Linux operating system. It offers visibility into critical metrics such as CPU temperature, utilization, and power consumption, making it essential for power saving initiatives and workload migration strategies. By leveraging telemetry frameworks, this plugin enables users to gain insights into platform-level metrics that help with monitoring and analytics systems in the context of Management and Orchestration (MANO). It facilitates the ability to make informed decisions and perform corrective actions based on the state of the platform, ultimately contributing to better system efficiency and reliability.

Parquet

The Parquet output plugin for Telegraf writes metrics to parquet files, which are columnar storage formats optimized for analytics. By default, this plugin groups metrics by their name, writing them to a single file. If a metric’s schema does not align with existing schemas, those metrics are dropped. The plugin generates an Apache Arrow schema based on all grouped metrics, ensuring that the schema reflects the union of all fields and tags. It operates in a buffered manner, meaning it temporarily holds metrics in memory before writing them to disk for efficiency. Parquet files require proper closure to ensure readability, and this is crucial when using the plugin, as improper closure can lead to unreadable files. Additionally, the plugin supports file rotation after specific time intervals, preventing overwrites of existing files and schema conflicts when a file with the same name already exists.

Configuration

Intel PowerStat

[[inputs.intel_powerstat]]
  # package_metrics = ["current_power_consumption", "current_dram_power_consumption", "thermal_design_power"]
  # cpu_metrics = []
  # included_cpus = []
  # excluded_cpus = []
  # event_definitions = ""
  # msr_read_timeout = "0ms"

Parquet

[[outputs.parquet]]
  ## Directory to write parquet files in. If a file already exists the output
  ## will attempt to continue using the existing file.
  # directory = "."
  
  ## Files are rotated after the time interval specified. When set to 0 no time
  ## based rotation is performed.
  # rotation_interval = "0h"
  
  ## Timestamp field name
  ## Field name to use to store the timestamp. If set to an empty string, then
  ## the timestamp is omitted.
  # timestamp_field_name = "timestamp"

Input and output integration examples

Intel PowerStat

1. Optimizing Data Center Energy Usage: Monitor power consumption metrics across all CPUs in a data center. By capturing real-time data, administrators can identify which servers consume the most power and implement shutdowns or load balancing strategies during low demand periods, effectively reducing operational costs.

2. Dynamic Workload Migration Based on Power Efficiency: Integrate this plugin with a cloud orchestration tool to enable dynamic migration of workloads based on power usage metrics. If a particular server is recorded as consuming excessive power without corresponding output, the orchestrator can seamlessly migrate workloads to more efficient nodes, ensuring optimal resource utilization and lower energy expenses.

3. Monitoring and Alerting Mechanism for Overheating CPUs: Implement an alerting system using the CPU temperature metrics captured by Intel PowerStat. Setting thresholds for temperature can alert system administrators when a CPU is prone to overheating, allowing proactive measures to be taken before hardware damage occurs, ultimately extending the life of the components.

4. Performance Benchmarking for CPU-intensive Applications: Use the metrics provided to benchmark the performance of CPU-intensive applications. By analyzing the cpu_frequency, cpu_temperature, and power metrics under load, developers can optimize application performance and make informed decisions regarding scaling and resource allocation.

Parquet

1. Data Lake Ingestion: Utilize the Parquet plugin to store metrics from various sources into a data lake. By writing metrics in parquet format, you establish a standardized and efficient way to manage time-series data, enabling faster querying capabilities and seamless integration with analytics tools like Apache Spark or AWS Athena. This setup can significantly improve data retrieval times and analysis workflows.

2. Long-term Storage of Metrics: Implement the Parquet plugin in a monitoring setup where metrics are collected over time from multiple applications. This allows for long-term storage of performance data in a compact format, making it cost-effective to store vast amounts of historical data while preserving the ability for quick retrieval and analysis later on. By archiving metrics in parquet files, organizations can maintain compliance and create detailed reports from historical performance trends.

3. Analytics and Reporting: After writing metrics to parquet files, leverage tools like Apache Arrow or PyArrow to perform complex analytical queries directly on the files without needing to load all the data into memory. This can enhance reporting capabilities, allowing teams to generate insights and visualization from large datasets efficiently, thereby improving decision-making processes based on accurate, up-to-date performance metrics.

4. Integrating with Data Warehouses: Use the Parquet plugin as part of a data integration pipeline that feeds into a modern data warehouse. By converting metrics to parquet format, the data can be easily ingested by systems like Snowflake or Google BigQuery, enabling powerful analytics and business intelligence capabilities that drive actionable insights from the collected metrics.

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