OpenStack and Parquet Integration

Input and output integration overview

This plugin collects metrics from essential OpenStack services, facilitating the monitoring and management of cloud infrastructures.

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

OpenStack

The OpenStack plugin allows users to collect performance metrics from various OpenStack services such as CINDER, GLANCE, HEAT, KEYSTONE, NEUTRON, and NOVA. It supports multiple OpenStack APIs to fetch critical metrics related to these services, enabling comprehensive monitoring and management of cloud resources. As organizations increasingly adopt OpenStack for their cloud infrastructure, this plugin plays a vital role in providing insights into resource usage, availability, and performance across the cloud environment. Configuration options allow for customized polling intervals and filtering unwanted tags to optimize performance and cardinals.

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

OpenStack

[[inputs.openstack]]
  ## The recommended interval to poll is '30m'

  ## The identity endpoint to authenticate against and get the service catalog from.
  authentication_endpoint = "https://my.openstack.cloud:5000"

  ## The domain to authenticate against when using a V3 identity endpoint.
  # domain = "default"

  ## The project to authenticate as.
  # project = "admin"

  ## User authentication credentials. Must have admin rights.
  username = "admin"
  password = "password"

  ## Available services are:
  ## "agents", "aggregates", "cinder_services", "flavors", "hypervisors",
  ## "networks", "nova_services", "ports", "projects", "servers",
  ## "serverdiagnostics", "services", "stacks", "storage_pools", "subnets",
  ## "volumes"
  # enabled_services = ["services", "projects", "hypervisors", "flavors", "networks", "volumes"]

  ## Query all instances of all tenants for the volumes and server services
  ## NOTE: Usually this is only permitted for administrators!
  # query_all_tenants = true

  ## output secrets (such as adminPass(for server) and UserID(for volume)).
  # output_secrets = false

  ## Amount of time allowed to complete the HTTP(s) request.
  # timeout = "5s"

  ## HTTP Proxy support
  # http_proxy_url = ""

  ## Optional TLS Config
  # tls_ca = /path/to/cafile
  # tls_cert = /path/to/certfile
  # tls_key = /path/to/keyfile
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false

  ## Options for tags received from Openstack
  # tag_prefix = "openstack_tag_"
  # tag_value = "true"

  ## Timestamp format for timestamp data received from Openstack.
  ## If false format is unix nanoseconds.
  # human_readable_timestamps = false

  ## Measure Openstack call duration
  # measure_openstack_requests = false

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

OpenStack

1. Cross-Cloud Management: Leverage the OpenStack plugin to monitor and manage multiple OpenStack clouds from a single Telegraf instance. By aggregating metrics across different clouds, organizations can gain insights into resource utilization and optimize their cloud architecture for cost and performance.

2. Automated Scaling Based on Metrics: Integrate the metrics gathered from OpenStack into an automated scaling solution. For example, if the plugin detects that a specific service’s performance is degraded, it can trigger auto-scaling rules to launch additional instances, ensuring that system performance remains optimal under varying workloads.

3. Performance Monitoring Dashboard: Use data collected by the OpenStack Telegraf plugin to power real-time monitoring dashboards. This setup provides visualizations of key metrics from OpenStack services, enabling stakeholders to quickly identify trends, pinpoint issues, and make data-driven decisions in managing their cloud infrastructure.

4. Reporting and Analysis of Service Availability: By utilizing the metrics collected from various OpenStack services, teams can generate detailed reports on service availability and performance over time. This information can help identify recurring issues, improve service delivery, and make informed decisions regarding changes in infrastructure or service configuration.

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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