HTTP and Parquet Integration

Input and output integration overview

The HTTP plugin allows for the collection of metrics from specified HTTP endpoints, handling various data formats and authentication methods.

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

HTTP

The HTTP plugin collects metrics from one or more HTTP(S) endpoints, which should have metrics formatted in one of the supported input data formats. It also supports secrets from secret-stores for various authentication options and includes globally supported configuration settings.

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

HTTP

[[inputs.http]]
  ## One or more URLs from which to read formatted metrics.
  urls = [
    "http://localhost/metrics",
    "http+unix:///run/user/420/podman/podman.sock:/d/v4.0.0/libpod/pods/json"
  ]

  ## HTTP method
  # method = "GET"

  ## Optional HTTP headers
  # headers = {"X-Special-Header" = "Special-Value"}

  ## HTTP entity-body to send with POST/PUT requests.
  # body = ""

  ## HTTP Content-Encoding for write request body, can be set to "gzip" to
  ## compress body or "identity" to apply no encoding.
  # content_encoding = "identity"

  ## Optional Bearer token settings to use for the API calls.
  ## Use either the token itself or the token file if you need a token.
  # token = "eyJhbGc...Qssw5c"
  # token_file = "/path/to/file"

  ## Optional HTTP Basic Auth Credentials
  # username = "username"
  # password = "pa$$word"

  ## OAuth2 Client Credentials. The options 'client_id', 'client_secret', and 'token_url' are required to use OAuth2.
  # client_id = "clientid"
  # client_secret = "secret"
  # token_url = "https://indentityprovider/oauth2/v1/token"
  # scopes = ["urn:opc:idm:__myscopes__"]

  ## HTTP Proxy support
  # use_system_proxy = false
  # http_proxy_url = ""

  ## Optional TLS Config
  ## Set to true/false to enforce TLS being enabled/disabled. If not set,
  ## enable TLS only if any of the other options are specified.
  # tls_enable =
  ## Trusted root certificates for server
  # tls_ca = "/path/to/cafile"
  ## Used for TLS client certificate authentication
  # tls_cert = "/path/to/certfile"
  ## Used for TLS client certificate authentication
  # tls_key = "/path/to/keyfile"
  ## Password for the key file if it is encrypted
  # tls_key_pwd = ""
  ## Send the specified TLS server name via SNI
  # tls_server_name = "kubernetes.example.com"
  ## Minimal TLS version to accept by the client
  # tls_min_version = "TLS12"
  ## List of ciphers to accept, by default all secure ciphers will be accepted
  ## See https://pkg.go.dev/crypto/tls#pkg-constants for supported values.
  ## Use "all", "secure" and "insecure" to add all support ciphers, secure
  ## suites or insecure suites respectively.
  # tls_cipher_suites = ["secure"]
  ## Renegotiation method, "never", "once" or "freely"
  # tls_renegotiation_method = "never"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false

  ## Optional Cookie authentication
  # cookie_auth_url = "https://localhost/authMe"
  # cookie_auth_method = "POST"
  # cookie_auth_username = "username"
  # cookie_auth_password = "pa$$word"
  # cookie_auth_headers = { Content-Type = "application/json", X-MY-HEADER = "hello" }
  # cookie_auth_body = '{"username": "user", "password": "pa$$word", "authenticate": "me"}'
  ## cookie_auth_renewal not set or set to "0" will auth once and never renew the cookie
  # cookie_auth_renewal = "5m"

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

  ## List of success status codes
  # success_status_codes = [200]

  ## Data format to consume.
  ## Each data format has its own unique set of configuration options, read
  ## more about them here:
  ## https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md
  # data_format = "influx"

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

HTTP

1. Collecting Metrics from Localhost: The plugin can fetch metrics from an HTTP endpoint like http://localhost/metrics, allowing for easy local monitoring.
2. Using Unix Domain Sockets: You can specify metrics collection from services over Unix domain sockets by using the http+unix scheme, for example, http+unix:///path/to/service.sock:/api/endpoint.

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.

Feedback

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