Google Cloud Stackdriver and Microsoft SQL Server Integration

[[inputs.stackdriver]]
  ## GCP Project
  project = "erudite-bloom-151019"

  ## Include timeseries that start with the given metric type.
  metric_type_prefix_include = [
    "compute.googleapis.com/",
  ]

  ## Exclude timeseries that start with the given metric type.
  # metric_type_prefix_exclude = []

  ## Most metrics are updated no more than once per minute; it is recommended
  ## to override the agent level interval with a value of 1m or greater.
  interval = "1m"

  ## Maximum number of API calls to make per second.  The quota for accounts
  ## varies, it can be viewed on the API dashboard:
  ##   https://cloud.google.com/monitoring/quotas#quotas_and_limits
  # rate_limit = 14

  ## The delay and window options control the number of points selected on
  ## each gather.  When set, metrics are gathered between:
  ##   start: now() - delay - window
  ##   end:   now() - delay
  #
  ## Collection delay; if set too low metrics may not yet be available.
  # delay = "5m"
  #
  ## If unset, the window will start at 1m and be updated dynamically to span
  ## the time between calls (approximately the length of the plugin interval).
  # window = "1m"

  ## TTL for cached list of metric types.  This is the maximum amount of time
  ## it may take to discover new metrics.
  # cache_ttl = "1h"

  ## If true, raw bucket counts are collected for distribution value types.
  ## For a more lightweight collection, you may wish to disable and use
  ## distribution_aggregation_aligners instead.
  # gather_raw_distribution_buckets = true

  ## Aggregate functions to be used for metrics whose value type is
  ## distribution.  These aggregate values are recorded in in addition to raw
  ## bucket counts; if they are enabled.
  ##
  ## For a list of aligner strings see:
  ##   https://cloud.google.com/monitoring/api/ref_v3/rpc/google.monitoring.v3#aligner
  # distribution_aggregation_aligners = [
  #  "ALIGN_PERCENTILE_99",
  #  "ALIGN_PERCENTILE_95",
  #  "ALIGN_PERCENTILE_50",
  # ]

  ## Filters can be added to reduce the number of time series matched.  All
  ## functions are supported: starts_with, ends_with, has_substring, and
  ## one_of.  Only the '=' operator is supported.
  ##
  ## The logical operators when combining filters are defined statically using
  ## the following values:
  ##   filter ::=  {AND  AND  AND }
  ##   resource_labels ::=  {OR }
  ##   metric_labels ::=  {OR }
  ##   user_labels ::=  {OR }
  ##   system_labels ::=  {OR }
  ##
  ## For more details, see https://cloud.google.com/monitoring/api/v3/filters
  #
  ## Resource labels refine the time series selection with the following expression:
  ##   resource.labels. = 
  # [[inputs.stackdriver.filter.resource_labels]]
  #   key = "instance_name"
  #   value = 'starts_with("localhost")'
  #
  ## Metric labels refine the time series selection with the following expression:
  ##   metric.labels. = 
  #  [[inputs.stackdriver.filter.metric_labels]]
  #    key = "device_name"
  #    value = 'one_of("sda", "sdb")'
  #
  ## User labels refine the time series selection with the following expression:
  ##   metadata.user_labels."" = 
  #  [[inputs.stackdriver.filter.user_labels]]
  #    key = "environment"
  #    value = 'one_of("prod", "staging")'
  #
  ## System labels refine the time series selection with the following expression:
  ##   metadata.system_labels."" = 
  #  [[inputs.stackdriver.filter.system_labels]]
  #    key = "machine_type"
  #    value = 'starts_with("e2-")'
</code></pre>

[[outputs.sql]]
  ## Database driver
  ## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres),
  ## sqlite (SQLite3), snowflake (snowflake.com), clickhouse (ClickHouse)
  driver = "mssql"

  ## Data source name
  ## For Microsoft SQL Server, the DSN typically includes the server, port, username, password, and database name.
  ## Example DSN: "sqlserver://username:password@localhost:1433?database=telegraf"
  data_source_name = "sqlserver://username:password@localhost:1433?database=telegraf"

  ## Timestamp column name
  timestamp_column = "timestamp"

  ## Table creation template
  ## Available template variables:
  ##  {TABLE}        - table name as a quoted identifier
  ##  {TABLELITERAL} - table name as a quoted string literal
  ##  {COLUMNS}      - column definitions (list of quoted identifiers and types)
  table_template = "CREATE TABLE {TABLE} ({COLUMNS})"

  ## Table existence check template
  ## Available template variables:
  ##  {TABLE} - table name as a quoted identifier
  table_exists_template = "SELECT 1 FROM {TABLE} LIMIT 1"

  ## Initialization SQL (optional)
  init_sql = ""

  ## Maximum amount of time a connection may be idle. "0s" means connections are never closed due to idle time.
  connection_max_idle_time = "0s"

  ## Maximum amount of time a connection may be reused. "0s" means connections are never closed due to age.
  connection_max_lifetime = "0s"

  ## Maximum number of connections in the idle connection pool. 0 means unlimited.
  connection_max_idle = 2

  ## Maximum number of open connections to the database. 0 means unlimited.
  connection_max_open = 0

  ## Metric type to SQL type conversion
  ## You can customize the mapping if needed.
  #[outputs.sql.convert]
  #  integer       = "INT"
  #  real          = "DOUBLE"
  #  text          = "TEXT"
  #  timestamp     = "TIMESTAMP"
  #  defaultvalue  = "TEXT"
  #  unsigned      = "UNSIGNED"
  #  bool          = "BOOL"