cpu input plugin error on mac m2

[phiremande]

Relevant telegraf.conf

# Telegraf Configuration
#
# Telegraf is entirely plugin driven. All metrics are gathered from the
# declared inputs, and sent to the declared outputs.
#
# Plugins must be declared in here to be active.
# To deactivate a plugin, comment out the name and any variables.
#
# Use 'telegraf -config telegraf.conf -test' to see what metrics a config
# file would generate.
#
# Environment variables can be used anywhere in this config file, simply surround
# them with ${}. For strings the variable must be within quotes (ie, "${STR_VAR}"),
# for numbers and booleans they should be plain (ie, ${INT_VAR}, ${BOOL_VAR})

# Global tags can be specified here in key="value" format.
[global_tags]
  # dc = "us-east-1" # will tag all metrics with dc=us-east-1
  # rack = "1a"
  ## Environment variables can be used as tags, and throughout the config file
  # user = "$USER"

# Configuration for telegraf agent
[agent]
  ## Default data collection interval for all inputs
  interval = "10s"
  ## Rounds collection interval to 'interval'
  ## ie, if interval="10s" then always collect on :00, :10, :20, etc.
  round_interval = true

  ## Telegraf will send metrics to outputs in batches of at most
  ## metric_batch_size metrics.
  ## This controls the size of writes that Telegraf sends to output plugins.
  metric_batch_size = 1000

  ## Maximum number of unwritten metrics per output.  Increasing this value
  ## allows for longer periods of output downtime without dropping metrics at the
  ## cost of higher maximum memory usage.
  metric_buffer_limit = 10000

  ## Collection jitter is used to jitter the collection by a random amount.
  ## Each plugin will sleep for a random time within jitter before collecting.
  ## This can be used to avoid many plugins querying things like sysfs at the
  ## same time, which can have a measurable effect on the system.
  collection_jitter = "0s"

  ## Collection offset is used to shift the collection by the given amount.
  ## This can be be used to avoid many plugins querying constraint devices
  ## at the same time by manually scheduling them in time.
  # collection_offset = "0s"

  ## Default flushing interval for all outputs. Maximum flush_interval will be
  ## flush_interval + flush_jitter
  flush_interval = "10s"
  ## Jitter the flush interval by a random amount. This is primarily to avoid
  ## large write spikes for users running a large number of telegraf instances.
  ## ie, a jitter of 5s and interval 10s means flushes will happen every 10-15s
  flush_jitter = "0s"

  ## Collected metrics are rounded to the precision specified. Precision is
  ## specified as an interval with an integer + unit (e.g. 0s, 10ms, 2us, 4s).
  ## Valid time units are "ns", "us" (or "µs"), "ms", "s".
  ##
  ## By default or when set to "0s", precision will be set to the same
  ## timestamp order as the collection interval, with the maximum being 1s:
  ##   ie, when interval = "10s", precision will be "1s"
  ##       when interval = "250ms", precision will be "1ms"
  ##
  ## Precision will NOT be used for service inputs. It is up to each individual
  ## service input to set the timestamp at the appropriate precision.
  precision = "0s"

  ## Log at debug level.
  # debug = false
  ## Log only error level messages.
  # quiet = false

  ## Log target controls the destination for logs and can be one of "file",
  ## "stderr" or, on Windows, "eventlog".  When set to "file", the output file
  ## is determined by the "logfile" setting.
  # logtarget = "file"

  ## Name of the file to be logged to when using the "file" logtarget.  If set to
  ## the empty string then logs are written to stderr.
  # logfile = ""

  ## The logfile will be rotated after the time interval specified.  When set
  ## to 0 no time based rotation is performed.  Logs are rotated only when
  ## written to, if there is no log activity rotation may be delayed.
  # logfile_rotation_interval = "0h"

  ## The logfile will be rotated when it becomes larger than the specified
  ## size.  When set to 0 no size based rotation is performed.
  # logfile_rotation_max_size = "0MB"

  ## Maximum number of rotated archives to keep, any older logs are deleted.
  ## If set to -1, no archives are removed.
  # logfile_rotation_max_archives = 5

  ## Pick a timezone to use when logging or type 'local' for local time.
  ## Example: America/Chicago
  # log_with_timezone = ""

  ## Override default hostname, if empty use os.Hostname()
  hostname = ""
  ## If set to true, do no set the "host" tag in the telegraf agent.
  omit_hostname = false

  ## Method of translating SNMP objects. Can be "netsnmp" (deprecated) which
  ## translates by calling external programs snmptranslate and snmptable,
  ## or "gosmi" which translates using the built-in gosmi library.
  # snmp_translator = "netsnmp"

  ## Name of the file to load the state of plugins from and store the state to.
  ## If uncommented and not empty, this file will be used to save the state of
  ## stateful plugins on termination of Telegraf. If the file exists on start,
  ## the state in the file will be restored for the plugins.
  # statefile = ""

###############################################################################
#                            SECRETSTORE PLUGINS                              #
###############################################################################

# # Secret-store to access Docker Secrets
# [[secretstores.docker]]
#   ## Unique identifier for the secretstore.
#   ## This id can later be used in plugins to reference the secrets
#   ## in this secret-store via @{<id>:<secret_key>} (mandatory)
#   id = "docker_secretstore"
#
#   ## Default Path to directory where docker stores the secrets file
#   ## Current implementation in docker compose v2 only allows the following
#   ## value for the path where the secrets are mounted at runtime
#   # path = "/run/secrets"
#
#   ## Allow dynamic secrets that are updated during runtime of telegraf
#   ## Dynamic Secrets work only with `file` or `external` configuration
#   ## in `secrets` section of the `docker-compose.yml` file
#   # dynamic = false

# # Read secrets from a HTTP endpoint
# [[secretstores.http]]
#   ## Unique identifier for the secret-store.
#   ## This id can later be used in plugins to reference the secrets
#   ## in this secret-store via @{<id>:<secret_key>} (mandatory)
#   id = "secretstore"
#
#   ## URLs from which to read the secrets
#   url = "http://localhost/secrets"
#
#   ## Optional HTTP headers
#   # headers = {"X-Special-Header" = "Special-Value"}
#
#   ## Optional Token for Bearer Authentication via
#   ## "Authorization: Bearer <token>" header
#   # token = "your-token"
#
#   ## Optional Credentials for HTTP Basic Authentication
#   # username = "username"
#   # password = "pa$$word"
#
#   ## OAuth2 Client Credentials. The options 'client_id', 'client_secret', and 'token_url' are required to use OAuth2.
#   # client_id = "clientid"
#   # client_secret = "secret"
#   # token_url = "https://indentityprovider/oauth2/v1/token"
#   # scopes = ["urn:opc:idm:__myscopes__"]
#
#   ## HTTP Proxy support
#   # use_system_proxy = false
#   # http_proxy_url = ""
#
#   ## Optional TLS Config
#   # tls_ca = "/etc/telegraf/ca.pem"
#   # tls_cert = "/etc/telegraf/cert.pem"
#   # tls_key = "/etc/telegraf/key.pem"
#   ## Minimal TLS version to accept by the client
#   # tls_min_version = "TLS12"
#   ## Use TLS but skip chain & host verification
#   # insecure_skip_verify = false
#
#   ## Optional Cookie authentication
#   # cookie_auth_url = "https://localhost/authMe"
#   # cookie_auth_method = "POST"
#   # cookie_auth_username = "username"
#   # cookie_auth_password = "pa$$word"
#   # cookie_auth_headers = { Content-Type = "application/json", X-MY-HEADER = "hello" }
#   # cookie_auth_body = '{"username": "user", "password": "pa$$word", "authenticate": "me"}'
#   ## When unset or set to zero the authentication will only happen once
#   ## and will never renew the cookie. Set to a suitable duration if you
#   ## require cookie renewal!
#   # cookie_auth_renewal = "0s"
#
#   ## Amount of time allowed to complete the HTTP request
#   # timeout = "5s"
#
#   ## List of success status codes
#   # success_status_codes = [200]
#
#   ## JSONata expression to transform the server response into a
#   ##   { "secret name": "secret value", ... }
#   ## form. See https://jsonata.org for more information and a playground.
#   # transformation = ''
#
#   ## Cipher used to decrypt the secrets.
#   ## In case your secrets are transmitted in an encrypted form, you need
#   ## to specify the cipher used and provide the corresponding configuration.
#   ## Please refer to https://github.com/influxdata/telegraf/blob/master/plugins/secretstores/http/README.md
#   ## for supported values.
#   # cipher = "none"
#
#   ## AES cipher parameters
#   # [secretstores.http.aes]
#   #   ## Key (hex-encoded) and initialization-vector (IV) for the decryption.
#   #   ## In case the key (and IV) is derived from a password, the values can
#   #   ## be omitted.
#   #   key = ""
#   #   init_vector = ""
#   #
#   #   ## Parameters for password-based-key derivation.
#   #   ## These parameters must match the encryption side to derive the same
#   #   ## key on both sides!
#   #   # kdf_algorithm = "PBKDF2-HMAC-SHA256"
#   #   # password = ""
#   #   # salt = ""
#   #   # iterations = 0

# # File based Javascript Object Signing and Encryption based secret-store
# [[secretstores.jose]]
#   ## Unique identifier for the secret-store.
#   ## This id can later be used in plugins to reference the secrets
#   ## in this secret-store via @{<id>:<secret_key>} (mandatory)
#   id = "secretstore"
#
#   ## Directory for storing the secrets
#   path = "/etc/telegraf/secrets"
#
#   ## Password to access the secrets.
#   ## If no password is specified here, Telegraf will prompt for it at startup time.
#   # password = ""

# # Secret-store to retrieve and maintain tokens from various OAuth2 services
# [[secretstores.oauth2]]
#   ## Unique identifier for the secret-store.
#   ## This id can later be used in plugins to reference the secrets
#   ## in this secret-store via @{<id>:<secret_key>} (mandatory)
#   id = "secretstore"
#
#   ## Service to retrieve the token(s) from
#   ## Currently supported services are "custom", "auth0" and "AzureAD"
#   # service = "custom"
#
#   ## Setting to overwrite the queried token-endpoint
#   ## This setting is optional for some serices but mandatory for others such
#   ## as "custom" or "auth0". Please check the documentation at
#   ## https://github.com/influxdata/telegraf/blob/master/plugins/secretstores/oauth2/README.md
#   # token_endpoint = ""
#
#   ## Tenant ID for the AzureAD service
#   # tenant_id = ""
#
#   ## Minimal remaining time until the token expires
#   ## If a token expires less than the set duration in the future, the token is
#   ## renewed. This is useful to avoid race-condition issues where a token is
#   ## still valid, but isn't when the request reaches the API endpoint of
#   ## your service using the token.
#   # token_expiry_margin = "1s"
#
#   ## Section for defining a token secret
#   [[secretstores.oauth2.token]]
#     ## Unique secret-key used for referencing the token via @{<id>:<secret_key>}
#     key = ""
#     ## Client-ID and secret for the 2-legged OAuth flow
#     client_id = ""
#     client_secret = ""
#     ## Scopes to send in the request
#     # scopes = []
#
#     ## Additional (optional) parameters to include in the token request
#     ## This might for example include the "audience" parameter required for
#     ## auth0.
#     # [secretstores.oauth2.token.parameters]
#     #     audience = ""

# # Operating System native secret-store
# [[secretstores.os]]
#   ## Unique identifier for the secret-store.
#   ## This id can later be used in plugins to reference the secrets
#   ## in this secret-store via @{<id>:<secret_key>} (mandatory)
#   id = "secretstore"
#
#   ## Keyring Name & Collection
#   ## * Linux: keyring name used for the secrets, collection is unused
#   ## * macOS: keyring specifies the macOS' Keychain name and collection is an
#   ##     optional Keychain service name
#   ## * Windows: keys follow a fixed pattern in the form
#   ##     `<collection>:<keyring>:<key_name>`. Please keep this in mind when
#   ##     creating secrets with the Windows credential tool.
#   # keyring = "telegraf"
#   # collection = ""
#
#   ## macOS Keychain password
#   ## If no password is specified here, Telegraf will prompt for it at startup
#   ## time.
#   # password = ""
#
#   ## Allow dynamic secrets that are updated during runtime of telegraf
#   # dynamic = false

# Send telegraf metrics to file(s)
[[outputs.file]]
  ## Files to write to, "stdout" is a specially handled file.
  files = ["stdout", "/tmp/metrics.out"]

  ## Use batch serialization format instead of line based delimiting.  The
  ## batch format allows for the production of non line based output formats and
  ## may more efficiently encode and write metrics.
  # use_batch_format = false

  ## The file will be rotated after the time interval specified.  When set
  ## to 0 no time based rotation is performed.
  # rotation_interval = "0h"

  ## The logfile will be rotated when it becomes larger than the specified
  ## size.  When set to 0 no size based rotation is performed.
  # rotation_max_size = "0MB"

  ## Maximum number of rotated archives to keep, any older logs are deleted.
  ## If set to -1, no archives are removed.
  # rotation_max_archives = 5

  ## Data format to output.
  ## 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_OUTPUT.md
  data_format = "influx"

  ## Compress output data with the specifed algorithm.
  ## If empty, compression will be disabled and files will be plain text.
  ## Supported algorithms are "zstd", "gzip" and "zlib".
  # compression_algorithm = ""

  ## Compression level for the algorithm above.
  ## Please note that different algorithms support different levels:
  ##   zstd  -- supports levels 1, 3, 7 and 11.
  ##   gzip -- supports levels 0, 1 and 9.
  ##   zlib -- supports levels 0, 1, and 9.
  ## By default the default compression level for each algorithm is used.
  # compression_level = -1

###############################################################################
#                            PROCESSOR PLUGINS                                #
###############################################################################

# # Attach AWS EC2 metadata to metrics
# [[processors.aws_ec2]]
#   ## Instance identity document tags to attach to metrics.
#   ## For more information see:
#   ## https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instance-identity-documents.html
#   ##
#   ## Available tags:
#   ## * accountId
#   ## * architecture
#   ## * availabilityZone
#   ## * billingProducts
#   ## * imageId
#   ## * instanceId
#   ## * instanceType
#   ## * kernelId
#   ## * pendingTime
#   ## * privateIp
#   ## * ramdiskId
#   ## * region
#   ## * version
#   imds_tags = []
#
#   ## EC2 instance tags retrieved with DescribeTags action.
#   ## In case tag is empty upon retrieval it's omitted when tagging metrics.
#   ## Note that in order for this to work, role attached to EC2 instance or AWS
#   ## credentials available from the environment must have a policy attached, that
#   ## allows ec2:DescribeTags.
#   ##
#   ## For more information see:
#   ## https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribeTags.html
#   ec2_tags = []
#
#   ## Timeout for http requests made by against aws ec2 metadata endpoint.
#   timeout = "10s"
#
#   ## ordered controls whether or not the metrics need to stay in the same order
#   ## this plugin received them in. If false, this plugin will change the order
#   ## with requests hitting cached results moving through immediately and not
#   ## waiting on slower lookups. This may cause issues for you if you are
#   ## depending on the order of metrics staying the same. If so, set this to true.
#   ## Keeping the metrics ordered may be slightly slower.
#   ordered = false
#
#   ## max_parallel_calls is the maximum number of AWS API calls to be in flight
#   ## at the same time.
#   ## It's probably best to keep this number fairly low.
#   max_parallel_calls = 10
#
#   ## cache_ttl determines how long each cached item will remain in the cache before
#   ## it is removed and subsequently needs to be queried for from the AWS API. By
#   ## default, no items are cached.
#   # cache_ttl = "0s"
#
#   ## tag_cache_size determines how many of the values which are found in imds_tags
#   ## or ec2_tags will be kept in memory for faster lookup on successive processing
#   ## of metrics. You may want to adjust this if you have excessively large numbers
#   ## of tags on your EC2 instances, and you are using the ec2_tags field. This
#   ## typically does not need to be changed when using the imds_tags field.
#   # tag_cache_size = 1000
#
#   ## log_cache_stats will emit a log line periodically to stdout with details of
#   ## cache entries, hits, misses, and evacuations since the last time stats were
#   ## emitted. This can be helpful in determining whether caching is being effective
#   ## in your environment. Stats are emitted every 30 seconds. By default, this
#   ## setting is disabled.

# # Apply metric modifications using override semantics.
# [[processors.clone]]
#   ## All modifications on inputs and aggregators can be overridden:
#   # name_override = "new_name"
#   # name_prefix = "new_name_prefix"
#   # name_suffix = "new_name_suffix"
#
#   ## Tags to be added (all values must be strings)
#   # [processors.clone.tags]
#   #   additional_tag = "tag_value"

# # Convert values to another metric value type
# [[processors.converter]]
#   ## Tags to convert
#   ##
#   ## The table key determines the target type, and the array of key-values
#   ## select the keys to convert.  The array may contain globs.
#   ##   <target-type> = [<tag-key>...]
#   [processors.converter.tags]
#     measurement = []
#     string = []
#     integer = []
#     unsigned = []
#     boolean = []
#     float = []
#
#     ## Optional tag to use as metric timestamp
#     # timestamp = []
#
#     ## Format of the timestamp determined by the tag above. This can be any of
#     ## "unix", "unix_ms", "unix_us", "unix_ns", or a valid Golang time format.
#     ## It is required, when using the timestamp option.
#     # timestamp_format = ""
#
#   ## Fields to convert
#   ##
#   ## The table key determines the target type, and the array of key-values
#   ## select the keys to convert.  The array may contain globs.
#   ##   <target-type> = [<field-key>...]
#   [processors.converter.fields]
#     measurement = []
#     tag = []
#     string = []
#     integer = []
#     unsigned = []
#     boolean = []
#     float = []
#
#     ## Optional field to use as metric timestamp
#     # timestamp = []
#
#     ## Format of the timestamp determined by the field above. This can be any
#     ## of "unix", "unix_ms", "unix_us", "unix_ns", or a valid Golang time
#     ## format. It is required, when using the timestamp option.
#     # timestamp_format = ""

# # Dates measurements, tags, and fields that pass through this filter.
# [[processors.date]]
#   ## New tag to create
#   tag_key = "month"
#
#   ## New field to create (cannot set both field_key and tag_key)
#   # field_key = "month"
#
#   ## Date format string, must be a representation of the Go "reference time"
#   ## which is "Mon Jan 2 15:04:05 -0700 MST 2006".
#   date_format = "Jan"
#
#   ## If destination is a field, date format can also be one of
#   ## "unix", "unix_ms", "unix_us", or "unix_ns", which will insert an integer field.
#   # date_format = "unix"
#
#   ## Offset duration added to the date string when writing the new tag.
#   # date_offset = "0s"
#
#   ## Timezone to use when creating the tag or field using a reference time
#   ## string.  This can be set to one of "UTC", "Local", or to a location name
#   ## in the IANA Time Zone database.
#   ##   example: timezone = "America/Los_Angeles"
#   # timezone = "UTC"

# # Filter metrics with repeating field values
# [[processors.dedup]]
#   ## Maximum time to suppress output
#   dedup_interval = "600s"

# ## Set default fields on your metric(s) when they are nil or empty
# [[processors.defaults]]
#   ## Ensures a set of fields always exists on your metric(s) with their
#   ## respective default value.
#   ## For any given field pair (key = default), if it's not set, a field
#   ## is set on the metric with the specified default.
#   ##
#   ## A field is considered not set if it is nil on the incoming metric;
#   ## or it is not nil but its value is an empty string or is a string
#   ## of one or more spaces.
#   ##   <target-field> = <value>
#   [processors.defaults.fields]
#     field_1 = "bar"
#     time_idle = 0
#     is_error = true

# # Map enum values according to given table.
# [[processors.enum]]
#   [[processors.enum.mapping]]
#     ## Name of the field to map. Globs accepted.
#     field = "status"
#
#     ## Name of the tag to map. Globs accepted.
#     # tag = "status"
#
#     ## Destination tag or field to be used for the mapped value.  By default the
#     ## source tag or field is used, overwriting the original value.
#     dest = "status_code"
#
#     ## Default value to be used for all values not contained in the mapping
#     ## table.  When unset and no match is found, the original field will remain
#     ## unmodified and the destination tag or field will not be created.
#     # default = 0
#
#     ## Table of mappings
#     [processors.enum.mapping.value_mappings]
#       green = 1
#       amber = 2
#       red = 3

# # Run executable as long-running processor plugin
# [[processors.execd]]
#   ## One program to run as daemon.
#   ## NOTE: process and each argument should each be their own string
#   ## eg: command = ["/path/to/your_program", "arg1", "arg2"]
#   command = ["cat"]
#
#   ## Environment variables
#   ## Array of "key=value" pairs to pass as environment variables
#   ## e.g. "KEY=value", "USERNAME=John Doe",
#   ## "LD_LIBRARY_PATH=/opt/custom/lib64:/usr/local/libs"
#   # environment = []
#
#   ## Delay before the process is restarted after an unexpected termination
#   # restart_delay = "10s"
#
#   ## Serialization format for communicating with the executed program
#   ## Please note that the corresponding data-format must exist both in
#   ## parsers and serializers
#   # data_format = "influx"

# # Performs file path manipulations on tags and fields
# [[processors.filepath]]
#   ## Treat the tag value as a path and convert it to its last element, storing the result in a new tag
#   # [[processors.filepath.basename]]
#   #   tag = "path"
#   #   dest = "basepath"
#
#   ## Treat the field value as a path and keep all but the last element of path, typically the path's directory
#   # [[processors.filepath.dirname]]
#   #   field = "path"
#
#   ## Treat the tag value as a path, converting it to its the last element without its suffix
#   # [[processors.filepath.stem]]
#   #   tag = "path"
#
#   ## Treat the tag value as a path, converting it to the shortest path name equivalent
#   ## to path by purely lexical processing
#   # [[processors.filepath.clean]]
#   #   tag = "path"
#
#   ## Treat the tag value as a path, converting it to a relative path that is lexically
#   ## equivalent to the source path when joined to 'base_path'
#   # [[processors.filepath.rel]]
#   #   tag = "path"
#   #   base_path = "/var/log"
#
#   ## Treat the tag value as a path, replacing each separator character in path with a '/' character. Has only
#   ## effect on Windows
#   # [[processors.filepath.toslash]]
#   #   tag = "path"

# # Add a tag of the network interface name looked up over SNMP by interface number
# [[processors.ifname]]
#   ## Name of tag holding the interface number
#   # tag = "ifIndex"
#
#   ## Name of output tag where service name will be added
#   # dest = "ifName"
#
#   ## Name of tag of the SNMP agent to request the interface name from
#   ##   example: agent = "source"
#   # agent = "agent"
#
#   ## Timeout for each request.
#   # timeout = "5s"
#
#   ## SNMP version; can be 1, 2, or 3.
#   # version = 2
#
#   ## SNMP community string.
#   # community = "public"
#
#   ## Number of retries to attempt.
#   # retries = 3
#
#   ## The GETBULK max-repetitions parameter.
#   # max_repetitions = 10
#
#   ## SNMPv3 authentication and encryption options.
#   ##
#   ## Security Name.
#   # sec_name = "myuser"
#   ## Authentication protocol; one of "MD5", "SHA", or "".
#   # auth_protocol = "MD5"
#   ## Authentication password.
#   # auth_password = "pass"
#   ## Security Level; one of "noAuthNoPriv", "authNoPriv", or "authPriv".
#   # sec_level = "authNoPriv"
#   ## Context Name.
#   # context_name = ""
#   ## Privacy protocol used for encrypted messages; one of "DES", "AES" or "".
#   # priv_protocol = ""
#   ## Privacy password used for encrypted messages.
#   # priv_password = ""
#
#   ## max_parallel_lookups is the maximum number of SNMP requests to
#   ## make at the same time.
#   # max_parallel_lookups = 100
#
#   ## ordered controls whether or not the metrics need to stay in the
#   ## same order this plugin received them in. If false, this plugin
#   ## may change the order when data is cached.  If you need metrics to
#   ## stay in order set this to true.  keeping the metrics ordered may
#   ## be slightly slower
#   # ordered = false
#
#   ## cache_ttl is the amount of time interface names are cached for a
#   ## given agent.  After this period elapses if names are needed they
#   ## will be retrieved again.
#   # cache_ttl = "8h"

# # Lookup a key derived from metrics in a static file
# [[processors.lookup]]
#   ## List of files containing the lookup-table
#   files = ["path/to/lut.json", "path/to/another_lut.json"]
#
#   ## Format of the lookup file(s)
#   ## Available formats are:
#   ##    json               -- JSON file with 'key: {tag-key: tag-value, ...}' mapping
#   ##    csv_key_name_value -- CSV file with 'key,tag-key,tag-value,...,tag-key,tag-value' mapping
#   ##    csv_key_values     -- CSV file with a header containing tag-names and
#   ##                          rows with 'key,tag-value,...,tag-value' mappings
#   # format = "json"
#
#   ## Template for generating the lookup-key from the metric.
#   ## This is a Golang template (see https://pkg.go.dev/text/template) to
#   ## access the metric name (`{{.Name}}`), a tag value (`{{.Tag "name"}}`) or
#   ## a field value (`{{.Field "name"}}`).
#   key = '{{.Tag "host"}}'

# # Adds noise to numerical fields
# [[processors.noise]]
#   ## Specified the type of the random distribution.
#   ## Can be "laplacian", "gaussian" or "uniform".
#   # type = "laplacian
#
#   ## Center of the distribution.
#   ## Only used for Laplacian and Gaussian distributions.
#   # mu = 0.0
#
#   ## Scale parameter for the Laplacian or Gaussian distribution
#   # scale = 1.0
#
#   ## Upper and lower bound of the Uniform distribution
#   # min = -1.0
#   # max = 1.0
#
#   ## Apply the noise only to numeric fields matching the filter criteria below.
#   ## Excludes takes precedence over includes.
#   # include_fields = []
#   # exclude_fields = []

# # Apply metric modifications using override semantics.
# [[processors.override]]
#   ## All modifications on inputs and aggregators can be overridden:
#   # name_override = "new_name"
#   # name_prefix = "new_name_prefix"
#   # name_suffix = "new_name_suffix"
#
#   ## Tags to be added (all values must be strings)
#   # [processors.override.tags]
#   #   additional_tag = "tag_value"

# # Parse a value in a specified field(s)/tag(s) and add the result in a new metric
# [[processors.parser]]
#   ## The name of the fields whose value will be parsed.
#   parse_fields = ["message"]
#
#   ## The name of the tags whose value will be parsed.
#   # parse_tags = []
#
#   ## If true, incoming metrics are not emitted.
#   # drop_original = false
#
#   ## Merge Behavior
#   ## Only has effect when drop_original is set to false. Possible options
#   ## include:
#   ##  * override: emitted metrics are merged by overriding the original metric
#   ##    using the newly parsed metrics, but retains the original metric
#   ##    timestamp.
#   ##  * override-with-timestamp: the same as "override", but the timestamp is
#   ##    set based on the new metrics if present.
#   # merge = ""
#
#   ## The dataformat to be read from files
#   ## 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"

# # Rotate a single valued metric into a multi field metric
# [[processors.pivot]]
#   ## Tag to use for naming the new field.
#   tag_key = "name"
#   ## Field to use as the value of the new field.
#   value_key = "value"

# # Given a tag/field of a TCP or UDP port number, add a tag/field of the service name looked up in the system services file
# [[processors.port_name]]
#   ## Name of tag holding the port number
#   # tag = "port"
#   ## Or name of the field holding the port number
#   # field = "port"
#
#   ## Name of output tag or field (depending on the source) where service name will be added
#   # dest = "service"
#
#   ## Default tcp or udp
#   # default_protocol = "tcp"
#
#   ## Tag containing the protocol (tcp or udp, case-insensitive)
#   # protocol_tag = "proto"
#
#   ## Field containing the protocol (tcp or udp, case-insensitive)
#   # protocol_field = "proto"

# # Print all metrics that pass through this filter.
# [[processors.printer]]

# # Transforms tag and field values as well as measurement, tag and field names with regex pattern
# [[processors.regex]]
#   namepass = ["nginx_requests"]
#
#   ## Tag value conversion(s). Multiple instances are allowed.
#   [[processors.regex.tags]]
#     ## Tag(s) to process with optional glob expressions such as '*'.
#     key = "resp_code"
#     ## Regular expression to match the tag value. If the value doesn't
#     ## match the tag is ignored.
#     pattern = "^(\\d)\\d\\d$"
#     ## Replacement expression defining the value of the target tag. You can
#     ## use regexp groups or named groups e.g. ${1} references the first group.
#     replacement = "${1}xx"
#     ## Name of the target tag defaulting to 'key' if not specified.
#     ## In case of wildcards being used in `key` the currently processed
#     ## tag-name is used as target.
#     # result_key = "method"
#     ## Appends the replacement to the target tag instead of overwriting it when
#     ## set to true.
#     # append = false
#
#   ## Field value conversion(s). Multiple instances are allowed.
#   [[processors.regex.fields]]
#     ## Field(s) to process with optional glob expressions such as '*'.
#     key = "request"
#     ## Regular expression to match the field value. If the value doesn't
#     ## match or the field doesn't contain a string the field is ignored.
#     pattern = "^/api(?P<method>/[\\w/]+)\\S*"
#     ## Replacement expression defining the value of the target field. You can
#     ## use regexp groups or named groups e.g. ${method} references the group
#     ## named "method".
#     replacement = "${method}"
#     ## Name of the target field defaulting to 'key' if not specified.
#     ## In case of wildcards being used in `key` the currently processed
#     ## field-name is used as target.
#     # result_key = "method"
#
#   ## Rename metric fields
#   [[processors.regex.field_rename]]
#     ## Regular expression to match on the field name
#     pattern = "^search_(\\w+)d$"
#     ## Replacement expression defining the name of the new field
#     replacement = "${1}"
#     ## If the new field name already exists, you can either "overwrite" the
#     ## existing one with the value of the renamed field OR you can "keep"
#     ## both the existing and source field.
#     # result_key = "keep"
#
#   ## Rename metric tags
#   [[processors.regex.tag_rename]]
#     ## Regular expression to match on a tag name
#     pattern = "^search_(\\w+)d$"
#     ## Replacement expression defining the name of the new tag
#     replacement = "${1}"
#     ## If the new tag name already exists, you can either "overwrite" the
#     ## existing one with the value of the renamed tag OR you can "keep"
#     ## both the existing and source tag.
#     # result_key = "keep"
#
#   ## Rename metrics
#   [[processors.regex.metric_rename]]
#     ## Regular expression to match on an metric name
#     pattern = "^search_(\\w+)d$"
#     ## Replacement expression defining the new name of the metric
#     replacement = "${1}"

# # Rename measurements, tags, and fields that pass through this filter.
# [[processors.rename]]
#   ## Specify one sub-table per rename operation.
#   [[processors.rename.replace]]
#     measurement = "network_interface_throughput"
#     dest = "throughput"
#
#   [[processors.rename.replace]]
#     tag = "hostname"
#     dest = "host"
#
#   [[processors.rename.replace]]
#     field = "lower"
#     dest = "min"
#
#   [[processors.rename.replace]]
#     field = "upper"
#     dest = "max"

# # ReverseDNS does a reverse lookup on IP addresses to retrieve the DNS name
# [[processors.reverse_dns]]
#   ## For optimal performance, you may want to limit which metrics are passed to this
#   ## processor. eg:
#   ## namepass = ["my_metric_*"]
#
#   ## cache_ttl is how long the dns entries should stay cached for.
#   ## generally longer is better, but if you expect a large number of diverse lookups
#   ## you'll want to consider memory use.
#   cache_ttl = "24h"
#
#   ## lookup_timeout is how long should you wait for a single dns request to repsond.
#   ## this is also the maximum acceptable latency for a metric travelling through
#   ## the reverse_dns processor. After lookup_timeout is exceeded, a metric will
#   ## be passed on unaltered.
#   ## multiple simultaneous resolution requests for the same IP will only make a
#   ## single rDNS request, and they will all wait for the answer for this long.
#   lookup_timeout = "3s"
#
#   ## max_parallel_lookups is the maximum number of dns requests to be in flight
#   ## at the same time. Requesting hitting cached values do not count against this
#   ## total, and neither do mulptiple requests for the same IP.
#   ## It's probably best to keep this number fairly low.
#   max_parallel_lookups = 10
#
#   ## ordered controls whether or not the metrics need to stay in the same order
#   ## this plugin received them in. If false, this plugin will change the order
#   ## with requests hitting cached results moving through immediately and not
#   ## waiting on slower lookups. This may cause issues for you if you are
#   ## depending on the order of metrics staying the same. If so, set this to true.
#   ## keeping the metrics ordered may be slightly slower.
#   ordered = false
#
#   [[processors.reverse_dns.lookup]]
#     ## get the ip from the field "source_ip", and put the result in the field "source_name"
#     field = "source_ip"
#     dest = "source_name"
#
#   [[processors.reverse_dns.lookup]]
#     ## get the ip from the tag "destination_ip", and put the result in the tag
#     ## "destination_name".
#     tag = "destination_ip"
#     dest = "destination_name"
#
#     ## If you would prefer destination_name to be a field instead, you can use a
#     ## processors.converter after this one, specifying the order attribute.

# # Add the S2 Cell ID as a tag based on latitude and longitude fields
# [[processors.s2geo]]
#   ## The name of the lat and lon fields containing WGS-84 latitude and
#   ## longitude in decimal degrees.
#   # lat_field = "lat"
#   # lon_field = "lon"
#
#   ## New tag to create
#   # tag_key = "s2_cell_id"
#
#   ## Cell level (see https://s2geometry.io/resources/s2cell_statistics.html)
#   # cell_level = 9

# # Scale values with a predefined range to a different output range.
# [[processors.scale]]
#     ## It is possible to define multiple different scaling that can be applied
#     ## do different sets of fields. Each scaling expects the following
#     ## arguments:
#     ##   - input_minimum: Minimum expected input value
#     ##   - input_maximum: Maximum expected input value
#     ##   - output_minimum: Minimum desired output value
#     ##   - output_maximum: Maximum desired output value
#     ## alternatively you can specify a scaling with factor and offset
#     ##   - factor: factor to scale the input value with
#     ##   - offset: additive offset for value after scaling
#     ##   - fields: a list of field names (or filters) to apply this scaling to
#
#     ## Example: Scaling with minimum and maximum values
#     # [processors.scale.scaling]
#     #    input_minimum = 0
#     #    input_maximum = 1
#     #    output_minimum = 0
#     #    output_maximum = 100
#     #    fields = ["temperature1", "temperature2"]
#
#     ## Example: Scaling with factor and offset
#     # [processors.scale.scaling]
#     #    factor = 10.0
#     #    offset = -5.0
#     #    fields = ["voltage*"]

# # Split a metric into one or more metrics with the specified field(s)/tag(s)
# [[processors.split]]
#   ## Keeps the original metric by default
#   # drop_original = false
#
#   ## Template for an output metric
#   ## Users can define multiple templates to split the original metric into
#   ## multiple, potentially overlapping, metrics.
#   [[processors.split.template]]
#     ## New metric name
#     name = ""
#
#     ## List of tag keys for this metric template, accepts globs, e.g. "*"
#     tags = []
#
#     ## List of field keys for this metric template, accepts globs, e.g. "*"
#     fields = []

# # Process metrics using a Starlark script
# [[processors.starlark]]
#   ## The Starlark source can be set as a string in this configuration file, or
#   ## by referencing a file containing the script.  Only one source or script
#   ## should be set at once.
#
#   ## Source of the Starlark script.
#   source = '''
# def apply(metric):
#   return metric
# '''
#
#   ## File containing a Starlark script.
#   # script = "/usr/local/bin/myscript.star"
#
#   ## The constants of the Starlark script.
#   # [processors.starlark.constants]
#   #   max_size = 10
#   #   threshold = 0.75
#   #   default_name = "Julia"
#   #   debug_mode = true

# # Perform string processing on tags, fields, and measurements
# [[processors.strings]]
#   ## Convert a field value to lowercase and store in a new field
#   # [[processors.strings.lowercase]]
#   #   field = "uri_stem"
#   #   dest = "uri_stem_normalised"
#
#   ## Convert a tag value to uppercase
#   # [[processors.strings.uppercase]]
#   #   tag = "method"
#
#   ## Convert a field value to titlecase
#   # [[processors.strings.titlecase]]
#   #   field = "status"
#
#   ## Trim leading and trailing whitespace using the default cutset
#   # [[processors.strings.trim]]
#   #   field = "message"
#
#   ## Trim leading characters in cutset
#   # [[processors.strings.trim_left]]
#   #   field = "message"
#   #   cutset = "\t"
#
#   ## Trim trailing characters in cutset
#   # [[processors.strings.trim_right]]
#   #   field = "message"
#   #   cutset = "\r\n"
#
#   ## Trim the given prefix from the field
#   # [[processors.strings.trim_prefix]]
#   #   field = "my_value"
#   #   prefix = "my_"
#
#   ## Trim the given suffix from the field
#   # [[processors.strings.trim_suffix]]
#   #   field = "read_count"
#   #   suffix = "_count"
#
#   ## Replace all non-overlapping instances of old with new
#   # [[processors.strings.replace]]
#   #   measurement = "*"
#   #   old = ":"
#   #   new = "_"
#
#   ## Trims strings based on width
#   # [[processors.strings.left]]
#   #   field = "message"
#   #   width = 10
#
#   ## Decode a base64 encoded utf-8 string
#   # [[processors.strings.base64decode]]
#   #   field = "message"
#
#   ## Sanitize a string to ensure it is a valid utf-8 string
#   ## Each run of invalid UTF-8 byte sequences is replaced by the replacement string, which may be empty
#   # [[processors.strings.valid_utf8]]
#   #   field = "message"
#   #   replacement = ""

# # Restricts the number of tags that can pass through this filter and chooses which tags to preserve when over the limit.
# [[processors.tag_limit]]
#   ## Maximum number of tags to preserve
#   limit = 3
#
#   ## List of tags to preferentially preserve
#   keep = ["environment", "region"]

# # Uses a Go template to create a new tag
# [[processors.template]]
#   ## Go template used to create the tag name of the output. In order to
#   ## ease TOML escaping requirements, you should use single quotes around
#   ## the template string.
#   tag = "topic"
#
#   ## Go template used to create the tag value of the output. In order to
#   ## ease TOML escaping requirements, you should use single quotes around
#   ## the template string.
#   template = '{{ .Tag "hostname" }}.{{ .Tag "level" }}'

# # Print all metrics that pass through this filter.
# [[processors.topk]]
#   ## How many seconds between aggregations
#   # period = 10
#
#   ## How many top buckets to return per field
#   ## Every field specified to aggregate over will return k number of results.
#   ## For example, 1 field with k of 10 will return 10 buckets. While 2 fields
#   ## with k of 3 will return 6 buckets.
#   # k = 10
#
#   ## Over which tags should the aggregation be done. Globs can be specified, in
#   ## which case any tag matching the glob will aggregated over. If set to an
#   ## empty list is no aggregation over tags is done
#   # group_by = ['*']
#
#   ## The field(s) to aggregate
#   ## Each field defined is used to create an independent aggregation. Each
#   ## aggregation will return k buckets. If a metric does not have a defined
#   ## field the metric will be dropped from the aggregation. Considering using
#   ## the defaults processor plugin to ensure fields are set if required.
#   # fields = ["value"]
#
#   ## What aggregation function to use. Options: sum, mean, min, max
#   # aggregation = "mean"
#
#   ## Instead of the top k largest metrics, return the bottom k lowest metrics
#   # bottomk = false
#
#   ## The plugin assigns each metric a GroupBy tag generated from its name and
#   ## tags. If this setting is different than "" the plugin will add a
#   ## tag (which name will be the value of this setting) to each metric with
#   ## the value of the calculated GroupBy tag. Useful for debugging
#   # add_groupby_tag = ""
#
#   ## These settings provide a way to know the position of each metric in
#   ## the top k. The 'add_rank_field' setting allows to specify for which
#   ## fields the position is required. If the list is non empty, then a field
#   ## will be added to each and every metric for each string present in this
#   ## setting. This field will contain the ranking of the group that
#   ## the metric belonged to when aggregated over that field.
#   ## The name of the field will be set to the name of the aggregation field,
#   ## suffixed with the string '_topk_rank'
#   # add_rank_fields = []
#
#   ## These settings provide a way to know what values the plugin is generating
#   ## when aggregating metrics. The 'add_aggregate_field' setting allows to
#   ## specify for which fields the final aggregation value is required. If the
#   ## list is non empty, then a field will be added to each every metric for
#   ## each field present in this setting. This field will contain
#   ## the computed aggregation for the group that the metric belonged to when
#   ## aggregated over that field.
#   ## The name of the field will be set to the name of the aggregation field,
#   ## suffixed with the string '_topk_aggregate'
#   # add_aggregate_fields = []

# # Rotate multi field metric into several single field metrics
# [[processors.unpivot]]
#   ## Metric mode to pivot to
#   ## Set to "tag", metrics are pivoted as a tag and the metric is kept as
#   ## the original measurement name. Tag key name is set by tag_key value.
#   ## Set to "metric" creates a new metric named the field name. With this
#   ## option the tag_key is ignored. Be aware that this could lead to metric
#   ## name conflicts!
#   # use_fieldname_as = "tag"
#
#   ## Tag to use for the name.
#   # tag_key = "name"
#
#   ## Field to use for the name of the value.
#   # value_key = "value"

###############################################################################
#                            AGGREGATOR PLUGINS                               #
###############################################################################

# # Keep the aggregate basicstats of each metric passing through.
# [[aggregators.basicstats]]
#   ## The period on which to flush & clear the aggregator.
#   period = "30s"
#
#   ## If true, the original metric will be dropped by the
#   ## aggregator and will not get sent to the output plugins.
#   drop_original = false
#
#   ## Configures which basic stats to push as fields
#   # stats = ["count","diff","rate","min","max","mean","non_negative_diff","non_negative_rate","percent_change","stdev","s2","sum","interval"]

# # Calculates a derivative for every field.
# [[aggregators.derivative]]
#   ## The period in which to flush the aggregator.
#   period = "30s"
#   ##
#   ## Suffix to append for the resulting derivative field.
#   # suffix = "_rate"
#   ##
#   ## Field to use for the quotient when computing the derivative.
#   ## When using a field as the derivation parameter the name of that field will
#   ## be used for the resulting derivative, e.g. *fieldname_by_parameter*.
#   ## By default the timestamps of the metrics are used and the suffix is omitted.
#   # variable = ""
#   ##
#   ## Maximum number of roll-overs in case only one measurement is found during a period.
#   # max_roll_over = 10

# # Report the final metric of a series
# [[aggregators.final]]
#   ## The period on which to flush & clear the aggregator.
#   period = "30s"
#   ## If true, the original metric will be dropped by the
#   ## aggregator and will not get sent to the output plugins.
#   # drop_original = false
#
#   ## The time that a series is not updated until considering it final.
#   # series_timeout = "5m"
#
#   ## Output strategy, supported values:
#   ##   timeout  -- output a metric if no new input arrived for `series_timeout`;
#   ##               useful for filling gaps in input data
#   ##   periodic -- ouput the last received metric every `period`; useful to
#   ##               downsample the input data
#   # output_strategy = "timeout"

# # Configuration for aggregate histogram metrics
# [[aggregators.histogram]]
#   ## The period in which to flush the aggregator.
#   period = "30s"
#
#   ## If true, the original metric will be dropped by the
#   ## aggregator and will not get sent to the output plugins.
#   drop_original = false
#
#   ## If true, the histogram will be reset on flush instead
#   ## of accumulating the results.
#   reset = false
#
#   ## Whether bucket values should be accumulated. If set to false, "gt" tag will be added.
#   ## Defaults to true.
#   cumulative = true
#
#   ## Expiration interval for each histogram. The histogram will be expired if
#   ## there are no changes in any buckets for this time interval. 0 == no expiration.
#   # expiration_interval = "0m"
#
#   ## If true, aggregated histogram are pushed to output only if it was updated since
#   ## previous push. Defaults to false.
#   # push_only_on_update = false
#
#   ## Example config that aggregates all fields of the metric.
#   # [[aggregators.histogram.config]]
#   #   ## Right borders of buckets (with +Inf implicitly added).
#   #   buckets = [0.0, 15.6, 34.5, 49.1, 71.5, 80.5, 94.5, 100.0]
#   #   ## The name of metric.
#   #   measurement_name = "cpu"
#
#   ## Example config that aggregates only specific fields of the metric.
#   # [[aggregators.histogram.config]]
#   #   ## Right borders of buckets (with +Inf implicitly added).
#   #   buckets = [0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0]
#   #   ## The name of metric.
#   #   measurement_name = "diskio"
#   #   ## The concrete fields of metric
#   #   fields = ["io_time", "read_time", "write_time"]

# # Merge metrics into multifield metrics by series key
# [[aggregators.merge]]
#   ## If true, the original metric will be dropped by the
#   ## aggregator and will not get sent to the output plugins.
#   drop_original = true

# # Keep the aggregate min/max of each metric passing through.
# [[aggregators.minmax]]
#   ## General Aggregator Arguments:
#   ## The period on which to flush & clear the aggregator.
#   period = "30s"
#   ## If true, the original metric will be dropped by the
#   ## aggregator and will not get sent to the output plugins.
#   drop_original = false

# # Keep the aggregate quantiles of each metric passing through.
# [[aggregators.quantile]]
#   ## General Aggregator Arguments:
#   ## The period on which to flush & clear the aggregator.
#   period = "30s"
#
#   ## If true, the original metric will be dropped by the
#   ## aggregator and will not get sent to the output plugins.
#   drop_original = false
#
#   ## Quantiles to output in the range [0,1]
#   # quantiles = [0.25, 0.5, 0.75]
#
#   ## Type of aggregation algorithm
#   ## Supported are:
#   ##  "t-digest" -- approximation using centroids, can cope with large number of samples
#   ##  "exact R7" -- exact computation also used by Excel or NumPy (Hyndman & Fan 1996 R7)
#   ##  "exact R8" -- exact computation (Hyndman & Fan 1996 R8)
#   ## NOTE: Do not use "exact" algorithms with large number of samples
#   ##       to not impair performance or memory consumption!
#   # algorithm = "t-digest"
#
#   ## Compression for approximation (t-digest). The value needs to be
#   ## greater or equal to 1.0. Smaller values will result in more
#   ## performance but less accuracy.
#   # compression = 100.0

# # Aggregate metrics using a Starlark script
# [[aggregators.starlark]]
#   ## The Starlark source can be set as a string in this configuration file, or
#   ## by referencing a file containing the script.  Only one source or script
#   ## should be set at once.
#   ##
#   ## Source of the Starlark script.
#   source = '''
# state = {}
#
# def add(metric):
#   state["last"] = metric
#
# def push():
#   return state.get("last")
#
# def reset():
#   state.clear()
# '''
#
#   ## File containing a Starlark script.
#   # script = "/usr/local/bin/myscript.star"
#
#   ## The constants of the Starlark script.
#   # [aggregators.starlark.constants]
#   #   max_size = 10
#   #   threshold = 0.75
#   #   default_name = "Julia"
#   #   debug_mode = true

# # Count the occurrence of values in fields.
# [[aggregators.valuecounter]]
#   ## General Aggregator Arguments:
#   ## The period on which to flush & clear the aggregator.
#   period = "30s"
#   ## If true, the original metric will be dropped by the
#   ## aggregator and will not get sent to the output plugins.
#   drop_original = false
#   ## The fields for which the values will be counted
#   fields = ["status"]

###############################################################################
#                            INPUT PLUGINS                                    #
###############################################################################

# Read metrics about cpu usage
[[inputs.cpu]]
  ## Whether to report per-cpu stats or not
  percpu = true
  ## Whether to report total system cpu stats or not
  totalcpu = true
  ## If true, collect raw CPU time metrics
  collect_cpu_time = false
  ## If true, compute and report the sum of all non-idle CPU states
  report_active = false
  ## If true and the info is available then add core_id and physical_id tags
  core_tags = false

# Read metrics about memory usage
[[inputs.mem]]
  # no configuration

# Gather metrics about network interfaces
[[inputs.net]]
  ## By default, telegraf gathers stats from any up interface (excluding loopback)
  ## Setting interfaces will tell it to gather these explicit interfaces,
  ## regardless of status. When specifying an interface, glob-style
  ## patterns are also supported.
  ##
  # interfaces = ["eth*", "enp0s[0-1]", "lo"]
  ##
  ## On linux systems telegraf also collects protocol stats.
  ## Setting ignore_protocol_stats to true will skip reporting of protocol metrics.
  ##
  ## DEPRECATION NOTICE: A value of 'false' is deprecated and discouraged!
  ##                     Please set this to `true` and use the 'inputs.nstat'
  ##                     plugin instead.
  # ignore_protocol_stats = false

# Read metrics about swap memory usage
[[inputs.swap]]
  # no configuration

Logs from Telegraf

./telegraf --debug --config ./telegraf.conf
2023-10-25T08:55:56Z I! Loading config: ./telegraf.conf
2023-10-25T08:55:56Z I! Starting Telegraf 1.29.0-3fbe624b brought to you by InfluxData the makers of InfluxDB
2023-10-25T08:55:56Z I! Available plugins: 241 inputs, 9 aggregators, 29 processors, 24 parsers, 60 outputs, 5 secret-stores
2023-10-25T08:55:56Z I! Loaded inputs: cpu mem net swap
2023-10-25T08:55:56Z I! Loaded aggregators: 
2023-10-25T08:55:56Z I! Loaded processors: 
2023-10-25T08:55:56Z I! Loaded secretstores: 
2023-10-25T08:55:56Z I! Loaded outputs: file
2023-10-25T08:55:56Z I! Tags enabled: host=Apples-MacBook-Pro.local
2023-10-25T08:55:56Z I! [agent] Config: Interval:10s, Quiet:false, Hostname:"Apples-MacBook-Pro.local", Flush Interval:10s
2023-10-25T08:55:56Z D! [agent] Initializing plugins
2023-10-25T08:55:56Z W! DeprecationWarning: Value "false" for option "ignore_protocol_stats" of plugin "inputs.net" deprecated since version 1.27.3 and will be removed in 1.36.0: use the 'inputs.nstat' plugin instead
2023-10-25T08:55:56Z D! [agent] Connecting outputs
2023-10-25T08:55:56Z D! [agent] Attempting connection to [outputs.file]
2023-10-25T08:55:56Z D! [agent] Successfully connected to outputs.file
2023-10-25T08:55:56Z D! [agent] Starting service inputs
2023-10-25T08:56:00Z E! [inputs.cpu] Error in plugin: error getting CPU info: not implemented yet
swap,host=Apples-MacBook-Pro.local total=0i,used=0i,free=0i,used_percent=0 1698224160000000000
swap,host=Apples-MacBook-Pro.local in=0i,out=0i 1698224160000000000

System info

macOS ventura 13.6, telegraf 1.29.0-3fbe624b

Docker

No response

Steps to reproduce

1. Follow the steps in https://github.com/influxdata/telegraf to build from source.
2. Generate telegraf.conf for relevant plugins. In this case cpu is one input plugin.
3. Run telegraf using the generated config.

Expected behavior

No error should be shown for cpu input plugin.

Actual behavior

2023-10-25T08:56:00Z E! [inputs.cpu] Error in plugin: error getting CPU info: not implemented yet

Additional info

No response

[powersj]

That error message means exactly what it says ;) the cpu plugin is not implemented for macOS. However, if you build telelgraf with CGO enabled, it will work. Our official builds are built without CGO enabled, but the homebrew builds do enable it.

The CPU plugin pulls data from gopsutil library. It requires the use of CGO to pull data for macOS cpus.