目录
强大的性能,无限的扩展能力
收集、组织和处理海量高速数据。当您将任何数据视为时间序列数据时,它都会更有价值。借助 InfluxDB,排名第一的旨在与 Telegraf 扩展的时间序列平台。
查看入门方法
输入和输出集成概述
OPC UA 插件提供了一个接口,用于从 OPC UA 服务器设备检索数据,从而促进有效的数据收集和监控。
Azure 数据资源管理器插件允许指标收集与 Azure 数据资源管理器集成,使用户能够高效地分析和查询其遥测数据。借助此插件,用户可以配置摄取设置以满足其需求,并利用 Azure 强大的分析功能。
集成详情
OPC UA
OPC UA 插件从使用 OPC UA 协议通信的设备检索数据,使您可以收集和监控来自 OPC UA 服务器的数据。
Azure 数据资源管理器
Azure 数据资源管理器插件允许用户将从各种 Telegraf 输入插件收集的指标、日志和时间序列数据写入 Azure 数据资源管理器、Azure Synapse 和 Fabric 中的实时分析。此集成充当桥梁,使应用程序和服务能够高效地监控其性能指标或日志。Azure 数据资源管理器针对分析大量不同类型的数据进行了优化,使其成为云环境中实时分析和监控解决方案的绝佳选择。该插件使用户能够根据其需求配置指标摄取,动态定义表架构,并设置各种摄取方法,同时保留数据库操作所需的角色和权限方面的灵活性。这支持现代应用程序的可扩展和安全监控设置,这些应用程序利用云服务。
配置
OPC UA
[[inputs.opcua]]
## Metric name
# name = "opcua"
#
## OPC UA Endpoint URL
# endpoint = "opc.tcp://localhost:4840"
#
## Maximum time allowed to establish a connect to the endpoint.
# connect_timeout = "10s"
#
## Maximum time allowed for a request over the established connection.
# request_timeout = "5s"
# Maximum time that a session shall remain open without activity.
# session_timeout = "20m"
#
## Security policy, one of "None", "Basic128Rsa15", "Basic256",
## "Basic256Sha256", or "auto"
# security_policy = "auto"
#
## Security mode, one of "None", "Sign", "SignAndEncrypt", or "auto"
# security_mode = "auto"
#
## Path to cert.pem. Required when security mode or policy isn't "None".
## If cert path is not supplied, self-signed cert and key will be generated.
# certificate = "/etc/telegraf/cert.pem"
#
## Path to private key.pem. Required when security mode or policy isn't "None".
## If key path is not supplied, self-signed cert and key will be generated.
# private_key = "/etc/telegraf/key.pem"
#
## Authentication Method, one of "Certificate", "UserName", or "Anonymous". To
## authenticate using a specific ID, select 'Certificate' or 'UserName'
# auth_method = "Anonymous"
#
## Username. Required for auth_method = "UserName"
# username = ""
#
## Password. Required for auth_method = "UserName"
# password = ""
#
## Option to select the metric timestamp to use. Valid options are:
## "gather" -- uses the time of receiving the data in telegraf
## "server" -- uses the timestamp provided by the server
## "source" -- uses the timestamp provided by the source
# timestamp = "gather"
#
## Client trace messages
## When set to true, and debug mode enabled in the agent settings, the OPCUA
## client's messages are included in telegraf logs. These messages are very
## noisey, but essential for debugging issues.
# client_trace = false
#
## Include additional Fields in each metric
## Available options are:
## DataType -- OPC-UA Data Type (string)
# optional_fields = []
#
## Node ID configuration
## name - field name to use in the output
## namespace - OPC UA namespace of the node (integer value 0 thru 3)
## identifier_type - OPC UA ID type (s=string, i=numeric, g=guid, b=opaque)
## identifier - OPC UA ID (tag as shown in opcua browser)
## tags - extra tags to be added to the output metric (optional); deprecated in 1.25.0; use default_tags
## default_tags - extra tags to be added to the output metric (optional)
##
## Use either the inline notation or the bracketed notation, not both.
#
## Inline notation (default_tags not supported yet)
# nodes = [
# {name="", namespace="", identifier_type="", identifier="", tags=[["tag1", "value1"], ["tag2", "value2"]},
# {name="", namespace="", identifier_type="", identifier=""},
# ]
#
## Bracketed notation
# [[inputs.opcua.nodes]]
# name = "node1"
# namespace = ""
# identifier_type = ""
# identifier = ""
# default_tags = { tag1 = "value1", tag2 = "value2" }
#
# [[inputs.opcua.nodes]]
# name = "node2"
# namespace = ""
# identifier_type = ""
# identifier = ""
#
## Node Group
## Sets defaults so they aren't required in every node.
## Default values can be set for:
## * Metric name
## * OPC UA namespace
## * Identifier
## * Default tags
##
## Multiple node groups are allowed
#[[inputs.opcua.group]]
## Group Metric name. Overrides the top level name. If unset, the
## top level name is used.
# name =
#
## Group default namespace. If a node in the group doesn't set its
## namespace, this is used.
# namespace =
#
## Group default identifier type. If a node in the group doesn't set its
## namespace, this is used.
# identifier_type =
#
## Default tags that are applied to every node in this group. Can be
## overwritten in a node by setting a different value for the tag name.
## example: default_tags = { tag1 = "value1" }
# default_tags = {}
#
## Node ID Configuration. Array of nodes with the same settings as above.
## Use either the inline notation or the bracketed notation, not both.
#
## Inline notation (default_tags not supported yet)
# nodes = [
# {name="node1", namespace="", identifier_type="", identifier=""},
# {name="node2", namespace="", identifier_type="", identifier=""},
#]
#
## Bracketed notation
# [[inputs.opcua.group.nodes]]
# name = "node1"
# namespace = ""
# identifier_type = ""
# identifier = ""
# default_tags = { tag1 = "override1", tag2 = "value2" }
#
# [[inputs.opcua.group.nodes]]
# name = "node2"
# namespace = ""
# identifier_type = ""
# identifier = ""
## Enable workarounds required by some devices to work correctly
# [inputs.opcua.workarounds]
## Set additional valid status codes, StatusOK (0x0) is always considered valid
# additional_valid_status_codes = ["0xC0"]
# [inputs.opcua.request_workarounds]
## Use unregistered reads instead of registered reads
# use_unregistered_reads = false
Azure 数据资源管理器
[[outputs.azure_data_explorer]]
## The URI property of the Azure Data Explorer resource on Azure
## ex: endpoint_url = https://myadxresource.australiasoutheast.kusto.windows.net
endpoint_url = ""
## The Azure Data Explorer database that the metrics will be ingested into.
## The plugin will NOT generate this database automatically, it's expected that this database already exists before ingestion.
## ex: "exampledatabase"
database = ""
## Timeout for Azure Data Explorer operations
# timeout = "20s"
## Type of metrics grouping used when pushing to Azure Data Explorer.
## Default is "TablePerMetric" for one table per different metric.
## For more information, please check the plugin README.
# metrics_grouping_type = "TablePerMetric"
## Name of the single table to store all the metrics (Only needed if metrics_grouping_type is "SingleTable").
# table_name = ""
## Creates tables and relevant mapping if set to true(default).
## Skips table and mapping creation if set to false, this is useful for running Telegraf with the lowest possible permissions i.e. table ingestor role.
# create_tables = true
## Ingestion method to use.
## Available options are
## - managed -- streaming ingestion with fallback to batched ingestion or the "queued" method below
## - queued -- queue up metrics data and process sequentially
# ingestion_type = "queued"
输入和输出集成示例
OPC UA
-
基本配置:使用您的 OPC UA 服务器端点和所需的指标设置插件。这允许 Telegraf 开始从配置的节点收集指标。
-
节点 ID 设置:使用配置指定特定节点,例如温度传感器,以实时监控其值。例如,配置节点
ns=3;s=Temperature
以直接收集温度数据。 -
组配置:通过将多个节点分组在单个配置下,简化对多个节点的监控 - 这将为该组中的所有节点设置默认值,从而减少设置中的冗余。
Azure 数据资源管理器
-
实时监控仪表板:通过使用此插件将来自各种服务的指标集成到 Azure 数据资源管理器中,组织可以构建反映实时性能指标的综合仪表板。这使团队能够主动响应性能问题并优化系统健康状况,而不会延误。
-
集中式日志管理:利用 Azure 数据资源管理器整合来自多个应用程序和服务的日志。通过利用该插件,组织可以简化其日志分析流程,从而更轻松地搜索、过滤和从随着时间推移积累的历史数据中获取见解。
-
数据驱动的警报系统:通过根据通过此插件发送的指标配置警报来增强监控功能。组织可以设置阈值并自动化事件响应,从而显着减少停机时间并提高关键操作的可靠性。
-
机器学习模型训练:通过利用发送到 Azure 数据资源管理器的数据,组织可以执行大规模分析并准备数据以馈送到机器学习模型中。此插件支持构建可随后用于预测分析的数据结构,从而增强决策能力。
反馈
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强大的性能,无限的扩展能力
收集、组织和处理海量高速数据。当您将任何数据视为时间序列数据时,它都会更有价值。借助 InfluxDB,排名第一的旨在与 Telegraf 扩展的时间序列平台。
查看入门方法