目录
输入和输出集成概述
gNMI(gRPC 网络管理接口)输入插件使用 gNMI Subscribe 方法从网络设备收集遥测数据。它支持 TLS 以实现安全身份验证和数据传输。
Azure Data Explorer 插件允许将指标收集与 Azure Data Explorer 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 强大的分析能力。
集成详情
gNMI
此输入插件与供应商无关,可以与任何支持 gNMI 规范的平台一起使用。它基于 gNMI Subscribe 方法使用遥测数据,从而可以实时监控网络设备。
Azure Data Explorer
Azure Data Explorer 插件允许用户将从各种 Telegraf 输入插件收集的指标、日志和时序数据写入 Azure Data Explorer、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有效地监控其性能指标或日志。Azure Data Explorer 针对对大量不同数据类型进行分析进行了优化,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。这为利用云服务的现代应用程序支持可扩展且安全的监控设置。
配置
gNMI
[[inputs.gnmi]]
## Address and port of the gNMI GRPC server
addresses = ["10.49.234.114:57777"]
## define credentials
username = "cisco"
password = "cisco"
## gNMI encoding requested (one of: "proto", "json", "json_ietf", "bytes")
# encoding = "proto"
## redial in case of failures after
# redial = "10s"
## gRPC Keepalive settings
## See https://pkg.go.dev/google.golang.org/grpc/keepalive
## The client will ping the server to see if the transport is still alive if it has
## not see any activity for the given time.
## If not set, none of the keep-alive setting (including those below) will be applied.
## If set and set below 10 seconds, the gRPC library will apply a minimum value of 10s will be used instead.
# keepalive_time = ""
## Timeout for seeing any activity after the keep-alive probe was
## sent. If no activity is seen the connection is closed.
# keepalive_timeout = ""
## gRPC Maximum Message Size
# max_msg_size = "4MB"
## Enable to get the canonical path as field-name
# canonical_field_names = false
## Remove leading slashes and dots in field-name
# trim_field_names = false
## Guess the path-tag if an update does not contain a prefix-path
## Supported values are
## none -- do not add a 'path' tag
## common path -- use the common path elements of all fields in an update
## subscription -- use the subscription path
# path_guessing_strategy = "none"
## Prefix tags from path keys with the path element
# prefix_tag_key_with_path = false
## Optional client-side TLS to authenticate the device
## 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
## gNMI subscription prefix (optional, can usually be left empty)
## See: https://github.com/openconfig/reference/blob/master/rpc/gnmi/gnmi-specification.md#222-paths
# origin = ""
# prefix = ""
# target = ""
## Vendor specific options
## This defines what vendor specific options to load.
## * Juniper Header Extension (juniper_header): some sensors are directly managed by
## Linecard, which adds the Juniper GNMI Header Extension. Enabling this
## allows the decoding of the Extension header if present. Currently this knob
## adds component, component_id & sub_component_id as additional tags
# vendor_specific = []
## YANG model paths for decoding IETF JSON payloads
## Model files are loaded recursively from the given directories. Disabled if
## no models are specified.
# yang_model_paths = []
## Define additional aliases to map encoding paths to measurement names
# [inputs.gnmi.aliases]
# ifcounters = "openconfig:/interfaces/interface/state/counters"
[[inputs.gnmi.subscription]]
## Name of the measurement that will be emitted
name = "ifcounters"
## Origin and path of the subscription
## See: https://github.com/openconfig/reference/blob/master/rpc/gnmi/gnmi-specification.md#222-paths
##
## origin usually refers to a (YANG) data model implemented by the device
## and path to a specific substructure inside it that should be subscribed
## to (similar to an XPath). YANG models can be found e.g. here:
## https://github.com/YangModels/yang/tree/master/vendor/cisco/xr
origin = "openconfig-interfaces"
path = "/interfaces/interface/state/counters"
## Subscription mode ("target_defined", "sample", "on_change") and interval
subscription_mode = "sample"
sample_interval = "10s"
## Suppress redundant transmissions when measured values are unchanged
# suppress_redundant = false
## If suppression is enabled, send updates at least every X seconds anyway
# heartbeat_interval = "60s"
Azure Data Explorer
[[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
gNMI
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监控 Cisco 设备:使用 gNMI 插件从 Cisco IOS XR、NX-OS 或 IOS XE 设备收集遥测数据以进行性能监控。
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实时网络洞察:借助 gNMI 插件,网络管理员可以深入了解实时指标,例如接口统计信息和 CPU 使用率。
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安全数据收集:配置带有 TLS 设置的 gNMI 插件,以确保在从设备收集敏感遥测数据时进行安全通信。
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灵活的数据处理:使用订阅选项自定义您要根据特定需求或要求收集的遥测数据。
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错误处理:该插件包括故障排除选项,用于处理常见问题,例如缺少指标名称或 TLS 握手失败。
Azure Data Explorer
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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 Data Explorer 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立即优化系统健康状况。
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集中式日志管理:利用 Azure Data Explorer 来整合来自多个应用程序和服务的日志。通过使用该插件,组织可以简化其日志分析流程,从而更轻松地搜索、过滤和从随时间积累的历史数据中获得见解。
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数据驱动的警报系统:通过基于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执行事件响应,从而显着减少停机时间并提高关键操作的可靠性。
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机器学习模型训练:通过利用发送到 Azure Data Explorer 的数据,组织可以执行大规模分析并准备数据以供输入到机器学习模型中。此插件能够构建数据结构,随后可用于预测分析,从而增强决策能力。
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