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,它都更有价值。借助 InfluxDB,这个 #1 的时间序列平台旨在与 Telegraf 一起扩展。
查看入门方法
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,用于安全身份验证和数据传输。
Redis 插件使用户能够将 Telegraf 收集的指标直接发送到 Redis。此集成非常适合需要强大的时间序列数据存储和分析的应用程序。
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。
Redis
Redis Telegraf 插件旨在将指标写入 RedisTimeSeries,这是一个专为时间序列数据设计的专用 Redis 数据库模块。此插件促进了 Telegraf 与 RedisTimeSeries 的集成,从而可以高效地存储和检索带时间戳的数据。借助 RedisTimeSeries,用户可以利用增强的功能来管理时间序列数据,包括聚合视图和范围查询。该插件提供了各种配置选项,以实现安全连接到 Redis 数据库所需的灵活性,包括对身份验证、超时、数据类型转换和 TLS 配置的支持。底层技术利用了 Redis 的效率和可扩展性,使其成为高容量指标环境的绝佳选择,在这些环境中,实时处理至关重要。
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"
Redis
[[outputs.redistimeseries]]
## The address of the RedisTimeSeries server.
address = "127.0.0.1:6379"
## Redis ACL credentials
# username = ""
# password = ""
# database = 0
## Timeout for operations such as ping or sending metrics
# timeout = "10s"
## Enable attempt to convert string fields to numeric values
## If "false" or in case the string value cannot be converted the string
## field will be dropped.
# convert_string_fields = true
## Optional TLS Config
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
# insecure_skip_verify = false
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。
-
灵活的数据处理:使用订阅选项自定义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。
Redis
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监控物联网传感器数据:利用 Redis Telegraf 插件实时收集和存储来自物联网传感器的数据。通过将插件连接到 RedisTimeSeries 数据库,用户可以分析温度、湿度或其他环境因素的趋势。有效查询历史传感器数据的能力将有助于预测性维护并帮助进行资源管理。
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金融市场数据聚合:使用此插件跟踪和存储来自各种来源的时间敏感型金融数据。通过将指标发送到 Redis,金融机构可以聚合和分析市场趋势或价格随时间的变化,从而为他们提供从可靠的时间序列分析中获得的可操作的见解。
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应用程序性能监控 (APM):实施 Redis 插件以收集应用程序性能指标,例如响应时间和 CPU 使用率。用户可以使用 RedisTimeSeries 可视化其应用程序随时间的性能,从而使他们能够快速识别瓶颈并优化资源分配。
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能源消耗跟踪:利用此插件来监控建筑物随时间的能源使用情况。通过与智能电表集成并将数据发送到 RedisTimeSeries,市政当局或企业可以分析能源消耗模式,从而帮助实施节能措施和可持续发展实践。
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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,它都更有价值。借助 InfluxDB,这个 #1 的时间序列平台旨在与 Telegraf 一起扩展。
查看入门方法