目录
输入和输出集成概述
StatsD 输入插件通过在后台运行侦听器服务,从 StatsD 服务器捕获指标,从而实现全面的性能监控和指标聚合。
此插件将 Telegraf 指标保存到 Apache IoTDB 后端,支持会话连接和数据插入。
集成详情
StatsD
StatsD 输入插件旨在通过在 Telegraf 处于活动状态时运行后台 StatsD 侦听器服务,从 StatsD 服务器收集指标。此插件利用原始 Etsy 实现建立的 StatsD 消息格式,该格式允许各种类型的指标,包括仪表、计数器、集合、计时、直方图和分布。StatsD 插件的功能扩展到解析标签和扩展标准协议,以适应 InfluxDB 的标记系统。它可以处理通过不同协议(UDP 或 TCP)发送的消息,有效地管理多个指标,并为最佳指标处理提供高级配置,例如百分位数计算和数据转换模板。这种灵活性使户能够全面跟踪应用程序性能,使其成为强大的监控设置的必备工具。
IoTDB
Apache IoTDB(物联网数据库)是一种物联网原生数据库,具有高性能的数据管理和分析能力,可部署在边缘和云端。其轻量级架构、高性能和丰富的功能集使其非常适合物联网工业领域的大规模数据存储、高速数据摄取和复杂分析。IoTDB 与 Apache Hadoop、Spark 和 Flink 深度集成,进一步增强了其处理大规模数据和复杂处理任务的能力。
配置
StatsD
[[inputs.statsd]]
## Protocol, must be "tcp", "udp4", "udp6" or "udp" (default=udp)
protocol = "udp"
## MaxTCPConnection - applicable when protocol is set to tcp (default=250)
max_tcp_connections = 250
## Enable TCP keep alive probes (default=false)
tcp_keep_alive = false
## Specifies the keep-alive period for an active network connection.
## Only applies to TCP sockets and will be ignored if tcp_keep_alive is false.
## Defaults to the OS configuration.
# tcp_keep_alive_period = "2h"
## Address and port to host UDP listener on
service_address = ":8125"
## The following configuration options control when telegraf clears it's cache
## of previous values. If set to false, then telegraf will only clear it's
## cache when the daemon is restarted.
## Reset gauges every interval (default=true)
delete_gauges = true
## Reset counters every interval (default=true)
delete_counters = true
## Reset sets every interval (default=true)
delete_sets = true
## Reset timings & histograms every interval (default=true)
delete_timings = true
## Enable aggregation temporality adds temporality=delta or temporality=commulative tag, and
## start_time field, which adds the start time of the metric accumulation.
## You should use this when using OpenTelemetry output.
# enable_aggregation_temporality = false
## Percentiles to calculate for timing & histogram stats.
percentiles = [50.0, 90.0, 99.0, 99.9, 99.95, 100.0]
## separator to use between elements of a statsd metric
metric_separator = "_"
## Parses tags in the datadog statsd format
## http://docs.datadoghq.com/guides/dogstatsd/
## deprecated in 1.10; use datadog_extensions option instead
parse_data_dog_tags = false
## Parses extensions to statsd in the datadog statsd format
## currently supports metrics and datadog tags.
## http://docs.datadoghq.com/guides/dogstatsd/
datadog_extensions = false
## Parses distributions metric as specified in the datadog statsd format
## https://docs.datadoghq.com/developers/metrics/types/?tab=distribution#definition
datadog_distributions = false
## Keep or drop the container id as tag. Included as optional field
## in DogStatsD protocol v1.2 if source is running in Kubernetes
## https://docs.datadoghq.com/developers/dogstatsd/datagram_shell/?tab=metrics#dogstatsd-protocol-v12
datadog_keep_container_tag = false
## Statsd data translation templates, more info can be read here:
## https://github.com/influxdata/telegraf/blob/master/docs/TEMPLATE_PATTERN.md
# templates = [
# "cpu.* measurement*"
# ]
## Number of UDP messages allowed to queue up, once filled,
## the statsd server will start dropping packets
allowed_pending_messages = 10000
## Number of worker threads used to parse the incoming messages.
# number_workers_threads = 5
## Number of timing/histogram values to track per-measurement in the
## calculation of percentiles. Raising this limit increases the accuracy
## of percentiles but also increases the memory usage and cpu time.
percentile_limit = 1000
## Maximum socket buffer size in bytes, once the buffer fills up, metrics
## will start dropping. Defaults to the OS default.
# read_buffer_size = 65535
## Max duration (TTL) for each metric to stay cached/reported without being updated.
# max_ttl = "10h"
## Sanitize name method
## By default, telegraf will pass names directly as they are received.
## However, upstream statsd now does sanitization of names which can be
## enabled by using the "upstream" method option. This option will a) replace
## white space with '_', replace '/' with '-', and remove characters not
## matching 'a-zA-Z_\-0-9\.;='.
#sanitize_name_method = ""
## Replace dots (.) with underscore (_) and dashes (-) with
## double underscore (__) in metric names.
# convert_names = false
## Convert all numeric counters to float
## Enabling this would ensure that both counters and guages are both emitted
## as floats.
# float_counters = false
IoTDB
[[outputs.iotdb]]
## Configuration of IoTDB server connection
host = "127.0.0.1"
# port = "6667"
## Configuration of authentication
# user = "root"
# password = "root"
## Timeout to open a new session.
## A value of zero means no timeout.
# timeout = "5s"
## Configuration of type conversion for 64-bit unsigned int
## IoTDB currently DOES NOT support unsigned integers (version 13.x).
## 32-bit unsigned integers are safely converted into 64-bit signed integers by the plugin,
## however, this is not true for 64-bit values in general as overflows may occur.
## The following setting allows to specify the handling of 64-bit unsigned integers.
## Available values are:
## - "int64" -- convert to 64-bit signed integers and accept overflows
## - "int64_clip" -- convert to 64-bit signed integers and clip the values on overflow to 9,223,372,036,854,775,807
## - "text" -- convert to the string representation of the value
# uint64_conversion = "int64_clip"
## Configuration of TimeStamp
## TimeStamp is always saved in 64bits int. timestamp_precision specifies the unit of timestamp.
## Available value:
## "second", "millisecond", "microsecond", "nanosecond"(default)
# timestamp_precision = "nanosecond"
## Handling of tags
## Tags are not fully supported by IoTDB.
## A guide with suggestions on how to handle tags can be found here:
## https://iotdb.apache.org/UserGuide/Master/API/InfluxDB-Protocol.html
##
## Available values are:
## - "fields" -- convert tags to fields in the measurement
## - "device_id" -- attach tags to the device ID
##
## For Example, a metric named "root.sg.device" with the tags `tag1: "private"` and `tag2: "working"` and
## fields `s1: 100` and `s2: "hello"` will result in the following representations in IoTDB
## - "fields" -- root.sg.device, s1=100, s2="hello", tag1="private", tag2="working"
## - "device_id" -- root.sg.device.private.working, s1=100, s2="hello"
# convert_tags_to = "device_id"
## Handling of unsupported characters
## Some characters in different versions of IoTDB are not supported in path name
## A guide with suggetions on valid paths can be found here:
## for iotdb 0.13.x -> https://iotdb.apache.org/UserGuide/V0.13.x/Reference/Syntax-Conventions.html#identifiers
## for iotdb 1.x.x and above -> https://iotdb.apache.org/UserGuide/V1.3.x/User-Manual/Syntax-Rule.html#identifier
##
## Available values are:
## - "1.0", "1.1", "1.2", "1.3" -- enclose in `` the world having forbidden character
## such as @ $ # : [ ] { } ( ) space
## - "0.13" -- enclose in `` the world having forbidden character
## such as space
##
## Keep this section commented if you don't want to sanitize the path
# sanitize_tag = "1.3"
输入和输出集成示例
StatsD
-
实时应用程序性能监控:利用 StatsD 输入插件实时监控应用程序性能指标。通过配置您的应用程序向 StatsD 服务器发送各种指标,团队可以利用此插件动态分析性能瓶颈、跟踪用户活动并确保资源优化。历史和实时指标的结合允许主动故障排除并增强问题解决过程的响应能力。
-
跟踪 Web 应用程序中的用户参与度指标:使用 StatsD 插件收集用户参与度统计信息,例如页面浏览量、点击事件和交互时间。通过将这些指标发送到 StatsD 服务器,企业可以深入了解用户行为,从而能够根据定量反馈做出数据驱动的决策,以改善用户体验和界面设计。这可以显着提高营销策略和产品开发工作的有效性。
-
基础设施健康监控:部署 StatsD 插件,通过跟踪资源利用率、服务器响应时间和网络性能等指标来监控服务器基础设施的健康状况。通过这种设置,DevOps 团队可以详细了解系统性能,有效预测问题,防止问题升级。这使得能够采取主动的基础设施管理方法,最大限度地减少停机时间并确保最佳服务交付。
-
创建全面的服务仪表板:将 StatsD 与可视化工具集成,以创建全面的仪表板,反映整个架构中服务的状态和健康状况。例如,将通过 StatsD 记录的多个服务的数据组合在一起,可以将原始指标转换为可操作的见解,从而显示系统性能随时间变化的趋势。此功能使利益相关者能够保持监督,并根据可视化数据集驱动决策,从而提高整体运营透明度。
IoTDB
-
实时物联网监控:利用 IoTDB 插件从各种物联网设备收集传感器数据,并将其保存在 Apache IoTDB 后端,从而方便实时监控温度和湿度等环境条件。此用例使组织能够分析随时间变化的趋势,并根据历史数据做出明智的决策,同时还可以利用 IoTDB 的高效存储和查询功能。
-
智慧农业数据收集:使用 IoTDB 插件从部署在田野中的智慧农业传感器收集指标。通过将湿度水平、养分含量和大气条件传输到 IoTDB,农民可以详细了解最佳种植和浇水时间表,从而提高作物产量和资源管理水平。
-
能源消耗分析:利用 IoTDB 插件跟踪公用事业网络中智能电表的能源消耗指标。这种集成使分析能够识别使用高峰并预测未来消耗模式,最终支持节能措施和改进的公用事业管理。
-
自动化工业设备监控:使用此插件收集制造工厂中机械设备的操作指标,并将其存储在 IoTDB 中以进行分析。此设置可以帮助识别效率低下、预测性维护需求和操作异常,从而确保最佳性能并最大限度地减少意外停机时间。
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