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,它的价值都会更高。 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
Docker 输入插件允许您使用 Docker Engine API 从 Docker 容器收集指标,从而增强容器化应用程序的可视性和监控。
AWS Timestream Telegraf 插件使用户能够将指标直接发送到 Amazon 的 Timestream 服务,该服务专为时间序列数据管理而设计。 该插件为身份验证、数据组织和保留设置提供了各种配置选项。
集成详情
Docker
Telegraf 的 Docker 输入插件从 Docker Engine API 收集有价值的指标,从而深入了解正在运行的容器。 此插件利用官方 Docker 客户端与 Engine API 交互,允许用户监控各种容器状态、资源分配和性能指标。 该插件具有按名称和状态过滤容器的选项,以及可自定义的标签和标签,支持在各种环境中监控容器化应用程序的灵活性,无论是在本地系统上还是在 Kubernetes 等编排平台中。 此外,它还通过要求访问 Docker 守护进程的权限来解决安全问题,并强调在容器化环境中部署时进行正确的配置。
AWS Timestream
此插件旨在高效地将指标写入 Amazon 的 Timestream 服务,这是一种针对物联网和运营应用程序优化的时间序列数据库。 借助此插件,Telegraf 可以发送从各种来源收集的数据,并支持灵活的身份验证、数据组织和保留管理配置。 它使用凭证链进行身份验证,允许各种方法,例如 Web 身份、承担的角色和共享配置文件。 用户可以定义指标在 Timestream 中的组织方式——是使用单个表还是多个表,以及控制磁存储和内存存储的保留期等方面。 一个关键特性是它能够处理多指标记录,从而实现高效的数据摄取并有助于减少多次写入的开销。 在错误处理方面,该插件包含用于解决数据写入期间与 AWS 错误相关的常见问题的机制,例如针对节流的重试逻辑以及根据需要创建表的功能。
配置
Docker
[[inputs.docker]]
## Docker Endpoint
## To use TCP, set endpoint = "tcp://[ip]:[port]"
## To use environment variables (ie, docker-machine), set endpoint = "ENV"
endpoint = "unix:///var/run/docker.sock"
## Set to true to collect Swarm metrics(desired_replicas, running_replicas)
## Note: configure this in one of the manager nodes in a Swarm cluster.
## configuring in multiple Swarm managers results in duplication of metrics.
gather_services = false
## Only collect metrics for these containers. Values will be appended to
## container_name_include.
## Deprecated (1.4.0), use container_name_include
container_names = []
## Set the source tag for the metrics to the container ID hostname, eg first 12 chars
source_tag = false
## Containers to include and exclude. Collect all if empty. Globs accepted.
container_name_include = []
container_name_exclude = []
## Container states to include and exclude. Globs accepted.
## When empty only containers in the "running" state will be captured.
# container_state_include = []
# container_state_exclude = []
## Objects to include for disk usage query
## Allowed values are "container", "image", "volume"
## When empty disk usage is excluded
storage_objects = []
## Timeout for docker list, info, and stats commands
timeout = "5s"
## Whether to report for each container per-device blkio (8:0, 8:1...),
## network (eth0, eth1, ...) and cpu (cpu0, cpu1, ...) stats or not.
## Usage of this setting is discouraged since it will be deprecated in favor of 'perdevice_include'.
## Default value is 'true' for backwards compatibility, please set it to 'false' so that 'perdevice_include' setting
## is honored.
perdevice = true
## Specifies for which classes a per-device metric should be issued
## Possible values are 'cpu' (cpu0, cpu1, ...), 'blkio' (8:0, 8:1, ...) and 'network' (eth0, eth1, ...)
## Please note that this setting has no effect if 'perdevice' is set to 'true'
# perdevice_include = ["cpu"]
## Whether to report for each container total blkio and network stats or not.
## Usage of this setting is discouraged since it will be deprecated in favor of 'total_include'.
## Default value is 'false' for backwards compatibility, please set it to 'true' so that 'total_include' setting
## is honored.
total = false
## Specifies for which classes a total metric should be issued. Total is an aggregated of the 'perdevice' values.
## Possible values are 'cpu', 'blkio' and 'network'
## Total 'cpu' is reported directly by Docker daemon, and 'network' and 'blkio' totals are aggregated by this plugin.
## Please note that this setting has no effect if 'total' is set to 'false'
# total_include = ["cpu", "blkio", "network"]
## docker labels to include and exclude as tags. Globs accepted.
## Note that an empty array for both will include all labels as tags
docker_label_include = []
docker_label_exclude = []
## Which environment variables should we use as a tag
tag_env = ["JAVA_HOME", "HEAP_SIZE"]
## Optional TLS Config
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
AWS Timestream
[[outputs.timestream]]
## Amazon Region
region = "us-east-1"
## Amazon Credentials
## Credentials are loaded in the following order:
## 1) Web identity provider credentials via STS if role_arn and web_identity_token_file are specified
## 2) Assumed credentials via STS if role_arn is specified
## 3) explicit credentials from 'access_key' and 'secret_key'
## 4) shared profile from 'profile'
## 5) environment variables
## 6) shared credentials file
## 7) EC2 Instance Profile
#access_key = ""
#secret_key = ""
#token = ""
#role_arn = ""
#web_identity_token_file = ""
#role_session_name = ""
#profile = ""
#shared_credential_file = ""
## Endpoint to make request against, the correct endpoint is automatically
## determined and this option should only be set if you wish to override the
## default.
## ex: endpoint_url = "http://localhost:8000"
# endpoint_url = ""
## Timestream database where the metrics will be inserted.
## The database must exist prior to starting Telegraf.
database_name = "yourDatabaseNameHere"
## Specifies if the plugin should describe the Timestream database upon starting
## to validate if it has access necessary permissions, connection, etc., as a safety check.
## If the describe operation fails, the plugin will not start
## and therefore the Telegraf agent will not start.
describe_database_on_start = false
## Specifies how the data is organized in Timestream.
## Valid values are: single-table, multi-table.
## When mapping_mode is set to single-table, all of the data is stored in a single table.
## When mapping_mode is set to multi-table, the data is organized and stored in multiple tables.
## The default is multi-table.
mapping_mode = "multi-table"
## Specifies if the plugin should create the table, if the table does not exist.
create_table_if_not_exists = true
## Specifies the Timestream table magnetic store retention period in days.
## Check Timestream documentation for more details.
## NOTE: This property is valid when create_table_if_not_exists = true.
create_table_magnetic_store_retention_period_in_days = 365
## Specifies the Timestream table memory store retention period in hours.
## Check Timestream documentation for more details.
## NOTE: This property is valid when create_table_if_not_exists = true.
create_table_memory_store_retention_period_in_hours = 24
## Specifies how the data is written into Timestream.
## Valid values are: true, false
## When use_multi_measure_records is set to true, all of the tags and fields are stored
## as a single row in a Timestream table.
## When use_multi_measure_record is set to false, Timestream stores each field in a
## separate table row, thereby storing the tags multiple times (once for each field).
## The recommended setting is true.
## The default is false.
use_multi_measure_records = "false"
## Specifies the measure_name to use when sending multi-measure records.
## NOTE: This property is valid when use_multi_measure_records=true and mapping_mode=multi-table
measure_name_for_multi_measure_records = "telegraf_measure"
## Specifies the name of the table to write data into
## NOTE: This property is valid when mapping_mode=single-table.
# single_table_name = ""
## Specifies the name of dimension when all of the data is being stored in a single table
## and the measurement name is transformed into the dimension value
## (see Mapping data from Influx to Timestream for details)
## NOTE: This property is valid when mapping_mode=single-table.
# single_table_dimension_name_for_telegraf_measurement_name = "namespace"
## Only valid and optional if create_table_if_not_exists = true
## Specifies the Timestream table tags.
## Check Timestream documentation for more details
# create_table_tags = { "foo" = "bar", "environment" = "dev"}
## Specify the maximum number of parallel go routines to ingest/write data
## If not specified, defaulted to 1 go routines
max_write_go_routines = 25
## Please see README.md to know how line protocol data is mapped to Timestream
##
输入和输出集成示例
Docker
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监控容器化应用程序的性能:使用 Docker 输入插件来跟踪 Docker 容器中运行的应用程序的 CPU、内存、磁盘 I/O 和网络活动。 通过收集这些指标,DevOps 团队可以主动管理资源分配、排除性能瓶颈,并确保跨不同环境的最佳应用程序性能。
-
与 Kubernetes 集成:利用此插件收集由 Kubernetes 编排的 Docker 容器的指标。 通过过滤掉不必要的 Kubernetes 标签并专注于关键指标,团队可以简化其监控解决方案并创建仪表板,以深入了解 Kubernetes 集群中运行的微服务的整体运行状况。
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容量规划和资源优化:使用 Docker 输入插件收集的指标来执行 Docker 部署的容量规划。 分析使用模式有助于识别未充分利用的资源和过度配置的容器,从而指导根据实际使用趋势进行纵向或横向扩展的决策。
-
容器异常的自动警报:根据通过 Docker 插件收集的指标设置警报规则,以通知团队资源使用量异常激增或服务中断。 这种主动监控方法有助于维护服务可靠性并优化容器化应用程序的性能。
AWS Timestream
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物联网数据指标:使用 Timestream 插件将来自物联网设备的实时指标发送到 Timestream,从而可以快速分析和可视化传感器数据。 通过将设备读数组织成时间序列格式,用户可以跟踪趋势、识别异常并根据设备性能简化运营决策。
-
应用程序性能监控:将 Timestream 与应用程序监控工具结合使用,以发送有关服务性能随时间变化的指标。 这种集成使工程师能够对应用程序性能执行历史分析,将其与业务指标相关联,并根据随时间推移的使用模式优化资源分配。
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自动数据归档:配置 Timestream 插件以将数据写入 Timestream,同时管理保留期。 此设置可以自动化归档策略,确保根据预定义的标准保留旧数据。 这对于合规性和历史分析特别有用,使企业能够以最少的人工干预来维护其数据生命周期。
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多应用程序指标聚合:利用 Timestream 插件将来自多个应用程序的指标聚合到 Timestream 中。 通过创建性能指标的统一数据库,组织可以获得跨各种服务的整体洞察力,从而提高对全系统性能的可见性并促进跨应用程序故障排除。
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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,它的价值都会更高。 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