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,它会更有价值。借助 InfluxDB,这是基于 Telegraf 构建的排名第一的时间序列平台,可进行扩展。
查看入门方法
输入和输出集成概述
此插件通过 gRPC 接收来自 OpenTelemetry 客户端和代理的跟踪、指标和日志,从而实现对应用程序的全面可观测性。
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。此插件为身份验证、数据组织和保留设置提供了各种配置选项。
集成详细信息
OpenTelemetry
OpenTelemetry 插件旨在通过 gRPC 接收来自客户端和代理的遥测数据,例如跟踪、指标和日志,这些客户端和代理实现了 OpenTelemetry。此插件启动一个 gRPC 服务,该服务侦听传入的遥测数据,这使其与按定义的间隔收集指标的标准插件不同。OpenTelemetry 生态系统通过提供一种供应商中立的方式来检测、生成、收集和导出遥测数据,从而帮助开发人员观察和了解其应用程序的性能。此插件的主要功能包括可自定义的连接超时、传入数据的可调整最大消息大小以及用于指定跨度、日志和配置文件维度以标记传入指标的选项。凭借这种灵活性,组织可以定制其遥测收集,以满足精确的可观测性要求,并确保数据无缝集成到 InfluxDB 等系统中。
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,并支持身份验证、数据组织和保留管理的灵活配置。它利用凭证链进行身份验证,允许各种方法,例如 Web 身份、承担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。一个关键功能是它能够处理多度量记录,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,该插件包括用于解决数据写入期间与 AWS 错误相关的常见问题的机制,例如用于限制的重试逻辑以及根据需要创建表的能力。
配置
OpenTelemetry
[[inputs.opentelemetry]]
## Override the default (0.0.0.0:4317) destination OpenTelemetry gRPC service
## address:port
# service_address = "0.0.0.0:4317"
## Override the default (5s) new connection timeout
# timeout = "5s"
## gRPC Maximum Message Size
# max_msg_size = "4MB"
## Override the default span attributes to be used as line protocol tags.
## These are always included as tags:
## - trace ID
## - span ID
## Common attributes can be found here:
## - https://github.com/open-telemetry/opentelemetry-collector/tree/main/semconv
# span_dimensions = ["service.name", "span.name"]
## Override the default log record attributes to be used as line protocol tags.
## These are always included as tags, if available:
## - trace ID
## - span ID
## Common attributes can be found here:
## - https://github.com/open-telemetry/opentelemetry-collector/tree/main/semconv
## When using InfluxDB for both logs and traces, be certain that log_record_dimensions
## matches the span_dimensions value.
# log_record_dimensions = ["service.name"]
## Override the default profile attributes to be used as line protocol tags.
## These are always included as tags, if available:
## - profile_id
## - address
## - sample
## - sample_name
## - sample_unit
## - sample_type
## - sample_type_unit
## Common attributes can be found here:
## - https://github.com/open-telemetry/opentelemetry-collector/tree/main/semconv
# profile_dimensions = []
## Override the default (prometheus-v1) metrics schema.
## Supports: "prometheus-v1", "prometheus-v2"
## For more information about the alternatives, read the Prometheus input
## plugin notes.
# metrics_schema = "prometheus-v1"
## Optional TLS Config.
## For advanced options: https://github.com/influxdata/telegraf/blob/v1.18.3/docs/TLS.md
##
## Set one or more allowed client CA certificate file names to
## enable mutually authenticated TLS connections.
# tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]
## Add service certificate and key.
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
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
OpenTelemetry
-
跨服务的统一监控:使用 OpenTelemetry 插件收集和整合来自 Kubernetes 环境中各种微服务的遥测数据。通过使用 OpenTelemetry 检测每个服务,您可以利用此插件收集应用程序性能和依赖关系的整体视图,从而更快地进行故障排除并提高复杂系统的可靠性。
-
通过跟踪增强调试:实施此插件以捕获流经多个服务的请求的端到端跟踪。例如,当用户发起触发多个后端服务的事务时,OpenTelemetry 插件可以记录详细的跟踪,突出显示性能瓶颈,从而为开发人员提供必要的见解来调试问题并优化其代码。
-
动态负载测试和性能监控:通过在模拟更高负载下收集实时指标和跟踪,在负载测试阶段利用此插件的功能。这种方法有助于评估应用程序组件的弹性,并抢先识别潜在的性能下降,从而确保在生产中获得流畅的用户体验。
-
用于实时监控的集成日志记录和指标:将 OpenTelemetry 插件与日志记录框架结合使用,以收集实时日志以及指标数据,从而创建一个功能强大的可观测性平台。例如,将其集成到 CI/CD 管道中以监控构建和部署,同时收集有助于实时诊断故障或性能问题的日志。
AWS Timestream
-
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执行应用程序性能的历史分析,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。
-
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尤其有用,使企业能够以最少的人工干预来维护其数据生命周期。
-
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见解,从而提高系统范围性能的可视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,它会更有价值。借助 InfluxDB,这是基于 Telegraf 构建的排名第一的时间序列平台,可进行扩展。
查看入门方法