目录
输入和输出集成概述
Kinesis 插件使您能够从 Kinesis 数据流中读取数据,支持各种数据格式和配置。
AWS Timestream Telegraf 插件户能够直接向 Amazon 的 Timestream 服务发送指标,该服务专为时序数据管理而设计。此插件提供了各种配置选项,用于身份验证、数据组织和保留设置。
集成详情
Kinesis
Kinesis Telegraf 插件旨在从 Amazon Kinesis 数据流中读取数据,使用户能够实时收集指标。作为服务输入插件,它通过监听传入数据而不是定期轮询来运行。配置指定了各种选项,包括 AWS 区域、流名称、身份验证凭据和数据格式。它支持跟踪未送达的消息以防止数据丢失,用户可以利用 DynamoDB 来维护上次处理记录的检查点。此插件对于需要可靠且可扩展的流处理以及其他监控需求的应用程序特别有用。
AWS Timestream
此插件旨在高效地将指标写入 Amazon 的 Timestream 服务,这是一种为物联网和运营应用程序优化的时序数据库。借助此插件,Telegraf 可以发送从各种来源收集的数据,并支持灵活的配置,用于身份验证、数据组织和保留管理。它利用凭证链进行身份验证,允许各种方法,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,并控制磁存储和内存存储的保留期限等方面。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,该插件包括用于解决与数据写入期间 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
Kinesis
# Configuration for the AWS Kinesis input.
[[inputs.kinesis_consumer]]
## Amazon REGION of kinesis endpoint.
region = "ap-southeast-2"
## 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 = ""
## Kinesis StreamName must exist prior to starting telegraf.
streamname = "StreamName"
## Shard iterator type (only 'TRIM_HORIZON' and 'LATEST' currently supported)
# shard_iterator_type = "TRIM_HORIZON"
## Max undelivered messages
## This plugin uses tracking metrics, which ensure messages are read to
## outputs before acknowledging them to the original broker to ensure data
## is not lost. This option sets the maximum messages to read from the
## broker that have not been written by an output.
##
## This value needs to be picked with awareness of the agent's
## metric_batch_size value as well. Setting max undelivered messages too high
## can result in a constant stream of data batches to the output. While
## setting it too low may never flush the broker's messages.
# max_undelivered_messages = 1000
## Data format to consume.
## Each data format has its own unique set of configuration options, read
## more about them here:
## https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md
data_format = "influx"
##
## The content encoding of the data from kinesis
## If you are processing a cloudwatch logs kinesis stream then set this to "gzip"
## as AWS compresses cloudwatch log data before it is sent to kinesis (aws
## also base64 encodes the zip byte data before pushing to the stream. The base64 decoding
## is done automatically by the golang sdk, as data is read from kinesis)
##
# content_encoding = "identity"
## Optional
## Configuration for a dynamodb checkpoint
[inputs.kinesis_consumer.checkpoint_dynamodb]
## unique name for this consumer
app_name = "default"
table_name = "default"
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
Kinesis
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使用 Kinesis 进行实时数据处理:此用例涉及将 Kinesis 插件与监控仪表板集成,以实时分析传入的数据指标。例如,应用程序可以从多个服务消耗日志,并以可视化方式呈现它们,使运营团队能够快速识别趋势并对发生的异常做出反应。
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无服务器日志聚合:在无服务器架构中使用此插件,其中 Kinesis 流聚合来自各种微服务的日志。该插件可以创建有助于检测系统中问题的指标,通过第三方集成自动化警报流程,使团队能够最大限度地减少停机时间并提高可靠性。
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基于流指标的动态扩展:实施一种解决方案,其中 Kinesis 插件消耗的流指标可用于动态调整资源。例如,如果处理的记录数量激增,则可以触发相应的扩展操作来处理增加的负载,从而确保最佳的资源分配和性能。
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带检查点的 S3 数据管道:创建一个强大的数据管道,其中 Kinesis 流数据通过 Telegraf Kinesis 插件处理,检查点存储在 DynamoDB 中。这种方法可以确保数据一致性和可靠性,因为它管理已处理数据的状态,从而实现与下游数据湖或存储解决方案的无缝集成。
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序格式,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:利用 Timestream 和应用程序监控工具一起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,并根据随时间查看的使用模式优化资源分配。
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自动化数据归档:配置 Timestream 插件以将数据写入 Timestream,同时管理保留期限。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:使用 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,从而提高对系统范围性能的可见性并促进跨应用程序故障排除。
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