目录
强大的性能,无限的扩展
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。 使用 InfluxDB,第一的时间序列平台,旨在与 Telegraf 一起扩展。
查看入门方法
输入和输出集成概述
Azure Event Hubs 输入插件允许 Telegraf 从 Azure Event Hubs 和 Azure IoT Hub 消费数据,从而能够高效地处理数据并监控来自这些云服务的事件流。
Telegraf 的 SQL 插件使用简单的表模式和动态列生成将收集的指标发送到 SQL 数据库。 当配置为 ClickHouse 时,它会调整 DSN 格式和类型转换设置,以确保无缝的数据集成。
集成详情
Azure Event Hubs
此插件充当 Azure Event Hubs 和 Azure IoT Hub 的消费者,允许用户有效地从这些平台摄取数据流。Azure Event Hubs 是一个高度可扩展的数据流平台和事件摄取服务,能够每秒接收和处理数百万个事件,而 Azure IoT Hub 支持 IoT 应用中安全的设备到云和云到设备通信。Event Hubs 输入插件与这些服务无缝交互,提供可靠的消息消费和流处理能力。主要功能包括消费者组的动态管理、防止数据丢失的消息跟踪以及用于预取计数、用户代理和元数据处理的可自定义设置。此插件旨在支持各种用例,包括实时遥测数据收集、IoT 数据处理以及与更广泛 Azure 生态系统中的各种数据分析和监控工具集成。
Clickhouse
Telegraf 的 SQL 插件旨在通过基于传入指标动态创建表和列,将指标数据写入 SQL 数据库。当配置为 ClickHouse 时,它使用 clickhouse-go v1.5.4 驱动程序,该驱动程序采用独特的 DSN 格式和一组专门的类型转换规则,将 Telegraf 的数据类型直接映射到 ClickHouse 的本机类型。这种方法确保了高吞吐量环境中的最佳存储和检索性能,使其非常适合实时分析和大规模数据仓库。动态模式创建和精确的类型映射实现了详细的时间序列数据日志记录,这对于监控现代分布式系统至关重要。
配置
Azure Event Hubs
[[inputs.eventhub_consumer]]
## The default behavior is to create a new Event Hub client from environment variables.
## This requires one of the following sets of environment variables to be set:
##
## 1) Expected Environment Variables:
## - "EVENTHUB_CONNECTION_STRING"
##
## 2) Expected Environment Variables:
## - "EVENTHUB_NAMESPACE"
## - "EVENTHUB_NAME"
## - "EVENTHUB_KEY_NAME"
## - "EVENTHUB_KEY_VALUE"
## 3) Expected Environment Variables:
## - "EVENTHUB_NAMESPACE"
## - "EVENTHUB_NAME"
## - "AZURE_TENANT_ID"
## - "AZURE_CLIENT_ID"
## - "AZURE_CLIENT_SECRET"
## Uncommenting the option below will create an Event Hub client based solely on the connection string.
## This can either be the associated environment variable or hard coded directly.
## If this option is uncommented, environment variables will be ignored.
## Connection string should contain EventHubName (EntityPath)
# connection_string = ""
## Set persistence directory to a valid folder to use a file persister instead of an in-memory persister
# persistence_dir = ""
## Change the default consumer group
# consumer_group = ""
## By default the event hub receives all messages present on the broker, alternative modes can be set below.
## The timestamp should be in https://github.com/toml-lang/toml#offset-date-time format (RFC 3339).
## The 3 options below only apply if no valid persister is read from memory or file (e.g. first run).
# from_timestamp =
# latest = true
## Set a custom prefetch count for the receiver(s)
# prefetch_count = 1000
## Add an epoch to the receiver(s)
# epoch = 0
## Change to set a custom user agent, "telegraf" is used by default
# user_agent = "telegraf"
## To consume from a specific partition, set the partition_ids option.
## An empty array will result in receiving from all partitions.
# partition_ids = ["0","1"]
## 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
## Set either option below to true to use a system property as timestamp.
## You have the choice between EnqueuedTime and IoTHubEnqueuedTime.
## It is recommended to use this setting when the data itself has no timestamp.
# enqueued_time_as_ts = true
# iot_hub_enqueued_time_as_ts = true
## Tags or fields to create from keys present in the application property bag.
## These could for example be set by message enrichments in Azure IoT Hub.
# application_property_tags = []
# application_property_fields = []
## Tag or field name to use for metadata
## By default all metadata is disabled
# sequence_number_field = "SequenceNumber"
# enqueued_time_field = "EnqueuedTime"
# offset_field = "Offset"
# partition_id_tag = "PartitionID"
# partition_key_tag = "PartitionKey"
# iot_hub_device_connection_id_tag = "IoTHubDeviceConnectionID"
# iot_hub_auth_generation_id_tag = "IoTHubAuthGenerationID"
# iot_hub_connection_auth_method_tag = "IoTHubConnectionAuthMethod"
# iot_hub_connection_module_id_tag = "IoTHubConnectionModuleID"
# iot_hub_enqueued_time_field = "IoTHubEnqueuedTime"
## 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"
Clickhouse
[[outputs.sql]]
## Database driver
## Valid options include mssql, mysql, pgx, sqlite, snowflake, clickhouse
driver = "clickhouse"
## Data source name
## For ClickHouse, the DSN follows the clickhouse-go v1.5.4 format.
## Example DSN: "tcp://localhost:9000?debug=true"
data_source_name = "tcp://localhost:9000?debug=true"
## Timestamp column name
timestamp_column = "timestamp"
## Table creation template
## Available template variables:
## {TABLE} - table name as a quoted identifier
## {TABLELITERAL} - table name as a quoted string literal
## {COLUMNS} - column definitions (list of quoted identifiers and types)
table_template = "CREATE TABLE {TABLE} ({COLUMNS})"
## Table existence check template
## Available template variables:
## {TABLE} - table name as a quoted identifier
table_exists_template = "SELECT 1 FROM {TABLE} LIMIT 1"
## Initialization SQL (optional)
init_sql = ""
## Maximum amount of time a connection may be idle. "0s" means connections are never closed due to idle time.
connection_max_idle_time = "0s"
## Maximum amount of time a connection may be reused. "0s" means connections are never closed due to age.
connection_max_lifetime = "0s"
## Maximum number of connections in the idle connection pool. 0 means unlimited.
connection_max_idle = 2
## Maximum number of open connections to the database. 0 means unlimited.
connection_max_open = 0
## Metric type to SQL type conversion for ClickHouse.
## The conversion maps Telegraf metric types to ClickHouse native data types.
[outputs.sql.convert]
conversion_style = "literal"
integer = "Int64"
text = "String"
timestamp = "DateTime"
defaultvalue = "String"
unsigned = "UInt64"
bool = "UInt8"
real = "Float64"
输入和输出集成示例
Azure Event Hubs
-
实时 IoT 设备监控:使用 Azure Event Hubs 插件监控来自 IoT 设备(如传感器和执行器)的遥测数据。通过将设备数据流式传输到监控仪表板,组织可以深入了解系统性能、跟踪使用模式并快速响应异常情况。此设置允许对设备进行主动管理,提高运营效率并减少停机时间。
-
事件驱动的数据处理工作流:利用此插件来响应从 Azure Event Hubs 接收的事件触发数据处理工作流。例如,当新事件到达时,它可以启动数据转换、聚合或存储过程,使企业能够更有效地自动化其工作流。这种集成增强了响应能力并简化了跨系统的操作。
-
与分析平台集成:实施该插件以将事件数据导入 Azure Synapse 或 Power BI 等分析平台。通过将实时流数据集成到分析工具中,组织可以执行全面的数据分析、推动商业智能工作并创建交互式可视化效果,从而为决策提供信息。
-
跨平台数据同步:使用 Azure Event Hubs 插件在不同的系统或平台之间同步数据流。通过从 Azure Event Hubs 消费数据并将其转发到其他系统(如数据库或云存储),组织可以在其整个架构中维护一致且最新的信息,从而实现有凝聚力的数据策略。
Clickhouse
-
高容量数据的实时分析:使用该插件将来自大规模系统的流式指标馈送到 ClickHouse。此设置支持超快的查询性能和近乎实时的分析,非常适合监控高流量应用程序。
-
时间序列数据仓库:将该插件与 ClickHouse 集成以创建强大的时间序列数据仓库。此用例允许组织存储详细的历史指标并执行复杂的查询以进行趋势分析和容量规划。
-
分布式环境中的可扩展监控:利用该插件在 ClickHouse 中为每个指标类型动态创建表,从而更轻松地管理和查询来自大量分布式系统的数据,而无需事先定义模式。
-
针对 IoT 部署的优化存储:部署该插件以将来自 IoT 传感器的数据摄取到 ClickHouse 中。其高效的模式创建和本机类型映射有助于处理海量数据,从而实现实时监控和预测性维护。
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强大的性能,无限的扩展
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。 使用 InfluxDB,第一的时间序列平台,旨在与 Telegraf 一起扩展。
查看入门方法