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,它会变得更有价值。借助 InfluxDB,这款排名第一的时间序列平台旨在与 Telegraf 一起扩展。
查看入门方法
输入和输出集成概览
Azure 事件中心输入插件允许 Telegraf 从 Azure 事件中心和 Azure IoT 中心使用数据,从而实现来自这些云服务的事件流的高效数据处理和监控。
Telegraf 的 SQL 输出插件通过为每种指标类型动态创建表,将指标存储在 SQL 数据库中。当配置为 SQLite 时,它使用基于文件的 DSN 和针对轻量级嵌入式数据库使用量定制的最小 SQL 模式。
集成详情
Azure 事件中心
此插件充当 Azure 事件中心和 Azure IoT 中心的消费者,允许用户有效地从这些平台摄取数据流。Azure 事件中心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 中心则支持 IoT 应用中安全的设备到云和云到设备通信。事件中心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、物联网数据处理以及与更广泛的 Azure 生态系统中的各种数据分析和监控工具集成。
SQLite
SQL 输出插件使用动态模式将 Telegraf 指标写入 SQL 数据库,其中每种指标类型对应一个表。对于 SQLite,该插件使用 modernc.org/sqlite 驱动程序,并且需要采用文件 URI 格式的 DSN(例如,“file:/path/to/telegraf.db?cache=shared”)。此配置利用标准 ANSI SQL 进行表创建和数据插入,确保与 SQLite 的功能兼容。
配置
Azure 事件中心
[[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"
SQLite
[[outputs.sql]]
## Database driver
## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres),
## sqlite (SQLite3), snowflake (snowflake.com), clickhouse (ClickHouse)
driver = "sqlite"
## Data source name
## For SQLite, the DSN is a filename or URL with the scheme "file:".
## Example: "file:/path/to/telegraf.db?cache=shared"
data_source_name = "file:/path/to/telegraf.db?cache=shared"
## 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
## The values on the left are the data types Telegraf has and the values on the right are the SQL types used when writing to SQLite.
#[outputs.sql.convert]
# integer = "INT"
# real = "DOUBLE"
# text = "TEXT"
# timestamp = "TIMESTAMP"
# defaultvalue = "TEXT"
# unsigned = "UNSIGNED"
# bool = "BOOL"
输入和输出集成示例
Azure 事件中心
-
实时物联网设备监控:使用 Azure 事件中心插件来监控来自物联网设备(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,从而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:利用此插件来触发数据处理工作流,以响应从 Azure 事件中心接收的事件。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。此集成增强了响应能力并简化了跨系统的运营。
-
与分析平台集成:实施该插件以将事件数据导入到 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用于指导决策的交互式可视化效果。
-
跨平台数据同步:利用 Azure 事件中心插件跨不同的系统或平台同步数据流。通过从 Azure 事件中心使用数据并将其转发到数据库或云存储等其他系统,组织可以在其整个架构中保持一致且最新的信息,从而实现有凝聚力的数据策略。
SQLite
- 本地监控存储:配置插件以将指标写入本地 SQLite 数据库文件。这非常适合不需要设置全规模数据库服务器的轻量级部署。
- 嵌入式应用程序:将 SQLite 用作边缘设备中嵌入式应用程序的后端,受益于其基于文件的架构和最低的资源要求。
- 快速设置进行测试:利用 SQLite 的易用性,快速设置 Telegraf 指标收集的测试环境,而无需外部数据库服务。
- 自定义模式管理:如果您需要特定的列类型或索引,请调整表创建模板以预定义您的模式,从而确保与您的应用程序需求兼容。
反馈
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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,它会变得更有价值。借助 InfluxDB,这款排名第一的时间序列平台旨在与 Telegraf 一起扩展。
查看入门方法