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。
Telegraf 的 SQL 插件有助于将指标存储在 SQL 数据库中。当配置为 Microsoft SQL Server 时,它支持特定的 DSN 格式和模式要求,从而实现与 SQL Server 的无缝集成。
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。
Microsoft SQL Server
Telegraf 的 Microsoft SQL Server SQL 输出插件旨在通过动态创建与传入数据结构匹配的表和列来捕获和存储指标数据。此集成利用 go-mssqldb 驱动程序,该驱动程序通过包含服务器、端口和数据库详细信息的 DSN 遵循 SQL Server 连接协议。尽管由于单元测试有限,该驱动程序被认为是实验性的,但它为动态模式生成和数据插入提供了强大的支持,从而实现了系统性能的详细时间戳记录。尽管其状态为实验性,但这种灵活性使其成为需要可靠且精细的指标日志记录的环境的宝贵工具。
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"
Microsoft SQL Server
[[outputs.sql]]
## Database driver
## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres),
## sqlite (SQLite3), snowflake (snowflake.com), clickhouse (ClickHouse)
driver = "mssql"
## Data source name
## For Microsoft SQL Server, the DSN typically includes the server, port, username, password, and database name.
## Example DSN: "sqlserver://username:password@localhost:1433?database=telegraf"
data_source_name = "sqlserver://username:password@localhost:1433?database=telegraf"
## 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
## You can customize the mapping if needed.
#[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
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中消耗日志,并以可视化方式呈现它们,从而使运营团队能够快速识别趋势并对发生的异常做出反应。
-
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。该插件可以创建指标,帮助检测系统中的问题,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,如果处理的记录数激增,则可以触发相应的扩展操作以处理增加的负载,从而确保最佳的资源分配和性能。
-
带有检查点的到 S3 的数据管道: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。
Microsoft SQL Server
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企业应用程序监控:利用该插件捕获在 SQL Server 上运行的企业应用程序的详细性能指标。此设置允许 IT 团队分析系统性能、跟踪事务时间并识别跨复杂多层环境的瓶颈。
-
动态基础设施审计:部署该插件以在 SQL Server 中创建基础设施更改和性能指标的动态审计日志。此用例非常适合需要实时监控和系统性能历史分析以进行合规性和优化的组织。
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自动化性能基准测试:使用该插件持续记录和分析 SQL Server 数据库的性能指标。这实现了自动化基准测试,其中将历史数据与当前性能进行比较,从而帮助快速识别异常或服务降级。
-
集成 DevOps 仪表板:将插件与 DevOps 监控工具集成,以将来自 SQL Server 的实时指标馈送到集中式仪表板中。这提供了应用程序健康状况的整体视图,使团队能够将 SQL Server 性能与应用程序级事件相关联,从而实现更快的故障排除和主动维护。
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