目录
输入和输出集成概述
AMQP Consumer 输入插件允许您从符合 AMQP 0-9-1 标准的消息代理(如 RabbitMQ)中提取数据,从而实现无缝数据收集,用于监控和分析。
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,以确保无缝数据集成。
集成详情
AMQP
此插件为 AMQP 0-9-1 提供了一个消费者,RabbitMQ 是其重要的实现之一。 AMQP,即高级消息队列协议,最初是为了在网络中不同系统之间实现可靠、可互操作的消息传递而开发的。 该插件使用配置的队列和绑定键从主题交换中读取指标,从而提供了一种灵活高效的方式,用于从符合 AMQP 标准的消息传递系统中收集数据。 这使用户能够利用现有的 RabbitMQ 实现来有效地监控其应用程序,方法是捕获详细的指标以进行分析和警报。
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 的原生类型。 这种方法确保了在高吞吐量环境中实现最佳的存储和检索性能,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序数据日志记录,这对于监控现代分布式系统至关重要。
配置
AMQP
[[inputs.amqp_consumer]]
## Brokers to consume from. If multiple brokers are specified a random broker
## will be selected anytime a connection is established. This can be
## helpful for load balancing when not using a dedicated load balancer.
brokers = ["amqp://localhost:5672/influxdb"]
## Authentication credentials for the PLAIN auth_method.
# username = ""
# password = ""
## Name of the exchange to declare. If unset, no exchange will be declared.
exchange = "telegraf"
## Exchange type; common types are "direct", "fanout", "topic", "header", "x-consistent-hash".
# exchange_type = "topic"
## If true, exchange will be passively declared.
# exchange_passive = false
## Exchange durability can be either "transient" or "durable".
# exchange_durability = "durable"
## Additional exchange arguments.
# exchange_arguments = { }
# exchange_arguments = {"hash_property" = "timestamp"}
## AMQP queue name.
queue = "telegraf"
## AMQP queue durability can be "transient" or "durable".
queue_durability = "durable"
## If true, queue will be passively declared.
# queue_passive = false
## Additional arguments when consuming from Queue
# queue_consume_arguments = { }
# queue_consume_arguments = {"x-stream-offset" = "first"}
## A binding between the exchange and queue using this binding key is
## created. If unset, no binding is created.
binding_key = "#"
## Maximum number of messages server should give to the worker.
# prefetch_count = 50
## 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
## Timeout for establishing the connection to a broker
# timeout = "30s"
## Auth method. PLAIN and EXTERNAL are supported
## Using EXTERNAL requires enabling the rabbitmq_auth_mechanism_ssl plugin as
## described here: https://rabbitmq.cn/plugins.html
# auth_method = "PLAIN"
## Optional TLS Config
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
## Content encoding for message payloads, can be set to
## "gzip", "identity" or "auto"
## - Use "gzip" to decode gzip
## - Use "identity" to apply no encoding
## - Use "auto" determine the encoding using the ContentEncoding header
# content_encoding = "identity"
## Maximum size of decoded message.
## Acceptable units are B, KiB, KB, MiB, MB...
## Without quotes and units, interpreted as size in bytes.
# max_decompression_size = "500MB"
## 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
AMQP
-
通过 AMQP 集成应用程序指标:使用 AMQP Consumer 插件收集发布到 RabbitMQ 交换机的应用程序指标。 通过配置插件以侦听特定队列,团队可以深入了解应用程序性能,实时跟踪请求率、错误计数和延迟指标。 此设置不仅有助于异常检测,还为容量规划和系统优化提供了有价值的数据。
-
事件驱动型监控:配置 AMQP Consumer 以在应用程序中满足特定条件时触发特定的监控事件。 例如,如果收到指示高错误率的消息,则插件可以将此数据馈送到监控工具中,从而生成警报或扩展事件。 这种集成可以提高对问题的响应速度,并自动化部分操作工作流程。
-
跨平台数据聚合:利用 AMQP Consumer 插件整合来自分布在不同平台上的各种应用程序的指标。 通过使用 RabbitMQ 作为集中式消息代理,组织可以统一其监控数据,从而允许通过 Telegraf 进行全面的分析和仪表板显示,从而在异构环境中保持可见性。
-
实时日志处理:扩展 AMQP Consumer 的使用范围,以捕获发送到 RabbitMQ 交换机的日志数据,实时处理日志以进行监控和警报。 此应用程序通过分析日志模式、趋势和异常情况,确保及时检测和解决操作问题。
Clickhouse
-
用于高容量数据的实时分析:使用插件将来自大型系统的流式指标馈送到 ClickHouse 中。 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。
-
时序数据仓库:将插件与 ClickHouse 集成,以创建强大的时序数据仓库。 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。
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分布式环境中的可扩展监控:利用插件在 ClickHouse 中为每种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,而无需预先定义模式。
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针对物联网部署的优化存储:部署插件以将来自物联网传感器的数据提取到 ClickHouse 中。 其高效的模式创建和原生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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