目录
强大的性能,无限的扩展性
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。借助 InfluxDB,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
输入和输出集成概述
此插件允许您从 Kafka 主题实时收集指标,从而增强 Telegraf 设置中的数据监控和收集能力。
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
Kafka
Kafka Telegraf 插件旨在从 Kafka 主题读取数据,并使用支持的输入数据格式创建指标。作为服务输入插件,它持续监听传入的指标和事件,这与以固定间隔运行的标准输入插件不同。此特定插件可以使用各种 Kafka 版本的功能,并且能够使用 SASL 等安全凭据以及使用消息偏移量和消费者组管理消息处理等配置,从指定主题消费消息。此插件的灵活性使其能够处理各种消息格式和用例,使其成为依赖 Kafka 进行数据摄取的应用程序的宝贵资产。
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
Kafka
[[inputs.kafka_consumer]]
## Kafka brokers.
brokers = ["localhost:9092"]
## Set the minimal supported Kafka version. Should be a string contains
## 4 digits in case if it is 0 version and 3 digits for versions starting
## from 1.0.0 separated by dot. This setting enables the use of new
## Kafka features and APIs. Must be 0.10.2.0(used as default) or greater.
## Please, check the list of supported versions at
## https://pkg.go.dev/github.com/Shopify/sarama#SupportedVersions
## ex: kafka_version = "2.6.0"
## ex: kafka_version = "0.10.2.0"
# kafka_version = "0.10.2.0"
## Topics to consume.
topics = ["telegraf"]
## Topic regular expressions to consume. Matches will be added to topics.
## Example: topic_regexps = [ "*test", "metric[0-9A-z]*" ]
# topic_regexps = [ ]
## When set this tag will be added to all metrics with the topic as the value.
# topic_tag = ""
## The list of Kafka message headers that should be pass as metric tags
## works only for Kafka version 0.11+, on lower versions the message headers
## are not available
# msg_headers_as_tags = []
## The name of kafka message header which value should override the metric name.
## In case when the same header specified in current option and in msg_headers_as_tags
## option, it will be excluded from the msg_headers_as_tags list.
# msg_header_as_metric_name = ""
## Set metric(s) timestamp using the given source.
## Available options are:
## metric -- do not modify the metric timestamp
## inner -- use the inner message timestamp (Kafka v0.10+)
## outer -- use the outer (compressed) block timestamp (Kafka v0.10+)
# timestamp_source = "metric"
## Optional Client id
# client_id = "Telegraf"
## Optional TLS Config
# enable_tls = false
# 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
## Period between keep alive probes.
## Defaults to the OS configuration if not specified or zero.
# keep_alive_period = "15s"
## SASL authentication credentials. These settings should typically be used
## with TLS encryption enabled
# sasl_username = "kafka"
# sasl_password = "secret"
## Optional SASL:
## one of: OAUTHBEARER, PLAIN, SCRAM-SHA-256, SCRAM-SHA-512, GSSAPI
## (defaults to PLAIN)
# sasl_mechanism = ""
## used if sasl_mechanism is GSSAPI
# sasl_gssapi_service_name = ""
# ## One of: KRB5_USER_AUTH and KRB5_KEYTAB_AUTH
# sasl_gssapi_auth_type = "KRB5_USER_AUTH"
# sasl_gssapi_kerberos_config_path = "/"
# sasl_gssapi_realm = "realm"
# sasl_gssapi_key_tab_path = ""
# sasl_gssapi_disable_pafxfast = false
## used if sasl_mechanism is OAUTHBEARER
# sasl_access_token = ""
## SASL protocol version. When connecting to Azure EventHub set to 0.
# sasl_version = 1
# Disable Kafka metadata full fetch
# metadata_full = false
## Name of the consumer group.
# consumer_group = "telegraf_metrics_consumers"
## Compression codec represents the various compression codecs recognized by
## Kafka in messages.
## 0 : None
## 1 : Gzip
## 2 : Snappy
## 3 : LZ4
## 4 : ZSTD
# compression_codec = 0
## Initial offset position; one of "oldest" or "newest".
# offset = "oldest"
## Consumer group partition assignment strategy; one of "range", "roundrobin" or "sticky".
# balance_strategy = "range"
## Maximum number of retries for metadata operations including
## connecting. Sets Sarama library's Metadata.Retry.Max config value. If 0 or
## unset, use the Sarama default of 3,
# metadata_retry_max = 0
## Type of retry backoff. Valid options: "constant", "exponential"
# metadata_retry_type = "constant"
## Amount of time to wait before retrying. When metadata_retry_type is
## "constant", each retry is delayed this amount. When "exponential", the
## first retry is delayed this amount, and subsequent delays are doubled. If 0
## or unset, use the Sarama default of 250 ms
# metadata_retry_backoff = 0
## Maximum amount of time to wait before retrying when metadata_retry_type is
## "exponential". Ignored for other retry types. If 0, there is no backoff
## limit.
# metadata_retry_max_duration = 0
## When set to true, this turns each bootstrap broker address into a set of
## IPs, then does a reverse lookup on each one to get its canonical hostname.
## This list of hostnames then replaces the original address list.
## resolve_canonical_bootstrap_servers_only = false
## Strategy for making connection to kafka brokers. Valid options: "startup",
## "defer". If set to "defer" the plugin is allowed to start before making a
## connection. This is useful if the broker may be down when telegraf is
## started, but if there are any typos in the broker setting, they will cause
## connection failures without warning at startup
# connection_strategy = "startup"
## Maximum length of a message to consume, in bytes (default 0/unlimited);
## larger messages are dropped
max_message_len = 1000000
## 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
## Maximum amount of time the consumer should take to process messages. If
## the debug log prints messages from sarama about 'abandoning subscription
## to [topic] because consuming was taking too long', increase this value to
## longer than the time taken by the output plugin(s).
##
## Note that the effective timeout could be between 'max_processing_time' and
## '2 * max_processing_time'.
# max_processing_time = "100ms"
## The default number of message bytes to fetch from the broker in each
## request (default 1MB). This should be larger than the majority of
## your messages, or else the consumer will spend a lot of time
## negotiating sizes and not actually consuming. Similar to the JVM's
## `fetch.message.max.bytes`.
# consumer_fetch_default = "1MB"
## 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
Kafka
-
实时数据处理:使用 Kafka 插件将来自 Kafka 主题的实时数据馈送到监控系统。这对于需要即时反馈性能指标或用户活动的应用尤其有用,使企业能够更快地对其环境中的变化条件做出反应。
-
动态指标收集:利用此插件根据 Kafka 内发生的事件动态调整正在捕获的指标。例如,通过与其他服务集成,用户可以让插件即时重新配置自身,从而确保始终根据业务或应用程序的需求收集相关指标。
-
集中式日志记录和监控:使用 Kafka Consumer Plugin 实施集中式日志记录系统,以将来自多个服务的日志聚合到统一的监控仪表板中。此设置可以帮助识别跨不同服务的问题,并提高整体系统可观察性和故障排除能力。
-
异常检测系统:将 Kafka 与机器学习算法结合使用,以进行实时异常检测。通过不断分析流数据,此设置可以自动识别异常模式,触发警报并更有效地缓解潜在问题。
Clickhouse
-
高容量数据的实时分析:使用此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:将此插件与 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:利用此插件在 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。
-
物联网部署的优化存储:部署此插件以将来自物联网传感器的数据摄取到 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。
反馈
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强大的性能,无限的扩展性
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。借助 InfluxDB,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