目录
输入和输出集成概述
StatsD 输入插件通过在后台运行侦听器服务来捕获来自 StatsD 服务器的指标,从而实现全面的性能监控和指标聚合。
Telegraf 的 SQL 插件允许在 SQL 数据库中无缝存储指标。当配置为 Snowflake 时,它采用专门的 DSN 格式和动态表创建,以将指标映射到适当的模式。
集成详情
StatsD
StatsD 输入插件旨在通过在 Telegraf 处于活动状态时运行后台 StatsD 侦听器服务,从 StatsD 服务器收集指标。此插件利用原始 Etsy 实施建立的 StatsD 消息格式,该格式允许各种类型的指标,包括 gauges、counters、sets、timings、histograms 和 distributions。StatsD 插件的功能扩展到解析标签,并使用适应 InfluxDB 标记系统的功能扩展标准协议。它可以处理通过不同协议(UDP 或 TCP)发送的消息,有效管理多个指标,并为最佳指标处理提供高级配置,例如百分位数计算和数据转换模板。这种灵活性使用户能够全面跟踪应用程序性能,使其成为强大监控设置的重要工具。
Snowflake
Telegraf 的 SQL 插件旨在通过基于传入数据创建表和列,将指标动态写入 SQL 数据库。当配置为 Snowflake 时,它采用 gosnowflake 驱动程序,该驱动程序使用 DSN,DSN 将凭据、帐户详细信息和数据库配置封装在紧凑的格式中。此设置允许自动生成表,在表中记录每个指标以及精确的时间戳,从而确保详细的历史跟踪。尽管该集成被认为是实验性的,但它利用了 Snowflake 强大的数据仓库功能,使其适用于可扩展的、基于云的分析和报告解决方案。
配置
StatsD
[[inputs.statsd]]
## Protocol, must be "tcp", "udp4", "udp6" or "udp" (default=udp)
protocol = "udp"
## MaxTCPConnection - applicable when protocol is set to tcp (default=250)
max_tcp_connections = 250
## Enable TCP keep alive probes (default=false)
tcp_keep_alive = false
## Specifies the keep-alive period for an active network connection.
## Only applies to TCP sockets and will be ignored if tcp_keep_alive is false.
## Defaults to the OS configuration.
# tcp_keep_alive_period = "2h"
## Address and port to host UDP listener on
service_address = ":8125"
## The following configuration options control when telegraf clears it's cache
## of previous values. If set to false, then telegraf will only clear it's
## cache when the daemon is restarted.
## Reset gauges every interval (default=true)
delete_gauges = true
## Reset counters every interval (default=true)
delete_counters = true
## Reset sets every interval (default=true)
delete_sets = true
## Reset timings & histograms every interval (default=true)
delete_timings = true
## Enable aggregation temporality adds temporality=delta or temporality=commulative tag, and
## start_time field, which adds the start time of the metric accumulation.
## You should use this when using OpenTelemetry output.
# enable_aggregation_temporality = false
## Percentiles to calculate for timing & histogram stats.
percentiles = [50.0, 90.0, 99.0, 99.9, 99.95, 100.0]
## separator to use between elements of a statsd metric
metric_separator = "_"
## Parses tags in the datadog statsd format
## http://docs.datadoghq.com/guides/dogstatsd/
## deprecated in 1.10; use datadog_extensions option instead
parse_data_dog_tags = false
## Parses extensions to statsd in the datadog statsd format
## currently supports metrics and datadog tags.
## http://docs.datadoghq.com/guides/dogstatsd/
datadog_extensions = false
## Parses distributions metric as specified in the datadog statsd format
## https://docs.datadoghq.com/developers/metrics/types/?tab=distribution#definition
datadog_distributions = false
## Keep or drop the container id as tag. Included as optional field
## in DogStatsD protocol v1.2 if source is running in Kubernetes
## https://docs.datadoghq.com/developers/dogstatsd/datagram_shell/?tab=metrics#dogstatsd-protocol-v12
datadog_keep_container_tag = false
## Statsd data translation templates, more info can be read here:
## https://github.com/influxdata/telegraf/blob/master/docs/TEMPLATE_PATTERN.md
# templates = [
# "cpu.* measurement*"
# ]
## Number of UDP messages allowed to queue up, once filled,
## the statsd server will start dropping packets
allowed_pending_messages = 10000
## Number of worker threads used to parse the incoming messages.
# number_workers_threads = 5
## Number of timing/histogram values to track per-measurement in the
## calculation of percentiles. Raising this limit increases the accuracy
## of percentiles but also increases the memory usage and cpu time.
percentile_limit = 1000
## Maximum socket buffer size in bytes, once the buffer fills up, metrics
## will start dropping. Defaults to the OS default.
# read_buffer_size = 65535
## Max duration (TTL) for each metric to stay cached/reported without being updated.
# max_ttl = "10h"
## Sanitize name method
## By default, telegraf will pass names directly as they are received.
## However, upstream statsd now does sanitization of names which can be
## enabled by using the "upstream" method option. This option will a) replace
## white space with '_', replace '/' with '-', and remove characters not
## matching 'a-zA-Z_\-0-9\.;='.
#sanitize_name_method = ""
## Replace dots (.) with underscore (_) and dashes (-) with
## double underscore (__) in metric names.
# convert_names = false
## Convert all numeric counters to float
## Enabling this would ensure that both counters and guages are both emitted
## as floats.
# float_counters = false
Snowflake
[[outputs.sql]]
## Database driver
## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres),
## sqlite (SQLite3), snowflake (snowflake.com), clickhouse (ClickHouse)
driver = "snowflake"
## Data source name
## For Snowflake, the DSN format typically includes the username, password, account identifier, and optional warehouse, database, and schema.
## Example DSN: "username:password@account/warehouse/db/schema"
data_source_name = "username:password@account/warehouse/db/schema"
## 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
## Defaults to ANSI/ISO SQL types unless overridden. Adjust if needed for Snowflake compatibility.
#[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
StatsD
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实时应用程序性能监控:利用 StatsD 输入插件实时监控应用程序性能指标。通过配置您的应用程序向 StatsD 服务器发送各种指标,团队可以利用此插件动态分析性能瓶颈、跟踪用户活动并确保资源优化。历史指标和实时指标的结合可以实现主动故障排除,并提高问题解决流程的响应速度。
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跟踪 Web 应用程序中的用户参与度指标:使用 StatsD 插件收集用户参与度统计信息,例如页面浏览量、点击事件和交互时间。通过将这些指标发送到 StatsD 服务器,企业可以获得有关用户行为的宝贵见解,使他们能够根据定量反馈做出数据驱动的决策,以改善用户体验和界面设计。这可以显着提高营销策略和产品开发工作的有效性。
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基础设施健康监控:部署 StatsD 插件,通过跟踪资源利用率、服务器响应时间和网络性能等指标来监控服务器基础设施的健康状况。通过此设置,DevOps 团队可以详细了解系统性能,从而有效地在问题升级之前预测问题。这使得能够采用主动的基础设施管理方法,最大限度地减少停机时间并确保最佳服务交付。
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创建全面的服务仪表板:将 StatsD 与可视化工具集成,以创建全面的仪表板,反映整个架构中服务的状态和健康状况。例如,组合通过 StatsD 记录的来自多个服务的数据可以将原始指标转换为可操作的见解,从而展示系统性能随时间变化的趋势。此功能使利益干系人能够保持监督并根据可视化的数据集推动决策,从而提高整体运营透明度。
Snowflake
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基于云的数据湖集成:利用此插件将来自各种来源的实时指标流式传输到 Snowflake,从而创建集中的数据湖。此集成支持云数据上的复杂分析和机器学习工作流程。
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动态商业智能仪表板:利用此插件从传入指标自动生成表,并将它们馈送到 BI 工具。这使企业能够创建动态仪表板,可视化性能趋势和运营见解,而无需手动模式管理。
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可扩展的物联网分析:部署此插件以捕获来自 IoT 设备的高频数据到 Snowflake 中。此用例有助于传感器数据的聚合和分析,从而实现大规模的预测性维护和实时监控。
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用于合规性的历史趋势分析:使用此插件在 Snowflake 中记录和存档详细的指标数据,然后可以查询这些数据以进行长期趋势分析和合规性报告。此设置确保组织可以维护可靠的审计跟踪并在需要时执行取证分析。
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