Google Cloud Stackdriver 和 MariaDB 集成

借助由 InfluxData 构建的开源数据连接器 Telegraf 提供支持,实现强大的性能和简单的集成。

info

这不是实时大规模查询的推荐配置。为了优化查询和压缩、高速摄取和高可用性,您可能需要考虑Stackdriver 和 InfluxDB

50 亿+

Telegraf 下载量

#1

时序数据库
来源:DB Engines

10 亿+

InfluxDB 下载量

2,800+

贡献者

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序数据时,它都会更有价值。借助 InfluxDB,排名第一的时序平台,旨在与 Telegraf 一起扩展。

查看入门方法

输入和输出集成概述

此插件支持通过 Stackdriver Monitoring API 从 Google Cloud 服务收集监控数据。它旨在通过收集相关指标来帮助用户监控其云基础设施的性能和健康状况。

此插件使用参数化的 SQL INSERT 语句将指标从 Telegraf 直接写入 MariaDB,从而提供了一种将指标存储在结构化关系表中的灵活方法。

集成详细信息

Google Cloud Stackdriver

Stackdriver Telegraf 插件允许用户使用 Cloud Monitoring API v3 从 Google Cloud Monitoring 查询时序数据。借助此插件,用户可以轻松地将 Google Cloud 监控指标集成到其监控堆栈中。此 API 提供了有关 Google Cloud 中运行的资源和应用程序的大量见解,包括性能、正常运行时间和运营指标。该插件支持各种配置选项来过滤和优化检索到的数据,使用户可以根据其特定需求自定义其监控设置。此集成有助于更顺畅地维护云资源的健康和性能,并帮助团队根据历史和当前性能统计数据做出数据驱动的决策。

MariaDB

Telegraf 中的 SQL 输出插件支持通过执行参数化的 SQL 语句将指标直接写入与 SQL 兼容的数据库(如 MariaDB)。该插件支持 MySQL 驱动程序,可与 MariaDB 无缝集成,以实现可靠、结构化的指标存储。对于喜欢基于 SQL 的分析或希望将指标与业务数据一起存储以进行统一查询的用户来说,此设置是理想的选择。MariaDB 是 MySQL 的一个社区开发的、企业级的分支,强调性能、安全性和开放性。该插件支持将时序指标插入自定义架构,从而实现灵活的分析以及与使用 SQL 连接器的 BI 工具(如 Metabase 或 Grafana)的集成。

配置

Google Cloud Stackdriver

[[inputs.stackdriver]]
  ## GCP Project
  project = "erudite-bloom-151019"

  ## Include timeseries that start with the given metric type.
  metric_type_prefix_include = [
    "compute.googleapis.com/",
  ]

  ## Exclude timeseries that start with the given metric type.
  # metric_type_prefix_exclude = []

  ## Most metrics are updated no more than once per minute; it is recommended
  ## to override the agent level interval with a value of 1m or greater.
  interval = "1m"

  ## Maximum number of API calls to make per second.  The quota for accounts
  ## varies, it can be viewed on the API dashboard:
  ##   https://cloud.google.com/monitoring/quotas#quotas_and_limits
  # rate_limit = 14

  ## The delay and window options control the number of points selected on
  ## each gather.  When set, metrics are gathered between:
  ##   start: now() - delay - window
  ##   end:   now() - delay
  #
  ## Collection delay; if set too low metrics may not yet be available.
  # delay = "5m"
  #
  ## If unset, the window will start at 1m and be updated dynamically to span
  ## the time between calls (approximately the length of the plugin interval).
  # window = "1m"

  ## TTL for cached list of metric types.  This is the maximum amount of time
  ## it may take to discover new metrics.
  # cache_ttl = "1h"

  ## If true, raw bucket counts are collected for distribution value types.
  ## For a more lightweight collection, you may wish to disable and use
  ## distribution_aggregation_aligners instead.
  # gather_raw_distribution_buckets = true

  ## Aggregate functions to be used for metrics whose value type is
  ## distribution.  These aggregate values are recorded in in addition to raw
  ## bucket counts; if they are enabled.
  ##
  ## For a list of aligner strings see:
  ##   https://cloud.google.com/monitoring/api/ref_v3/rpc/google.monitoring.v3#aligner
  # distribution_aggregation_aligners = [
  #  "ALIGN_PERCENTILE_99",
  #  "ALIGN_PERCENTILE_95",
  #  "ALIGN_PERCENTILE_50",
  # ]

  ## Filters can be added to reduce the number of time series matched.  All
  ## functions are supported: starts_with, ends_with, has_substring, and
  ## one_of.  Only the '=' operator is supported.
  ##
  ## The logical operators when combining filters are defined statically using
  ## the following values:
  ##   filter ::=  {AND  AND  AND }
  ##   resource_labels ::=  {OR }
  ##   metric_labels ::=  {OR }
  ##   user_labels ::=  {OR }
  ##   system_labels ::=  {OR }
  ##
  ## For more details, see https://cloud.google.com/monitoring/api/v3/filters
  #
  ## Resource labels refine the time series selection with the following expression:
  ##   resource.labels. = 
  # [[inputs.stackdriver.filter.resource_labels]]
  #   key = "instance_name"
  #   value = 'starts_with("localhost")'
  #
  ## Metric labels refine the time series selection with the following expression:
  ##   metric.labels. = 
  #  [[inputs.stackdriver.filter.metric_labels]]
  #    key = "device_name"
  #    value = 'one_of("sda", "sdb")'
  #
  ## User labels refine the time series selection with the following expression:
  ##   metadata.user_labels."" = 
  #  [[inputs.stackdriver.filter.user_labels]]
  #    key = "environment"
  #    value = 'one_of("prod", "staging")'
  #
  ## System labels refine the time series selection with the following expression:
  ##   metadata.system_labels."" = 
  #  [[inputs.stackdriver.filter.system_labels]]
  #    key = "machine_type"
  #    value = 'starts_with("e2-")'
</code></pre>

MariaDB

[[outputs.sql]]
  ## Database driver
  ## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres),
  ##  sqlite (SQLite3), snowflake (snowflake.com) clickhouse (ClickHouse)
  driver = "mysql"

  ## Data source name
  ## The format of the data source name is different for each database driver.
  ## See the plugin readme for details.
  data_source_name = "username:password@tcp(host:port)/dbname"

  ## 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})"

  ## SQL INSERT statement with placeholders. Telegraf will substitute values at runtime.
  ## table_template = "INSERT INTO metrics (timestamp, name, value, tags) VALUES (?, ?, ?, ?)"

  ## Table existence check template
  ## Available template variables:
  ##  {TABLE} - tablename as a quoted identifier
  table_exists_template = "SELECT 1 FROM {TABLE} LIMIT 1"

  ## Initialization SQL
  init_sql = "SET sql_mode='ANSI_QUOTES';"

  ## 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

  ## NOTE: Due to the way TOML is parsed, tables must be at the END of the
  ## plugin definition, otherwise additional config options are read as part of the
  ## table

  ## 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 data types Telegraf will use when sending to a database.
  ##
  ## The database values used must be data types the destination database
  ## understands. It is up to the user to ensure that the selected data type is
  ## available in the database they are using. Refer to your database
  ## documentation for what data types are available and supported.
  #[outputs.sql.convert]
  #  integer              = "INT"
  #  real                 = "DOUBLE"
  #  text                 = "TEXT"
  #  timestamp            = "TIMESTAMP"
  #  defaultvalue         = "TEXT"
  #  unsigned             = "UNSIGNED"
  #  bool                 = "BOOL"
  #  ## This setting controls the behavior of the unsigned value. By default the
  #  ## setting will take the integer value and append the unsigned value to it. The other
  #  ## option is "literal", which will use the actual value the user provides to
  #  ## the unsigned option. This is useful for a database like ClickHouse where
  #  ## the unsigned value should use a value like "uint64".
  #  # conversion_style = "unsigned_suffix"

输入和输出集成示例

Google Cloud Stackdriver

  1. 将云指标集成到自定义仪表板中:借助此插件,团队可以将 Google Cloud 中的指标传输到个性化仪表板中,从而可以实时监控应用程序性能和资源利用率。通过自定义云指标的可视化表示,运营团队可以轻松识别趋势和异常情况,从而在问题升级之前进行主动管理。

  2. 自动化警报和分析:用户可以设置自动化警报机制,利用插件的指标来跟踪资源阈值。此功能使团队能够通过提供即时通知来快速响应性能下降或中断,从而缩短平均恢复时间并确保持续的运营效率。

  3. 跨平台资源比较:该插件可用于提取各种 Google Cloud 服务的指标,并将其与本地资源进行比较。这种跨平台可见性有助于组织就资源分配和扩展策略做出明智的决策,并优化云支出与本地基础设施的比较。

  4. 用于容量规划的历史数据分析:通过随着时间的推移收集历史指标,该插件使团队能够进行彻底的容量规划。了解过去的性能趋势有助于准确预测资源需求,从而实现更好的预算编制和投资策略。

MariaDB

  1. 商业智能集成:将应用程序性能指标直接存储到 MariaDB 中,并将其连接到 BI 工具(如 Metabase 或 Apache Superset)。此设置允许将运营数据与业务 KPI 相结合,以实现统一的仪表板,从而提高跨部门的可见性。

  2. 符合性报告与历史指标:使用此插件将指标记录到 MariaDB 中,用于审计和合规性用例。关系模型支持使用带时间戳的条目精确查询过去的绩效指标,从而支持监管文档。

  3. 基于 SQL 逻辑的自定义警报:将指标插入 MariaDB,并使用自定义 SQL 查询来定义警报阈值或条件。与 cron 作业或计划脚本结合使用,这可以实现传统指标平台无法实现的高级警报工作流程。

  4. 物联网传感器指标存储:通过 Telegraf 收集来自物联网设备的传感器数据,并使用规范化架构将其存储在 MariaDB 中。这种方法具有成本效益,并且可以很好地与现有的基于 SQL 的系统集成,以进行实时或历史分析。

反馈

感谢您成为我们社区的一份子!如果您有任何一般性反馈或在这些页面上发现了任何错误,我们欢迎并鼓励您提出意见。请在 InfluxDB 社区 Slack 中提交您的反馈。

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序数据时,它都会更有价值。借助 InfluxDB,排名第一的时序平台,旨在与 Telegraf 一起扩展。

查看入门方法

相关集成

HTTP 和 InfluxDB 集成

HTTP 插件从一个或多个 HTTP(S) 端点收集指标。它支持各种身份验证方法和数据格式的配置选项。

查看集成

Kafka 和 InfluxDB 集成

此插件从 Kafka 读取消息,并允许根据这些消息创建指标。它支持各种配置,包括不同的 Kafka 设置和消息处理选项。

查看集成

Kinesis 和 InfluxDB 集成

Kinesis 插件允许从 AWS Kinesis 流中读取指标。它支持多种输入数据格式,并提供 DynamoDB 的检查点功能,以实现可靠的消息处理。

查看集成