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Google Cloud Stackdriver and MariaDB Integration

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This is not the recommended configuration for real-time query at scale. For query and compression optimization, high-speed ingest, and high availability, you may want to consider <a href="/integrations/stackdriver-influxdb/">Stackdriver and InfluxDB</a>.

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Infrastructure Monitoring with Telegraf

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Input and output integration overview

<p>This plugin enables the collection of monitoring data from Google Cloud services through the Stackdriver Monitoring API. It is designed to help users monitor their cloud infrastructure’s performance and health by gathering relevant metrics.</p>

<p>This plugin writes metrics from Telegraf directly into MariaDB using parameterized SQL INSERT statements, offering a flexible way to store metrics in structured, relational tables.</p>

Integration details

Google Cloud Stackdriver

<p>The Stackdriver Telegraf plugin allows users to query timeseries data from Google Cloud Monitoring using the Cloud Monitoring API v3. With this plugin, users can easily integrate Google Cloud monitoring metrics into their monitoring stacks. This API provides a wealth of insights about resources and applications running in Google Cloud, including performance, uptime, and operational metrics. The plugin supports various configuration options to filter and refine the data retrieved, enabling users to customize their monitoring setup according to their specific needs. This integration facilitates a smoother experience in maintaining the health and performance of cloud resources and assists teams in making data-driven decisions based on historical and current performance statistics.</p>

MariaDB

<p>The SQL output plugin in Telegraf enables direct writing of metrics into SQL-compatible databases like MariaDB by executing parameterized SQL statements. With support for the MySQL driver, the plugin seamlessly integrates with MariaDB for reliable, structured metric storage. This setup is ideal for users who prefer SQL-based analytics or want to store metrics alongside business data for unified querying. MariaDB is a community-developed, enterprise-grade fork of MySQL that emphasizes performance, security, and openness. The plugin supports inserting time series metrics into custom schemas, enabling flexible analytics and integrations with BI tools like Metabase or Grafana using SQL connectors.</p>

Configuration

Google Cloud Stackdriver

<pre><code class="language-toml">[[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 ::= <resource_labels> {AND <metric_labels> AND <user_labels> AND <system_labels>} ## resource_labels ::= <resource_labels> {OR <resource_label>} ## metric_labels ::= <metric_labels> {OR <metric_label>} ## user_labels ::= <user_labels> {OR <user_label>} ## system_labels ::= <system_labels> {OR <system_label>} ## ## 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.<key> = <value> # [[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.<key> = <value> # [[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."<key>" = <value> # [[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."<key>" = <value> # [[inputs.stackdriver.filter.system_labels]] # key = "machine_type" # value = 'starts_with("e2-")' &lt;/code&gt;&lt;/pre&gt; </value></key></value></key></value></key></value></key></system_label></system_labels></user_label></user_labels></metric_label></metric_labels></resource_label></resource_labels></system_labels></user_labels></metric_labels></resource_labels></code></pre>

MariaDB

<pre><code class="language-toml">[[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" </code></pre>

Input and output integration examples

Google Cloud Stackdriver

<ol> <li> <p><strong>Integrating Cloud Metrics into Custom Dashboards</strong>: With this plugin, teams can funnel metrics from Google Cloud into personalized dashboards, allowing for real-time monitoring of application performance and resource utilization. By customizing the visual representation of cloud metrics, operations teams can easily identify trends and anomalies, enabling proactive management before issues escalate.</p> </li> <li> <p><strong>Automated Alerts and Analysis</strong>: Users can set up automated alerting mechanisms leveraging the plugin’s metrics to track resource thresholds. This capability allows teams to act swiftly in response to performance degradation or outages by providing immediate notifications, thus reducing the mean time to recovery and ensuring continued operational efficiency.</p> </li> <li> <p><strong>Cross-Platform Resource Comparison</strong>: The plugin can be used to draw metrics from various Google Cloud services and compare them with on-premise resources. This cross-platform visibility helps organizations make informed decisions about resource allocation and scaling strategies, as well as optimize cloud spending versus on-premise infrastructure.</p> </li> <li> <p><strong>Historical Data Analysis for Capacity Planning</strong>: By collecting historical metrics over time, the plugin empowers teams to conduct thorough capacity planning. Understanding past performance trends facilitates accurate forecasting for resource needs, leading to better budgeting and investment strategies.</p> </li> </ol>

MariaDB

<ol> <li> <p><strong>Business Intelligence Integration</strong>: Store application performance metrics directly into MariaDB and connect it to BI tools like Metabase or Apache Superset. This setup allows blending of operational data with business KPIs for unified dashboards, enhancing visibility across departments.</p> </li> <li> <p><strong>Compliance Reporting with Historical Metrics</strong>: Use this plugin to log metrics into MariaDB for audit and compliance use cases. The relational model enables precise querying of past performance indicators with timestamped entries, supporting regulatory documentation.</p> </li> <li> <p><strong>Custom Alerting Based on SQL Logic</strong>: Insert metrics into MariaDB and use custom SQL queries to define alert thresholds or conditions. Combined with cron jobs or scheduled scripts, this enables advanced alerting workflows not possible with traditional metric platforms.</p> </li> <li> <p><strong>IoT Sensor Metrics Storage</strong>: Collect sensor data from IoT devices via Telegraf and store it in MariaDB using a normalized schema. This approach is cost-effective and integrates well with existing SQL-based systems for real-time or historical analysis.</p> </li> </ol>

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Useful Links

Documentation

Telegraf Quickstart Training

Infrastructure Monitoring with Telegraf

Powerful Performance, Limitless Scale

Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.

See Ways to Get Started

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