Google Cloud PubSub and MySQL Integration

Input and output integration overview

This plugin ingests metrics from Google Cloud PubSub, allowing for real-time data processing and integration into monitoring setups.

The Telegraf SQL plugin allows you to store metrics from Telegraf directly into a MySQL database, making it easier to analyze and visualize the collected metrics.

Integration details

Google Cloud PubSub

The Google Cloud PubSub input plugin is designed to ingest metrics from Google Cloud PubSub, a messaging service that facilitates real-time communication between different systems. It allows users to create and process metrics by pulling messages from a specified subscription in a Google Cloud Project. One of the critical features of this plugin is its ability to operate as a service input, actively listening for incoming messages rather than merely polling for metrics at set intervals. Through various configuration options, users can customize the behavior of message ingestion, such as handling credentials, managing message sizes, and tuning the acknowledgment settings to ensure that messages are only acknowledged after successful processing. By leveraging the strengths of Google PubSub, this plugin integrates seamlessly with cloud-native architectures, enabling users to build robust and scalable applications that can react to events in real-time.

MySQL

Telegraf’s SQL output plugin is designed to seamlessly write metric data to a SQL database by dynamically creating tables and columns based on the incoming metrics. When configured for MySQL, the plugin leverages the go-sql-driver/mysql, which requires enabling the ANSI_QUOTES SQL mode to ensure proper handling of quoted identifiers. This dynamic schema creation approach ensures that each metric is stored in its own table with a structure derived from its fields and tags, providing a detailed, timestamped record of system performance. The flexibility of the plugin allows it to handle high-throughput environments, making it ideal for scenarios that demand robust, granular metric logging and historical data analysis.

Configuration

Google Cloud PubSub

[[inputs.cloud_pubsub]]
  project = "my-project"
  subscription = "my-subscription"
  data_format = "influx"
  # credentials_file = "path/to/my/creds.json"
  # retry_delay_seconds = 5
  # max_message_len = 1000000
  # max_undelivered_messages = 1000
  # max_extension = 0
  # max_outstanding_messages = 0
  # max_outstanding_bytes = 0
  # max_receiver_go_routines = 0
  # base64_data = false
  # content_encoding = "identity"
  # max_decompression_size = "500MB"

MySQL

[[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})"

  ## 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"

Input and output integration examples

Google Cloud PubSub

1. Real-Time Analytics for IoT Devices: Utilize the Google Cloud PubSub plugin to aggregate metrics from IoT devices scattered across various locations. By streaming data from devices to Google PubSub and using this plugin to ingest metrics, organizations can create a centralized dashboard for real-time monitoring and alerting. This setup allows for immediate insights into device performance, facilitating proactive maintenance and operational efficiency.

2. Dynamic Log Processing and Monitoring: Ingest logs from numerous sources via Google Cloud PubSub into a Telegraf pipeline, utilizing the plugin to parse and analyze log messages. This can help teams quickly identify anomalies or patterns in logs and streamline the process of troubleshooting issues across distributed systems. By consolidating log data, organizations can enhance their observability and response capabilities.

3. Event-Driven Workflow Integrations: Use the Google Cloud PubSub plugin to connect various cloud functions or services. Each time a new message is pushed to a subscription, actions can be triggered in other parts of the cloud architecture, such as starting data processing jobs, notifications, or even updates to reports. This event-driven approach allows for a more reactive system architecture that can adapt to changing business needs.

MySQL

1. Real-Time Web Analytics Storage: Leverage the plugin to capture website performance metrics and store them in MySQL. This setup enables teams to monitor user interactions, analyze traffic patterns, and dynamically adjust site features based on real-time data insights.

2. IoT Device Monitoring: Utilize the plugin to collect metrics from a network of IoT sensors and log them into a MySQL database. This use case supports continuous monitoring of device health and performance, allowing for predictive maintenance and immediate response to anomalies.

3. Financial Transaction Logging: Record high-frequency financial transaction data with precise timestamps. This approach supports robust audit trails, real-time fraud detection, and comprehensive historical analysis for compliance and reporting purposes.

4. Application Performance Benchmarking: Integrate the plugin with application performance monitoring systems to log metrics into MySQL. This facilitates detailed benchmarking and trend analysis over time, enabling organizations to identify performance bottlenecks and optimize resource allocation effectively.

Feedback

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