Google Cloud PubSub and SQLite 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.

Telegraf’s SQL output plugin stores metrics in an SQL database by creating tables dynamically for each metric type. When configured for SQLite, it utilizes a file-based DSN and a minimal SQL schema tailored for lightweight, embedded database usage.

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.

SQLite

The SQL output plugin writes Telegraf metrics to an SQL database using a dynamic schema where each metric type corresponds to a table. For SQLite, the plugin uses the modernc.org/sqlite driver and requires a DSN in the format of a file URI (e.g., ‘file:/path/to/telegraf.db?cache=shared’). This configuration leverages standard ANSI SQL for table creation and data insertion, ensuring compatibility with SQLite’s capabilities.

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"

SQLite

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

  ## Data source name
  ## For SQLite, the DSN is a filename or URL with the scheme "file:".
  ## Example: "file:/path/to/telegraf.db?cache=shared"
  data_source_name = "file:/path/to/telegraf.db?cache=shared"

  ## 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
  ## The values on the left are the data types Telegraf has and the values on the right are the SQL types used when writing to SQLite.
  #[outputs.sql.convert]
  #  integer       = "INT"
  #  real          = "DOUBLE"
  #  text          = "TEXT"
  #  timestamp     = "TIMESTAMP"
  #  defaultvalue  = "TEXT"
  #  unsigned      = "UNSIGNED"
  #  bool          = "BOOL"

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.

SQLite

1. Local Monitoring Storage: Configure the plugin to write metrics to a local SQLite database file. This is ideal for lightweight deployments where setting up a full-scale database server is not required.
2. Embedded Applications: Use SQLite as the backend for applications embedded in edge devices, benefiting from its file-based architecture and minimal resource requirements.
3. Quick Setup for Testing: Leverage SQLite’s ease of use to quickly set up a testing environment for Telegraf metrics collection without the need for external database services.
4. Custom Schema Management: Adjust the table creation templates to predefine your schema if you require specific column types or indexes, ensuring compatibility with your application’s needs.

Feedback

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