Apache Aurora and MariaDB Integration

Input and output integration overview

This plugin gathers metrics from Apache Aurora schedulers, providing insights necessary for effective monitoring of Aurora clusters.

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.

Integration details

Apache Aurora

The Aurora plugin is designed to gather metrics from Apache Aurora schedulers. This plugin connects to specified schedulers using their respective URLs and retrieves operational metrics that help in monitoring the health and performance of Aurora clusters. It primarily captures numeric data from the /vars endpoint, ensuring key metrics related to task execution and resource utilization are monitored. The plugin enhances operational insights by utilizing HTTP Basic Authentication for secure access. With optional TLS configuration, it further bolsters security when transmitting data. The plugin provides a robust way to interface with Apache Aurora, reflecting a focus on operational reliability and ongoing performance assessment across distributed systems.

MariaDB

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.

Configuration

Apache Aurora

[[inputs.aurora]]
  ## Schedulers are the base addresses of your Aurora Schedulers
  schedulers = ["http://127.0.0.1:8081"]

  ## Set of role types to collect metrics from.
  ##
  ## The scheduler roles are checked each interval by contacting the
  ## scheduler nodes; zookeeper is not contacted.
  # roles = ["leader", "follower"]

  ## Timeout is the max time for total network operations.
  # timeout = "5s"

  ## Username and password are sent using HTTP Basic Auth.
  # username = "username"
  # password = "pa$$word"

  ## Optional TLS Config
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false

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"

Input and output integration examples

Apache Aurora

1. Dynamic Resource Allocation Monitoring: Utilize the Aurora plugin to build a real-time dashboard displaying metrics related to resource allocation in your Aurora clusters. By aggregating data from multiple schedulers, you can visualize how resources are distributed among various roles (leader and follower), enabling proactive management of resource utilization and helping prevent bottlenecks in production workloads.

2. Alerting on Scheduler Health: Implement alerting mechanisms where the Aurora plugin checks the health of schedulers periodically. If a scheduler role responds with a status that indicates a failure to communicate (non-200 status), alerts can be automatically generated and sent to the operations team via email or messaging apps, ensuring immediate attention to critical issues and maintaining availability in service management.

3. Performance Benchmarking Over Time: By continuously collecting metrics such as job update events and execution times, this plugin can assist teams in benchmarking the performance of their Apache Aurora deployment over time. Relevant metrics can be logged into a time-series database, enabling historical analysis, trend identification, and understanding how changes in the system, such as configuration adjustments or workload changes, impact performance.

4. Integration with CI/CD Pipelines: Integrate the metrics collected via the Aurora plugin with CI/CD pipeline tools to monitor how deployments affect runtime metrics in Aurora. Teams can thereby ensure that new releases do not adversely impact scheduler performance and can roll back changes seamlessly if any metric exceeds predefined thresholds after deployment.

MariaDB

1. Business Intelligence Integration: 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.

2. Compliance Reporting with Historical Metrics: 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.

3. Custom Alerting Based on SQL Logic: 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.

4. IoT Sensor Metrics Storage: 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.

Feedback

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