Apache and TimescaleDB Integration

Input and output integration overview

This plugin interfaces with the Apache HTTP Server’s mod_status to gather and report performance metrics from the server.

This output plugin delivers a reliable and efficient mechanism for routing Telegraf collected metrics directly into TimescaleDB. By leveraging PostgreSQL’s robust ecosystem combined with TimescaleDB’s time series optimizations, it supports high-performance data ingestion and advanced querying capabilities.

Integration details

Apache

The Apache plugin collects server performance information using the mod_status module of the Apache HTTP Server. It relies on the mod_status feature, which must be explicitly enabled in the Apache configuration to access a machine-readable status page. This plugin allows users to fetch several metrics related to Apache’s operational performance, including worker status, connection statistics, and server load, thereby facilitating effective monitoring and troubleshooting of web server performance in real-time.

TimescaleDB

TimescaleDB is an open source time series database built as an extension to PostgreSQL, designed to handle large scale, time-oriented data efficiently. Launched in 2017, TimescaleDB emerged in response to the growing need for a robust, scalable solution that could manage vast volumes of data with high insert rates and complex queries. By leveraging PostgreSQL’s familiar SQL interface and enhancing it with specialized time series capabilities, TimescaleDB quickly gained popularity among developers looking to integrate time series functionality into existing relational databases. Its hybrid approach allows users to benefit from PostgreSQL’s flexibility, reliability, and ecosystem while providing optimized performance for time series data.

The database is particularly effective in environments that demand fast ingestion of data points combined with sophisticated analytical queries over historical periods. TimescaleDB has a number of innovative features like hypertables which transparently partition data into manageable chunks and built-in continuous aggregation. These allow for significantly improved query speed and resource efficiency.

Configuration

Apache

[[inputs.apache]]
  ## An array of URLs to gather from, must be directed at the machine
  ## readable version of the mod_status page including the auto query string.
  ## Default is "http://localhost/server-status?auto".
  urls = ["http://localhost/server-status?auto"]

  ## Credentials for basic HTTP authentication.
  # username = "myuser"
  # password = "mypassword"

  ## Maximum time to receive response.
  # response_timeout = "5s"

  ## 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

TimescaleDB

# Publishes metrics to a TimescaleDB database
[[outputs.postgresql]]
  ## Specify connection address via the standard libpq connection string:
  ##   host=... user=... password=... sslmode=... dbname=...
  ## Or a URL:
  ##   postgres://[user[:password]]@localhost[/dbname]?sslmode=[disable|verify-ca|verify-full]
  ## See https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING
  ##
  ## All connection parameters are optional. Environment vars are also supported.
  ## e.g. PGPASSWORD, PGHOST, PGUSER, PGDATABASE
  ## All supported vars can be found here:
  ##  https://www.postgresql.org/docs/current/libpq-envars.html
  ##
  ## Non-standard parameters:
  ##   pool_max_conns (default: 1) - Maximum size of connection pool for parallel (per-batch per-table) inserts.
  ##   pool_min_conns (default: 0) - Minimum size of connection pool.
  ##   pool_max_conn_lifetime (default: 0s) - Maximum connection age before closing.
  ##   pool_max_conn_idle_time (default: 0s) - Maximum idle time of a connection before closing.
  ##   pool_health_check_period (default: 0s) - Duration between health checks on idle connections.
  # connection = ""

  ## Postgres schema to use.
  # schema = "public"

  ## Store tags as foreign keys in the metrics table. Default is false.
  # tags_as_foreign_keys = false

  ## Suffix to append to table name (measurement name) for the foreign tag table.
  # tag_table_suffix = "_tag"

  ## Deny inserting metrics if the foreign tag can't be inserted.
  # foreign_tag_constraint = false

  ## Store all tags as a JSONB object in a single 'tags' column.
  # tags_as_jsonb = false

  ## Store all fields as a JSONB object in a single 'fields' column.
  # fields_as_jsonb = false

  ## Name of the timestamp column
  ## NOTE: Some tools (e.g. Grafana) require the default name so be careful!
  # timestamp_column_name = "time"

  ## Type of the timestamp column
  ## Currently, "timestamp without time zone" and "timestamp with time zone"
  ## are supported
  # timestamp_column_type = "timestamp without time zone"

  ## Templated statements to execute when creating a new table.
  # create_templates = [
  #   '''CREATE TABLE {{ .table }} ({{ .columns }})''',
  # ]

  ## Templated statements to execute when adding columns to a table.
  ## Set to an empty list to disable. Points containing tags for which there is
  ## no column will be skipped. Points containing fields for which there is no
  ## column will have the field omitted.
  # add_column_templates = [
  #   '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
  # ]

  ## Templated statements to execute when creating a new tag table.
  # tag_table_create_templates = [
  #   '''CREATE TABLE {{ .table }} ({{ .columns }}, PRIMARY KEY (tag_id))''',
  # ]

  ## Templated statements to execute when adding columns to a tag table.
  ## Set to an empty list to disable. Points containing tags for which there is
  ## no column will be skipped.
  # tag_table_add_column_templates = [
  #   '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
  # ]

  ## The postgres data type to use for storing unsigned 64-bit integer values
  ## (Postgres does not have a native unsigned 64-bit integer type).
  ## The value can be one of:
  ##   numeric - Uses the PostgreSQL "numeric" data type.
  ##   uint8 - Requires pguint extension (https://github.com/petere/pguint)
  # uint64_type = "numeric"

  ## When using pool_max_conns > 1, and a temporary error occurs, the query is
  ## retried with an incremental backoff. This controls the maximum duration.
  # retry_max_backoff = "15s"

  ## Approximate number of tag IDs to store in in-memory cache (when using
  ## tags_as_foreign_keys). This is an optimization to skip inserting known
  ## tag IDs. Each entry consumes approximately 34 bytes of memory.
  # tag_cache_size = 100000

  ## Cut column names at the given length to not exceed PostgreSQL's
  ## 'identifier length' limit (default: no limit)
  ## (see https://www.postgresql.org/docs/current/limits.html)
  ## Be careful to not create duplicate column names!
  # column_name_length_limit = 0

  ## Enable & set the log level for the Postgres driver.
  # log_level = "warn" # trace, debug, info, warn, error, none

Input and output integration examples

Apache

1. Real-Time Performance Monitoring: Use the Apache input plugin to set up a real-time dashboard displaying critical performance metrics of your Apache server. By visualizing metrics such as BusyWorkers, and Load averages, you can quickly identify performance bottlenecks and server health issues, aiding in proactive management of web traffic loads.

2. Automated Alerting for Server Issues: Implement alerts based on metrics collected by this plugin to notify administrators in case of performance degradation. For instance, if the BusyWorkers metric exceeds a certain threshold, automatic alerts can be triggered, ensuring prompt incident response to maintain uptime and service reliability.

3. Historical Performance Analysis: Combine data collected by the Apache plugin with long-term storage solutions to track performance trends over time. This accumulated data helps in understanding usage patterns, forecasting resource needs, and making informed decisions regarding server scaling or optimization.

4. Cross-System Monitoring: Integrate metrics gathered from Apache alongside metrics from other components of your web stack using Telegraf’s capabilities to send data to a centralized monitoring solution. This holistic view can simplify troubleshooting and coordination between different technologies, ensuring optimal system performance across the board.

TimescaleDB

1. Real-Time IoT Data Ingestion: Use the plugin to collect and store sensor data from thousands of IoT devices in real time. This setup facilitates immediate analysis, helping organizations monitor operational efficiency and respond quickly to changing conditions.

2. Cloud Application Performance Monitoring: Leverage the plugin to feed detailed performance metrics from distributed cloud applications into TimescaleDB. This integration supports real-time dashboards and alerts, enabling teams to swiftly identify and mitigate performance bottlenecks.

3. Historical Data Analysis and Reporting: Implement a system where long-term metrics are stored in TimescaleDB for comprehensive historical analysis. This approach allows businesses to perform trend analysis, generate detailed reports, and make data-driven decisions based on archived time-series data.

4. Adaptive Alerting and Anomaly Detection: Integrate the plugin with automated anomaly detection workflows. By continuously streaming metrics to TimescaleDB, machine learning models can analyze data patterns and trigger alerts when anomalies occur, enhancing system reliability and proactive maintenance.

Feedback

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