Apache and DuckDB Integration
Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.
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Time series database
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Table of Contents
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.
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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 plugin enables Telegraf to write structured metrics into DuckDB using SQLite-compatible SQL connections, supporting lightweight local analytics and offline metric analysis.
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.
DuckDB
Use the Telegraf SQL plugin to write metrics into a local DuckDB database. DuckDB is an in-process OLAP database designed for efficient analytical queries on columnar data. Although it does not provide a traditional client-server interface, DuckDB can be accessed via SQLite-compatible drivers in embedded mode. This allows Telegraf to store time series metrics in DuckDB using SQL, enabling powerful analytics workflows using familiar SQL syntax, Jupyter notebooks, or integration with data science tools like Python and R. DuckDB’s columnar storage and vectorized execution make it ideal for compact and high-performance metric archives.
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
DuckDB
[[outputs.sql]]
## Use the SQLite driver to connect to DuckDB via Go's database/sql
driver = "sqlite3"
## DSN should point to the DuckDB database file
dsn = "file:/var/lib/telegraf/metrics.duckdb"
## SQL INSERT statement with placeholders for metrics
table_template = "INSERT INTO metrics (timestamp, name, value, tags) VALUES (?, ?, ?, ?)"
## Optional: manage connection pooling
# max_open_connections = 1
# max_idle_connections = 1
# conn_max_lifetime = "0s"
## DuckDB does not require TLS or authentication by default
Input and output integration examples
Apache
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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.
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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. -
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.
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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.
DuckDB
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Embedded Metric Warehousing for Notebooks: Write metrics to a local DuckDB file from Telegraf and analyze them in Jupyter notebooks using Python or R. This workflow supports reproducible analytics, ideal for data science experiments or offline troubleshooting.
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Batch Time-Series Processing on the Edge: Use Telegraf with DuckDB on edge devices to log metrics locally in SQL format. The compact storage and fast analytical capabilities of DuckDB make it ideal for batch processing and low-bandwidth environments.
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Exploratory Querying of Historical Metrics: Accumulate system metrics over time in DuckDB and perform exploratory data analysis (EDA) using SQL joins, window functions, and aggregates. This enables insights that go beyond what typical time-series dashboards provide.
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Self-Contained Metric Snapshots: Use DuckDB as a portable metrics archive by shipping
.duckdb
files between systems. Telegraf can collect and store data in this format, and analysts can later load and query it using the DuckDB CLI or integrations with tools like Tableau and Apache Arrow.
Feedback
Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.
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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