Prometheus vs InfluxDB: Choosing the Best Time-Series Database for Monitoring

When it comes to monitoring the performance and health of your applications, systems, and infrastructure, time-series data plays a key role. A time-series database is essential for managing and analyzing this data effectively.

InfluxDB and Prometheus are two of the most popular open-source tools for handling time-series data. They are both widely used, but each serves a different purpose and has its own advantages. InfluxDB has a broad range of time-series data storage capabilities, including system metrics and IoT data, while Prometheus is popular for monitoring real-time metrics and cloud-native environments.

What Is Prometheus?
Prometheus is an open-source monitoring and alerting toolkit developed by SoundCloud and later contributed to the Cloud Native Computing Foundation (CNCF). It is widely adopted for monitoring the health of applications, microservices, containers, and infrastructure, particularly in Kubernetes-based environments.
Prometheus collects and stores metrics in a time-series format, where each time-series is identified by a metric name and associated labels (key-value pairs). Prometheus uses a pull-based model to scrape data from various sources like application endpoints, servers, or exporters.
Key Features of Prometheus:
    Pull-based model: Prometheus scrapes metrics from configured endpoints, which allows for a decentralized and flexible architecture.
    PromQL: A powerful query language designed specifically for time-series data. PromQL allows for aggregating, filtering, and visualizing metrics.
    Alerting: Built-in alerting capabilities through Alertmanager, enabling users to define alert rules based on metric values.
    Data retention: Prometheus stores data on disk using a custom, time-series optimized format and allows you to configure retention periods manually.
    Integration with Grafana: Prometheus integrates seamlessly with Grafana to visualize metrics on customizable dashboards.
    
What Is InfluxDB?
InfluxDB is another popular open-source time-series database developed by InfluxData. Unlike Prometheus, which is primarily focused on monitoring and alerting, InfluxDB is a more general-purpose time-series database that can handle various types of time-series data, including metrics, events, logs, and IoT data.
InfluxDB follows a push-based model, where data is written to the database using an HTTP API or other ingestion methods like Telegraf (an open-source agent for collecting, processing, and sending metrics).
Key Features of InfluxDB:
    Push-based model: Data is pushed to InfluxDB either via its API or through Telegraf agents, making it suitable for scenarios where the data is generated by external systems or devices.
    InfluxQL and Flux: InfluxDB uses InfluxQL, a SQL-like query language, for querying time-series data. Flux is a more powerful, functional query language that enables complex transformations, aggregations, and analytics.
    Continuous queries: InfluxDB supports continuous queries to automatically downsample and aggregate data over time, making it ideal for long-term data retention and historical analysis.
    Retention policies: InfluxDB allows users to define automatic retention policies, meaning older data can be automatically dropped or downsampled as needed.
    Clustering and High Availability: InfluxDB Enterprise provides support for clustering, data replication, and high availability (HA), enabling horizontal scaling for large-scale environments.
    Integration with Grafana: Like Prometheus, InfluxDB integrates with Grafana for visualizing time-series data on interactive dashboards.

Prometheus vs InfluxDB: A Detailed Comparison

Feature/Aspect	Prometheus	InfluxDB
Data Model	Pull-based with metric names and labels	Push-based with measurements, tags, and fields
Data Collection Model	Pull-based (scraping)	Push-based (data is sent to InfluxDB)
Query Language	PromQL (Prometheus Query Language)	InfluxQL (SQL-like) / Flux (more advanced)
Alerting	Built-in alerting with Alertmanager	Built-in alerting with Kapacitor
Data Retention	Configurable retention period through prometheus.yml	Automatic retention policies and continuous queries
Scalability	Federation for horizontal scaling, no native clustering in open-source	Clustering and horizontal scaling available in Enterprise version
Storage	Time-series optimized format with local storage	Time-series optimized with Time-Structured Merge Tree (TSM)
Integration with Grafana	Seamless integration with Grafana for dashboards	Seamless integration with Grafana for dashboards
Best Use Cases	Monitoring metrics for cloud-native and containerized applications, particularly in Kubernetes environments	General-purpose time-series storage for metrics, IoT, logs, and events
Ecosystem	Strong ecosystem with exporters for various services	Part of InfluxData stack (Telegraf, Kapacitor, Chronograf)
Cost	Free and open-source, though scaling may require additional components like Cortex or Thanos	Free open-source version, but scaling and clustering require Enterprise version

 

HAProxy Log Rotation Not Working? Here’s How to Fix It

When running HAProxy in production, it's crucial that log files are rotated properly to prevent excessive disk usage and system slowdowns. If HAProxy logs are not rotating as expected, it could lead to your disk filling up, affecting the performance and reliability of your system.
If your HAProxy logs are not rotating, it could be due to several possible reasons.
 In this post, we'll walk through the most common causes of log rotation issues, how to troubleshoot them, and provide a real-world use case with a solution.

1.Logrotate Configuration Missing or Incorrect
HAProxy typically uses logrotate to handle log file rotation. If your log files are not rotating, it could be due to a missing or misconfigured logrotate configuration.
How to Check Logrotate Configuration:
Ensure there is a logrotate configuration file for HAProxy in /etc/logrotate.d/
It should look similar to the following:
 /var/log/haproxy.log {
        daily
        missingok
        rotate 7
        compress
        notifempty
        create 0640 haproxy adm
        sharedscripts
        postrotate
     /etc/init.d/haproxy reload > /dev/null 2>/dev/null || true
        endscript
    }

Explanation of Directives:
daily: Rotate the log files daily. You can also use weekly, monthly, etc., depending on your requirements.
rotate 7: Keep 7 backup log files before deleting the oldest.
compress: Compress old log files to save disk space.
create 0640 haproxy adm: This ensures that new log files are created with proper permissions (0640), and the owner is set to haproxy, with the group as adm.
postrotate: This ensures that HAProxy is reloaded after log rotation to begin writing to the new log file. If HAProxy is still writing to the old log file, logrotate will not be able to rename the rotated file.

Troubleshooting:
If the logrotate configuration is missing or incorrectly configured, you can either create or update the configuration file as shown above.
To check if logrotate is working correctly, run the following command to simulate the log rotation process:
sudo logrotate -d /etc/logrotate.conf
This command will display what logrotate would do, but will not actually rotate any logs. This is useful for troubleshooting.

2. Permissions Issues
If the HAProxy log files are not being written to or rotated due to permission issues, you need to verify that HAProxy has write access to its log file and the directory.
Check the permissions of /var/log/haproxy.log and ensure the user HAProxy runs as (usually haproxy) has the correct permissions:
ls -l /var/log/haproxy.log
Check that the logrotate user (usually root) has the necessary permissions to rotate the file.
If permissions are incorrect, adjust them with chown and chmod:
sudo chown haproxy:adm /var/log/haproxy.log
sudo chmod 0640 /var/log/haproxy.log

3. Log Output Configuration in HAProxy
HAProxy must be configured to log to a file (e.g., /var/log/haproxy.log). Ensure your HAProxy configuration includes proper logging directives:
In /etc/haproxy/haproxy.cfg, make sure you have something like the following:
global
    log /dev/log local0
defaults
    log     global
    option  httplog
This tells HAProxy to log to the syslog facility local0, which is often associated with the HAProxy logs. If this is not set correctly, HAProxy may not be logging to the expected location.

4. Logfile Being Open by HAProxy Process
If the HAProxy process is holding the log file open (e.g., if HAProxy is still running with the old log file after rotation), logrotate might fail to rename the file. You can ensure that HAProxy is properly reloading by sending a SIGHUP signal to HAProxy, or by using the postrotate script in the logrotate config (mentioned above).
To manually reload HAProxy, you can:
sudo systemctl reload haproxy
or
sudo service haproxy reload

5. Logrotate Not Running
If logrotate is not running automatically (e.g., if the cron job for logrotate is not configured or working), the logs will not rotate.
Check cron jobs: Ensure that the logrotate cron job is enabled. You can check cron jobs by listing them with:
crontab -l
Alternatively, check if the logrotate service is running (on systems that use systemd):
systemctl status logrotate
To test logrotate manually, run:
sudo logrotate /etc/logrotate.conf

6. Disk Space Issues
If your disk is full, logrotate may not be able to create new log files or rotate old ones. You can check disk usage with:
df -h
If the disk is full, free up some space or increase the disk size.