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Metrics Monitoring and Alerting System : A robust log and metrics collection system is vital for monitoring application performance, diagnosing issues, and maintaining security. It enables teams to track resource usage, detect anomalies, and receive real-time alerts to proactively prevent system failures.
By correlating logs with metrics, organizations can optimize performance, automate incident response, and scale efficiently. It also supports compliance efforts, forensic analysis, and business intelligence by delivering actionable insights. Ultimately, such a system enhances reliability, operational efficiency, and informed decision-making across both technical and business teams.
Metric Sources: In the log collection process, metrics are derived from various sources, including system-level data (e.g., CPU usage, memory utilization, disk activity, and network traffic), application-level indicators (e.g., request latency, error rates, and database query performance), and custom business metrics (e.g., user transactions, conversion rates, and feature adoption). These metrics are collected from logs, traces, and event data, typically through the use of advanced monitoring tools.
Metrics collection can be achieved using either the push or pull method. In the pull method, a collection service periodically retrieves data from servers by querying a predefined list, typically through an HTTP endpoint like /metrics, which requires a client library on the server. However, managing dynamic server IPs and handling the addition or removal of servers can introduce complexity. In the push method, servers send metrics directly to a collector endpoint, eliminating the need to track server IPs. To manage high traffic volumes, the collector should be auto-scalable, with incoming data routed through a load balancer to ensure the system remains stable and avoids overload.
Message Queue: To prevent downstream systems from being overwhelmed by the high volume of incoming metrics, a message queue can be used to buffer and manage the data flow from metric collectors. Technologies like Kafka are commonly employed for this purpose. To ensure scalability and reliability, the message queue cluster should ideally be hosted on the cloud, such as using Pub/Sub or a SaaS solution like Kafka on the cloud.
It’s crucial to avoid creating an unlimited number of topics; instead, each source server should have its own dedicated topic to prevent system overload. While limiting the number of topics, the message payload should remain flexible enough to accommodate a variety of metrics and simplify processing. Additionally, it’s important to route all metrics from the same server to the same partition, ensuring that aggregations at the receiving end are accurate and consistent.
One possible message payload structure can be: {“server”: “A111”, “timestamp”: “2023-10:32T23:33:22”, “metric”: “cpu_utilization”, “value”: “20”, “unit”: “percentage”, “addn_info_1”: “”, “addn_info_2”: “”, “addn_info_3”: “}
The consumer service will aggregate incoming metrics from the message queue and prepare the data for storage in the database. To ensure scalability and efficiency, it's a good idea to distribute the processing across multiple consumers, making Kafka Streams an ideal solution for this use case.
Kafka Streams allows for distributed message processing and integrates seamlessly with Apache Kafka, removing the need for external systems. It processes data in real-time as it arrives in Kafka, enabling near-instantaneous analytics. Unlike Flink or Spark, Kafka Streams operates as a simple Java application, which can be deployed like a microservice without requiring additional infrastructure.
It’s also crucial to account for late-arriving data by using tumbling windows and watermarks, ensuring proper processing of delayed data. Additionally, enabling checkpointing is essential for maintaining reliability and consistency during processing.
Database: Using an RDBMS for time-series data is generally not ideal due to performance bottlenecks, particularly when dealing with large-scale aggregations and time-series operations. Most use cases focus on recent data, making traditional relational databases less efficient for this purpose.
Time-series databases like InfluxDB or Prometheus are better suited for this task, as they support optimized queries, exponential moving averages, and downsampling, allowing data to be stored at various time intervals for improved performance. These databases also employ data encoding, which compresses data before storage, reducing space and enhancing efficiency.
To optimize performance further, it’s beneficial to create tags in the dataset when writing data into InfluxDB, based on system types, metric types, and server groups. InfluxDB excels in high-performance reads based on these tags, and this approach ensures a flexible schema, allowing you to add additional tags as needed for different use cases. For example, a simple moving average calculation in InfluxDB can be achieved with an efficient query.
InfluxDB executes time-window queries significantly faster than traditional databases, primarily due to its time-optimized storage format (TSM). This eliminates the need for full-table scans, enhancing performance. InfluxDB also supports automatic downsampling and pre-aggregation, leveraging precomputed data whenever possible. Additionally, it utilizes tag-based indexing, enabling extremely fast filtering and avoiding the inefficiencies of full-table scans.
Query Service: The query service handles requests from dashboards, executes queries on the time-series database, and returns the results. By separating the visualization layer from the database, it provides flexibility to swap out the dashboard system if needed in the future. If a dedicated query service engine is integrated with the dashboard, this approach can be highly beneficial.
However, if you require a custom query service, you will also need to implement a caching layer to store query results. This cache layer will help prevent the need for costly reprocessing of data for repeated queries, improving overall performance.
Alerting System: The alerting system allows businesses to set threshold-based alerts for important metrics and choose notification methods like email or messages. It periodically queries the system to check for triggers and sends notifications when thresholds are met.
Each Notification Service can have its own queue where the alerting system publishes messages. The service then pulls messages from the queue and triggers the appropriate actions in response.
For better scalability and maintainability, it's a good practice to implement a dedicated microservice for each alerting channel. These microservices can read messages from a messaging queue like Kafka, process the event payloads, and trigger the relevant actions—such as sending an SMS, push notification, or triggering an incident in PagerDuty.
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