This alert is designed to monitor and detect significant increases in the number of evicted keys in GCP Memorystore for Redis. Tracking key evictions is critical for maintaining optimal performance and ensuring that applications relying on Redis for caching and data storage function smoothly. The alert is triggered when the sum of the evicted keys exceeds a specified threshold of 0 over a monitoring window. This indicates that at least one key has been evicted, which may lead to delays in data retrieval and negatively impact overall application performance. Increased evictions can occur due to various reasons, such as insufficient memory allocation, inefficient data management strategies, or high write loads that exceed the available capacity. Prolonged evictions can lead to degraded user experiences, slower response times, and increased latency for Redis operations. Customization Guidance: Threshold: The default threshold is set to 0 evictions. Adjust this threshold to reflect your application’s tolerance for key evictions and historical performance data. Lower thresholds may be appropriate for latency-sensitive applications or those with high throughput requirements. Monitoring Period: The default monitoring period can be set to 10 minutes to ensure timely detection of issues. You may adjust this based on traffic patterns and Redis usage. Shorter intervals may help identify problems during peak usage, while longer periods can help smooth out sporadic spikes in evictions. Evictions Metrics: Consider customizing this alert to focus on specific workloads or Redis instances that are more critical to your operations. Some applications may be more sensitive to key evictions based on their architecture and usage patterns. Notification Frequency: Tune the alert frequency to avoid excessive notifications while ensuring prompt responses to critical issues. Depending on the sensitivity of your operations, you may increase or decrease the frequency of notifications. Action: Upon triggering this alert, immediately investigate the cause of the increased evictions. Review performance metrics, memory usage, and data management strategies for GCP Memorystore for Redis. Consider optimizing data storage strategies, adjusting memory allocation, or implementing auto-scaling to accommodate load. Engage technical support if the issue persists due to infrastructural constraints beyond your control.

This alert is designed to monitor and detect significant increases in the number of blocked clients in GCP Memorystore for Redis. Tracking the count of blocked clients is critical for maintaining optimal performance and ensuring that applications relying on Redis for caching and data storage operate smoothly. The alert is triggered when the sum of blocked clients exceeds a specified threshold of 10 over a monitoring window. This indicates that a significant number of client connections are being blocked, which may lead to delays in data retrieval and negatively impact overall application performance. High blocked clients can occur due to various reasons, such as contention for resources, slow-running commands, or insufficient instance capacity. Prolonged blocking can lead to degraded user experiences, slower response times, and increased latency for Redis operations. Customization Guidance: Threshold: The default threshold is set to 10 blocked clients. Adjust this threshold to reflect your application’s tolerance for blocked connections and historical performance data. Lower thresholds may be appropriate for latency-sensitive applications or those with high throughput requirements. Monitoring Period: The default monitoring period is set to 10 minutes to ensure timely detection of issues. You may adjust this based on traffic patterns and Redis usage. Shorter intervals may help identify problems during peak usage, while longer periods can help smooth out sporadic spikes in blocking. Blocked Clients Metrics: Consider customizing this alert to focus on specific workloads or Redis instances that are more critical to your operations. Some applications may be more sensitive to blocked connections based on their architecture and usage patterns. Notification Frequency: Tune the alert frequency to avoid excessive notifications while ensuring prompt responses to critical issues. Depending on the sensitivity of your operations, you may increase or decrease the frequency of notifications. Action: Upon triggering this alert, immediately investigate the cause of the high blocked clients count. Review performance metrics, command execution times, and infrastructure configurations for GCP Memorystore for Redis. Consider optimizing Redis queries, adjusting instance sizes, or implementing auto-scaling to accommodate load. Engage technical support if the issue persists due to infrastructural constraints beyond your control.

This alert is designed to monitor and detect significant increases in the number of rejected connections in GCP Memorystore for Redis. Tracking rejected connections is crucial for maintaining the reliability of Redis and ensuring that applications depending on Redis for caching and data storage can connect and operate efficiently. The alert is triggered when the sum of rejected connections exceeds a specified threshold of 10 over a 10-minute monitoring window. This indicates that Redis is rejecting a substantial number of client connections, which may cause application disruptions, delays, or failures in accessing Redis. High rejected connections can occur due to various reasons, such as exceeding the maximum number of allowed connections, network issues, or Redis being under heavy load. Prolonged rejection of connections may result in degraded application performance and increased error rates. Customization Guidance: Threshold: The default threshold is set to 10 rejected connections. Adjust this threshold to reflect your application's tolerance for rejected connections and historical performance data. Lower thresholds may be appropriate for applications where maintaining consistent connections is critical to performance. Monitoring Period: The default monitoring period is set to 10 minutes to ensure timely detection of issues. You may adjust this based on Redis usage patterns and traffic. Shorter intervals may help identify connection spikes during peak load, while longer periods can smooth out transient issues. Rejected Connections Metrics: Customize this alert for specific Redis instances or workloads that are critical to your operations. Applications with higher connection rates or traffic surges may require more sensitive thresholds. Notification Frequency: Adjust the notification frequency to prevent alert fatigue while ensuring swift responses to connection issues. Depending on the criticality of your application, you may choose to increase or decrease the frequency of notifications. Action: Upon triggering this alert, immediately investigate the cause of the high rejected connections count. Review Redis connection settings, check for capacity constraints (e.g., connection limits), and monitor the overall load on the Redis instance. Consider increasing the maximum number of allowed connections, scaling the Redis instance, or optimizing connection management within your application. Engage technical support if connection rejections persist due to resource constraints or network issues beyond your control.

This alert is designed to monitor and detect significant increases in memory usage in GCP Memorystore for Redis. Tracking memory usage is critical for maintaining optimal performance and ensuring that applications relying on Redis for caching and data storage operate smoothly. The alert is triggered when the average memory usage ratio exceeds a specified threshold of 80% over a monitoring window. This indicates that the Redis instance is consuming a significant amount of memory, which may lead to degraded performance, potential key evictions, or failures to store new data if memory limits are reached. High memory usage can occur due to various reasons, such as increased data ingestion, suboptimal memory configuration, or inefficient key expiration strategies. Prolonged high memory usage can lead to evictions, slower response times, and negatively impact overall application performance. Customization Guidance: Threshold: The default threshold is set to 80%. Adjust this threshold to reflect your application’s tolerance for memory usage and historical performance data. Lower thresholds may be appropriate for applications with strict memory limits or high data churn. Monitoring Period: The default monitoring period can be set to 10 minutes to ensure timely detection of issues. You may adjust this based on traffic patterns and Redis usage. Shorter intervals may help identify problems during peak usage, while longer periods can help smooth out sporadic spikes in memory usage. Memory Usage Metrics: Consider customizing this alert to focus on specific Redis roles (e.g., master, replica) or instances that are more critical to your operations. Some applications may be more sensitive to memory consumption based on their architecture and data storage needs. Notification Frequency: Tune the alert frequency to avoid excessive notifications while ensuring prompt responses to critical issues. Depending on the sensitivity of your operations, you may increase or decrease the frequency of notifications. Action: Upon triggering this alert, immediately investigate the cause of the increased memory usage. Review memory allocation, key expiration strategies, and data ingestion patterns in GCP Memorystore for Redis. Consider optimizing memory configurations, adjusting instance sizes, or implementing auto-scaling to handle higher loads. Engage technical support if the issue persists due to infrastructural constraints beyond your control.