Etcd Memory Test

Etcd relies heavily on memory for efficient data storage and access. etcd's memory usage increases with the size of the stored data, the number of keys, and active connections. It caches frequently accessed data in memory to reduce disk I/O and improve performance. As a result, memory consumption can grow depending on the workload. High memory usage may indicate large datasets or frequent read/write operations. Optimizing etcd's memory usage involves proper resource allocation, tuning parameters, and scaling based on cluster needs.

Monitoring etcd memory is crucial to ensure optimal performance, as excessive memory usage can lead to slow response times or failures. etcd stores critical Kubernetes data, and high memory usage may indicate issues like inefficient queries or growing datasets. Regular monitoring helps prevent memory exhaustion, allowing timely adjustments to improve performance and ensure cluster stability.

The Etcd Memory Test continuously monitors the Etcd memory and reports the key metrics. Through the analysis of these metrics administrators can identify if there are any issues with system.

Target of the test : A Kubernetes Master Node

Agent deploying the test : A remote agent

Outputs of the test : One set of results for the target Kubernetes master node being monitored

Configurable parameters for the test
Parameter	Description
Test Period	How often should the test be executed.
Host	The IP address of the host for which this test is to be configured.
Port	Specify the port at which the specified Host listens. By default, this is 6443.
Timeout	Specify the duration (in seconds) beyond which the test will timeout in the Timeout text box. The default value is 10 seconds.
Health URL	The Kubernetes API server has a built-in health check endpoint. Enter the Health URL in Health URL text box.
Metric URL	Each of the Kubernetes system components expose monitoring metrics through /metrics endpoint of the HTTP server. For components that don't expose endpoint by default, refer official Kubernetes distribution documentation site. Specify the metric URL textbox.

Measurements made by the test
Measurement	Description	Measurement Unit	Interpretation
Allocated memory and still in use	Indicates the amount of memory reserved for a process or application, but they have not been released or freed.	MB
Allocated memory, even if freed	Indicates the amount of memory reserved for a process or application including the freed memory.	MB	This can lead to increased memory consumption, eventually causing performance degradation or crashes if the system runs out of memory. Monitoring memory usage and analyzing memory allocation patterns can help identify and mitigate issues like memory leaks or excessive caching.
Memory used by profiling bucket hash table	Indicates the memory used by profiling bucket hash table.	MB	The memory used by such a hash table depends on several factors, including the number of keys, the size of the values associated with each key, and the structure of the hash table itself.
Pointer lookups	Indicates the number of pointer lookups.	Number	A pointer is a variable that holds the memory address of another variable or data structure. When you perform a pointer lookup, you're dereferencing the pointer to access the value or data it points to.
Mallocs	Indicates the number of dynamically allocated memory operations.	Number	Malloc requires careful management to avoid issues such as memory leaks and fragmentation.
Other system memory	Indicates the size of other system memory.	MB
Total Memory size	Indicates the total amount of memory used by all components of Etcd.	MB
Total freed heap objects	Indicates the number of objects on heap which are no longer in use and have been Garbage collected.	Number	Efficient memory management, including garbage collection and the freeing of unused heap memory, is crucial for the performance and stability of etcd.
Heap memory waiting to be used	Indicates the heap memory available for use.	MB
Used heap memory	Indicates the portion of memory dynamically allocated during the lifetime of the application.	MB	This memory is used for storing data that is created and modified at runtime, such as key-value pairs, internal data structures (e.g., B+ trees, indexes), and objects allocated on the heap in Go. Unlike stack memory, which is used for function calls and local variables, heap memory is managed manually by the garbage collector in Go.
Heap allocated objects	Indicates the number of objects on heap which are in use.	Number
Heap memory released to OS	Indicates the size of memory released and freed up for the use by OS.	MB
Heap memory	Indicates the size of total dynamically allocated memory.	MB
Heap memory in-use before next GC	Indicates the size of heap memory before the garbage collection.	MB
Used cache memory	Indicates the memory used to store data temporarily for fast access.	MB	Etcd uses various caching mechanisms to speed up read and write operations, minimizing the need to repeatedly access disk or perform expensive operations. This is particularly important for etcd as it is the core data store in Kubernetes, and its performance is critical for the overall cluster health.
Cache memory	Indicates the total cache memory available to store data temporarily for fast access.	MB
Used span memory	Indicates the total span memory used by Etcd.	MB	In Go memory is allocated in large blocks called "spans". These spans are chunks of memory that the Go runtime allocates to manage heap objects efficiently
Span memory	Indicates the total span memory available to store data.	MB
Used stack memory	Indicates the total stack memory used by Etcd to store temporary variables for program execution.	MB	It is called "stack" memory because of the way it is structured: memory is allocated and deallocated in a Last-In-First-Out (LIFO) order, similar to how a stack works in data structures.
Stack memory	Indicates the total stack memory available for Etcd to store temporary variables for program execution.	MB	It is fast and automatically managed but limited in size. Understanding how stack memory works, especially in languages like Go (used by etcd), is important for efficient programming and avoiding issues like stack overflows.
Fraction of available CPU time used by the GC	Indicates the fraction of available CPU time used by the GC	MB	The garbage collector periodically stops the program’s execution to perform the cleanup, which can result in CPU overhead. This can affect the performance of applications, particularly in memory-intensive workloads like etcd.
Memory used by garbage collection system metadata	Indicates the memory used for collecting the metadata for garbage collector.	MB	This metadata is essential for tracking objects, references, and the lifecycle of memory, but it also consumes resources. By understanding the nature of this metadata and monitoring relevant Go runtime metrics, you can optimize memory usage and ensure the garbage collector operates efficiently.
Time since last GC	Indicates the time spent since the last Garbage collection was run.	Seconds