Performance problems are indicated by long suspensions, random long GC pauses, and most GCs occurring as full blocking GCs.
If the long GC pauses affect the performance metrics that are important to you, you should prioritize and debug the issue. owever, if they don't affect these metrics, it may be more productive to focus on other issues.
The GCStats view in PerfView is used to show the symptoms of performance problems.
Suspension should normally take less than 1 millisecond each time it occurs.
If the suspension takes in the 10s or 100s of milliseconds, especially consistently, it's a definitive sign of a performance problem.
The "Suspend Msec" and "Pause Msec" columns indicate the duration of suspension and pause time respectively for each GC.
"Random long GC pauses" refers to unexpected long pauses by the GC, even when it doesn't promote more memory than usual.
If a GC doesn't promote more than usual but takes much longer, it's an indication that something went wrong during that GC.
Full blocking GCs usually take a while, especially with a large heap. If most GCs are happening as full blocking GCs, it can ignificantly slow down the system.
The purpose of a full blocking GC is to reduce the heap size, so the memory load is no longer high.
The "provisional mode" is a method used by the GC to handle high memory load and heavy pinning situations, to avoid doing more full blocking GCs than necessary.
The most common cause for this is actually induced full blocking GCs.
The GCStats view in PerfView shows the trigger reason for each GC.
"Perf problems" is short for performance problems, which are issues that cause a system or application to run less efficiently.
GC suspension refers to the time during which the Garbage Collector stops the execution of programs so it can reclaim memory.
The GC index is a reference to a specific Garbage Collector activity in the log or metrics data.
'Pause Msec' refers to the time taken by the Garbage
If most of your GC pauses are taken up by suspension, it indicates a performance problem that needs to be debugged.
The "Promoted MB" column indicates the amount of memory (in megabytes) that has been promoted or moved to a longer-lived generation by the garbage collector.
"2NI" stands for second-generation non-incremental collection, a type of full blocking GC.
If you observe that a GC doesn't promote more than usual but takes much longer, what should be your next steps?
This is an indication that something went wrong during that GC. You should debug this issue to understand the cause.
You can refer to detailed examples of debugging a long suspension issue provided in resources such as this blog entry.
"Induced" means the GC was manually triggered by the application code, rather than being automatically triggered by the system's memory management.
Yes, the GC has ways to combat challenging scenarios, such as the provisional mode for high memory load and heavy pinning, to avoid doing more full blocking GCs than necessary.
During a GC suspension, the garbage collector stops the execution of an application so it can clean up and reclaim memory, which can result in a performance issue if the suspension time is too long.
What can you infer from the GCStats view if the 'Pause Msec' is significantly higher than the 'Suspend Msec' for a GC?
If the 'Pause Msec' is significantly higher than the 'Suspend Msec', it implies that other aspects of the GC process (like compacting memory or determining what to collect) are taking a long time, and may need to be investigated.
The GCStats view in PerfView provides detailed metrics about garbage collection activities, such as suspension times, pause times, and promotion rates. This information can help identify and debug performance issues.
Why is it important to provide the file version of the runtime when debugging performance issues related to garbage collection (GC)?
The file version of the runtime helps identify the specific GC changes in that version, allowing us to provide more accurate guidance and solutions.
You can use the following PowerShell command: (Get-Item C:\Windows\Microsoft.NET\Framework64\v4.0.30319\clr.dll).VersionInfo.FileVersion
You can use the following PowerShell command: (Get-Item C:\temp\coreclr.dll).VersionInfo.FileVersion
Yes, you can use the lmvm command in the debugger to retrieve the file version. For example: lmvm coreclr
In the ETW trace, look for the KernelTraceControl/ImageID/FileVersion event, which provides the file version information.
The version information is still valuable as it might not always be captured in the trace, and having it readily available saves time in the debugging process.
Share the diagnostics you have conducted and the conclusions you reached. This helps us understand what you have already tried and enables us to provide more targeted assistance.
Perf data allows us to pinpoint the problem accurately. Without it, we can only offer general guidelines and suggestions.
Use the command PerfView /nogui /KernelEvents=Process+Thread+ImageLoad+Profile /ClrEvents:GC+Stack /clrEventLevel=Informational /BufferSize:3000 /CircularMB:3000 /MaxCollectSec:600 collect gc-with-cpu.etl to collect a GC trace for 10 minutes.
Yes, collecting a trace with CPU samples can provide more detailed information about GC work. You can include CPU samples in the same command: PerfView /nogui /KernelEvents=Process+Thread+ImageLoad+Profile /ClrEvents:GC+Stack /clrEventLevel=Informational /BufferSize:3000 /CircularMB:3000 /MaxCollectSec:600 collect gc-with-cpu.etl
Use the command PerfView /nogui /KernelEvents=Process+Thread+ImageLoad+Profile /ClrEvents:GC+Stack /clrEventLevel=Informational /BufferSize:3000 /CircularMB:3000 /MaxCollectSec:600 collect gc-with-cpu.etl to collect a GC trace for 10 minutes.
We might ask you to collect additional traces to gather more information and identify the root cause of the performance issues.
Memory dumps are generally not ideal for performance problem investigation. However, if no traces are available, we can analyze the dumps if you provide them and ensure there are no privacy concerns.
Yes, performance engineers can work with memory dumps if no traces are available. However, traces are generally more helpful for investigating performance problems.
If you have memory dumps and can share them, ensure that there are no privacy concerns and that performance engineers have access to the dumps for analysis.
The specific version and commit information are typically included in the file version string. For example, "42,42,42,42424 @Commit: a545d13cef55534995115eb5f761fd0cecf66fc1".
Is it necessary to provide the version info if there is a time zone difference between the performance engineer and the person seeking help?
Yes, it's still important to provide the version info, even if there is a time zone difference, to expedite the troubleshooting process and avoid additional delays.
How can performance engineers make adjustments to the information they provide based on your diagnostics?
By sharing your diagnostics, performance engineers can understand how the provided information helped or didn't help in your specific case, allowing them to refine their guidance and support for future customers.
Changes in memory behavior can occur not only due to changes in the garbage collector (GC), but also due to changes in the library code you are using. Upgrading .NET involves changes in both the runtime and the libraries, which can affect allocation and survival patterns. It's essential to consider the impact of these changes when analyzing memory regressions.
When upgrading .NET, both the runtime code (including the GC) and the library code undergo changes. These changes can alter the allocation and survival patterns, leading to variations in memory behavior. It's important to understand that your process includes not only your code but also the libraries you use, which can have a significant impact on memory usage.
To determine if the larger heap size is caused by your code or the libraries you are using, you can capture a trace with top-level GC metrics. Check if there are full blocking GCs marked as "2N" (2: full GC, N: blocking) in the trace. If these GCs occur and the heap size remains large, it indicates that either your code or the libraries are holding onto more memory. Analyzing the differences between the old and new versions of the trace can help diagnose the problem further.
If you are experiencing memory regressions after upgrading .NET, you can begin the analysis by checking if there is an increase in allocations. Measure allocations using various techniques explained in the "Measure allocations" section. If the issue is not related to allocations, collecting a trace with top-level GC metrics on both the old and new versions can provide insights into the differences and help diagnose the problem. Keep in mind that diagnosing behavior changes resulting from both runtime and library changes can be challenging due to the significant number of modifications.
Starting from .NET 6, efforts have been made to maintain backward compatibility of the interface between the GC and the rest of the runtime. To isolate GC changes when upgrading .NET, you can use a standalone GC DLL that applies only the GC changes. Another option is to replace only the coreclr.dll to isolate runtime changes from library changes. However, it's important to note that changes at the system.private.corelib.dll layer may introduce significant interface modifications between releases.
If the GC is not collecting certain objects, it indicates that these objects are still being held live by user roots. When a full blocking GC occurs, the GC does not participate in determining object lifetime. If an object remains on the heap, it means that either your code or the library code you are using is holding it alive. This behavior is by design, as the GC must not collect objects that are still in use.
A larger heap size and full blocking GCs can occur if the heap is at its smallest possible size and there are objects that are still held onto by the application or libraries. If you observe full blocking GCs and a larger heap size, it suggests that memory is being held and not reclaimed. You can use tools to capture a trace with top-level GC metrics to analyze the situation further.
To identify whether the regression is caused by allocation changes or survival pattern changes, you can collect a trace that includes top-level GC metrics for both the old and new versions. Analyze the differences in the allocation and survival patterns between the two traces to gain insights into the category of problems causing the regression.
If the heap size remains large after a full blocking GC, it indicates that something is holding onto the memory and preventing the GC from reclaiming it. Possible causes can include pinned objects that the GC cannot move or objects held live by user roots. Investigating pinning and user roots can help identify the source of the memory retention.
During the first few full blocking GCs, if the heap size doesn't decrease, it means there are objects still held live by user roots. The GC's role is to collect objects that are no longer live, but if something is still holding onto these objects during the initial GCs, they won't be collected until they are no longer held live.
Finalization can affect the collection of objects by promoting them to a higher generation so that their finalizers can run. If a finalizable object holds references to other managed objects, those objects can only be collected when the finalizable object itself is collected. Delays in finalizer execution can cause objects to remain live and persist across multiple GC cycles.
The GC may not collect an object immediately after calling GC.Collect if the object's lifetime is extended by the JIT compiler. The JIT compiler can optimize code by extending the lifetime of objects until the end of a method. To ensure that an object is collected, you can place its usage in a separate method and disable inlining to avoid lifetime extension.
Although .NET strives to abstract away OS differences, some variations can still affect behavior. OS differences can impact performance and functionality, such as case sensitivity in directory names or different behaviors of OS APIs. It's important to be aware of these differences and capture performance data to investigate any variations that arise.
The "% time in GC" counter represents the percentage of elapsed time spent in performing a GC since the last GC cycle. It's not an average value but rather reflects the last observed value. If the counter shows 99%, it may indicate that a recent expensive GC occurred. To obtain a more accurate representation, consider capturing an event trace with top-level GC metrics or sampling the counter more frequently.
Top-level application metrics and performance goals provide a clear indication of the performance aspects of your application. These metrics, such as request latency or concurrent requests, directly reflect the performance of your application. Setting perf goals helps you track regressions or improvements as you develop your product and allows you to measure the impact of optimizations.
Workload variations can make it challenging to have stable top-level application metrics. Workloads may fluctuate from day to day, especially with tail latency measurements. To combat this, it's important to measure factors that can affect these metrics and adaptively add new factors as you discover them. Additionally, tracking component-level metrics that reflect workload variations, such as allocation rate for memory, can provide insights into changes in stress on the garbage collector (GC).
The relevant top-level GC metrics to track depend on your performance goals. For throughput, you should monitor metrics such as "% Pause time in GC" and "% CPU time in GC" to understand the impact of GC pauses and CPU usage during GC. For tail latency, it's important to measure individual GC pauses to assess their contribution to latency. In terms of memory footprint, tracking the GC heap size histogram provides insights into memory usage patterns.
As a performance engineer, you should start worrying about GC when the relevant GC metrics indicate that GC has a significant impact on your application's performance goals. If the GC metrics suggest that GC has a small effect, it would be more productive to focus efforts elsewhere. However, if the metrics indicate a substantial impact, it's time to delve into managed memory analysis to identify and address performance issues.
GC can impact throughput by interrupting threads in two ways. First, GC can pause threads during garbage collection, either for the entire duration of the GC (blocking GC) or for a short time (background GC). The "% Pause time in GC" metric indicates the percentage of time that threads are paused due to GC. Second, GC threads compete for CPU resources with application threads, even in the case of background GC. The "% CPU time in GC" metric reflects the CPU usage by GC threads.
To understand and improve tail latency, it's essential to measure individual GC pauses and assess their contribution to latency during long requests. By analyzing GC pauses and their impact on tail latency, you can identify whether GC is a significant factor affecting latency and take appropriate optimization measures.
GC metrics, such as the GC heap size histogram, provide insights into the memory footprint of a .NET application. By monitoring the GC heap size and understanding the relationship between the GC heap and the total process memory size, you can determine if the GC heap size is a significant portion of the overall memory usage. This helps determine if optimizing the GC heap size is a priority for memory footprint optimization.
The primary GC metric to consider for memory footprint optimization is the GC heap size histogram. This metric provides information about the distribution of memory allocations in the GC heap, helping you understand the memory usage patterns and identify opportunities for optimization.
To measure the impact of factors that affect performance metrics, you can use various techniques such as A/B testing, performance profiling, and monitoring. By isolating and manipulating individual factors while keeping others constant, you can analyze their influence on performance and identify areas for improvement.
Understanding GC fundamentals is crucial in performance analysis because the behavior of the garbage collector directly impacts memory management and overall application performance. Having knowledge of GC concepts, such as generations, garbage collection modes, and heap compaction, helps in diagnosing and optimizing GC-related performance issues.
To determine if GC pauses contribute to latency issues, you can analyze individual GC pauses during the execution of latency-sensitive operations. By correlating latency spikes with GC pause events, you can assess their impact on request processing time and identify any optimization opportunities.
Measuring CPU time in GC provides insights into the amount of CPU resources consumed by garbage collection. It helps evaluate the efficiency of the GC algorithm and its impact on the overall CPU utilization of the application. Monitoring CPU time in GC can identify scenarios where GC is competing for CPU resources with application threads.
High CPU time in GC can be caused by factors such as excessive memory allocations, frequent garbage collection due to memory pressure, inefficient object finalization, or complex object graph traversals. Identifying the specific causes requires analyzing GC logs, heap snapshots, and performance profiles to pinpoint areas for optimization.
To analyze GC pauses, you can collect detailed GC logs, including the duration and frequency of each pause. Additionally, using performance profiling tools or memory profilers, you can examine heap snapshots and object lifetimes to understand the objects and memory regions contributing to GC pauses.
The GC heap size represents a specific portion of the overall process memory size. While the GC heap accounts for managed object storage, the process memory size includes additional memory regions such as native allocations, loaded libraries, and thread stacks. Understanding this relationship helps differentiate between the GC heap and other memory usage in your application.
Optimizing the GC heap size involves balancing memory consumption and performance. Techniques such as reducing unnecessary allocations, managing large objects efficiently, and tuning GC-related settings can help control the size of the GC heap. Additionally, monitoring the GC heap size and its impact on memory footprint can guide optimization efforts.
A fragmented GC heap can result from long-lived objects, pinned objects, and objects with varying lifetimes. Fragmentation can occur when objects are allocated and deallocated in non-contiguous memory regions, leading to inefficient memory utilization. Addressing fragmentation involves strategies such as compacting the heap and optimizing allocation patterns.
To diagnose and optimize memory usage, you can utilize memory profilers and heap analysis tools to identify memory leaks, excessive allocations, and inefficient memory utilization patterns. Analyzing object lifetimes, finalization behavior, and large object allocations can guide optimization efforts for efficient memory management.
In virtual memory fundamentals, reserved memory refers to a range of virtual addresses that are allocated for future use, but they do not consume physical memory or have storage backing. On the other hand, committed memory is a range of virtual addresses that are not only reserved but also backed by physical memory or storage. Committed memory can be used to store data and is available for direct access.
Virtual memory fragmentation can impact memory usage in a process by causing inefficient utilization of address space. Fragmentation creates "holes" or free blocks in the address space, making it challenging to find contiguous blocks of memory for allocations. This can lead to wasted space and may limit the amount of memory that can be effectively utilized by the process.
The garbage collector in .NET is responsible for automatically managing memory and reclaiming unused objects. It ensures memory safety by freeing developers from manual memory management and eliminates issues like memory leaks and heap corruption. The garbage collector tracks object lifetimes and automatically releases memory occupied by objects that are no longer referenced by the application.
The garbage collector is aware of the global physical memory load on the machine. When the memory load reaches a certain threshold, indicating high memory pressure, the garbage collector enters a more aggressive mode. In this mode, it may prioritize more frequent full blocking garbage collections to reduce the heap size and avoid excessive paging. This behavior helps manage memory efficiently and prevent resource exhaustion.
The allocation budget is a concept in garbage collection that determines when a garbage collection should be triggered. It represents the amount of memory that can be allocated before a garbage collection becomes necessary. As allocations occur, the allocation budget decreases, and when it reaches a certain threshold, a garbage collection is triggered to reclaim unused memory and reset the budget.
There are several ways to measure the cost of allocations in a .NET application. One approach is to monitor the frequency of garbage collections since most GCs are triggered due to allocations. Sampling techniques can also be used to profile frequent allocations. Additionally, by examining CPU usage information, you can analyze the cost in GC methods that involve memory clearing, which is an expensive part of the allocation process.
Memory clearing plays a significant role in the allocation process as it ensures that all allocations provided by the garbage collector are zero-filled. Clearing memory is done for safety, security, and reliability reasons. While memory clearing incurs a cost, it is an essential step to maintain memory integrity and prevent data leaks or sensitive information exposure.
The garbage collector in .NET manages memory fragmentation in the heap by performing compaction. Compaction involves moving objects closer together to reduce fragmentation and create contiguous free space. By compacting the heap, the garbage collector optimizes memory utilization and reduces the likelihood of fragmented memory blocks.
Garbage collections in a .NET application can be triggered primarily due to allocations. When the allocation of objects consumes memory and the allocation budget reaches a certain threshold, a garbage collection is triggered to reclaim unused memory. Additionally, high physical memory pressure on the machine or explicit calls to GC.Collect.
In native code, virtual memory management is typically handled through native allocators like the CRT heap or C++ new/delete helpers. Developers manually allocate and deallocate memory using these allocators. In managed code, virtual memory management is abstracted by the garbage collector (GC). The GC automatically handles memory allocation and deallocation on behalf of the developer, freeing them from manual memory management responsibilities.
The working set refers to the set of memory pages actively used by a process at a given time. It represents the portion of memory that is currently in physical RAM and readily accessible. Optimizing the working set is important for efficient memory management as keeping frequently accessed pages in RAM reduces the need for costly page swaps between RAM and disk.
The garbage collector in a .NET application aims to avoid excessive paging, which can significantly impact performance. By prioritizing the retention of actively used pages in the working set, the garbage collector reduces the likelihood of page faults and subsequent disk access. This helps maintain optimal performance by minimizing the time spent on costly disk operations.
Memory safety refers to the prevention of memory-related errors such as accessing deallocated memory or reading/writing beyond the boundaries of allocated memory. The garbage collector ensures memory safety in a managed environment by automatically tracking object lifetimes and reclaiming memory occupied by objects that are no longer referenced. This prevents common memory errors and reduces the risk of memory leaks or heap corruption.
The garbage collector is primarily responsible for managing memory allocated by the managed heap. However, it does not directly deallocate unmanaged resources such as file handles or database connections. To handle the deallocation of unmanaged resources, developers should implement proper disposal patterns, such as implementing the IDisposable interface and using using statements, to ensure timely and proper cleanup of unmanaged resources.
Yes, the garbage collector can reclaim memory from objects with finalizers. However, objects with finalizers go through an additional GC cycle known as the finalization process. During this process, the object is put in a finalization queue, and after the finalizer execution, the memory is reclaimed in a subsequent GC cycle. It's important to note that objects with finalizers may have a longer lifetime and can introduce additional overhead in the garbage collection process.
The garbage collector in .NET is designed to handle memory allocation in multi-threaded applications. It employs synchronization mechanisms, such as write barriers and coordination with threads, to ensure memory consistency and prevent race conditions during the allocation and deallocation process. These mechanisms allow for safe and efficient memory management in concurrent environments.
Compaction is a process performed by the garbage collector to reduce memory fragmentation in the managed heap. During compaction, the garbage collector moves objects closer together, eliminating free space and creating contiguous blocks of memory. This optimization helps improve memory utilization and reduces the impact of memory fragmentation on performance.
The Virtual Memory Manager (VMM) plays a crucial role in memory management by providing each process with its own virtual address space. It enables processes to have the illusion of dedicated memory, even though physical memory is shared among multiple processes.
The VMM manages virtual memory states such as free, reserved, and committed. When a range of virtual addresses is marked as free, it indicates that the addresses are available for use. Reserved virtual memory range indicates the intention to use that memory range, but it cannot be utilized for storing data yet. To use the memory range, it needs to be committed, meaning the VMM will back it up with physical storage so that data can be stored in it.
In the context of virtual memory, private memory refers to memory that is exclusively used by the current process. It cannot be accessed or shared by other processes. On the other hand, shared memory can be accessed and utilized by multiple processes. However, all memory used by the garbage collector (GC) is considered private.
Yes, virtual address space can become fragmented, leading to the presence of "holes" or free blocks in the address space. Fragmentation can affect memory management because when reserving a chunk of virtual memory, the VMM needs to find a continuous block of free addresses that can satisfy the request. If there are only scattered free blocks, the reservation may fail, even if the total free space is sufficient. However, with the ample virtual address range in 64-bit systems, fragmentation is less of a concern compared to physical memory availability.
The VMM is aware of the global physical memory load on the machine. It recognizes a certain memory load percentage as a "high memory load situation." When the memory load percentage exceeds this threshold, the VMM goes into a more aggressive mode to reduce the heap size and avoid paging. By monitoring physical memory usage, the VMM adjusts the garbage collector's behavior to prioritize efficient memory usage and minimize the need for costly paging operations.
The garbage collector offers several key benefits in memory management, including:
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Memory safety: The GC ensures memory safety by automatically reclaiming objects that are no longer in use, preventing memory leaks and dangling references.
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Automatic memory management: Developers are freed from manual memory allocation and deallocation, reducing the risk of memory-related bugs and heap corruption.
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Simplified memory management: The GC eliminates the need for explicit memory cleanup code, making the development process more efficient and less error-prone.
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Performance optimization: The GC employs various strategies to optimize memory usage and minimize the impact of memory deallocation, such as generational garbage collection and concurrent garbage collection.
Measuring the GC heap size with respect to when GCs happen is crucial for accurate memory analysis. Simply taking heap size measurements without considering GC occurrences can be misleading. The timing of GCs impacts the heap size, and without that context, it's challenging to interpret the data correctly. Measuring the heap size before and after each GC provides valuable information about memory usage and helps identify the impact of GCs on the heap.
The allocation budget represents the amount of allocation the garbage collector (GC) allows before triggering the next GC. It is the difference between the heap size at the end of the previous GC and the heap size at the beginning of the current GC. The allocation budget is specific to each generation (gen0, gen1, gen2) and determines when a GC should be triggered to reclaim memory based on the rate of object allocations.
The generational GC in .NET divides objects in the GC heap into three generations: gen0, gen1, and gen2. Younger objects are placed in gen0, and with each subsequent GC, surviving objects are promoted to the next generation. Gen2 represents the oldest generation. The generational GC is designed to perform more frequent GCs on younger generations (ephemeral GCs) and less frequent GCs on the older generation (full GCs). This approach optimizes memory management by focusing on the most recently allocated objects and reducing the cost of full GCs.
The Large Object Heap (LOH) is a separate part of the GC heap that is dedicated to storing large objects. Objects larger than a certain threshold (default: 85,000 bytes) are allocated in the LOH instead of the regular Small Object Heap (SOH). The LOH has its own allocation budget and is only collected during gen2 GCs. The presence of large objects in the LOH can introduce performance challenges, and managing them requires specific considerations.
Fragmentation refers to the presence of free space or "holes" in the GC heap. The .NET GC can perform compacting or sweeping GCs. Compacting GCs move objects, which reduces fragmentation but requires updating references. Sweeping GCs coalesce adjacent dead objects into free objects, leading to fragmentation. The size occupied by the free list is included in the reported heap size. Fragmentation impacts memory usage because the free space is used to accommodate survivors from younger generations. Monitoring fragmentation helps understand memory utilization and the effectiveness of GC's memory management strategies.
Concurrent garbage collection (Background GC) in .NET aims to minimize pauses caused by full GCs. Background GC allows GC threads to run concurrently with user threads, reducing the impact on application responsiveness. During Background GC, only sweeping GCs are performed, meaning objects are not compacted. The free list built during Background GC is used by younger generation GCs to accommodate survivors. Understanding the timing and impact of concurrent GCs is important for assessing memory performance and optimizing GC behavior.
Pinning objects in .NET, which prevents them from being moved by the GC, can introduce additional fragmentation in memory. When objects are pinned, the GC cannot compact the heap effectively, leading to fragmentation as free spaces become scattered. This fragmentation can impact memory usage and the efficiency of memory allocation. Managing pinned objects carefully is important to minimize fragmentation and optimize memory utilization.
The Large Object Heap (LOH) in .NET can impact garbage collection performance due to the presence of large objects. Managing the LOH requires additional considerations because full GCs are necessary to collect the LOH. Full GCs, especially when triggered frequently, can have a higher performance cost compared to ephemeral GCs. It's important to minimize unnecessary allocations in the LOH and ensure efficient memory usage to avoid excessive full GCs and improve overall GC performance.
While reducing allocations can be beneficial for memory performance in a .NET application, there are challenges to consider. Reducing allocations may require significant changes to the codebase and can make the logic more complex or awkward. In scenarios where third-party libraries are used, controlling allocations within those libraries may not be feasible. Additionally, replacing allocations with alternative approaches may not always result in a reduction in GC workload. Careful analysis and consideration of the specific application's performance characteristics are necessary when pursuing allocation reduction optimizations.
The allocation budget, specific to each generation (gen0, gen1, gen2), influences garbage collection behavior in .NET. The allocation budget determines when a GC should be triggered based on the rate of object allocations. As allocations occur, objects consume the allocation budget of their respective generation. Once the allocation budget is exhausted, the GC triggers to reclaim memory. Adjusting the allocation budget can control GC frequency and heap size, allowing for optimizations tailored to the specific memory requirements and performance goals of the application.
Measuring allocation cost in a .NET application is essential for understanding memory usage and optimizing performance. While much focus is often placed on measuring the cost of garbage collection, allocation cost is equally important. Allocating objects incurs a certain cost, including memory clearing and associated runtime overhead. Monitoring allocation cost provides insights into the impact of object creation on performance and can help identify opportunities to reduce unnecessary allocations or optimize allocation patterns for better memory utilization.
The garbage collector (GC) in .NET is aware of the global physical memory load on the machine. When the memory load exceeds a certain threshold (e.g., 90% for smaller machines), the GC adjusts its behavior to avoid excessive memory usage and potential paging situations. In high memory load situations, the GC may perform more aggressive GC cycles, including more frequent full GCs, to reduce the heap size and free up memory. Adjusting the threshold for high memory load can be done using configuration settings to optimize GC behavior based on the specific machine and application requirements.
Virtual memory provides a layer of abstraction that allows each process to have its own virtual address space, regardless of the physical memory available. The virtual memory manager (VMM) maps the virtual addresses to physical memory or disk storage as needed. This abstraction simplifies memory management for applications, as they can work with virtual addresses without directly managing physical memory.
The GC heap in .NET is physically organized into segments. When the GC heap is initialized, initial segments are reserved for the Small Object Heap (SOH) and the Large Object Heap (LOH). As allocations happen, memory is committed within these segments. For the SOH, there is typically one segment that contains all three generations (gen0, gen1, gen2). If the SOH grows beyond the capacity of a single segment, additional segments are acquired during garbage collection. The LOH, on the other hand, acquires new segments as needed during allocation time. Segments may be released when no live objects are present, and the space within the segment, except for the ephemeral segment, is decommitted. Understanding the physical organization of the GC heap can provide insights into memory allocation and usage patterns.
The ephemeral segment in the GC heap is a specific segment that holds the ephemeral generations (gen0 and gen1). The ephemeral segment is where newly allocated objects reside. It is designed such that the ephemeral generations are never larger than a single segment, simplifying the memory management and garbage collection process. The space after the last live object in the ephemeral segment is kept committed after a garbage collection cycle since gen0 allocations will immediately use this space. The committed space in the ephemeral segment corresponds to the gen0 budget, which explains why the GC commits more memory than the current heap size. This distinction is especially significant in server GC scenarios with multiple heaps and large gen0 budgets.
The physical organization of the GC heap can affect memory fragmentation. When objects are allocated and deallocated, free spaces can become scattered throughout the heap. Fragmentation occurs when these free spaces are not contiguous, leading to suboptimal memory utilization. The GC's memory management operations, such as acquiring new segments and decommitting memory, can contribute to fragmentation. Fragmentation can have implications for memory usage, heap size, and overall performance. Careful memory allocation and management practices can help minimize fragmentation and optimize memory utilization.
GC pause time refers to the duration during which the user threads executing managed code are paused due to garbage collection. The duration of a garbage collection cycle is determined by the amount of GC work required, which is roughly proportional to the amount of live memory or survivors. For blocking garbage collection, the GC pause time is equal to the GC duration since user threads are paused for the entire GC process. In the case of concurrent garbage collection, the pause time is shorter as GC work is performed concurrently with user threads. Understanding both the total pause time and individual pause times can help optimize performance and reduce the impact on request tail latency.
The GC bookkeeping in .NET consumes a certain amount of memory, roughly around 1% of the GC heap size. This bookkeeping overhead includes various data structures and information required by the GC for tracking and managing objects. The concurrent GC, which is enabled by default, incurs additional bookkeeping proportional to the heap reserve size. While the memory consumed by GC bookkeeping is relatively small, it's important to be aware of its existence, especially in memory-constrained environments or when optimizing for memory usage.
The GC in .NET tries to avoid throwing OutOfMemoryException (OOM) by making every effort to satisfy allocation requests. Before throwing an OOM, the GC attempts a full blocking GC and verifies that it cannot satisfy the allocation. However, there is a tuning heuristic that may prevent the GC from continuing with full blocking GCs if they are not effective at reducing the heap size. In such cases, the GC may throw an OOM even if it wasn't a full blocking GC. The likelihood of encountering an OOM due to GC is generally low, and the GC makes significant efforts to prevent it from happening.
The duration of a garbage collection cycle is roughly proportional to the amount of live memory or survivors. As the size of live memory increases, more objects need to be traced and processed by the GC, resulting in longer GC durations. This can impact GC pauses, as longer GC durations mean longer periods of time during which user threads are paused. Understanding the relationship between live memory size, GC duration, and pause times is crucial for managing the impact of garbage collection on application performance and responsiveness.
In .NET, Workstation GC (WKS GC) and Server GC (SVR GC) are two major flavors of the garbage collector designed for different workloads. WKS GC is typically used for workstation or client scenarios where the application shares the machine with other processes. SVR GC, on the other hand, is optimized for server workloads where the application is the dominant process on the machine and handles multiple user threads.
The key differences between WKS GC and SVR GC are as follows:
- SVR GC has one heap per logical core, while WKS GC has a single heap.
- SVR GC threads have their priority set to
THREAD_PRIORITY_HIGHEST, which allows them to preempt lower-priority threads. WKS GC runs on the user thread that triggered the GC, typically at normal priority. - SVR GC threads are hard affinitized to logical cores, ensuring better utilization of available CPU resources.
The choice between WKS GC and SVR GC depends on the nature of the workload and the specific requirements of the application. SVR GC is particularly suited for server scenarios with high thread concurrency, while WKS GC is more suitable for client applications.
The key rule to remember is: "What survives usually determines how much work GC needs to do; what doesn't survive usually determines how often a GC is triggered." This rule highlights the relationship between the survival of objects and garbage collection behavior. In extreme examples, if objects in gen0 don't survive at all, gen0 garbage collections will occur frequently but with short pauses since there is minimal work to do. On the other hand, if most objects in gen2 survive, gen2 garbage collections will be triggered infrequently, but individual pauses may be longer due to the increased amount of work.
The survival of objects in .NET garbage collection is influenced by various factors. One of the primary factors is the generational aspect, where objects that are not collected in a particular generation are considered live. User roots, including stack variables and GC handles, are responsible for keeping objects alive. Stack variables are automatically managed by the JIT compiler, and GC handles, such as strong and weak handles, can either hold objects live or allow them to be collected. Pinning objects can affect their survival and promotion between generations. Additionally, the presence of finalizers can impact object survival, as objects with finalizers are promoted and handled separately during garbage collection.
Pinning objects in .NET garbage collection indicates that an object cannot be moved in memory. From a garbage collection perspective, pinning creates free object spaces before pinned objects, which can be used to accommodate survivors from younger generations. To optimize memory usage, the garbage collector has a feature called demotion, where pinned objects can be demoted back to gen0, allowing the free spaces associated with them to be utilized for gen0 allocations. However, excessive pinning can lead to fragmentation issues in gen2. It is recommended to pin objects early in already compacted areas and allocate batches of buffers together to minimize fragmentation. In .NET 5, the Pinned Object Heap (POH) was introduced to allocate pinned objects on a separate heap, further mitigating fragmentation concerns.
Finalizers in .NET introduce additional considerations for memory management and garbage collection. Allocating a finalizable object incurs a cost as it needs to be registered on the finalize queue during garbage collection. Having a finalizer prevents the use of fast allocation helpers, as each finalizable object allocation requires interaction with the garbage collector. During garbage collection, the GC identifies live objects and promotes them, including finalizable objects. Objects on the finalize queue are checked for promotion and are considered dead if not promoted. However, running a finalizer requires the object to be alive again, which results in its promotion to a higher generation. This can delay the reclamation of memory associated with finalizable objects and potentially lead to memory leaks. Suppressing the finalizer with GC.SuppressFinalize can prevent unnecessary promotions and improve memory reclamation efficiency.
The one rule to remember is: "What survives usually determines how much work GC needs to do; what doesn't survive usually determines how often a GC is triggered." This rule emphasizes the importance of the survival rate of objects in each generation and its impact on garbage collection behavior. The survival rate influences both the frequency of GCs and the amount of work the GC needs to perform during each cycle.
The generational aspect of garbage collection in .NET plays a significant role in determining when and how GCs occur. If a generation isn't collected, it means that all objects in that generation are considered live. This means that the generational aspect becomes less relevant when collecting gen2, as all generations are collected. However, understanding the generational behavior is crucial for optimizing garbage collection pauses and overall memory management.
User roots refer to various runtime components that hold references to objects and influence the garbage collection process. The common types of user roots include stack variables and GC handles. Stack variables are automatically managed by the Just-In-Time (JIT) compiler, which efficiently determines when a stack variable is no longer in use. GC handles, on the other hand, allow user code to hold objects live or inspect objects without keeping them live.
GC handles can be either strong handles or weak handles. Strong handles indicate to the GC that the object needs to be live, while weak handles allow objects to be collected if there are no strong references to them. It's important to properly manage GC handles, releasing strong handles when they are no longer needed, to allow for effective garbage collection.
Pinning is a feature in .NET that allows objects to be marked as unpinned, indicating that they cannot be moved by the garbage collector. When objects are pinned, the dead space before the pinned objects can be used to accommodate survivors from the younger generation. However, if pinned objects are promoted to a higher generation, such as gen1 or gen2, they can cause fragmentation issues in those generations.
To mitigate the impact of pinning on garbage collection, it is recommended to pin objects in already compacted portions of the heap and allocate batches of pinned objects instead of allocating and pinning objects individually. Additionally, in .NET 5, the Pinned Object Heap (POH) feature was introduced, allowing pinned objects to be allocated on a separate heap to reduce fragmentation.
Finalizers, or destructors, are special methods in .NET that are invoked before an object is garbage collected. When an object with a finalizer is allocated, its address is recorded on the finalize queue. Allocating finalizable objects requires interaction with the GC, preventing the use of fast alloc helpers.
During garbage collection, the GC checks the objects on the finalize queue to determine if they have been promoted. Finalizable objects that have not been promoted are considered dead, but their memory cannot be reclaimed until the finalizer has run. Running finalizers introduces additional cost and can delay memory reclamation, as the object must be promoted to a higher generation.
To optimize memory management, it is recommended to suppress finalization for objects that do not require finalization using the GC.SuppressFinalize method. This prevents unnecessary promotions and delays in memory reclamation.
Knowing the performance goal is essential when optimizing a product because different products have different performance requirements. Understanding what aspect of performance to optimize for, such as memory footprint, throughput, or tail latency, helps prioritize the optimization efforts. It ensures that the optimizations align with the specific goals and constraints of the product. By clearly defining the performance goal, engineers can focus their efforts on the areas that will have the most significant impact and allocate resources efficiently.
Measuring is a fundamental aspect of performance analysis. It allows you to gather data and evaluate the performance of your system. By measuring relevant metrics, such as request processing time, memory usage, or latency, you can identify performance bottlenecks and areas for improvement.
Measuring performance should be an integral part of the development process, rather than an afterthought. It helps to understand the current state of the system and track performance changes over time. Measuring should not be limited to high-level metrics but also involve collecting detailed data that enables meaningful performance analysis. Additionally, measuring the impact of fixes and workarounds allows you to validate the effectiveness of optimizations and ensure they achieve the desired results.
Measuring the impact of factors that are likely to affect performance metrics is crucial for understanding their contribution to overall performance. By measuring the effect of these factors, you can observe how they influence performance as you develop your product.
For example, in the case of request latency, various factors like garbage collection pauses or network I/O can impact latency. By measuring the total latency of requests affected by each factor and calculating the impact percentage, you can determine the relative contribution of each factor to the overall latency. This information helps in prioritizing optimization efforts and focusing on the factors that have the most significant impact on performance.
Understanding the impact of factors becomes especially important when optimizing for specific percentiles, such as P95 or P99 latency, as the factors affecting different percentiles may vary. By measuring and analyzing the impact, you can make informed decisions about where to invest your optimization efforts.
When optimizing a framework or platform technology versus user code, different considerations come into play.
For those working on an end product, there is more freedom to optimize because they have control over the environment in which the product runs. They can make predictions about resource utilization, such as CPU or memory, and tailor their optimizations accordingly. They can choose specific machines or VMs, select libraries, and make tradeoffs between performance and usability based on the specific requirements of the product.
In contrast, those working on platform technologies or libraries need to be more cautious about memory usage, as their code will be used by various users in different environments. They need to ensure that their code is frugal in terms of memory usage to accommodate users who may have performance-critical requirements. Providing different APIs that offer performance and usability tradeoffs, along with educating users about these tradeoffs, can help optimize the usage of the platform technology or library.
By considering these factors, engineers can tailor their optimization strategies to the specific context in which they are working, whether it's optimizing an end product or a platform technology.
Understanding the impact of GC changes and changes in the framework is crucial for accurate performance analysis. When observing memory behavioral changes after upgrading or encountering different runtime environments, it's essential to identify whether the changes are due to GC-specific modifications or other framework-related factors. Upgrades and environment shifts can introduce alterations in memory allocation patterns, memory usage, or object survival rates. By distinguishing between GC-related changes and other framework changes, you can effectively pinpoint the root causes of performance variations and optimize the appropriate components accordingly.
Measuring enough to determine the focus of optimization efforts is essential for effective performance optimization. While it's tempting to optimize based on isolated metrics or hearsay, understanding the fundamentals and measuring multiple aspects of performance provides a more comprehensive view. By measuring various metrics, you can identify the areas that require optimization and allocate your efforts accordingly. Optimizing solely based on limited measurements can lead to suboptimal results or overlooking critical performance bottlenecks. Therefore, measuring enough to grasp the overall system behavior ensures that optimization efforts are targeted at the most impactful areas, resulting in significant performance improvements.
Measuring the impact of factors that contribute to request latency is instrumental in optimizing latency performance. Various factors, such as garbage collection pauses or network I/O, can affect request latency. By measuring the impact of these factors, you can quantitatively assess their influence on latency. This information enables you to prioritize optimization efforts and focus on mitigating the factors that have the most significant impact on request latency. Through careful measurement and analysis, you can identify opportunities to optimize the critical components of the system, resulting in reduced latency and improved overall performance.
What is the importance of understanding the performance goals when optimizing for memory footprint, throughput, or tail latency?
Understanding the performance goals when optimizing for memory footprint, throughput, or tail latency is crucial for aligning optimization efforts with the desired outcomes. Each performance aspect serves specific objectives, and optimizing without a clear understanding of the goals can lead to misguided efforts. By knowing the performance goal upfront, whether it's maximizing resource utilization, achieving high throughput, or meeting strict latency requirements, you can prioritize optimizations accordingly. This ensures that the optimization strategies are tailored to address the specific performance requirements, resulting in a more efficient and effective optimization process.
Measuring the impact of fixes and workarounds is important to validate their effectiveness in improving performance. When applying fixes or implementing workarounds to address performance issues, it's crucial to measure their impact on the system. Measuring the performance before and after applying the fix or workaround allows you to assess whether the intended improvements have been achieved. It helps in identifying whether the applied solution has effectively mitigated the performance problem or if further adjustments are required. By measuring the impact of fixes and workarounds, you can ensure that your optimization efforts are yielding the desired results and continuously refine your approach to achieve optimal performance.
Measuring realistic performance is essential for accurate performance analysis. It involves creating measurement scenarios that closely resemble the actual production environment and workload. Simulating real-world conditions helps capture the complex interactions and dynamics that impact performance. By measuring performance under realistic conditions, you can obtain reliable data that reflects the behavior of the system in its intended deployment. This enables you to identify potential bottlenecks, understand system behavior
perfview /GCCollectOnly /AcceptEULA /nogui collect after you are done, press s in the perfview cmd window to stop it.
perfview /GCCollectOnly /AcceptEULA /nogui collect after you are done, press s in the perfview cmd window to stop it.
perfview /GCCollectOnly /AcceptEULA /nogui collect after you are done, press s in the perfview cmd window to stop it.
perfview /GCCollectOnly /AcceptEULA /nogui collect after you are done, press s in the perfview cmd window to stop it.
dotnet trace collect -p <pid> -o <outputpath with .nettrace extension> --profile gc-collect --duration <in hh:mm:ss format>
dotnet trace collect -p <pid> -o <outputpath with .nettrace extension> --profile gc-collect --duration <in hh:mm:ss format>
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Picking the right tools for performance analysis is crucial to efficiently identify and resolve performance issues. Using the appropriate tools can significantly reduce the time and effort required to pinpoint the root causes of performance problems. It is common to encounter complex issues that require specialized tools for analysis. By selecting the right tools, you can gain deeper insights into the system behavior, measure performance accurately, and uncover bottlenecks or inefficiencies that might not be apparent with generic tools. Therefore, investing time in identifying and learning the right tools for your specific performance analysis needs is highly beneficial.
PerfView is a powerful performance analysis tool developed by the runtime team at Microsoft. At its core, PerfView utilizes TraceEvent, a library that decodes Event Tracing for Windows (ETW) events emitted by various components, including the runtime providers, kernel providers, and more. ETW events are emitted over time and share a common format, allowing tools like PerfView to interpret and analyze them together. This enables comprehensive performance investigation and analysis. PerfView also supports other eventing mechanisms on Linux, such as LTTng or EventPipe, to capture events in a cross-platform manner. Additionally, PerfView provides functionality for analyzing heap snapshots, which can be valuable when inspecting object relationships and connections. While PerfView is highly capable, it is important to explore and utilize its full range of features to leverage its potential for performance analysis.
Some common approaches that may be flawed when starting a memory performance analysis include focusing solely on capturing a CPU profile, opening a heap snapshot, or capturing allocations. While these approaches may be effective for specific types of problems, they can overlook critical factors contributing to the performance goal. For example, if the goal is to address tail latency issues, reducing CPU usage or eliminating unnecessary objects may not directly impact long garbage collection (GC) pauses, which could be the primary cause of the latency problem. Instead, a more productive approach involves reasoning about the factors that influence the performance goal and starting the analysis from that perspective. By understanding the top-level GC metrics and collecting relevant data, you can gain insights into the critical aspects of performance and make targeted optimizations.
To collect top-level GC metrics, you can utilize PerfView's command-line interface. The following command can be used to collect GC events at the Informational level:
perfview /GCCollectOnly /AcceptEULA /nogui collect
This command collects GC events, and you can run it for an extended period to capture sufficient GC activities. If you know the duration for collecting GC data, you can specify the maximum collection time using the /MaxCollectSec argument. For example:
perfview /GCCollectOnly /AcceptEULA /nogui /MaxCollectSec:1800 collect
This command collects GC events for a maximum of 1800 seconds (30 minutes). The resulting file is typically named PerfViewGCCollectOnly.etl.zip and contains the collected GC trace. On Linux, a similar result can be achieved using the dotnet trace command with the gc-collect profile. These methods allow you to obtain comprehensive GC metrics that contribute to performance analysis and optimization.
PerfView utilizes Event Tracing for Windows (ETW) events as a primary source of data for performance analysis. ETW events are emitted by various components and provide a wealth of information about system behavior and performance. PerfView's underlying library, TraceEvent, decodes and interprets these ETW events, allowing for comprehensive performance analysis. By collecting and analyzing ETW events, PerfView can provide insights into various aspects of the system, such as garbage collection, kernel events, and runtime behavior. This enables users to understand the interactions and dependencies between different components, identify performance bottlenecks, and optimize the system accordingly.
Collecting heap snapshots in PerfView can provide valuable insights into memory usage and object relationships within the system. Heap snapshots allow you to visualize the objects present in the heap and how they are connected to each other. This information can help identify memory leaks, inefficient memory usage patterns, or excessive object retention. By analyzing heap snapshots, you can make informed decisions on memory optimizations, such as eliminating unnecessary objects or improving memory allocation strategies. Although heap snapshots may not be directly related to garbage collection, they offer a complementary perspective on memory utilization and can assist in optimizing overall performance.
The debugger extension SoS (Son of Strike) is available in PerfView and is primarily used for debugging purposes rather than profiling or performance analysis. SoS provides functionality for examining the managed heap, heap statistics, and individual object details. While it can be helpful for inspecting heap-related information, such as identifying memory leaks or examining specific objects, SoS is not as focused on performance profiling as other tools like PerfView. Therefore, when conducting performance analysis, it is often more beneficial to leverage PerfView's capabilities, which provide a broader range of performance-related insights.
When starting a memory performance analysis, it is important to approach the problem systematically and consider the factors that contribute to the performance goal. Instead of relying on generic approaches like capturing a CPU profile, opening a heap snapshot, or tracking allocations, it is more productive to reason about the specific factors impacting performance. Understanding the top-level garbage collection metrics and their relation to the performance goal is key. By collecting the appropriate metrics and analyzing them in the context of the performance goal, you can identify the critical areas for optimization and make informed decisions on improving memory performance.
When collecting performance counters for memory and CPU usage on Windows, several key counters can provide valuable insights. For memory usage, the "Memory\Available MBytes" counter indicates the amount of available physical memory in megabytes, while the "Process\Private Bytes" counter measures the number of bytes allocated by a specific process. These counters help monitor the overall memory availability and the memory usage of the target process, respectively. For CPU usage, the "Process% Processor Time" counter provides the percentage of time the CPU is actively executing the target process. This counter allows you to gauge the CPU utilization of the process. By monitoring these counters, you can obtain essential data to analyze memory and CPU performance and identify areas for improvement.
PerfView provides a convenient view called GCStats that displays top-level garbage collection (GC) metrics. To access this view, open the PerfViewGCCollectOnly.etl.zip file in PerfView by either browsing to the directory and double-clicking the file or running the "PerfView PerfViewGCCollectOnly.etl.zip" command-line. In the GCStats view, you will find detailed information about each process's GC statistics. This includes the command line, CLR startup flags, percentage of time paused for GC, GC stats by generation, LOH (Large Object Heap) allocation pauses, gen2 GC stats, condemned reasons for GCs, finalized object counts, and more. The GCStats view offers comprehensive insights into the GC behavior and performance of the analyzed processes.
There are several ways to measure allocations and collect allocation data using PerfView. One approach is to use the GC.GetTotalAllocatedBytes API in .NET 3.0+ to obtain the total allocated bytes at a specific point in time. In PerfView's GCStats view, you can find the total number of allocated bytes in the summary of each process. Additionally, PerfView provides the Gen0 Alloc MB column, which shows the allocated bytes for gen0 GCs. Another method involves collecting sampled allocations with stacks using the AllocationTick event. By running PerfView with the appropriate command-line arguments, such as /ClrEvents:GC+Stack, you can collect the AllocationTick events and their associated call stacks. The resulting data can be viewed in the "GC Heap Alloc Ignore Free (Coarse Sampling)" view, which displays the allocations grouped by type and their respective call stacks.
PerfView offers a diffing functionality for stack views, such as the CPU stacks, Heap Snapshot, or GC Heap Alloc views. To perform a diffing analysis, open two traces in the same PerfView instance and open the stack view for each of them. In the Diff menu, you will find a "with Baseline" option that allows you to compare the two traces. This feature is useful for identifying regressions or changes in performance between two runs. It's important to note that when performing a diffing analysis, it's recommended to have similar workloads in both runs to ensure accurate comparisons. By leveraging the diffing capability in PerfView, you can gain insights into performance differences and optimize your application accordingly.
In addition to the GCStats view, PerfView offers several other relevant views that can be useful for performance analysis. One such view is the CPU Stacks view, which displays the CPU stacks when CPU sample events are collected in the trace. This view allows you to analyze CPU usage patterns and identify hotspots in the code. The Events view provides a raw events view, which allows you to filter and search for specific events of interest. You can filter events using the Filter textbox and explore detailed event occurrences. The GC Heap Alloc Ignore Free view is particularly useful for analyzing allocations, and the Any Stacks view displays events with their associated stacks. These views, along with the ability to perform diffing between traces, provide a comprehensive set of tools for performance analysis in PerfView.
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PerfView provides a dedicated view called GCStats that displays the top-level garbage collection (GC) metrics. To access this view, open the PerfViewGCCollectOnly.etl.zip file in PerfView either by browsing to the directory and double-clicking the file or using the command "PerfView PerfViewGCCollectOnly.etl.zip." In the GCStats view, you will find multiple nodes under the "Memory Group" node. The one of interest is the "GCStats" view. Opening it will present various details related to GC metrics.
The GCStats view includes the following information for each process:
- Summary: Provides general information such as the command-line, CLR startup flags, percentage of time spent pausing for GC, and more.
- GC stats rollup by generation: Displays statistics for each generation (gen0, gen1, gen2), including the number of GCs, mean/average pause times, and more.
- GC stats for GCs whose pause time was > 200ms: Shows details about GCs with pause times exceeding 200 milliseconds.
- LOH Allocation Pause (due to background GC) > 200 Msec for this process: Indicates if there were significant LOH allocations during background GC that caused pauses longer than 200 milliseconds.
- Gen2 GC stats: Provides specific statistics for gen2 GCs.
- All GC stats: Displays a comprehensive table with information about all GCs that occurred during the trace collection.
- Condemned reasons for GCs: Shows the reasons for GCs being triggered.
- Finalized Object Counts: Presents the counts of finalized objects.
- Summary explanation: Explains the various fields and their meanings in the summary section.
You can further explore specific views and tables within the GCStats view to gain insights into individual GC events, pause times, heap sizes, and more. The ability to view and analyze these metrics is instrumental in understanding GC behavior and optimizing memory performance.
The CPU Stacks view in PerfView provides a detailed analysis of CPU samples collected during a trace. By collecting CPU sample events in your trace, you can explore this view to understand the CPU utilization and identify hotspots in your code. When opening the CPU Stacks view, it is recommended to clear the three highlighted text boxes initially. This allows you to focus on the relevant information and update the view accordingly. The CPU Stacks view also supports grouping by modules, which can be useful for specific investigations. Overall, the CPU Stacks view helps pinpoint areas of high CPU usage and provides insights for optimizing code performance.
The Events view in PerfView presents raw event data collected during the trace. Although it may initially seem overwhelming, the Events view offers several functionalities to simplify the analysis process. You can filter events using the Filter textbox, specifying one or multiple strings separated by '|'. This allows you to focus on specific events of interest, such as file-related events or GC start events. Double-clicking on an event name displays all occurrences of that event, enabling you to search for specific details. For example, if you want to determine the exact version of the coreclr.dll, you can search for "coreclr" in the Find textbox. Additionally, you can use the "Start/End" feature to limit the time range of events, apply process filters to specific processes, and control the columns displayed for each event. The Events view provides flexibility and control for analyzing raw event data and extracting meaningful insights.
If you have CPU samples but not AllocationTick events, you can analyze the cost of memory clearing by examining the CPUAlloc view in PerfView.
By analyzing the caller of memset_repmovs, you can identify the locations in the code where the garbage collector (GC) performs memory clearing before allocating new objects.
In the provided example, what percentage of the total CPU usage is attributed to the allocation cost?
In the example, the allocation cost accounts for 25.6% of the exclusive cost of the total CPU usage.
In the GCStats view, there is a column called "Trigger Reason" that provides information about the reason why a GC was triggered. You can examine this column to understand the trigger reason for a specific GC.
The most common trigger reason you would encounter is AllocSmall, which indicates that the gen0 budget has been exceeded.
If AllocLarge is the most common trigger reason, it suggests that the GC was triggered because the LOH budget was exceeded by allocating large objects. This situation often leads to triggering a gen2 GC.
Trigger reasons such as OutOfSpaceSOH and OutOfSpaceLOH are less frequently observed and occur when the system is close to reaching its physical space limit. These trigger reasons indicate that the system is running out of memory in the respective segments.
The Induced trigger reasons imply that certain code is triggering GCs independently. This behavior can raise concerns as it may lead to unnecessary GCs, impacting performance. The GCTriggered event can be used to identify the code that triggered a GC with its callstack.
You can collect a lightweight trace using PerfView with the command: PerfView.exe /nogui /accepteula /KernelEvents=Process+Thread+ImageLoad /ClrEvents:GC+Stack /ClrEventLevel=Informational /BufferSize:3000 /CircularMB:3000 collect. This trace includes the necessary information to identify the code that triggered GCs.
The "Trigger reason" indicates how a GC starts or comes into existence. It represents the reason behind the initiation of a GC. The most common trigger reason determines whether the GC starts as a gen0 GC, while the condemned reasons influence whether the GC will escalate to higher generations.
After a GC starts, it can be escalated to a higher generation such as gen1 or gen2. The decision to escalate is based on "condemned reasons," which are factors that lead to the need for collecting higher generations. These reasons are determined early in the GC process.
How can I analyze the cost of memory clearing if I don't have AllocationTick events but only CPU samples?
If you don't have AllocationTick events, you can still analyze the cost of memory clearing by looking at the CPUAlloc view in PerfView. It provides insights into the memory clear operations performed by the garbage collector (GC) based on CPU samples.
What information can I gather from the caller of memset_repmovs in the context of GC's memory clear?
The caller of memset_repmovs represents the point where GC performs memory clearing before handing out new objects. By examining the callers, you can identify the memory clear operations carried out by the GC.
In the provided example, what percentage of the total CPU usage does the allocation cost account for?
In the example, the allocation cost represents 25.6% of the exclusive cost of the total CPU usage.
In the GCStats view, there's a column called "Trigger Reason" that provides information about the reason behind a GC being triggered. You can examine this column to understand why a GC collected a particular generation.
The most common trigger reason you would encounter is AllocSmall, indicating that the gen0 budget has been exceeded. Another common trigger reason is AllocLarge, suggesting that the GC was triggered due to exceeding the LOH budget by allocating large objects.
If AllocLarge is the most common trigger reason, it indicates a problem where the LOH budget has been exceeded by allocating large objects. This situation often leads to triggering a gen2 GC.
The Induced trigger reasons should raise a red flag as they indicate that some code is triggering GCs on its own. This behavior can have performance implications and needs to be investigated further.
Trigger reasons like OutOfSpaceSOH and OutOfSpaceLOH occur less frequently than AllocSmall and AllocLarge. They indicate situations where you are close to reaching the physical space limit, such as the end of the ephemeral segment.
You can collect a lightweight trace using PerfView with the GC informational level, stack, and minimal kernel events. By examining the stacks for the GCTriggered events in the Any Stacks view, you can find the code that triggered the GC.
The "Trigger reason" represents how a GC starts or comes into existence. It identifies the reason behind the GC's initiation. For example, if the most common reason for a GC to start is due to allocating on the SOH, that GC would start as a gen0 GC because the gen0 budget was exceeded. After the GC starts, the decision about which generation to collect is made based on "condemned reasons."
Long pauses in BGC are usually rare but can occur due to either a rare bug in the runtime or certain operations performed during the STW (Stop-The-World) marking phase of BGC. It's important to diagnose and investigate these scenarios to understand the cause of long pauses.
The amount of GC work in ephemeral GCs is roughly proportional to the amount of survivors. You can assess the amount of survivors by looking at the "Promoted Bytes" metric in the GCStats tables. Promoted Bytes indicate the amount of memory promoted during GC, and higher promotion can result in longer pause times.
If ephemeral GCs start promoting significantly more objects, it can lead to longer pause times. One possible reason for this behavior is when objects that are not meant to survive start surviving due to code paths that are infrequently executed. Tools like the Generational Aware view in PerfView (available in .NET 5) can help identify which old generation objects are causing young generation objects to survive.
If a GC is longer than expected and doesn't fit into any known scenarios, it's important to determine if the long pause is caused by GC work or other factors. PerfView provides a stop trigger feature that can be used to capture relevant information during the trace. By analyzing the event sequence and using the stop triggers, you can identify the root cause of long GC pauses.
In a typical blocking GC, the event sequence consists of GC/SuspendEEStart, GC/SuspendEEStop, GC/Start, GC/End, GC/RestartEEStart, and GC/RestartEEStop. For BGC, the event sequence is more complex and includes additional Suspend/Restart pairs. The complete event sequence can be observed in the Events view of PerfView.
PerfView provides three GC-specific stop triggers: StopOnGCOverMsec, StopOnGCSuspendOverMSec, and StopOnBGCFinalPauseOverMSec. These triggers allow you to specify conditions based on GC event durations or suspension times to capture relevant information during the trace.
To debug a GC that exhibits unexpected long pause times, you can use PerfView's CPU stack view. By examining the CPU samples during the problematic GC's time range, you can identify any periods where there is no CPU usage. This can indicate potential interference from other processes or modules. Additionally, analyzing the CPU samples from interfering processes can help confirm if they are causing delays in the GC process.
What information can be obtained from ThreadTime traces in the context of long suspension debugging?
ThreadTime traces, which include ContextSwitch and ReadyThread events, provide detailed information about what the GC thread is waiting for during the SuspendEE phase. These traces can help pinpoint the cause of long suspensions during GC and aid in the debugging process. However, ThreadTime traces can be voluminous, so they should be used judiciously considering their impact on application behavior.
Long pauses in BGC can occur due to rare bugs in the runtime or specific operations performed during the STW marking phase. These situations can lead to longer pause times and should be investigated for root cause analysis.
To identify the cause of a long GC pause, you can use PerfView's stop trigger feature. By analyzing the event sequence and using appropriate stop triggers, you can capture relevant information and determine if the pause is due to GC work or external factors.
The "Promoted Bytes" metric in the GCStats tables indicates the amount of memory promoted during GC. This metric can be used as an estimate of the amount of GC work involved in ephemeral GCs.
Ephemeral GCs can promote more objects if there are code paths that cause objects to survive when they are not meant to. This can result in longer pause times as more objects are promoted. Tools like the Generational Aware view in PerfView can help identify which objects are causing the increased promotion.
When debugging a GC with longer than expected pauses, you can use PerfView's CPU stack view. By analyzing the CPU samples during the problematic GC's time range, you can identify any periods where there is no CPU usage, which could indicate potential issues or interference.
The event sequence for a typical blocking GC includes GC/SuspendEEStart, GC/SuspendEEStop, GC/Start, GC/End, GC/RestartEEStart, and GC/RestartEEStop. These events mark the suspension and resumption phases of the GC process.
PerfView offers three stop triggers for GC debugging: StopOnGCOverMsec, StopOnGCSuspendOverMSec, and StopOnBGCFinalPauseOverMSec. These triggers help capture relevant information based on specified conditions related to GC events and suspensions.
To debug a GC with longer pauses compared to other GCs with similar promotion amounts, you can utilize PerfView's CPU stack view. By examining the CPU samples during the problematic GC's time range, you may identify any periods where there is little or no CPU usage, which can indicate potential issues or interference.
ThreadTime traces provide detailed information about what the GC thread is waiting for during the SuspendEE phase. By capturing ContextSwitch and ReadyThread events, these traces can help identify the cause of long suspensions during GC and aid in the debugging process.
PerfView.exe /nogui /accepteula /StopOnGCSuspendOverMSec:200 /Process:A /DelayAfterTriggerSec:0 /CollectMultiple:3 /KernelEvents=ThreadTime /ClrEvents:GC+Stack /BufferSize:3000 /CircularMB:3000 /Merge:TRUE /ZIP:True collect
However, ThreadTime traces can be too voluminous and might cause your application to not run "normal" enough to exhibit the behavior you were debugging. In that case I would take a trace with default kernel events to begin with which often would either reveal the problem or give you enough clues. You can simply replace ThreadTime with Default -
PerfView.exe /nogui /accepteula /StopOnGCSuspendOverMSec:200 /Process:A /DelayAfterTriggerSec:0 /CollectMultiple:3 /KernelEvents=Default /ClrEvents:GC+Stack /BufferSize:3000 /CircularMB:3000 /Merge:TRUE /ZIP:True collect
While ThreadTime traces can provide valuable information for debugging long suspensions, they can be voluminous and impact the normal behavior of the application. Therefore, it's important to use ThreadTime traces judiciously and consider their impact on the application's runtime.
PerfView.exe /nogui /accepteula /StopOnGCSuspendOverMSec:200 /Process:A /DelayAfterTriggerSec:0 /CollectMultiple:3 /KernelEvents=ThreadTime /ClrEvents:GC+Stack /BufferSize:3000 /CircularMB:3000 /Merge:TRUE /ZIP:True collect
However, ThreadTime traces can be too voluminous and might cause your application to not run "normal" enough to exhibit the behavior you were debugging. In that case I would take a trace with default kernel events to begin with which often would either reveal the problem or give you enough clues. You can simply replace ThreadTime with Default -
PerfView.exe /nogui /accepteula /StopOnGCSuspendOverMSec:200 /Process:A /DelayAfterTriggerSec:0 /CollectMultiple:3 /KernelEvents=Default /ClrEvents:GC+Stack /BufferSize:3000 /CircularMB:3000 /Merge:TRUE /ZIP:True collect
Top-level application metrics provide a broader understanding of the performance aspects of your application. These metrics, such as concurrent request counts, average/max/P95 request latency, and other application-specific measurements, help assess the overall performance of your application. In the context of GC analysis, top-level application metrics serve as a baseline for evaluating the impact of GC on application performance. By comparing GC metrics with the top-level application metrics, you can identify if GC-related issues are affecting the desired performance goals.
Workload variability can introduce challenges when measuring top-level application metrics. To combat this, you can employ several strategies:
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Measure factors that likely affect performance metrics: Identify and measure factors that impact the top-level application metrics. By understanding these factors, you can gain insights into the variability of the workload and its effect on performance.
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Monitor top-level component metrics: In addition to the primary application metrics, consider tracking secondary metrics that help assess workload variation. For example, monitoring the amount of allocation during peak hours can provide insights into the stress exerted on the GC by the workload. This metric is closely related to user code and can help understand the correlation between allocations and GC behavior.
By incorporating these strategies, you can better understand the impact of workload variability on top-level application metrics and refine your performance analysis.
The choice of relevant GC metrics depends on your specific performance goals. Table 3 provides a guideline for selecting relevant GC metrics based on different performance goals:
| Application perf goal | Relevant GC metrics |
|---|---|
| Throughput | % Pause time in GC (maybe also % CPU time in GC) |
| Tail latency | Individual GC pauses |
| Memory footprint | GC heap size histogram |
For improving throughput, monitoring the percentage of time spent in GC pauses (% Pause time in GC) is crucial. Additionally, % CPU time in GC can provide insights into CPU utilization during GC execution, especially in scenarios with background garbage collection (BGC).
To address tail latency, tracking individual GC pauses is important. By measuring the duration and frequency of GC pauses during the longest requests, you can assess their impact on latency and identify potential optimization opportunities.
For optimizing memory footprint, monitoring the GC heap size histogram is essential. This metric provides a breakdown of memory usage across different generations, allowing you to analyze memory utilization patterns and identify areas for optimization.
The behavior of the GC is directly influenced by the application itself. To properly interpret GC metrics, it is crucial to consider the application behavior and its impact on performance. The top-level application metrics act as a guide for understanding when GC-related issues should be a concern.
For example, looking at the average "% Pause time in GC" metric over a day may not provide meaningful insights if the workload has long periods of dormancy. Instead, it is more effective to focus on GC metrics during specific periods of interest, such as an outage or a high-demand scenario.
By aligning GC metrics with the application behavior, you can accurately assess the impact of GC on performance and determine when further investigation or optimization is required.
Top-level application metrics provide crucial insights into the performance aspects of your application. These metrics, such as concurrent requests, request latency, or maximum latency, directly reflect the application's performance goals. By monitoring these metrics, you can identify performance regressions or improvements. Additionally, top-level application metrics serve as a baseline to understand the impact of GC on your application's performance.
Workload variation can make it challenging to have stable top-level application metrics. To combat this, you can take the following steps:
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Measure factors that likely affect your performance metrics: By measuring factors such as allocation rates or specific workload characteristics, you can understand how workload variations impact your application's performance. As you gain more knowledge, you can add additional factors to your measurement repertoire.
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Monitor top-level component metrics: In the case of memory, you can track the amount of allocation done during different workload periods. If you notice significant variations in allocation rates, it indicates potential stress on the GC. This metric can help you understand the workload's impact on memory management, even if the exact correlation with GC is not straightforward.
The top-level GC metrics to monitor depend on your performance goals. The relevant metrics can be categorized as follows:
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Throughput: To optimize throughput, monitor the percentage of pause time in GC (% Pause time in GC). This metric indicates how much time your application's threads are paused due to garbage collection. Additionally, you can monitor the percentage of CPU time used by the GC (% CPU time in GC) to understand how much CPU resources the GC consumes.
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Tail latency: For latency-sensitive applications, monitor individual GC pauses to assess their impact on tail latency. By understanding the timing and duration of GC pauses during the longest requests, you can evaluate the contribution of GC to overall latency.
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Memory footprint: To evaluate memory footprint optimizations, monitor the GC heap size histogram. This metric provides insights into the size distribution of the GC heap, including the sizes of generations and the large object heap (LOH).
Monitoring these top-level GC metrics helps you assess the impact of GC on performance goals and identify areas for improvement.
The relevance of GC-related problems depends on the impact indicated by the top-level GC metrics. If the relevant GC metrics show that GC has a small effect on performance, it would be more productive to focus efforts elsewhere. However, if the metrics indicate that GC has a significant impact, it is crucial to start investigating managed memory analysis to address potential issues.
For example, if the % Pause time in GC is consistently below 5% while actively handling workloads, it suggests that GC is not a major performance bottleneck. However, if the % Pause time in GC is high, it indicates that GC interruptions might be affecting overall performance. By examining the top-level GC metrics, you can determine when to focus on GC-related problems and dive deeper into managed memory analysis.
Performance engineers should monitor top-level application metrics that directly represent the performance aspects of the application. These metrics could include the concurrent number of requests handled, average, maximum, and P95 request latency. By tracking these metrics, performance engineers can determine if there are any performance regressions or improvements during the development process. However, it's important to note that these metrics may not always be stable due to varying workloads. To combat this, it's recommended to measure factors that can affect the metrics and have top-level component metrics that indicate workload variations, such as tracking memory allocations during peak hours. Monitoring these metrics helps identify if the workload puts additional stress on the garbage collector (GC), which can be valuable in GC analysis.
The relevant top-level GC metrics depend on the specific performance goals of the application. Here are the relevant GC metrics based on different perf goals:
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For throughput optimization: % Pause time in GC and % CPU time in GC. These metrics indicate the extent to which GC pauses threads and competes with application threads for CPU resources.
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For tail latency optimization: Individual GC pauses. Monitoring individual GC pauses helps understand their contribution to latency, especially during long requests.
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For memory footprint optimization: GC heap size histogram. Analyzing the GC heap size helps assess the memory consumption of the application.
These metrics provide insights into the impact of GC on the performance goals and guide performance engineers in their investigation and analysis of performance issues related to the GC.
Performance engineers should worry about GC-related problems when the relevant GC metrics indicate that GC has a significant impact on the application's performance. The top-level application metrics serve as indicators of performance problems, and the corresponding GC metrics help in the investigation of these issues. Instead of solely relying on average GC metrics over long periods, it's more productive to focus on GC metrics during specific events or outages that coincide with performance degradation. By correlating these events with GC metrics, performance engineers can determine if GC is likely the cause of the performance problem and shift their focus to managed memory analysis.
How does GC affect application throughput, and which metrics should be monitored for throughput optimization?
GC can impact application throughput by interrupting threads through pauses and competing for CPU resources. To optimize throughput, performance engineers should monitor the following metrics:
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% Pause time in GC: This metric indicates the percentage of time that threads are paused due to GC. Lower values are desired, as they indicate fewer interruptions to application threads.
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% CPU time in GC: This metric represents the percentage of CPU time used by GC. While concurrent GC modes (such as BGC) minimize thread pauses, they still compete for CPU resources. Higher values indicate that GC is efficiently utilizing available CPU resources during its execution.
By monitoring these metrics, performance engineers can assess the impact of GC on thread pauses and CPU usage, helping optimize throughput by minimizing interruptions and maximizing CPU efficiency during GC execution.
Individual GC pauses can contribute to tail latency, particularly during long requests. To optimize tail latency, it's crucial to measure these individual GC pauses. By measuring individual pauses, performance engineers can assess the impact of GC on latency and understand how much they contribute to the overall tail latency.
Measuring individual GC pauses helps identify if specific requests are affected by GC pauses, enabling targeted optimization efforts. For example, if the goal is to reduce P95 latency, it's not productive to focus on optimizing GC pauses that don't affect those specific
There are several definitive signs of performance problems related to the GC. These signs indicate that there are issues affecting the GC's efficiency and overall performance. Here are some definitive signs to look out for:
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Suspension is too long: Normally, suspension during GC pauses should take less than 1ms. If you consistently observe suspension times in the range of 10s or 100s of ms, it is a clear indication of a performance problem. Monitoring the "Suspend Msec" and "Pause Msec" columns in the GCStats view can help identify this issue.
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Random long GC pauses: Random long GC pauses occur when a GC takes significantly longer than usual without promoting more objects than usual. For example, if most GC pauses complete in a few milliseconds but one GC pause takes much longer, it suggests something went wrong during that particular GC. Analyzing the "Pause Msec" and "Promoted MB" columns in the GCStats view can help identify this pattern.
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Most GCs are full blocking GCs: If a large portion of GCs are full blocking GCs, it indicates a performance problem. Full blocking GCs, which take a longer time to complete, should not occur frequently. Even in high memory load situations, full blocking GCs should be minimized. Monitoring the "Trigger Reason" and "Pause Msec" columns in the GCStats view can help identify induced full blocking GCs.
Identifying these signs allows performance engineers to focus their efforts on diagnosing and resolving the specific GC-related performance problems.
Long suspension times during GC pauses, exceeding the usual 1ms duration, are definitive signs of a performance problem. Suspension refers to the time when application threads are paused during GC activity. When suspension times are consistently longer, especially when it constitutes a significant portion of the total GC pause time, it indicates an underlying issue.
By monitoring the "Suspend Msec" and "Pause Msec" columns in the GCStats view, performance engineers can identify GC pauses with extended suspension times. Analyzing these longer suspension periods helps pinpoint the source of the performance problem and provides insights into potential areas for optimization.
Detailed examples and guidance on debugging long suspension issues can be found in relevant blog entries and resources provided by Microsoft.
Random long GC pauses occur when a specific GC takes significantly longer than expected, despite not promoting more objects than usual. This irregular behavior during garbage collection is a clear indication of a performance problem.
Monitoring the "Pause Msec" and "Promoted MB" columns in the GCStats view can help identify these random long GC pauses. By comparing the duration of individual GC pauses, performance engineers can detect outliers that deviate from the normal pattern. Analyzing such deviations can reveal potential issues and guide further investigation and debugging.
It's important to note that these long pauses may sometimes coincide with longer suspension times, as the same underlying reasons causing suspension can also impact the GC work. Debugging techniques and strategies for addressing random long GC pauses are covered in relevant resources provided by Microsoft.
A: There are several definitive signs that indicate performance problems related to GC. These signs include:
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Suspension is too long: Normal suspensions during GC pauses typically take less than 1 millisecond. If you observe suspensions lasting tens or hundreds of milliseconds, it's a clear indication of a performance problem.
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Random long GC pauses: Random long GC pauses occur when a GC takes significantly longer than usual, even though it doesn't promote more objects than other GCs. This irregular behavior suggests that something went wrong during that specific GC, and further investigation is necessary.
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Most GCs are full blocking GCs: Full blocking GCs, which can take a substantial amount of time for large heaps, should not be occurring frequently. If you notice that most GCs are full blocking, it indicates a performance problem. The purpose of a full blocking GC is to reduce heap size in situations of high memory load, but GC has mechanisms, such as provisional mode, to handle such scenarios more efficiently.
These signs serve as strong indicators that performance issues related to GC are present and warrant investigation and debugging. Monitoring the relevant GC metrics, such as suspension time, pause time, and trigger reasons, can help identify these signs.
Suspension during GC pauses refers to the time when application threads are paused to allow the GC to perform its work. Normally, suspensions last less than 1 millisecond. However, if you observe suspensions that take tens or hundreds of milliseconds, it is a clear sign of a performance problem. Long suspensions can impact the overall responsiveness and throughput of the application.
When suspension times become excessive, it indicates that there might be issues related to thread coordination, synchronization, or resource contention. To investigate and debug such problems, it's recommended to analyze the suspension times during GC pauses using tools like the GCStats view in PerfView or similar profiling tools.
Top-level application metrics, such as concurrent number of requests, average, maximum, and P95 request latency, should be monitored to evaluate the performance of your application.
To combat the instability of top-level application metrics caused by varying workloads, you can:
- Measure factors that can affect the metrics, even if you don't know all the factors upfront. As you gather more knowledge, add them to the list of things to measure.
- Monitor top-level component metrics that indicate workload variation. For memory, you can track metrics like the amount of allocation. If the allocation during peak hours is significantly higher than the previous day, it indicates increased stress on the GC. Monitoring such metrics can help identify performance impacts on the GC caused by user code.
The relevant top-level GC metrics for determining when to worry about GC depend on your performance goals. Based on Table 3, the following metrics are relevant:
- Throughput: % Pause time in GC and % CPU time in GC (optional).
- Tail latency: Individual GC pauses.
- Memory footprint: GC heap size histogram.
These metrics provide insights into GC's impact on the corresponding performance goals.
To assess the impact of GC on throughput, you need to consider two key metrics:
- % Pause time in GC: This metric indicates the percentage of time your threads are paused due to GC. A lower value is desirable as it minimizes interruptions to your threads.
- % CPU time in GC: This metric shows the percentage of CPU time consumed by the GC during its execution. Higher CPU usage during GC helps in completing the GC work faster. The calculation of % CPU time in GC takes into account the CPU usage during GC pauses and outside GC.
Monitoring % Pause time in GC is a cost-effective way to determine if you should be concerned about GC. % CPU time in GC is more expensive to monitor and becomes crucial only when your application performs a significant amount of background GCs and is highly CPU saturated.
When considering tail latency, it's important to measure individual GC pauses and their contribution to latency. GC pauses, including the longest ones, may occur during the longest requests, along with other factors impacting tail latency. By measuring individual GC pauses, you can determine their influence on latency.
However, if your goal is to reduce a specific percentile of latency (e.g., P95 latency), it's unproductive to focus on individual pauses that don't affect those requests. Eliminating pauses longer than the target latency won't impact that specific percentile. While it's beneficial to address longer GC pauses that might affect other percentiles, they don't directly contribute to the task of reducing the targeted percentile latency.
When optimizing memory footprint, it's crucial to understand the relationship between the GC heap size and the overall process memory usage. The GC heap is only one aspect of memory usage in a managed process. It's common for managed processes to have significant memory usage beyond the GC heap.
To properly measure the GC heap size and assess its significance, refer to the guidance on comparing GC heap size to the total process memory size. If the GC heap represents a small percentage of the total memory usage, focusing solely on reducing the GC heap size might not yield significant optimization benefits. It's essential to consider the overall memory utilization and identify any other areas contributing to memory consumption within your application.
To determine if you should worry about GC based on the top-level application metrics, you should analyze the metrics in relation to your performance goals. If the metrics indicate that GC has a small impact on your application's performance, it would be more productive to focus your efforts elsewhere. However, if the metrics indicate that GC has a significant impact, it's a sign that you should start considering managed memory analysis as a priority. The top-level application metrics serve as indicators of performance issues, and the GC metrics can help in investigating and understanding the underlying causes.
Why is monitoring the "% Pause time in GC" metric recommended as a top-level metric for determining GC impact?
Monitoring the "% Pause time in GC" metric is recommended as a top-level metric because it provides valuable insights into the impact of GC on your application. It is a cost-effective metric to monitor, as it doesn't require extensive resources or complex measurements. By tracking the percentage of time your threads are paused due to GC, you can assess the interruption caused by GC to your application's execution. In a well-behaved application, it is generally expected to have a low pause time (e.g., < 5%) during active workload handling. Monitoring this metric allows you to determine if GC pauses are within acceptable limits and whether they require further investigation or optimization.
How does the "% CPU time in GC" metric relate to the execution of GC and its impact on application performance?
The "% CPU time in GC" metric provides insights into how CPU resources are utilized during GC execution. While concurrent GC (e.g., background GC) minimizes thread pauses, it competes for CPU resources with the application's threads. This metric indicates the percentage of CPU time utilized by the GC during its execution. It's important to understand that a high CPU usage during GC execution is desirable as it helps complete the GC work more efficiently. It's no longer a concern if the GC uses a significant portion of CPU resources, especially when your threads are paused. Therefore, monitoring the "% CPU time in GC" metric becomes crucial when your application performs a considerable number of background GCs and operates in a CPU-saturated environment.
Individual GC pauses play a crucial role in understanding the impact of GC on tail latency. When investigating tail latency issues, it's important to measure and analyze individual GC pauses to identify their contribution to latency. These pauses may occur during the execution of long-running requests, potentially affecting their completion time. By measuring individual GC pauses and their duration, you can assess their impact on tail latency and determine if they need to be optimized or minimized to achieve the desired performance goals.
The GC heap size represents only one aspect of the overall memory footprint of a managed process. It's important to differentiate between the GC heap size and the total memory usage of the process. The GC heap size refers specifically to the memory allocated for managed objects, while the total memory usage includes other memory regions, such as native allocations or memory used by external libraries.
To properly assess memory footprint optimization, it's recommended to measure and compare the GC heap size against the total process memory size. If the GC heap size is relatively small compared to the overall memory usage, focusing solely on reducing the GC heap size may not result in significant memory optimization benefits. It's essential to consider the complete memory picture, including other memory-consuming factors within your application, to identify potential areas for optimization and memory management.