I'm using Flink 1.15 Docker images in Session mode pretty much the same as the Compose documentation. I have one Task Manager. A few minutes after starting my streaming job I get a stack dump log message from my Job Manager stating that the Task Manager is no longer reachable and I see that my Task Manager Docker container has exited with code 137 - which possibly indicates an out of memory error. Although docker inspect shows the OOMKilled flag as false indicating some sort of other issue.
End of stack trace from Job Manager log:
Caused by: org.apache.flink.runtime.jobmaster.JobMasterException: TaskManager with id 172.18.0.5:44333-7c7193 is no longer reachable.
The TaskManager Docker logs produce no error whatsoever before exiting. If I resurrect the dead Task Manager Docker container and have a look at the log file in /opt/flink/logs/ then the last messages state that the various components in my pipeline have switched from INITIALIZING to RUNNING.
I would have expected an out of memory stack dump from the task manager if my state had become too large. Also docker inspect shows that the container did not exit because of an out of memory error.
I have no idea what causes my Task Manager to die. Any ideas how I can figure out what is causing the issue? (This happens on 1.15.1 & 1.15.2. I haven't used any other version of Flink.)
This problem happened to me when a task manager runs out of memory and when the GC takes too much time trying to free some memory.
I know you said docker inspect doesn't show that it shuts down because of memory issues, but still try to use more RAM or decrease the memory requirements of your tasks and see if it still crashes.
I ended up using nothing more sophisticated than trial and error with a variety of different test jobs. I'm not 100% sure I fixed the problem as the issue of the Task Manager crashing without an stack dump occurred sporadically. However the Task Manager hasn't crashed for several days.
The simplest job to recreate my issue was with a SourceFunction outputting a continuous stream of incrementing Longs straight to a DiscardingSink. With this setup the Task Manager would crash after a while on my Linux machine sporadically but never on my Mac.
If I added a Thread.sleep to the SourceFunctions run loop then the crash would eventually occur but take a bit longer.
I tried Source framework instead of SourceFunction where a SingleThreadMultiplexSourceReaderBase repeatedly calls fetch on a SplitReader to output the Longs. There have been fewer crashes since I did this so it didn't work 100%.
I presume my SourceFunction was overfilling some sort of buffer or making a task slot unresponsive as it never relinquished a slot once it started. (Or some other completely different explanation.)
I wish the Task Manager gave some sort of indication why it stopped running.
We are getting gateway timeout error when doing load testing with 1000 users producing 90k sessions in an hour on Keycloak.
It seems like Heap is not releasing memory. Here are the configurations
We are using Cluster mode with 2 instances ( each with 2 core and 4GB RAM )
Keycloak Version : 16.0.0
With default Infinispan in-memory cache configuration
Standalone.config
JBOSS_JAVA_SIZING="-Xms128m -Xmx1024m -XX:MetaspaceSize=128M -XX:MaxMetaspaceSize=512m"
Heap Graph
CPU Utilization
We also tried increasing heap size upto 8GB but ran into same problem after 4hours. Looked into heap found that old G1 heap memory not releasing its increasing with the rate of 4-5MB/min.
I have a flink job that kept crashing. I asked question on debugging that in this post.
The issue was solved by increasing memory for task managers. I then checked the memory usage related metrics for all the containers at the time that this crash happened, and I saw 2 of them did have abnormal value for Status.JVM.Memory.Direct.MemoryUsed. I have a chart for that:
jvm.memory.direct.memory_used.png
From Flink official doc, it says The biggest driver of Direct memory is by far the number of Flink’s network buffers, which can be configured. However from task log I didn't see anything related to not enough network buffer. In order to prevent this from happening in the future, I would like to understand in detail what this portion of memory does in Flink and what could happen to these 2 outlier containers from the image. Thank you.
first, I've also need the behavior of TMs quitting without any logging of the problem, when it's an OutOfMemoryError.
Second, my experience with direct memory issues is that it didn't run out due to network buffers, but rather because I was using code that called through to compiled C code (Fasttext, in my case) which was allocating direct memory...are you sure you don't have a similar situation? Asking because usually Flink is good about not over-allocating memory - typically you get a failure like "Not enough memory for network buffers".
Is it possible to hardly limit a maximum memory consumption of a BaseX server instance process (something like a cache_size option in MongoDB) avoiding getting Java heap space errors simultaneously? Or, in the current implementation, it's required for the process to be able to allocate memory enough for storing a whole data set of the currently accessed database?
In the official documentation I've found nothing more than the following statement related to memory usage:
If BaseX terminates with an Out of Memory error, you can assign more RAM via the -Xmx flag (see below).
Does any know a good rule of thumb for the appropriate pagefile size for a Windows 2003 server running SQL Server?
With all due respect to Remus (whom I respect greatly), I strongly disagree. If your page file is large enough to support a full dump, it will perform a full dump every time. If you have a very large amount of RAM, this can cause a tiny blip to became a major outage.
You do NOT want your server to have to write out 1 TB of RAM to disk if there is a one-time transient issue. If there is a recurring issue, you can increase the page file to capture a full dump. I would wait to do this until you have been isntructed by PSS (or someone else qualified to analyze a full dump) request you to capture a full dump. An extremely small percentage of DBAs know how to analyze a full dump. A mini-dump is sufficent for troubleshooting most issues that pop up anyway.
Plus, if your server is configured to allow a 1 TB full dump and a recurring issue occurs, how much free disk space would you recommend having on hand? You could fill up an entire SAN in a single weekend.
A page file 1.5*RAM was the norm back in the days when you were lucky to have a SQL Server with 3 or 4 GB of RAM. This is not the case any more. I leave the page file at Windows default size and settings on all production servers (except for an SSAS server that is experiencing memory pressure).
And just for clarification, I've worked with servers ranging from 2 GB of RAM to 2 TB of RAM. After more than 11 years, I have only had to increae the paging file to capture a full dump one time.
Irrelevant of the size of the RAM, you still need a pagefile at least 1.5 times the amount of physical RAM. This is true even if you have a 1 TB RAM machine, you'll need 1.5 TB pagefile on disk (sounds crazy, but is true).
When a process asks MEM_COMMIT memory via VirtualAlloc/VirtualAllocEx, the requested size needs to be reserved in the pagefile. This was true in the first Win NT system, and is still true today see Managing Virtual Memory in Win32:
When memory is committed, physical
pages of memory are allocated and
space is reserved in a pagefile.
Bare some extreme odd cases, SQL Server will always ask for MEM_COMMIT pages. And given the fact that SQL uses a Dynamic Memory Management policy that reserves upfront as much buffer pool as possible (reserves and commits in terms of VAS), SQL Server will request at start up a huge reservation of space in the pagefile. If the pagefile is not properly sized errors 801/802 will start showing up in SQL's ERRORLOG file and operations.
This always causes some confusion, as administrators erroneously assume that a large RAM eliminates the need for a pagefile. In truth the contrary happens, a large RAM increases the need for pagefile, just because of the inner workings of the Windows NT memory manager. The reserved pagefile is, hopefully, never used.
According to Microsoft, "as the amount of RAM in a computer increases, the need for a page file decreases." The article then goes on to describe how to use Performance Logs to determine how much of the page file is actually being used. Try setting your page file to 1.5X system memory for a start, then do the recommended monitoring and make adjustments from there.
How to determine the appropriate page file size for 64-bit versions of Windows
The bigger the better up to the size of the working set of the application where you will start to get into diminishing returns. You can try to find this by slowly increasing or decreasing the size until you see a significant change in cache hit rates. However, if the cache hit rate is over 90% or so you're probably OK. Generally you should keep an eye on this on a production system to make sure it hasn't outgrown its RAM allocation.
We were recently having some performance issues with one of our SQL Server that we weren't able to completely narrow down, and actually used one of our Microsoft support tickets to have them help troubleshoot. The optimal pagefile size to use with SQL Server came up, and Microsoft's recommendation is that it be 1 1/2 times the amount of RAM.
In this case, the normal recommendation of 1.5 times total physical RAM is not the best. This very general recommendation is provided under the assumption that all memory is being used by "normal" processes, which can generally have their least-used pages moved to disk without generating massive performance issues for the application process the memory belongs to.
For servers running SQL Server (generally with very large amounts of RAM), the majority of the physical RAM is committed to the SQL Server process and should be (if configured correctly) locked in physical memory, preventing it from being paged out to the pagefile. SQL Server manages its own memory very carefully with performance in mind, using a large part of the RAM allocated to its process as a data cache to reduce disk I/O. It does not make sense to page out those data cache pages to the pagefile, as the sole purpose of having that data in RAM in the first place is to reduce disk I/O. (Note that the Windows OS also uses available RAM similarly as disk cache to speed up system operation.) Since SQL Server already manages its own memory space, this memory space should not be considered "pageable", and not included in a calculation for pagefile size.
In regard to MEM_COMMIT mentioned by Remus, the terminology is confusing because in the virtual memory parlance, "reserved" never refers to actual allocation, but to preventing use of an address space (not physical space) by another process. Memory available to be "committed" is basically equal to the sum of physical RAM and pagefile size, and doing a MEM_COMMIT just decrements the amount available in the committed pool. It does not allocate a matching page in the pagefile at that time. When a committed memory page is actually written to, that is when the virtual memory system will allocate a physical memory page and possibly bump another memory page from physical RAM to the pagefile. See MSDN's VirtualAlloc function reference.
The Windows OS keeps track of memory pressures between application processes and its own disk cache mechanism and decides when it should bump non-locked memory pages from physical to the pagefile. My understanding is that having a pagefile that is way too large compared to the actual non-locked memory space can result in Windows overzealously paging out application memory to the pagefile, resulting in those applications suffering the consequences of page misses (slow performance).
As long as the server is not running other memory-hungry processes, a pagefile size of 4GB should be plenty. If you have set SQL Server to allow locking pages in memory, you should also consider setting SQL Server's max memory setting so that it leaves some physical RAM available to the OS for itself and other processes.
802 errors in SQL Server indicate that the system cannot commit any more pages for the data cache. Increasing the pagefile size will only help in this situation insofar as Windows is able to page out memory from non-SQL Server processes. Allowing SQL Server memory to grow into the pagefile in this situation might get rid of the error messages, but it is counterproductive, due to the point earlier about the reason for the data cache in the first place.
If you're looking for high performance, you are going to want to avoid paging completely, so the page file size becomes less significant. Invest in as much RAM as feasible for the DB server.
After much research our dedicated SQL Servers running Enterprise x64 on Windows 2003 Enterprise x64 have no page file.
Simply, the page file is a cache for files that gets managed by the OS, and SQL has it's own internal memory management system.
The MS article referenced does not qualify that the advice is for the OS running out-of-the-box services such as file sharing.
Having a page file simply burdens the disk I/O because Windows is trying to help, when only the SQL OS can do the job.