Socksproxy example is suspected of having a memory leak

Expected behavior
Automatically release memory
Actual behavior
Direct Memory Leak
Steps to reproduce
I ran the socksproxy example project, setting the jvm parameters.
-Xmx200M -XX:MaxDirectMemorySize=50M -Dio.netty.leakDetection.level=PARANOID -Dio.netty.resourceLeakDetection

Then I start the edge browser and use socks5 to proxy the traffic.
Then use https://github.com/openspeedtest/Speed-Test to perform a proxy speed test. It was normal at first, when the test progressed to upload. There is a direct memory growth as shown in the figure, and it will not be released

Please tell me why it happened and how to solve it.
Minimal yet complete reproducer code (or URL to code)
https://github.com/netty/netty/tree/4.1/example/src/main/java/io/netty/example/socksproxy
Netty version
4.1.92.Final-SNAPSHOT
JVM version (e.g. java -version)
JVM: OpenJDK 64-Bit Server VM (11.0.19+7-LTS, mixed mode)
Java: version 11.0.19 2023-04-18 LTS, vendor Azul Systems, Inc.
Java Home: C:\Users\at.jdks\azul-11.0.19
OS version (e.g. uname -a)
Windows11

If there are no logs related memory leaks (reported stack traces) is unlikely there is any leak on Netty side. Let us know if you find anything suspicious in the logs

If there are no logs related memory leaks (reported stack traces) is unlikely there is any leak on Netty side. Let us know if you find anything suspicious in the logs

After using these two parameters, netty did not report any memory leaks.
-Dio.netty.leakDetection.level=PARANOID -Dio.netty.resourceLeakDetection

So I'm not too sure whether it's because of netty itself or because of the example.

So I'm not too sure whether it's because of netty itself or because of the example.

In either case I would say it's not a Netty thing.
One common misconception related Netty allocators is that it an release back memory to the Operating System: it won't.
When it grows it keep on growing to accommodate the maximum load, regardless which direct memory settings are used, or failing with OutOfMemory if beyond such.
If your memory just keep on growing until certain limits ( ie not unbounded) then is perfectly fine and it is not a problem.

In either case I would say it's not a Netty thing. One common misconception related Netty allocators is that it an release back memory to the Operating System: it won't. When it grows it keep on growing to accommodate the maximum load, regardless which direct memory settings are used, or failing with OutOfMemory if beyond such. If your memory just keep on growing until certain limits ( ie not unbounded) then is perfectly fine and it is not a bug.

But here netty not only does not release memory to the operating system, but also does not collect direct memory for reuse, and it has been kept occupied.
Perhaps I have misunderstood netty's memory recovery method.
Thank you for your answer, I'm doing some more testing around this issue.

@selcarpa you can print at interval the pinned memory used by the pooled allocators and you can see if memory grow unfounded or not (although it should fail with some OOM killer at OS level first, likely).
If Netty is not reusing the memory it should grow unbounded under further steady load.

public final class SocksServer {

    ...

    public static void main(String[] args) throws Exception {
        ...
        new Thread() {
            @Override
            public void run() {
                memStat();
            }
        }.start();
      ...
    }

    private static void memStat() {
        Executors.newSingleThreadScheduledExecutor().scheduleAtFixedRate(
                new Runnable() {
                    @Override
                    public void run() {
                        ByteBufAllocatorMetric metric = ((ByteBufAllocatorMetricProvider) ByteBufAllocator.DEFAULT).metric();
                        System.out.println("Direct: " + metric.usedDirectMemory() + ", Heap: " + metric.usedHeapMemory());
                    }
                }, 0, 1, java.util.concurrent.TimeUnit.SECONDS
        );
    }
}

I added these codes to https://github.com/netty/netty/blob/4.1/example/src/main/java/io/netty/example/socksproxy/SocksServer.java
And changed the startup parameters
-Xmx4000M -Dio.netty.leakDetection.level=PARANOID -Dio.netty.resourceLeakDetection

Still use https://github.com/openspeedtest/Speed-Test to test, this is the printed, when memory reaches 536870912, it will not change
Connected to the target VM, address: '<IP_ADDRESS>:6425', transport: 'socket'
Direct: 0, Heap: 0
Direct: 0, Heap: 0
22:49:33.621 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942] REGISTERED
22:49:33.630 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942] BIND: <IP_ADDRESS>/<IP_ADDRESS>:1080
22:49:33.635 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] ACTIVE
22:49:33.758 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ: [id: 0x4be01c88, L:/<IP_ADDRESS>:1080 - R:/<IP_ADDRESS>:6481]
22:49:33.760 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ COMPLETE
22:49:33.959 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ: [id: 0x13ddc8f8, L:/<IP_ADDRESS>:1080 - R:/<IP_ADDRESS>:6480]
22:49:33.960 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ COMPLETE
Direct: 8388608, Heap: 0
Direct: 8388608, Heap: 0
Direct: 8388608, Heap: 0
Direct: 8388608, Heap: 0
Direct: 8388608, Heap: 0
Direct: 8388608, Heap: 0
Direct: 8388608, Heap: 0
Direct: 8388608, Heap: 0
22:49:41.463 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ: [id: 0xb4eeb029, L:/<IP_ADDRESS>:1080 - R:/<IP_ADDRESS>:6486]
22:49:41.463 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ COMPLETE
22:49:41.770 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ: [id: 0x1e399b57, L:/<IP_ADDRESS>:1080 - R:/<IP_ADDRESS>:6488]
22:49:41.770 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ COMPLETE
22:49:42.076 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ: [id: 0x9d855687, L:/<IP_ADDRESS>:1080 - R:/<IP_ADDRESS>:6492]
22:49:42.077 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ COMPLETE
Direct: 20971520, Heap: 0
22:49:42.363 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ: [id: 0x8a3a52c9, L:/<IP_ADDRESS>:1080 - R:/<IP_ADDRESS>:6494]
22:49:42.364 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ COMPLETE
Direct: 25165824, Heap: 0
Direct: 25165824, Heap: 0
Direct: 25165824, Heap: 0
Direct: 25165824, Heap: 0
Direct: 25165824, Heap: 0
Direct: 25165824, Heap: 0
Direct: 25165824, Heap: 0
Direct: 25165824, Heap: 0
Direct: 25165824, Heap: 0
Direct: 25165824, Heap: 0
Direct: 25165824, Heap: 0
Direct: 25165824, Heap: 0
Direct: 25165824, Heap: 0
java.io.IOException: 你的主机中的软件中止了一个已建立的连接。
	at java.base/sun.nio.ch.SocketDispatcher.read0(Native Method)
	at java.base/sun.nio.ch.SocketDispatcher.read(SocketDispatcher.java:43)
	at java.base/sun.nio.ch.IOUtil.readIntoNativeBuffer(IOUtil.java:276)
	at java.base/sun.nio.ch.IOUtil.read(IOUtil.java:233)
	at java.base/sun.nio.ch.IOUtil.read(IOUtil.java:223)
	at java.base/sun.nio.ch.SocketChannelImpl.read(SocketChannelImpl.java:356)
	at io.netty.buffer.PooledByteBuf.setBytes(PooledByteBuf.java:256)
	at io.netty.buffer.AbstractByteBuf.writeBytes(AbstractByteBuf.java:1132)
	at io.netty.buffer.WrappedByteBuf.writeBytes(WrappedByteBuf.java:821)
	at io.netty.buffer.AdvancedLeakAwareByteBuf.writeBytes(AdvancedLeakAwareByteBuf.java:636)
	at io.netty.channel.socket.nio.NioSocketChannel.doReadBytes(NioSocketChannel.java:357)
	at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:151)
	at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:788)
	at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:724)
	at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:650)
	at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:562)
	at io.netty.util.concurrent.SingleThreadEventExecutor$4.run(SingleThreadEventExecutor.java:997)
	at io.netty.util.internal.ThreadExecutorMap$2.run(ThreadExecutorMap.java:74)
	at io.netty.util.concurrent.FastThreadLocalRunnable.run(FastThreadLocalRunnable.java:30)
	at java.base/java.lang.Thread.run(Thread.java:829)
Direct: 25165824, Heap: 0
Direct: 25165824, Heap: 0
Direct: 25165824, Heap: 0
22:49:58.893 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ: [id: 0x8613c619, L:/<IP_ADDRESS>:1080 - R:/<IP_ADDRESS>:6502]
22:49:58.894 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ COMPLETE
22:49:59.129 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ: [id: 0x81d5cc0e, L:/<IP_ADDRESS>:1080 - R:/<IP_ADDRESS>:6504]
22:49:59.130 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ COMPLETE
Direct: 109051904, Heap: 0
22:49:59.431 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ: [id: 0x70f52a61, L:/<IP_ADDRESS>:1080 - R:/<IP_ADDRESS>:6506]
22:49:59.432 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ COMPLETE
22:49:59.729 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ: [id: 0x66cb0880, L:/<IP_ADDRESS>:1080 - R:/<IP_ADDRESS>:6508]
22:49:59.729 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ COMPLETE
22:50:00.030 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ: [id: 0xf70d4152, L:/<IP_ADDRESS>:1080 - R:/<IP_ADDRESS>:6510]
22:50:00.030 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ COMPLETE
22:50:00.064 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ: [id: 0xb621073e, L:/<IP_ADDRESS>:1080 - R:/<IP_ADDRESS>:6512]
22:50:00.064 [nioEventLoopGroup-2-1] INFO  i.n.handler.logging.LoggingHandler - [id: 0x1f153942, L:/0:0:0:0:0:0:0:0:1080] READ COMPLETE
Direct: 293601280, Heap: 0
Direct: 419430400, Heap: 0
Direct: 562036736, Heap: 0
Direct: 675282944, Heap: 0
Direct: 738197504, Heap: 0
Direct: 775946240, Heap: 0
Direct: 775946240, Heap: 0
Direct: 775946240, Heap: 0
Direct: 826277888, Heap: 0
Direct: 843055104, Heap: 0
Direct: 830472192, Heap: 0
Direct: 868220928, Heap: 0
Direct: 864026624, Heap: 0
Direct: 855638016, Heap: 0
Direct: 855638016, Heap: 0
Direct: 855638016, Heap: 0
Direct: 834666496, Heap: 0
Direct: 813694976, Heap: 0
Direct: 805306368, Heap: 0
Direct: 780140544, Heap: 0
Direct: 763363328, Heap: 0
Direct: 738197504, Heap: 0
Direct: 717225984, Heap: 0
Direct: 692060160, Heap: 0
Direct: 671088640, Heap: 0
Direct: 641728512, Heap: 0
Direct: 612368384, Heap: 0
Direct: 583008256, Heap: 0
Direct: 553648128, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0
Direct: 536870912, Heap: 0

Is it as expected

I recommend you also log PooledByteBufAllocator.pinnedDirectMemory — this will show the memory that's in use by direct buffers. This is in contrast to the used memory, which is what the pool has taken up. There's also a version for heap memory.
Pinned memory should go up and down as buffers are allocated and released, while used memory will only grow due to pooling.

I recommend you also log PooledByteBufAllocator.pinnedDirectMemory — this will show the memory that's in use by direct buffers. This is in contrast to the used memory, which is what the pool has taken up. There's also a version for heap memory. Pinned memory should go up and down as buffers are allocated and released, while used memory will only grow due to pooling.

It is indeed so, I printed this part, it is indeed "go up and down as buffers are allocated and released".
What puzzles me is that the direct memory keeps rising until OutOfMemory. I am learning this part and trying to find out the reason.
Thanks

I am sure that there is some problem with the example code. I suspect this weird "Promise" code.
I had the same problem a while ago https://github.com/netty/netty/issues/12115


FW-751. Nordic nRF52840 port

Adds support for Nordic nRF52840 (Developer Kit and Dongle).
This is a continuation of PR #445 created by tharczos with merge conflicts fixed. I will try everything on real hardware as soon as I get some free time!

Ok, the project now compiles using the following command:
sudo scons board=nrf52840 revision=DK toolchain=armgcc oos_openwsn
It successfully runs in the boards too (LEDs turn on and core is running). I still have to check if the network is running and operational, I will attach a logic analyzer to debug using the debugging pins provided by the port.

@dragondgold great news! I will check it out on my local and will comment on this PR after.

@dragondgold I tried with the PR however I encounter the following issue when compiling:
arm-none-eabi-as -g -gdwarf-2 -mcpu=cortex-m4 -mthumb -c -x assembler-with-cpp -DNRF52840_XXAA=1 -o
build\nrf52840_armgcc\bsp\boards\nrf52840\sdk\modules\nrfx\mdk\gcc_startup_nrf52840.o build\nrf52840
_armgcc\bsp\boards\nrf52840\sdk\modules\nrfx\mdk\gcc_startup_nrf52840.S
arm-none-eabi-as: unrecognized option `-x'
scons: *** [build\nrf52840_armgcc\bsp\boards\nrf52840\sdk\modules\nrfx\mdk\gcc_startup_nrf52840.o] E
rror 1
scons: building terminated because of errors.

Do you have this issue? By searching online, it tells the assembly file suffix should be '.S' instead of '.s'. But it does end with '.S'

@dragondgold I tried this branch on Linux and the compile passed. I will check what's going on on Windows.   Beside,  the bootload is not supported for nRF52840. How do you problem the DK board?

@dragondgold I found the emProject and tried to program the board,  after comment out the openapp_init() function,  the project compiles.   After I flash two DK boards, when running OpenVisualizer, it's hardly to recognized their serial port.  Sometime it recognized but with an error triggered in the OpenVisualizer  terminal indicating
======= crash in moteProbe@COM84 =======
Error:

call stack:

Traceback (most recent call last):
  File "C:\Users\Tengfei\Desktop\nodirc_port\openvisualizer\openvisualizer\moteProbe\moteProbe.py", line 349, in run
    self.inputBuf        = self.hdlc.dehdlcify(self.inputBuf)
  File "C:\Users\Tengfei\Desktop\nodirc_port\openvisualizer\openvisualizer\moteProbe\OpenHdlc.py", line 99, in dehdlcify
    assert inBuf[-1]==self.HDLC_FLAG
AssertionError

Then I went to the uart.c implementation, it seems the clear/enable/disable interrupts for Tx and Rx are not implemented, also the implementation for uart_writeByte doesn't support XON/XOFF feature.  You can take the uart.c of OpenMote-B 24GHz as an example to fix the implementation.
For the current epidemic  situation, let me know  if you don't have time to work on that. So that I can take over the work if you don't mind.

Hi @changtengfei! I'm so sorry for the late reply, of course, you can take over the work! I haven't had time to continue it. I compiled everything in Ubuntu and flashed the device using nrfutil

I find this project and I want to know why to configure the UART by directly accessing the register instead of using the app_uart_init() function.


Fix choosereactor package name in examples/twisted/websocket/echo_endpoints

[Current coverage][cc-pull] is 59.27%

Merging [#687][cc-pull] into [master][cc-base-branch] will not change coverage

@@             master       #687   diff @@
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  Branches       1643       1643
==========================================
  Hits           6365       6365
  Misses         3861       3861
  Partials        513        513

Powered by Codecov. Last updated by [c54bc9e...662b1fe][cc-compare]
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[cc-pull]: https://codecov.io/gh/crossbario/autobahn-python/pull/687?src=pr


How to use in a map?

I have a react Navigation component, where I loop over routes, each route has a icon property. How would I use the icon in the map?
This would be the array of the routes:
const routes = [{
    route: '/',
    title: 'Dashboard',
    exact: true,
    icon: 'Code',
  }]

And this is what the component returns:
return (

      {routes.map(({route, title, icon, exact}, index) => (
        <NavLink
          className="flex items-center text-gray-400 rounded-full rounded-l-none text-sm font-semibold py-3 px-6 hover:text-white"
          activeClassName="navigation-link-active"
          key={index}
          to={route}
          exact={typeof exact !== 'undefined' && exact}
        >
          <Icons.Code className="inline-block h-5 w mr-3" />
          {title}
        </NavLink>
      ))}

  );

Any help is appreciated!
Thanks in advance.

@JPortegijs I ran into same issue. how did you solve this?

You could import the entire module as an object and use the icon names to lookup its exports:
import * as icons from "@heroicons/react/solid";

const ComponentWithIcon: React.FC<{ iconName: keyof typeof icons }> = (props) => {
  const Icon = icons[props.iconName];
  return (

      <Icon className="w-5 h-5" />
      {props.iconName}

  );
};

Full example here: https://codesandbox.io/s/tailwindlabsheroicons262-lw2gr
Although, be carful here since you may loose tree-shaking by doing so. I recommend you explicitly re-export the icons you need from another file (import * as icons from '../path/to/icons'). This way you have control over the icons that ends up included in the bundle.


Refactor array util

Hi @dlwh,
an attempt to remove duplications in ArrayUtil without affecting performances.
Does it make sense?

unfortunately this causes boxing, and possibly even reflection. If you want to do something like this, I'd recommend using the @expand macro like in zeroSkippingHashCodeImpl

Thanks, I guessed so!

Thanks for the feedback.


How to work with AJAX?

Hi,
How can I force this to load with ajax content being added?
Cheers
Andy

you probably just need to re-initiliaze the enterview instance on load.


Responsiveness of team page

The team page is not responsive right now and doesn't look good on small screens. This needs to be fixed.

please assign this issue to me


multiple sql statements in an sql script file

hi
in a script, executed using the 'sql' command.
if you have one sql statement, like select resetDB(), you get back the result of the select, very nicely
Query:  [resetDB] SELECT resetDB();

{ id: '8fdb0a61-59a5-40ea-bcc0-f3f53830ff55',
  sql:
   'SELECT resetDB();\r\n',
  start:<PHONE_NUMBER>119,
  name: 'resetDB',
  params: undefined,
  end:<PHONE_NUMBER>237,
  duration: 118,
  data: { row_count: 1, rows: [ [Object] ], command: 'SELECT' } }
Result: [resetDB] (118ms ) 1  row(s)
      = [ { resetdb: '' } ]

if you have more than one, you get back a result with all nulls (row_count, etc ...)
Query:  [resetDB] SELECT resetDB();
insert into types (is_type, name) values ('obj', 'e') returning * ;

result event: context
{ id: 'c8aedfe2-7da9-4220-a95e-0b993c244f2d',
  sql:
   'SELECT resetDB();\r\n',
  start:<PHONE_NUMBER>306,
  name: 'resetDB',
  params: undefined,
  end:<PHONE_NUMBER>408,
  duration: 102,
  error: null,
  data:
   { row_count: undefined, rows: undefined, command: undefined } }
Result: [resetDB] (102ms ) undefined  row(s)

Note: the result in the database is as expected, just the result returned to the caller is not..

I think support for multiple query results is a new thing in node pg. I will look to see how they've implemented it.

What version of postgres are you using?

Started work on supporting multiple statements:
https://github.com/joeandaverde/tinypg/pull/12

hi
i am using the postgres 10.4
On Wed, Jul 4, 2018 at 10:34 AM, Joe Andaverde<EMAIL_ADDRESS>wrote:

Started work on supporting multiple statements:
#12 https://github.com/joeandaverde/tinypg/pull/12
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/joeandaverde/tinypg/issues/11#issuecomment-402532012,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AhAzA6TaQ-CEEQtdv7oL0ZRpyJLlCohKks5uDPyugaJpZM4Uv-4D
.


macOS - Docker build - OSError: [Errno 18] Invalid cross-device link

When building a lambda using docker on macOS, the following error is raised.
OSError: [Errno 18] Invalid cross-device link: '/tmp/pip-target-aypwuoa4/lib/python/pandas' -> '/var/task/pandas'

The requirements.txt is
pandas==1.0.3
scipy==1.4.1
nltk==3.4.5
joblib==0.14.1

For some reason that happened to me as well, but running terraform plan && terraform apply immediately again, it went away. Really weird.

I am experiencing a similar issue here. Interestingly, the error on the invalid cross-device link for me occurs at a random python package, while sometimes the build is actually successful. At the moment I have no solution other than "try until it works", which is not really satisfying.

I have the same issue:
OSError: [Errno 18] Invalid cross-device link: '/tmp/pip-target-u3i7dyip/lib/python/google' -> '/var/task/google'
Do you have any solution? @niranjan94

Same issue OSError: [Errno 18] Invalid cross-device link: '/opt/conda/lib/python3.7/site-packages/pkg_resources' -> '/tmp/pip-uninstall-1k2j_mjl/opt/conda/lib/python3.7/site-packages/pkg_resources'


Output path on MacOS

Thank you for this fine tool.
A minor issue, but on MacOS it creates a weird folder so the path to a JSON file looks like this:
.\\docs\\java.lang.Thread.json

You probably want to use the System property file.separator there:
https://github.com/RaidAndFade/javadoc-json-doclet/blob/master/src/com/raidandfade/JsonDoclet/Main.java#L18
and there:
https://github.com/RaidAndFade/javadoc-json-doclet/blob/master/src/com/raidandfade/JsonDoclet/Main.java#L75

You're right. I'll fix this when i get the chance.


npm WARN EPEERINVALID<EMAIL_ADDRESS>requires a peer of<EMAIL_ADDRESS>but none was installed.

I'm fairly new to React Native so I don't understand this message and if I should be concerned:
I do npm install --save react-native-swipeout in my project directory and get this:
npm WARN EPEERINVALID<EMAIL_ADDRESS>requires a peer of<EMAIL_ADDRESS>but none was installed.

Hello!
ReactJS needs this. (react-tween-state requires it as a peer dependency)
But since you're using react-native I guess you're (and me :)) doesn't need to do npm install react-dom --save

https://github.com/chenglou/react-tween-state/blob/master/package.json#L41

It should work fine without react-dom.


naked /** @deprecated */ for toPixelsFromProjected

    /** @deprecated */
    @Deprecated
    public Point toPixelsFromProjected(PointL in, Point reuse) {

in https://github.com/osmdroid/osmdroid/blob/0aaa8dc31f9f8adae022910a7df1d55ca78efbaf/osmdroid-android/src/main/java/org/osmdroid/views/Projection.java#L265
It's not clear what to replace it with...
Issue Type
[x] Documentation
6.0.2-SNAPSHOT

@pasniak Satisfied by #995?

	/**
     * @since 6.0.0
     */
    public PointL getLongPixelsFromProjected(final PointL in, final double powerDifference, final boolean pCloser, final PointL reuse) {

The call to getLongPixelsFromProjected looks quite cryptic to me :) Maybe the names (in, powerDifference, pCloser, reuse) could be less cryptic?
I understand mMapCoordsTranslated is the result.  It's a bit like C++ optimization i.e passing the ref :)  if that is the case why do we return it (when it is suboptimal)?

They look cryptic to me too :)
But even the concept of "projected" is a bit cryptic; in a better world we wouldn't need that. It's only used when we have tons of GeoPoints to frequently project on a screen: instead of systematically call the heavy Math projection functions, we project once on a "projected" virtual screen with a "big" tile size, and then we just have to reduce into the actual smaller projection virtual screen.
I believe the names could be slightly less cryptic, but not that much; I'd rather say we need more comments instead.
Projection is a sort of work in progress, there are TODO tags and recent new concepts and optimization around map replication and Polygon drawing.
About the reuse + return part of your comment, I just "reused" (!) the method parameter pattern in Projection. I'm not against not returning a value: how suboptimal is the unnecessary "return" part?

Well, we have to deal with a code with a long history and many contributors...

@pasniak Just PR'ed #1088.

@pasniak ping

I assume it's decent now. Feel free to reopen if necessary.


In github-feed.js improve handling of options

Combine the arguments and the local variable "options" into a single object. It will replace the arguments.

Completed.


Add a prototype of an integration test of service loading [ECR-3010]

Overview

A simple single IT that successfully loads a fake-service.

See: https://jira.bf.local/browse/ECR-3010
Definition of Done

[ ] There are no TODOs left in the code
[ ] Change is covered by automated tests
[ ] The coding guidelines are followed
[ ] Public API has Javadoc
[ ] Method preconditions are checked and documented in the Javadoc of the method
[ ] Changelog is updated if needed (in case of notable or breaking changes)
[ ] The continuous integration build passes

Coverage increased (+0.2%) to 85.926% when pulling dcad5d36bfff7552b93e96d072848d2ac9d764ed on dmitry-timofeev:service-loader-its-prototype-ECR-3010 into c148c2650badf6df72ab72816cee1fea513e33f5 on exonum:master.

I'd leave it as a prototype, as the overhead of a separate module for such ITs is higher than the benefit (we already have ITs that generate artifacts at runtime using ServiceArtifactBuilder).


Inference on TFLite Model Gets Stuck In An Infinite Loop

Description
Problem
I'm trying to run inference on a model on the edge TPU, but there seems to be some issues. When I run the model without setting any environment variables, I get this error:
F driver/usb/usb_driver.cc:857] transfer on tag 1 failed. Abort. Deadline exceeded: USB transfer error 2 [LibUsbDataOutCallback]
Aborted (core dumped)

and the lights on the edge TPU USB accelerator gets stuck blinking. I have to unplug then replug the USB cable to get it working again. However, when I set the environment variable USB_ENABLE_BULK_DESCRIPTORS_FROM_DEVICE=1 and then run the model, I no longer receive that error, but inference gets stuck in an infinite loop and never terminates.
Steps to Reproduce

Download these files:

main.py
quant_model_edgetpu_new.tflite (compiled with edgetpu_compiler version 16.0.384591198)

Run inference on the tflite model with main.py

python main.py quant_model_edgetpu_new.tflite

Notes
I googled the first error, and found two related issues. One of the issues is related to power draw, so I made sure that I installed libedgetpu1-std instead of libedgetpu1-max, and that the USB cable is directly connected to a USB port on my PC, instead of going through a USB hub. The other issue is related to big input/output tensor sizes, which seems to be related to my issue, because I can successfully run inference on the same model with smaller input/output sizes.
However, if I compile the CPU tflite model with edgetpu_compiler version 15.0.340273435 instead of version 16.0.384591198, even though all the same operations are mapped to the TPU, I can now run inference on the model without any problems, so this issue seems to only appear with the latest version of the edgetpu_compiler. Here are the related files for your reference:

quant_model.tflite (CPU)
quant_model_edgetpu.tflite (compiled with edgetpu_compiler version 15.0.340273435)

You can run
python main.py quant_model_edgetpu.tflite

to try inference on the model compiled with edgetpu_compiler version 15.0.340273435.
Click to expand!
Issue Type
Bug
Operating System
Ubuntu
Coral Device
USB Accelerator
Other Devices
No response
Programming Language
Python 3.8
Relevant Log Output
No response

Hi, With the latest changes it is expected to get the below error to avoid overheating of USB accelerator.
F driver/usb/usb_driver.cc:857] transfer on tag 1 failed. Abort. Deadline exceeded: USB transfer error 2 [LibUsbDataOutCallback]
Aborted (core dumped)

@hjonnala Thanks for the information. So it is expected behavior that the CPU tflite model compiled with edgetpu_compiler version 15.0.340273435 can be run successfully on the edge TPU USB accelerator, but the same CPU tflite model compiled with edgetpu_compiler version 16.0.384591198 cannot be successfully run because of potential overheating issues? Does this mean that if the CPU tflite model is compiled with edgetpu_compiler version 15.0.340273435, it is possible that the USB accelerator will overheat without warning?

Hi, With USB accelerator for models with  15.0 thermal throttling is controlled at runtime level. But the inference shuts off when the temp raises.
with 16.0 such model are not allowed in first place.


Drop older asyncio workarounds for Python < 3.7

Since we've dropped 3.6, and 3.7 has A LOT of asyncio improvements (here's a glance of some -- already a long list!), we might want to start replacing all 'workarounds' that catered to Python < 3.7 to things that are 'more elegant' (and definitely more up-to-date).
Not marking this for any particular version. But definitely making this a tech-debt.

With PR #359 , we have completely removed any support for Python<3.7, and this issue can go forward.


Implement Overlay Banner For The Search Results Page

Context
The type of Implementation: New feature (non-breaking change which adds functionality)
On the search results page, our application displays a closable overlay banner message to first-time visitors, and we'll need to implement it.
Problem or idea

Based on the UI design, we'll build a closable overlay banner component to display to first-time visitors.
Solution or next step

Create a reusable overlay banner component
Utilize the local storage to create its logic

This feature branch will be created based on the PR #106 branch, thank you!


Set up project structure and development environment

This subtask of the issue #1
Create the initial project structure and set up the development environment for the smart document manager application.
Steps:

Initialize a new Next.js project with TypeScript support
Set up Tailwind CSS for styling
Configure TypeORM with SQLite
Set up the project directory structure
Configure ESLint and Prettier for code formatting
Set up a logging library (e.g., winston or pino) with configurable log levels
Create a basic README.md with project setup instructions

Technical details:

Use create-next-app with the --typescript flag
Follow the official Tailwind CSS installation guide for Next.js
Use typeorm package and configure it for SQLite
Create directories for components, pages, api, utils, and models
Use .env file for environment variables, including log level configuration

Expected output:

A functional Next.js project with TypeScript and Tailwind CSS
TypeORM configured with SQLite
ESLint and Prettier configured
Logging library installed and configured
Basic README.md with setup instructions

Testing:

Ensure the development server starts without errors
Verify that Tailwind CSS classes work
Test TypeORM connection to SQLite
Verify that logging works with different log levels

--logs--
Agent: dev
Task failed.
Details

Output:

Error:
Error: Command failed: git push origin
fatal: The current branch issue-2-set-up-project-structure-and-development-environme has no upstream branch.
To push the current branch and set the remote as upstream, use

    git push --set-upstream origin issue-2-set-up-project-structure-and-development-environme

  </p>
  </details>

--logs--
Agent: dev
Task failed.
Details

Output:

Error:
Error: Command failed: git clone -q -b main  --depth=1 git@github.com:SergKam/smart-doc-manager.git workspace/dev
fatal: could not create leading directories of 'workspace/dev': Permission denied

  </p>
  </details>

--logs--
Agent: dev
Task failed.
Details

Output:

Error:
Error: Command failed: git clone -q -b main  --depth=1 git@github.com:SergKam/smart-doc-manager.git workspace/dev
fatal: could not create leading directories of 'workspace/dev': Permission denied

  </p>
  </details>

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Agent: dev
Task completed.
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Two or more scenes and views

I tried to get the calculator example to work with two views.
It's a first step in implementing groups.
See the patch. It's got some qDebug() s added but just ignore them.
0001-Calculator-with-two-scenes-plus-some-qDebug-addition.patch.txt
To switch views, double click on the view.
Blender uses Tab to enter/exit a group.
Anyway, when I add a node it appears in both scenes/views, one without the text widget.

Thanks for the patch! I'll have a look asap

I figured it out.
You're storing the embedded QWidgets in the data model, e.g. NumberSourceDataModel.
This fixes it: create two data model registries - they don't need to be the same for embedded scenes anyway.
    std::shared_ptr<NodeDelegateModelRegistry> registry1 = registerDataModels();
    std::shared_ptr<NodeDelegateModelRegistry> registry2 = registerDataModels();

    QWidget mainWidget;

    auto menuBar = new QMenuBar();
    QMenu *menu = menuBar->addMenu("File");
    auto saveAction = menu->addAction("Save Scene");
    auto loadAction = menu->addAction("Load Scene");

    QVBoxLayout *l = new QVBoxLayout(&mainWidget);

    DataFlowGraphModel dataFlowGraphModel1(registry1);
    DataFlowGraphModel dataFlowGraphModel2(registry2);

    l->addWidget(menuBar);
    auto scene1 = new DataFlowGraphicsScene(dataFlowGraphModel1, &mainWidget);
    auto view1 = new CalcGraphicsView(scene1);
    l->addWidget(view1);

    auto scene2 = new DataFlowGraphicsScene(dataFlowGraphModel2, &mainWidget);
    auto view2 = new CalcGraphicsView(scene2);
    l->addWidget(view2);
    view2->hide();

Now, double-clicking anywhere on one scene takes you to the other scene.
That's not ideal, as in Blender you need to have the group node selected in order to tab into it, but at least it's a start.


[SVC-3422] Migrate CDN to Envoy + xDS with gRPC

Motivation
The current CDN system uses Traefik's HTTP provider to pull new configs frequently. This will not scale.
Implementation

Replace Traefik with Envoy.
Expose a standalone xDS service using gRPC that CDN can talk to to pull configs.

From SyncLinear.com | SVC-3422

Out of sync with Linear


Provide subscriber-oriented view of later-binding plugins

Is your feature request related to a problem? Please describe.
As a user of OpenSCD I would like to see a subscriber-oriented view of the later binding plugins.
This has been done for the "whole message" Subscriber plugins.
This is because sometimes I want to answer the question "What is IED x subscribed to?" which I expect to be a common question that users will raise.
At present, this is hard to answer.
Describe the solution you'd like
I'd like a radio button similar to the other plugins which allows me to swap the left and right panes similar to the Subscriber (GOOSE) and Subscriber (SMV) plugins:

On the LHS there would be a list of subscribing IEDs. Click on one of them would provide a list of control blocks to which the IED is subscribed and a view of the mappings between them.

Good idea!

Todo, describe left and right:
Screens

How often you think you need this? If I understand @JakobVogelsang well, this feature is primairy used for "whole message" subscribers.

I think any time one wants to configure an IED from the perspective of "what do I receive from the world" a subscriber-oriented view is necessary. I don't see it as in any way coupled to the "whole message" concept.
At present, for later binding, we cannot answer the question: "What does IED x subscribe to?" without going to look for every publisher which might send messages. Even if only for testing or understanding, this is an important feature.
I suspect users will prefer to configure an "IED at a time" based on "what is available", which is the subscriber view.
Where the publisher view is most valuable, in my opinion, is where there is a many-to-one relationship (e.g. configuring all merging units on a bus to a bus zone relay or configuring CBFail for all bays to a bus zone or an individual bay then the publisher view is best).

At our last refinement, I indicated I would provide a mock-up. It is a very mash-up style mockup, but perhaps we could discuss it further.
I am somewhat troubled by the busy-ness of the ExtRef view on the left in this arrangement and think I've missed something.

In its full glory, here: https://excalidraw.com/#json=QhxOhq5ECwpvdx76uB5WA,KAuNo-5CQ2MM8bOaynxXQQ

Placing the subscribed FCDA identity on the right-hand side is not something we do now in the later-binding plugin editors.

It has the advantage of providing an overview for a whole subscribing device and the disadvantage of: busyness and clutter.

Perhaps worth mentioning that this is now a separate plugin with development mostly complete: https://github.com/danyill/oscd-subscriber-later-binding


[Mapping issue] PCR results are not mapped

Describe the problem
PCR tests source codes in gold are:

37310282
1240581000000104
Severe acute respiratory syndrome coronavirus 2 detected
Clinical Finding
Non-standard
Valid
Measurement
SNOMED

37310281
1240591000000102
Severe acute respiratory syndrome coronavirus 2 not detected
Clinical Finding
Non-standard
Valid
Measurement
SNOMED

Both are mapped to:

756055
OMOP4873969
Measurement of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Lab Test
Standard
Valid
Measurement
OMOP Extension

Which is fine and allows us to see the PCR records.
But then the result is not mapped so we would expect that for 37310282 the value_as_concept_id is 9191 (Positive) and for 37310281 the value_as_concept_id is 9189 (Negative). But both have a value_as_concept_id = 0 (No matching concept)
Currently we have to identify the result from the source codes, it would be nice if we can map the result with standard codes.
Affected databases
As far as I know: cdm_gold_202207, cdm_gold_202201, cdm_aurum_p21_000557
Tag for awareness
@teenwyman @adelmestri @edward-burn @Xintong-Li-ZnCu

In GOLD, the source codes of PCR tests are

concept_id
concept_name
domain_id
vocabulary_id
concept_class_id
concept_code

940643
Cent lab SARS-CoV-2 spec detec
Measurement
Read
Read
4J3S100

940723
PoC SARS-CoV-2 species detect
Measurement
Read
Read
4J3S200

940771
SARS-CoV-2 detect reslt unknow
Measurement
Read
Read
4J3R400

940772
SARS-CoV-2 detect result equiv
Measurement
Read
Read
4J3R300

940802
SARS-CoV-2 species detect test
Measurement
Read
Read
4J3S.00

940842
Specimen SARS-CoV-2 qual exist
Measurement
Read
Read
4J3S300

940848
Taking of swab for SARS-CoV-2 (severe acute respiratory syndrome   coronavirus 2)
Meas/Procedure
Read
Read
4JF6.00

And they are mapped to

concept_id_1
concept_name
concept_id_2
relationship_id
valid_start_date
valid_end_date
invalid_reason

940643
Cent lab SARS-CoV-2 spec detec
32878
Maps to
10/08/2022
31/12/2099

940643
Cent lab SARS-CoV-2 spec detec
756055
Maps to
10/08/2022
31/12/2099

940723
PoC SARS-CoV-2 species detect
756055
Maps to
10/08/2022
31/12/2099

940771
SARS-CoV-2 detect reslt unknow
756055
Maps to
10/08/2022
31/12/2099

940772
SARS-CoV-2 detect result equiv
756055
Maps to
10/08/2022
31/12/2099

940802
SARS-CoV-2 species detect test
756055
Maps to
10/08/2022
31/12/2099

940842
Specimen SARS-CoV-2 qual exist
756055
Maps to
10/08/2022
31/12/2099

940848
Taking of swab for SARS-CoV-2 (severe acute respiratory syndrome   coronavirus 2)
4109225
Maps to
10/08/2022
31/12/2099

940848
Taking of swab for SARS-CoV-2 (severe acute respiratory syndrome   coronavirus 2)
756055
Maps to
10/08/2022
31/12/2099

And records with these source value came from source.clinical with adid=0.
The test results should be found in  source.additional (Please refer to the diagram below). However, there is no result found with these patid and adid=0.

@catalamarti
FYI.
https://ohdsi.github.io/ETL-LambdaBuilder/CPRD/CPRD_Test_STEM.html#covid-codes


Fix solid-frontpage-example

This renames the solid basic-typescript example to basic, so that it loads on tanstack query frontpage.
Also, it's aligned with svelte / vue, which also have the typescript examples called 'basic'

not sure why this never got merged, sorry for that. we re-structured the docs, let me know if this is still an issue. if renaming fixes it (every framework should have the basic example), we should do it.


Frustum culling support for three.js

Support frustum culling in three.js by using an updated angle (https://github.com/Almost-Done/angle/pull/2) which provides a projection matrix and allows an application to render using this matrix in automatic stereo mode. three.js can then use HolographicCamera's projectionMatrix to perform frustum culling. Should also enable more seamless culling support with other libraries.

Thank you very much for this contribution!


Improve testing for token requests

When controller runtime is released, Improve the unit tests for token requests to test for token generation and different expiration times.

https://github.com/kubernetes-sigs/controller-runtime/pull/2969


Add miner diagnostics output to diagnostics system

Request the same diagnostics information from the miner that the diagnostics function in the app uses.
This would then include:
If the hotspot thinks it is connected to the helium network.
Weather the hotspot is "dialable" (I'm guessing not relayed)
The height of the hotspot
And internet conneection type.

All the variables from this are added to the diagnostics json.
I've added I believe the main two parts, which are the height of the miner and weather it is synced on the blockchain.
@shawaj hopefully with the extra info added we can re-direct people to their diagnostics page rather than relying on explorer for hopefully a more up to date status.


Add support for maistra/istio's maistra-2.1 branch

The 2.1 Dockerfile is currently just a copy of the 2.0 one. I'm currently running the jobs locally to make sure they work, haven't encountered any issues so far. I'll keep a /hold on this PR until the tests have passed.

/hold

Can you add it to all repos? (envoy, proxy, operator, etc)

I won't in this PR, but yes we'll do that. I'm focusing on maistra/istio for now. It's way more work to do all of them

/hold release


[otbn,dv] Most "bad branches" are taken

In insn_bxx_cg, the eq_oob_cross and eq_neg_cross crosses track whether a branch was taken and whether the destination was above the top of memory or negative, respectively.
We generate branches hitting these crosses with BadBranch, but seem to very rarely pick the direction that means the branch wasn't taken, which means ew miss some of the bins.

The milestone was wrong here: this is about hitting a specific coverage point (from when I went through a coverage report trying to work out what we were missing). Moving to V3 and removing from the M2 project.

This coverage is getting hit now so closing as done


Enable parcel v2

Pixi's resource loader requires spritesheet files to be copied as is to the dist folder. Currently parcel-plugin-static-files-copy is used for this, but this won't work in v2. I need to figure out a new way to do this.

Decided to use Rollup instead of Parcel


Evaluate kops vs EKS

I'm playing with kops for managing AWS Kubernetes clusters instead of EKS. I've been frustrated with EKS a while:

It uses AWS VPC CNI for networking. This is AWS native, but has one major problem for us - pod density! Each pod requires its own Elastic Network Interface. There are fairly strong limits on how many ENIs a node can have. For example, an m5.large node can only have 28 pods! That's very low, and wastes a lot of money.
Managing worker nodes is actually quite painful, at least compared to GKE. Managed Node Groups don't scale down to 0, and so we can't really use them. You really end up managing them via eksctl - the aws CLI is too hard, and terraform is also pretty unnecessarily complex.
It costs 72$ a month. I want a lower base cost.

(1) was the motivating case. (2) makes me feel like I'm already managing a lot of infrastructure intimately - might as well embrace the extra control provided by kops, no?
Going to test it out, and keep this issue updated

AWS is aware of problem (1) and have a roadmap for pod density fixes. Realistically, I don't expect that for another 5-6 months at the earliest.

My current setup is:

t3.medium master, so it's about 30$ a month base cost. I also want the hub pods in there.
EFS for home directories, so you only pay for what you use
Autoscale to 0, so you only pay for what you use

Things I've had to patch so far:

kube-dns needs to tolerate being on master
Need to explicitly remove the node-role master from the master nodes, while leaving the taints.  See https://github.com/kubernetes/enhancements/pull/1144

So https://github.com/kubernetes/enhancements/pull/1144 ended up needing some work. It needed a couple feature gates enabled. And thankfully, kops makes this easy.
I now have a cluster working almost the way I want with the following kops config.
apiVersion: kops.k8s.io/v1alpha2
kind: Cluster
metadata:
  creationTimestamp: "2021-02-13T23:25:11Z"
  name: farallon-2i2c.k8s.local
spec:
  clusterAutoscaler:
    enabled: true
  api:
    loadBalancer:
      class: Classic
      type: Public
  authorization:
    rbac: {}

  dns:
    kubeDNS:
      provider: CoreDNS
  channel: stable
  cloudProvider: aws
  configBase: s3://2i2c-farallon-pangeo-kops/farallon-2i2c.k8s.local
  containerRuntime: docker
  etcdClusters:
  - cpuRequest: 200m
    etcdMembers:
    - instanceGroup: master-us-east-2a
      name: a
    memoryRequest: 100Mi
    name: main
  - cpuRequest: 100m
    etcdMembers:
    - instanceGroup: master-us-east-2a
      name: a
    memoryRequest: 100Mi
    name: events
  iam:
    allowContainerRegistry: true
    legacy: false
  kubelet:
    anonymousAuth: false
    featureGates:
      LegacyNodeRoleBehavior: "false"
      ServiceNodeExclusion: "false"
  kubeControllerManager:
    featureGates:
      LegacyNodeRoleBehavior: "false"
      ServiceNodeExclusion: "false"
  kubernetesApiAccess:
  - <IP_ADDRESS>/0
  kubernetesVersion: 1.19.7
  masterPublicName: api.farallon-2i2c.k8s.local
  networkCIDR: <IP_ADDRESS>/16
  networking:
    calico:
      majorVersion: v3
  nonMasqueradeCIDR: <IP_ADDRESS>/10
  sshAccess:
  - <IP_ADDRESS>/0
  subnets:
  - cidr: <IP_ADDRESS>/19
    name: us-east-2a
    type: Public
    zone: us-east-2a
  topology:
    dns:
      type: Public
    masters: public
    nodes: public

---

apiVersion: kops.k8s.io/v1alpha2
kind: InstanceGroup
metadata:
  creationTimestamp: "2021-02-13T23:25:12Z"
  labels:
    kops.k8s.io/cluster: farallon-2i2c.k8s.local
  name: master-us-east-2a
spec:
  image:<PHONE_NUMBER>77/ubuntu/images/hvm-ssd/ubuntu-focal-20.04-amd64-server-20210119.1
  machineType: t3.medium
  maxSize: 1
  minSize: 0
  nodeLabels:
    kops.k8s.io/instancegroup: master-us-east-2a
  role: Master
  subnets:
  - us-east-2a

---
apiVersion: kops.k8s.io/v1alpha2
kind: InstanceGroup
metadata:
  labels:
    kops.k8s.io/cluster: farallon-2i2c.k8s.local
    hub.jupyter.org/pool-name: notebook-m5-xlarge
  name: notebook-m5-xlarge-2021-02-15
spec:
  image:<PHONE_NUMBER>77/ubuntu/images/hvm-ssd/ubuntu-focal-20.04-amd64-server-20210119.1
  cloudLabels:
      k8s.io/cluster-autoscaler/node-template/label/hub.jupyter.org/pool-name: notebook-pool-m5-xlarge
      k8s.io/cluster-autoscaler/node-template/taint/hub.jupyter.org_dedicated: user:NoSchedule
      k8s.io/cluster-autoscaler/node-template/taint/hub.jupyter.org/dedicated: user:NoSchedule
  taints:
   - hub.jupyter.org_dedicated=user:NoSchedule
   - hub.jupyter.org/dedicated=user:NoSchedule
  nodeLabels:
    hub.jupyter.org/pool-name: notebook-m5-xlarge
  machineType: m5.xlarge
  maxSize: 20
  minSize: 0
  role: Node
  subnets:
  - us-east-2a
---
apiVersion: kops.k8s.io/v1alpha2
kind: InstanceGroup
metadata:
  labels:
    kops.k8s.io/cluster: farallon-2i2c.k8s.local
    hub.jupyter.org/pool-name: notebook-m5-2xlarge
  name: notebook-m5-2xlarge-2021-02-15
spec:
  image:<PHONE_NUMBER>77/ubuntu/images/hvm-ssd/ubuntu-focal-20.04-amd64-server-20210119.1
  cloudLabels:
      k8s.io/cluster-autoscaler/node-template/label/hub.jupyter.org/pool-name: notebook-pool-m5-2xlarge
      k8s.io/cluster-autoscaler/node-template/taint/hub.jupyter.org_dedicated: user:NoSchedule
      k8s.io/cluster-autoscaler/node-template/taint/hub.jupyter.org/dedicated: user:NoSchedule
  taints:
   - hub.jupyter.org_dedicated=user:NoSchedule
   - hub.jupyter.org/dedicated=user:NoSchedule
  nodeLabels:
    hub.jupyter.org/pool-name: notebook-m5-2xlarge
  machineType: m5.2xlarge
  maxSize: 20
  minSize: 0
  role: Node
  subnets:
  - us-east-2a
---
apiVersion: kops.k8s.io/v1alpha2
kind: InstanceGroup
metadata:
  labels:
    kops.k8s.io/cluster: farallon-2i2c.k8s.local
    hub.jupyter.org/pool-name: dask-worker
  name: dask-worker-2021-02-15
spec:
  image:<PHONE_NUMBER>77/ubuntu/images/hvm-ssd/ubuntu-focal-20.04-amd64-server-20210119.1
  cloudLabels:
      k8s.io/cluster-autoscaler/node-template/label/hub.jupyter.org/pool-name: dask-worker
      k8s.io/cluster-autoscaler/node-template/taint/k8s.dask.org_dedicated: worker:NoSchedule
      k8s.io/cluster-autoscaler/node-template/taint/k8s.dask.org/dedicated: worker:NoSchedule
  taints:
   - k8s.dask.org_dedicated=worker:NoSchedule
   - k8s.dask.org/dedicated=worker:NoSchedule
  nodeLabels:
    hub.jupyter.org/pool-name: dask-worker
  machineType: m5.2xlarge
  maxSize: 50
  minSize: 0
  role: Node
  subnets:
  - us-east-2a

I have so far absolutely liked this experience much much more than EKS! There is more control than EKS, which actually is a good thing - since EKS is only 'semi-managed', you are often stuck with places where they have made a constraining decision but not provided enough support to make that work easily. Maybe once managed node groups reach feature parity with other cloud providers.
Something else I really really like is that there's one master node that's running t3.medium, and it contains our JupyterHubs too! So the total base cost becomes a little over 30$ a month. With EKS, you've to pay the 72$ a month master fee, and then enough money for a node to run hub infra. And that extra node needs to be much bigger too, due to low pod density.
I still need to do spot instances, and test dask out.

With Calico CNI by default, you get networkpolicy enforcement. So gotta make sure your dask setup works with that.

Thanks for the example config and write up here. From my understanding of what you're trying to do here are some suggestions I would probably investigate if I were in your situation.
You may already have some of these things done. This was the research I've done and some ideas.
Reducing cost
Kops is great and removes a lot of the operational burden from you. If you want to stick with it I would look into using spot worker nodes with mixed ASG instance types. You can get a list of comparable instance types using ec2 instance selector and you'll be able to reduce your cost by a lot.
Two things to consider with spot is I'm not sure if the pods students connect to are stateful. If an instance gets shut down in the middle of a class it could be very disruptive. Especially at the pod density you're running.
For spot instances you'll also need to run a termination handler. You can use the official AWS termination handler.
The second thing I would look at is k3s. If you're already running a single node master k3s could reduce the overhead quite a bit by using SQLite instead of etcd. If it were me I would run k3s via terraform and mount an EBS volume and and store sqlite there. If you make the API server node an ASG with min 1 max 1 and store your state in EBS it'll be easy to replace the instance if/when things go bad.
There's a slightly older terraform project that you can use as a starting point. Even with k3s you can still create Spot based ASG worker nodes. You can also look at what kops is doing with --target terraform and look at the tf files it exports.
I suspect you'd be able to reduce your control plane node size if you use k3s and reduce your worker node cost by more than 50% with Spot.
Faster scaling
This problem was a bit more interesting to look at and I have 3 main recommendations.
Build a custom AMI with images pre-pulled
Since your container images are so large you can solve this problem a couple different ways. You can build a custom AMI with the images pulled and then the instance snapshotted. You'd want to build this AMI often to make sure you don't have to pull too much when nodes join the cluster. If you don't want to build a custom AMI you can also use EBS mounted at /var/run/docker and use snapshots and clone the disk for each worker node. I forget the exact syntax but you can do it in the launch template for the ASG.
It would be great if you had a breakdown of how long it takes to start a pod currently with each stage broken down.
How long does it take to add an instance until it's healthy in the cluster?
How long does it take to pull the image?
How long does it take to start the pod and become healthy?
Control ASG scale up outside of Kubernetes
The cluster autoscaler works for gradual increases but it's not great for sudden bursts and is naturally reactive rather than proactive. I think you mentioned that a teacher has a web portal to create the student notebooks. I'm assuming behind the web app is something that scales up your deployment and then you let the autoscaler add instances.
If you can change the web to scale up the ASG before it scales up the k8s deployment you can probably get ahead of the scaling needs buy 30-60 seconds. You could either have the deployment button talk directly to the aws API and ASG but that means you need to hard code region/ASG information. It would probably be easier to use EventBridge that can send a small amount of data like region and how many pods are going to be added. You can then do some basic math in a lambda to know how many instances to add to the ASG based on your desired pod density.
You can still use the cluster autoscaler to scale down the ASG (eventually to 0) but scaling up fast will always be slower if you're trying to react to scheduled pods or running metrics vs pre-scaling the instances.
Optimize container image
This option is a bit more experimental but would be interesting to look at as a possibility. It would also require you to switch to containerd for your container runtime in Kubernetes but you should probably do that within the next year anyway as docker will be removed at some point.
Looking at containerd plugins like stargz you can have large images that pull data on demand. This means you could probably have most of the initial UI files pulled but then pull the rest of the libraries after a student tries to run their notebook. This option may not work for you but if the other two options don't reduce your startup speed this would probably be the next thing I would look at.
I would love to hear back if you try any of these suggestions and would be curious to know how well they work. Feel free to reach out if you have other questions.

Thanks for the suggestions, @rothgar!
Unfortunately, student pods depend on in-memory state, so we can't use spot instances. That's also why we can't aggressively scale down. We do use spot instances with dask though, since those are much more resilient to terminations.
Building AMIs with container images pre-pulled is one of the things I'm most excited about with the move to kops. I just realized that this is possible with eksctl as well. Right now, we run student workflows mostly on Google Cloud - only research flows are on AWS. Will definitely put effort towards this when that changes.
I've never really considered k3s for anything more than single node workflows. We try very hard to be as uniform across cloud providers as possible, so k3s on AWS doesn't seem worth the trouble for the differential. I also don't think the etcd resource cost makes a lot of difference in our case, but will consider it! kops just seems a lot more supported...
stargz is definitely on our radar! Will report back when we start working on it.
Thanks for responding here, @rothgar - we appreciate it. I think pod density is really our biggest blocker with EKS for many use cases, so will look forward to that getting better so we can re-evaluate it vs kops.

I totally understand with spot and in memory state. Custom AMI support for managed node groups came out late last year so it should work for you if you start building an AMI with containers pre-pulled.
We're working on VPC CNI pod density, but it's not quite ready yet. I'm a big fan of cilium too. They have a walk through on using it with EKS that might be helpful if you want to try EKS again https://docs.cilium.io/en/v1.9/gettingstarted/k8s-install-eks/

@rothgar ah, thanks! I'm just wary of doing a custom CNI when I've no control over the master Hope that makes sense.


refactor: use exception wrapper on all commands

All commands communicating with DMSS should use the dmss_exception_wrapper() function to avoid duplicating exception handling

How can we handle cases where exception from API is accepted?
For example in the import_data_source command, we don't want the import to stop if API gives error.
Should we NOT use the exception wrapper in this case?
@ds_cli.command("import", help="Import a datasource, where <path> is the path to a data source definition (JSON).")
@click.argument("path", required=True)
@click.pass_context
def import_data_source(context, path: str):
    """
    Import a single data source definition to DMSS.
    """
    data_source_path = Path(path)
    if not data_source_path.is_file():
        raise FileNotFoundError(f"The path '{path}' is not a file.")

    click.echo(f"IMPORTING DATA SOURCE '{data_source_path.name}'")

    # Read the data source definition
    with open(data_source_path) as file:
        document = json.load(file)
        try:
            # Upload the data source definition to DMSS
            dmss_api.data_source_save(document["name"], data_source_request=document)
            click.echo(f"\tImported data source '{document['name']}'")
        except (ApiException, ApplicationException, KeyError) as error:
            if error.status == 400:
                click.echo(
                    emoji.emojize(
                        f"\t:warning: Could not import data source '{document['name']}'. "
                        "A data source with that name already exists"
                    )
                )
            else:
                raise ImportError(f"\tFailed to import data source '{data_source_path.name}': {error}")

The exception should be caught as close to the function calling raising it as possible if we want to continue.
Stopping the exception from "boubling up".


What is the format for --custom gradients?

The --custom option color format does not seem to be defined anywhere. I've tried every combination of hex values I can think of, but always get the error invalid hex format.
What is the custom gradient format?

It should work with named colors, hex, rgb, etc. What hex that gives you error?

Well, I just looked at what I was trying last night, and realized I was making a trivial mistake.
This worked:
lolcrab <file>
But when I tried something like this it failed:
lolcrab -c 00ff00 ff0000 0000ff <file>
In retrospect it was trivial mistake. The returned error even said that <file> was an invalid color.


Graph3

Use this one not #38

Codecov Report

Merging #40 into master will decrease coverage by 5.52%.
The diff coverage is 0%.

@@            Coverage Diff             @@
##           master      #40      +/-   ##
==========================================
- Coverage   97.86%   92.33%   -5.53%
==========================================
  Files           5        6       +1
  Lines         702      744      +42
==========================================
  Hits          687      687
- Misses         15       57      +42

Impacted Files
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streams/graph.py
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Is it possible to test this code?

one sec

Note that this doesn't do a good job with cyclic graphs since sink doesn't know what it is sinking to. Eg if we use sink to take data from one stream and emit it into another. This may be possible if we use map?

I don't want to try to test the plotting itself, although I have left an easy enough to run example in the examples folder. (mostly because I don't want to try to install graphviz/pygraphviz in travis). (making a conda package for that is on my todo list)

Just as long as we can test the majority of the code leading up to the plotting I'm happy.  Another thought is that we might choose to conform to the visualize API within Dask if we expect to share a userbase.  This might reduce surprise.  https://github.com/dask/distributed/pull/1270

Whoops, that was the wrong link entirely: http://dask.pydata.org/en/latest/delayed-overview.html?highlight=visualize

Ok I can add a method onto streams which will plot the graph. I'll look at the dask code to try to match the app.

Do you mind if I borrow the docs from Dask (and modify them as needed?)

Go for it
On Wed, Jul 19, 2017 at 5:54 PM, Christopher J. Wright <
<EMAIL_ADDRESS>wrote:

Ok I can add a method onto streams which will plot the graph. I'll look at
the dask code to try to match the app.
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Ready for review.
I've revised things so that stream has a visualize method, although I didn't do all the dot ipython visualization, it still writes to disk the image though.

@mrocklin Do you want the new "filename has both name and format in it" visualization API or do you want to keep with dask's?

I think in practice everyone uses dask's visualize function with a full filename.  I don't think I've seen anyone use the format= keyword in the wild.  I wouldn't mind deprecating it in the future.

Ok I'll change the API, any idea why the conda graphviz install isn't taking?

graphviz is the name of the system library, you might also want python-graphviz or graphviz-python or something like that.

@mrocklin @ordirules ready for review/merge.

This looks good to me.  I've added a small change to pass through keyword
arguments to graphviz.
Tried this out in a notebook and the results looks stellar!  thanks!
On Thu, Jul 20, 2017 at 7:07 PM, Christopher J. Wright <
<EMAIL_ADDRESS>wrote:

@mrocklin https://github.com/mrocklin @ordirules
https://github.com/ordirules ready for review/merge.
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Awesome, would there be any interest in putting an example image in the README.md?

looks awesome! i don't see any issues. good job!

Dask includes visualize as a method on dask objects as well as a top-level
dask.visualize function that takes in a list of objects to visualize all at
once.
On Fri, Jul 28, 2017 at 10:51 AM, Christopher J. Wright <
<EMAIL_ADDRESS>wrote:

@CJ-Wright commented on this pull request.
In streams/core.py
https://github.com/mrocklin/streams/pull/40#discussion_r130108889:

@@ -7,6 +7,7 @@
from tornado.ioloop import IOLoop
from tornado.queues import Queue
from collections import Iterable
+from streams.graph import visualize

This comes from the dask api which had visualize as a method. But I don't
have too much feeling either way. Although thinking about it now we may
want the graph creation exposed as a method in case users are interested.
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