HLS | Python M3U8 library , mainly use | Video Utils library

It is possible to:

1. Download the playlist using HTTP Request sampler and Save Responses to a file listener so it would be saved to your local drive. See Performance Testing: Upload and Download Scenarios with Apache JMeter article for more comprehensive instructions if needed

2. Amend the playlist as needed using JSR223 Sampler or OS Process Sampler

3. In the bzm - Streaming Sampler use local URL via file URI scheme i.e.

The problem is casued by routing.For a http.FileServer,"\simple" means a dir but not a url.So just fix like this:

GHC 9.2.1 enables GHC2021 by default.

The GHC2021 language is supported now. It builds on top of Haskell2010, adding several stable and conservative extensions, and removing deprecated ones. It is now also the “default” language set that is active when no other language set, such as Haskell98 or Haskell2010, is explicitly loaded (e.g via Cabal’s default-language). https://downloads.haskell.org/~ghc/9.2.1/docs/html/users_guide/9.2.1-notes.html

GHC2021 language enables InstanceSigs.

https://github.com/ghc-proposals/ghc-proposals/blob/master/proposals/0380-ghc2021.rst

The problem here is that modifySTRef takes an STRef s a, not a ST s (STRef s a). ST was designed to utilize monad properties to secure mutable operations from being abused, so all modifications can happen only in an ST context. Therefore, you need to use the powers of the context to extract the pure STRef reference.

Normally you would do it like this:

I figured it out. In my case, this exception is caused by the crash of the system tv application, which owns the Surface object. The codec goes into the Error state when the Surface becomes invalid and at the same moment the ExoPlayer tries to work with the codec's buffers, not knowing that the codec has changed the Executing state to Error.

And the reason for the crash of the system tv app was the following exception:

s3fs 1.88 and later buffers data locally and flushes according to the -o max_dirty_data flag, defaulting to 5 GB. If you reduce this value you should see updates more often. Note that these flushes require server-side copies and may do more IO than you anticipate.

To configure HLS, record ( MP4 or WebM ) or PNG output of Ant Media to upload, you can use the following setting;

settings.uploadExtensionsToS3

It is a number that its binary form digits represent upload or not. Least significant digit switches record files, second switches HLS, third for PNG.

Example: settings.uploadExtensionsToS3=5 ( 101 in binary ) means upload mp4 and PNG but not HLS

Possible values;

• Don't upload anything

  settings.uploadExtensionsToS3=0

• Only record file ( mp4 or webm ) upload

  settings.uploadExtensionsToS3=1

• Only HLS upload

  settings.uploadExtensionsToS3=2

• HLS + record upload

  settings.uploadExtensionsToS3=3

• Only PNG upload

  settings.uploadExtensionsToS3=4

• PNG and record upload

  settings.uploadExtensionsToS3=5

• PNG + HLS upload

  settings.uploadExtensionsToS3=6

• Upload everything ( default )upload

  settings.uploadExtensionsToS3=7

After making the changes, please restart ant media server.

As you use OriginCluster, then you must get lots of streams to serve, there are lots of encoders to publish streams to your media servers. The key to solve the problem:

1. Never use single server, use cluster for elastic ability, because you might get much more streams in future. So forward is not good, because you must config a special set of streams to foward to, similar to a manually hash algorithm.
2. Beside of bandwidth, the disk IO is also the bottleneck. You definitely need a high performance network storage cluster. But be careful, never let SRS directly write to the storage, it will block SRS coroutine.

So the best solution, as I know, is to:

1. Use SRS Origin Cluster, to write HLS on your local disk, or RAM disk is better, to make sure the disk IO never block the SRS coroutine(driven by state-threads network IO).
2. Use network storage cluster to store the HLS files, for example cloud storage like AWS S3, or NFS/K8S PV/Distributed File System whatever. Use nginx or CDN to deliver the HLS.

Now the problem is: How to move data from memory/disk to a network storage cluster?

You must build a service, by Python or Go:

• Use on_hls callback, to notify your service to move the HLS files.
• Use on_publish callback, to notify your service to start FFmpeg to convert RTMP to HLS.

Note that FFmpeg should pull stream from SRS edge, never from origin server directly.

You can do this with a template, but you've got the wrong syntax. It should be: