Renegotiate_Rate_Limit

renegotiate_rate_limit

If you are using OpenSSL and you want a renegotiation to happen after a certain number of bytes, you can use BIO_set_ssl_renegotiate_bytes. If you want it to happen after a certain interval of time has elapsed, you can use BIO_set_ssl_renegotiate_timeout.

If, instead, you want to set an upper limit on how often renegotiation is allowed, I don't think OpenSSL has explicit support for that. Instead, you might register an info callback with BIO_set_info_callback and then wait for SSL_ST_RENEGOTIATE notifications. If you observe them at a rate greater than you want, take some action (eg close the connection).

Unable to limit WebRTC P2P Multi-participant Receiving Bandwidth

RTCRtpSender only controls the sending bandwidth. If you want to limit the receiving bandwidth, you need to use either the b=AS / b=TIAS way or make the receiver use setParameters.

Npgsql. unrecognized configuration parameter ssl_renegotiation_limit

It's hard to know what's going on because you're not including any details on your PostreSQL version. However, All recent versions of PostgreSQL recognize (and ignore) ssl_renegotiation_limit. You're either using a very old version of PostgreSQL, or possibly Amazon Redshift, in which case you need to add Server Compatibility Mode=Redshift as specified in the Npgsql docs.

Maximum RTCDataChannels in Chrome

Chrome announces 1024 outgoing streams and uses the usrsctp default of 2048 incoming streams.

For comparison, Firefox announces 256 outgoing streams and 2048 incoming streams but allows to renegotiate for up to 2048 streams. However, there is a bug in the renegotiation procedure.

One of the peer takes odd stream IDs and the other takes even stream IDs to create a data channel (which avoids conflicts if both peers create data channels at the same time). The result is that you can create half as many data channels per peer as the amount of streams that have been negotiated (minimum of both).

Data channels can also be created in a way where you assign the id yourselves in which case you can create as many data channels as the negotiated amount of streams.

why spark reads and writes so fast from S3

The only bottlenecks within AWS are:

• A network bandwidth limitation on Amazon EC2 instances, based upon the Instance Type (Basically, larger instances have more network bandwidth)
• The speed of Amazon EBS storage volumes (Provisioned IOPS supports up to 20,000 IOPS)

Throughput within a Region, such as between Amazon EC2 and Amazon S3, is extremely high and is unlikely to limit your ability to transfer data (aside from the EC2 network bandwidth limitation mentioned above).

Amazon S3 is distributed over many servers across multiple Availability Zones within a Region. At very high speeds, Amazon S3 does have some recommended Request Rate and Performance Considerations, but this is only when making more than 300 PUT/LIST/DELETE requests per second or more than 800 GET requests per second for a particular bucket.

Apache Spark is typically deployed across multiple nodes. Each node has network bandwidth available based on its Instance Type. The parallel nature of Spark means that it can transfer data to/from Amazon S3 much faster than could be done by a single instance.

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