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https://issues.apache.org/jira/browse/CASSANDRA-7937?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14136794#comment-14136794
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Benedict commented on CASSANDRA-7937:
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Under these workload conditions, CASSANDRA-3852 is unlikely to help; it is more
for reads. CASSANDRA-6812 will help for writes. It in no way affects the
concept of load shedding or backpressure, just write latency spikes and hence
timeouts.
There are some issues with load shedding by itself, though, at least as it
stands in Cassandra. Take your example cluster: A..E, with RF=3. Let's say B
_and_ C are now load shedding. The result is that we see 1/5th of requests
queue up and block in A,D and E, which within a short window results in all
requests to those servers being blocked, as all rpc threads are waiting for
results that will never come. Now, with explicit back pressure (whether it be
blocking consumption of network messages or signalling the coordinator we are
load shedding) we could detect this problem and start dropping those requests
on the floor in A, D and E and continue to serve other requests.
FTR, we do already have an accidental back pressure system built into
Incoming/OutboundTcpConnection, with only negative consequences. If the remote
server is blocked due to GC (or some other event), it will not consume its
IncomingTcpConnection which will cause our coordinator's queue to that node to
back up. This is something we should deal with, and introducing explicit back
pressure of the same kind when a node cannot cope with its load would allow us
to deal with both situations using the same mechanism.
> Apply backpressure gently when overloaded with writes
> -----------------------------------------------------
>
> Key: CASSANDRA-7937
> URL: https://issues.apache.org/jira/browse/CASSANDRA-7937
> Project: Cassandra
> Issue Type: Bug
> Components: Core
> Environment: Cassandra 2.0
> Reporter: Piotr Kołaczkowski
> Labels: performance
>
> When writing huge amounts of data into C* cluster from analytic tools like
> Hadoop or Apache Spark, we can see that often C* can't keep up with the load.
> This is because analytic tools typically write data "as fast as they can" in
> parallel, from many nodes and they are not artificially rate-limited, so C*
> is the bottleneck here. Also, increasing the number of nodes doesn't really
> help, because in a collocated setup this also increases number of
> Hadoop/Spark nodes (writers) and although possible write performance is
> higher, the problem still remains.
> We observe the following behavior:
> 1. data is ingested at an extreme fast pace into memtables and flush queue
> fills up
> 2. the available memory limit for memtables is reached and writes are no
> longer accepted
> 3. the application gets hit by "write timeout", and retries repeatedly, in
> vain
> 4. after several failed attempts to write, the job gets aborted
> Desired behaviour:
> 1. data is ingested at an extreme fast pace into memtables and flush queue
> fills up
> 2. after exceeding some memtable "fill threshold", C* applies adaptive rate
> limiting to writes - the more the buffers are filled-up, the less writes/s
> are accepted, however writes still occur within the write timeout.
> 3. thanks to slowed down data ingestion, now flush can finish before all the
> memory gets used
> Of course the details how rate limiting could be done are up for a discussion.
> It may be also worth considering putting such logic into the driver, not C*
> core, but then C* needs to expose at least the following information to the
> driver, so we could calculate the desired maximum data rate:
> 1. current amount of memory available for writes before they would completely
> block
> 2. total amount of data queued to be flushed and flush progress (amount of
> data to flush remaining for the memtable currently being flushed)
> 3. average flush write speed
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