Details
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Improvement
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Status: Resolved
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Normal
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Resolution: Later
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None
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None
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Cassandra 2.0
Description
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