Fix Version/s: None
I was looking into how the row cache worked today and realized only row keys were saved and later pre-populated on start-up.
On the premise that row caches are typically used for small rows of which there may be many, this is highly likely to be seek bound on large data sets during pre-population.
The pre-population could be made faster by increasing I/O queue depth (by concurrency or by libaio as in 1576), but especially on large data sets the performance would be nowhere near what could be achieved if a reasonably sized file containing the actual rows were to be read in a sequential fashion on start.
On the one hand, Cassandra's design means that this should be possible to do efficiently much easier than in some other cases, but on the other hand it is still not entirely trivial.
The key problem with maintaining a continuously durable cache is that one must never read stale data on start-up. Stale could mean either data that was later deleted, or an old version of data that was updated.
In the case of Cassandra, this means that any cache restored on start-up must be up-to-date with whatever position in the commit log that commit log recovery will start at. (Because the row cache is for an entire row, we can't couple updating of an on-disk row cache with memtable flushes.)
I can see two main approaches:
(a) Periodically dump the entire row cache, deferring commit log eviction in synchronization with said dumping.
(b) Keep a change log of sorts, similar to the commit log but filtered to only contain data written to the commit log that affects keys that were in the row cache at the time. Eviction of commit logs or updating positional markers that affect the point of commit log recovery start, would imply fsync():ing this change log. An incremental traversal, or alternatively a periodic full dump, would have to be used to ensure that old row change log segments can be evicted without loss of cache warmness.
I like (b), but it is also the introduction of significant complexity (and potential write path overhead) for the purpose of the row cache. In the worst case where hotly read data is also hotly written, the overhead could be particularly significant.
I am not convinced whether this is a good idea for Cassandra, but I have a use-case where a similar cache might have to be written in the application to achieve the desired effect (pre-population being too slow for a sufficiently large row cache). But there are reasons why, in an ideal world, having such a continuously durable cache in Cassandra would be much better than something at the application level. The primary reason is that it does not interact poorly with consistency in the cluster, since the cache is node-local and appropriate measures would be taken to make it consistent locally on each node. I.e., it would be entirely transparent to the application.
Thoughts? Like/dislike/too complex/not worth it?
|Transition||Time In Source Status||Execution Times||Last Executer||Last Execution Date|
|611d 7h 40m||1||Jonathan Ellis||19/Jun/12 02:08|
|Workflow||patch-available, re-open possible [ 12752478 ]||reopen-resolved, no closed status, patch-avail, testing [ 12758324 ]|
|Workflow||no-reopen-closed, patch-avail [ 12524426 ]||patch-available, re-open possible [ 12752478 ]|
|Status||Open [ 1 ]||Resolved [ 5 ]|
|Resolution||Duplicate [ 3 ]|