Vladimir, What about moving the entire locking mechanism to a separate off-heap memory region which will be volatile wrt restarts, but will still support off-load to disk. In the current architecture, it means that we will need to allocate a separate DataRegion with no WAL and no crash recovery - locks are meaningless after a restart, and we will automatically drop them. I would be interesting to prototype this because I think we may be on-par with on-heap lock placement, as we already proved for in-memory caches.
2017-12-14 21:53 GMT+03:00 Denis Magda <[email protected]>: > Vladimir, > > No it’s crystal clear, thanks. > > If this approach works only for Ignite persistence based deployment, how > will we handle locking for pure in-memory and caching of 3rd party > databases scenarios? As I understand the tuples still will be stored in the > page memory while there won’t be any opportunity to fallback to disk if the > memory usage increases some threshold. > > — > Denis > > > On Dec 13, 2017, at 11:21 PM, Vladimir Ozerov <[email protected]> > wrote: > > > > Denis, > > > > Sorry, may be I was not clear enough - "tuple-approach" and "persistent > > approach" are the same. By "tuple" I mean a row stored inside a data > block. > > Currently we store lock information in Java heap and proposal is to move > it > > to data blocks. The main driver is memory - if there are a rows to be > > locked we will either run out of memory, or produce serious memory > > pressure. For example, currently update of 1M entries will consume ~500Mb > > of heap. With proposed approach it will consume almost nothing. The > > drawback is increased number of dirty data pages, but it should not be a > > problem because in final implementation we will update data rows before > > prepare phase anyway, so I do not expect any write amplification in usual > > case. > > > > This approach is only applicable for Ignite persistence. > > > > On Thu, Dec 14, 2017 at 1:53 AM, Denis Magda <[email protected]> wrote: > > > >> Vladimir, > >> > >> Thanks for a throughout overview and proposal. > >> > >>> Also we could try employing tiered approach > >>> 1) Try to keep everything in-memory to minimize writes to blocks > >>> 2) Fallback to persistent lock data if certain threshold is reached. > >> > >> What are the benefits of the backed-by-persistence approach in compare > to > >> the one based on tuples? Specifically: > >> - will the persistence approach work for both 3rd party and Ignite > >> persistence? > >> - any performance impacts depending on a chosen method? > >> - what’s faster to implement? > >> > >> — > >> Denis > >> > >>> On Dec 13, 2017, at 2:10 AM, Vladimir Ozerov <[email protected]> > >> wrote: > >>> > >>> Igniters, > >>> > >>> As you probably we know we work actively on MVCC [1] and transactional > >> SQL > >>> [2] features which could be treated as a single huge improvement. We > >> face a > >>> number of challenges and one of them is locking. > >>> > >>> At the moment information about all locks is kept in memory on > per-entry > >>> basis (see GridCacheMvccManager). For every locked key we maintain > >> current > >>> lock owner (XID) and the list of would-be-owner transactions. When > >>> transaction is about to lock an entry two scenarios are possible: > >>> 1) If entry is not locked we obtain the lock immediately > >>> 2) if entry is locked we add current transaction to the wait list and > >> jumps > >>> to the next entry to be locked. Once the first entry is released by > >>> conflicting transaction, current transaction becomes an owner of the > >> first > >>> entry and tries to promote itself for subsequent entries. > >>> > >>> Once all required locks are obtained, response is sent to the caller. > >>> > >>> This approach doesn't work well for transactional SQL - if we update > >>> millions of rows in a single transaction we will simply run out of > >> memory. > >>> To mitigate the problem other database vendors keep information about > >> locks > >>> inside the tuples. I propose to apply the similar design as follows: > >>> > >>> 1) No per-entry lock information is stored in memory anymore. > >>> 2) The list of active transactions are maintained in memory still > >>> 3) When TX locks an entry, it sets special marker to the tuple [3] > >>> 4) When TX meets already locked entry, it enlists itself to wait queue > of > >>> conflicting transaction and suspends > >>> 5) When first transaction releases conflicting lock, it notifies and > >> wakes > >>> up suspended transactions, so they resume locking > >>> 6) Entry lock data is cleared on transaction commit > >>> 7) Entry lock data is not cleared on rollback or node restart; Instead, > >> we > >>> will could use active transactions list to identify invalid locks and > >>> overwrite them as needed. > >>> > >>> Also we could try employing tiered approach > >>> 1) Try to keep everything in-memory to minimize writes to blocks > >>> 2) Fallback to persistent lock data if certain threshold is reached. > >>> > >>> Thoughts? > >>> > >>> [1] https://issues.apache.org/jira/browse/IGNITE-3478 > >>> [2] https://issues.apache.org/jira/browse/IGNITE-4191 > >>> [3] Depends on final MVCC design - it could be per-tuple XID, undo > >> vectors, > >>> per-block transaction lists, etc.. > >>> > >>> Vladimir. > >> > >> > >
