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From Maxim Muzafarov <maxmu...@gmail.com>
Subject Re: [DISCUSSION] Design document. Rebalance caches by transferring partition files
Date Wed, 14 Aug 2019 15:26:07 GMT
Nikolay,

In my message above I've described only internal local BackupManager
for the rebalance needs, but for the backup feature of the whole
Ignite cluster I also have some thoughts. I'll give you a detailed
answer in an appropriate discussion topic [1] a bit later.

[1] http://apache-ignite-developers.2346864.n4.nabble.com/DISCUSSION-Hot-cache-backup-td41034.html

On Wed, 14 Aug 2019 at 16:40, Nikolay Izhikov <nizhikov@apache.org> wrote:
>
> Hello, Maxim.
>
> I think backup is a great feature for Ignite.
> Let's have it!
>
> Few notes for it:
>
> 1. Backup directory should be taken from node configuration.
>
> 2. Backup should be stored on local node only.
> Ignite admin can write sh script to move all backuped partitions to one storage by himself.
>
> 3. Ignite should provide CLI tools to start backup/restore procedure.
>
> Questions:
>
> 1. How each backup would be identified?
> 2. Do you plan to implement backup of cache or cache group?
> 3. How restore process would be implemented from user point of view?
>         Can we interact with cache during restore?
>
> В Ср, 14/08/2019 в 16:13 +0300, Maxim Muzafarov пишет:
> > Igniters,
> >
> >
> > Since the file transmission between Ignite nodes [2] have been merged
> > to the master branch (it is the first mandatory part of file-based
> > rebalance procedure) I'd like to focus on the next step of the current
> > IEP-28 - the process of creating snapshots of cache group partitions.
> >
> > Previously, we've discussed a creation of cache group backups [3] for
> > the whole cluster. I'd like to take into account the GridGain
> > experience with such snapshot creation and, at first, focuses on the
> > local internal IgniteBackupManager which will be used for such purpose
> > [4] [1].
> >
> > Changes are almost ready. I need some additional time to finalize the
> > PR with backup creation.
> >
> >
> > API (create local partitions copy)
> >
> > /**
> >  * @param name Unique backup name.
> >  * @param parts Collection of pairs group and appropratate cache
> > partition to be backuped.
> >  * @param dir Local backup directory.
> >  */
> > public IgniteInternalFuture<?> backup(
> >     String name,
> >     Map<Integer, Set<Integer>> parts,
> >     File dir,
> >     ExecutorService backupSvc (this can be completely optional)
> > );
> >
> >
> > API (backup partitoins over the network)
> >
> > /**
> >  * @param name Unique backup name.
> >  * @param parts Collection of pairs group and appropratate cache
> > partition to be backuped.
> >  * @param snd File sender provider.
> >  */
> > public IgniteInternalFuture<?> backup(
> >     String name,
> >     Map<Integer, Set<Integer>> parts,
> >     Supplier<GridIoManager.TransmissionSender> snd
> > );
> >
> > [1] https://cwiki.apache.org/confluence/display/IGNITE/IEP-28%3A+Cluster+peer-2-peer+balancing#IEP-28:Clusterpeer-2-peerbalancing-Copypartitiononthefly
> > [2] https://issues.apache.org/jira/browse/IGNITE-10619
> > [3] http://apache-ignite-developers.2346864.n4.nabble.com/DISCUSSION-Hot-cache-backup-td41034.html
> > [4] https://issues.apache.org/jira/browse/IGNITE-11073
> >
> > On Wed, 12 Dec 2018 at 11:15, Vladimir Ozerov <vozerov@gridgain.com> wrote:
> > >
> > > Maxim,
> > >
> > > Thank you for excellent analysis! From profiling data I see the following:
> > > 1) Almost no parallelism - one rebalance thread is used (default), two responses
are sent per a single demand request (default)
> > > 2) All system resources are underutilized - CPU, disk, network
> > > 3) Huge hotspot ion free lists
> > >
> > > In general I would recommend to consider the following points during further
rebalance optimization:
> > > 1) Start with the fact that rebalance always causes system degradation due
to additional hardware resources required. Different deployments may require different degradation
modes. Sometimes "soft" mode is preferable - long rebalance with low system overhead. This
is what we see now. Sometimes the opposite - as short rebalance as possible at the cost of
severe degradation in operations. Sometimes - something in the middle. Every optimization
we made should have clear explanation on how system degrades.
> > > 2) We need to investigate the hotspot on free lists. Looks like this is the
main limiting factor for now. Alex, do you have any ideas what is this? Is it possible to
bypass freelists completely during rebalance at the cost of higher data fragmentation during
concurrent updates?
> > > 3) We need to investigate streaming rebalance mode, when supplier constantly
streams data to demander similarly to our data streamer. It should be fairly easy to implement,
applicable for all modes and may speedup rebalance up to 5-10 times. Great thing about this
approach is that it will allow users to choose between system stress level and rebalance throughput
easily.
> > > 4) File transfer rebalance: we need to have clear design of failure and concurrency
cases and degradation modes. Several questions to answer:
> > > 4.1) What would happen if another rebalance starts when previous is not finished
yet?
> > > 4.2) What would happen if supplier or demander fails in the middle? What kind
of cleanup would be required
> > > 4.3) Degradation: what kind of problems should users expect due to massive
disk and network load during file transfer and due to data merging on demander side?
> > > 4.4) Degradation: how secondary indexes would be rebuilt on demander side?
Note that until indexes are ready node is not operational and cannot become partition owner,
and index rebuild is essentially full data rescan with potentially the same issues with slow
updates of persistent data structures we have now.
> > >
> > > Vladimir.
> > >
> > > On Fri, Dec 7, 2018 at 3:32 PM Maxim Muzafarov <maxmuzaf@gmail.com> wrote:
> > > >
> > > > Vladimir,
> > > >
> > > >
> > > > Let me propose to consider the whole this rebalance process as having
> > > > three strategies:
> > > > - The classical message-based approach, preferable to use for in-memory
caches;
> > > > - Historical rebalance based on WAL, used for rebalancing persisted
> > > > caches deltas;
> > > > - (new) File-based rebalance (current IEP-28), used for relocation of
> > > > full cache partitions.
> > > >
> > > >
> > > > First of all, I want to show you that for the full cache relocation
> > > > file-based rebalancing strategy from my point has a set of advantages
> > > > prior to the message-based approach. Let's also assume that the time
> > > > spent on WAL logging during the rebalance procedure is already
> > > > optimized (we are not taking it into account at all).
> > > >
> > > > According to preliminary measurements [8] and the message above we
> > > > spend more than 65% of rebalancing time on creating K-V cache pair for
> > > > preloading entries and supporting internal data structures. It is true
> > > > as for in-memory cluster configuration and for a cluster with enabled
> > > > persistence. It is also true, that these data structures can be used
> > > > more efficiently by implementing batch entry processing for them. And
> > > > it should be done (a ticket for it is already created [3]).
> > > >
> > > > Let's have a look closer to the simple example.
> > > >
> > > > I've collected some information about a cache on my stress-testing cluster:
> > > > partitions (total): 65534
> > > > single partition size: 437 MB
> > > > rebalance batch: 512 Kb
> > > > batches per partition: 874
> > > > partitions per node: 606
> > > > batches per node: 529644
> > > >
> > > > Let's assume that we've already implemented batched entry processing
> > > > and we perform bulk operations over internal data structures.
> > > > Regarding these assumptions, we still need to process 874 batches per
> > > > each cache partition to transfer data. I will cost us up to 15 seconds
> > > > per each partition file, a lot of CPU cycles to maintain internal data
> > > > structures and block for a while other threads waiting for releasing
> > > > database checkpoint lock.
> > > >
> > > > Increasing the rebalance batch size is not efficient here because we
> > > > are starting to hold the database lock for too long. It will lead to
> > > > thread starvation and will only slow down the whole rebalance speed.
> > > > Exactly the same as increasing batch size, making the rebalance thread
> > > > pool bigger can lead to the cluster performance drop for almost the
> > > > same reasons.
> > > >
> > > > I think the file-based rebalance can provide us (prior to the batch
> > > > processing) for huge caches:
> > > >  - a fair non-blocking approach in each part of the rebalancing procedure;
> > > >  - reduce the number of locks being acquired (the other threads can
> > > > make bigger progress);
> > > >  - a zero-copy transmission on supplier saves CPU cycles and memory bandwidth;
> > > >  - as a result, the transferable batch size increased up to the whole
> > > > partition file size;
> > > >
> > > > SUMMARY TO DO
> > > >
> > > > The plan to do and other ideas (without risks evaluation):
> > > >
> > > > Message-based approach.
> > > > Optimization to do by priority [3] [2] and may be [9].
> > > >
> > > > Historical rebalance based on WAL.
> > > > Suppose, nothing to do here as Sergey already working on the issue [1]
> > > > with turning off WAL.
> > > >
> > > > (new) Full cache data relocation.
> > > > Prototyping current IEP-28.
> > > >
> > > > I think another approach can be also implemented.
> > > > During the rebalance procedure we can write entries to data pages
> > > > directly skipping free lists, PK index and secondary index. Once the
> > > > partition preloading is finished, we will rebuild free list and all
> > > > indexes.
> > > > Will it work for us?
> > > >
> > > > ANSWERS
> > > >
> > > > > 1) Is it correct that supplier sends only one message for every demand
> > > > > message? If yes, then streaming should improve network utilization
a lot.
> > > >
> > > > I think we already have such ability for the Apache Ignite (not
> > > > exactly streaming). The CacheConfiguration#rebalanceBatchesPrefetchCnt
> > > > can be used here to reduce the system delay between send\receive
> > > > message process. The default value is more than enough for most of the
> > > > cases. The testing results showed only 7 seconds (0.32%) delay during
> > > > the 40 min cache rebalance procedure. So, each supply message is ready
> > > > to be sent when the next demand message arrives.
> > > >
> > > >
> > > > > 2) Is it correct that for user caches we process supply messages
in a
> > > > > system pool? Did we consider moving it to striped pool? Because if
all
> > > > > operations on a single partition is ordered, we may apply a number
of
> > > > > critical optimizations - bypassing page cache and checkpointer for
new
> > > > > entries, batched index updates, batched free list updates, etc.
> > > >
> > > > I think the rebalance procedure should not cause a thousand messages
> > > > per second, so we don't need to move the rebalance procedure to the
> > > > stripped pool. We should have a limited stable load for rebalancing
> > > > procedure on the cluster. As for the second part, are you talking
> > > > about thread per partition model? If yes, we have tickets for it [4],
> > > > [5], [6].
> > > >
> > > > > 3) Seems that WAL should no longer be a problem for us [1]. What
are exact
> > > > > conditions when it could be disabled on supplier side?
> > > >
> > > > Do you mean the demander side? Why we should try to disable it on the
> > > > supplier node? I do not take it into account at all because it can be
> > > > easily done (suppose issue [1] is about it). But it doesn't help us
> > > > much for the full cache relocations.
> > > >
> > > > > 4) Most important - have we tried to profile plain single-threaded
> > > > > rebalance without concurrent write load? We need to have clear
> > > > > understanding on where time is spent - supplier/demander, cpu/network/disk,
> > > > > etc. Some Java tracing code should help.
> > > >
> > > > I've updated some information about profiling results on the
> > > > confluence page [8]. If you will find that I've missed something or
> > > > information is unclear, please, let me know and I will fix it.
> > > >
> > > > > And one question regarding proposed implementation - how are we going
to
> > > > > handle secondary indexes?
> > > >
> > > > Thank you for pointing this out. Actually, the current IEP page
> > > > doesn't cover this case. I think we can schedule rebuild indexes after
> > > > all partition files would be transferred. This approach was also
> > > > mentioned at [2] issue.
> > > > Will it be the correct?
> > > >
> > > >
> > > > [1] https://issues.apache.org/jira/browse/IGNITE-10505
> > > > [2] https://issues.apache.org/jira/browse/IGNITE-7934
> > > > [3] https://issues.apache.org/jira/browse/IGNITE-7935
> > > >
> > > > [4] https://issues.apache.org/jira/browse/IGNITE-4682
> > > > [5] https://issues.apache.org/jira/browse/IGNITE-4506
> > > > [6] https://issues.apache.org/jira/browse/IGNITE-4680
> > > >
> > > > [7] https://issues.apache.org/jira/browse/IGNITE-7027
> > > > [8] https://cwiki.apache.org/confluence/display/IGNITE/Rebalance+peer-2-peer
> > > > [9] https://issues.apache.org/jira/browse/IGNITE-9520
> > > > On Wed, 28 Nov 2018 at 23:00, Vladimir Ozerov <vozerov@gridgain.com>
wrote:
> > > > >
> > > > > Maxim,
> > > > >
> > > > > Regarding MVCC - this is essentially a copy-on-write approach. New
entry is
> > > > > created on every update. They are cleaned asynchronously by dedicated
> > > > > threads (aka "vacuum").
> > > > >
> > > > > I looked at the document you mentioned, thank you for pointing to
it. But
> > > > > it doesn't answer all questions around existing design, and what
I am
> > > > > trying to do is to get how deep do we understand current problems.
It is
> > > > > very true that various subsystems, such as buffer managers, WALs,
> > > > > supporting sctructures, etc. incur very serious overhead. And when
it comes
> > > > > to heavy operations implementors typically seek for a way to bypass
as much
> > > > > components as possible, taking in count that different shortcuts
lead to
> > > > > different types of side effects. And IMO our very important goal
for now is
> > > > > to create space of possible improvements, and estimate their costs,
risks
> > > > > and applicability for product's configuration space.
> > > > >
> > > > > Let me claridy several questions about current rebalance implementation,
as
> > > > > I am not a big expert here.
> > > > > 1) Is it correct that supplier sends only one message for every demand
> > > > > message? If yes, then streaming should improve network utilization
a lot.
> > > > > 2) Is it correct that for user caches we process supply messages
in a
> > > > > system pool? Did we consider moving it to striped pool? Because if
all
> > > > > operations on a single partition is ordered, we may apply a number
of
> > > > > critical optimizations - bypassing page cache and checkpointer for
new
> > > > > entries, batched index updates, batched free list updates, etc.
> > > > > 3) Seems that WAL should no longer be a problem for us [1]. What
are exact
> > > > > conditions when it could be disabled on supplier side?
> > > > > 4) Most important - have we tried to profile plain single-threaded
> > > > > rebalance without concurrent write load? We need to have clear
> > > > > understanding on where time is spent - supplier/demander, cpu/network/disk,
> > > > > etc. Some Java tracing code should help.
> > > > >
> > > > > And one question regarding proposed implementation - how are we going
to
> > > > > handle secondary indexes?
> > > > >
> > > > > [1] https://issues.apache.org/jira/browse/IGNITE-8017
> > > > >
> > > > >
> > > > > On Wed, Nov 28, 2018 at 6:43 PM Maxim Muzafarov <maxmuzaf@gmail.com>
wrote:
> > > > >
> > > > > > Eduard,
> > > > > >
> > > > > > Thank you very much for the discussion!
> > > > > >
> > > > > > Your algorithm looks much better for me too and easier to implement.
> > > > > > I'll update appropriate process points on IEP page of the proposed
> > > > > > rebalance procedure.
> > > > > > On Tue, 27 Nov 2018 at 18:52, Eduard Shangareev
> > > > > > <eduard.shangareev@gmail.com> wrote:
> > > > > > >
> > > > > > > So, after some discussion, I could describe another approach
on how to
> > > > > > > build consistent partition on the fly.
> > > > > > >
> > > > > > > 1. We make a checkpoint, fix the size of the partition
in OffheapManager.
> > > > > > > 2. After checkpoint finish, we start sending partition
file (without any
> > > > > > > lock) to the receiver from 0 to fixed size.
> > > > > > > 3. Next checkpoints if they detect that they would override
some pages of
> > > > > > > transferring file should write the previous state of a
page to a
> > > > > >
> > > > > > dedicated
> > > > > > > file.
> > > > > > > So, we would have a list of pages written 1 by 1, page
id is written in
> > > > > >
> > > > > > the
> > > > > > > page itself so we could determine page index. Let's name
it log.
> > > > > > > 4. When transfer finished checkpointer would stop updating
log-file. Now
> > > > > >
> > > > > > we
> > > > > > > are ready to send it to the receiver.
> > > > > > > 5. On receiver side we start merging the dirty partition
file with log
> > > > > > > (updating it with pages from log-file).
> > > > > > >
> > > > > > > So, an advantage of this method:
> > > > > > > - checkpoint-thread work couldn't  increase more than twice;
> > > > > > > - checkpoint-threads shouldn't wait for anything;
> > > > > > > - in best case, we receive partition without any extra
effort.
> > > > > > >
> > > > > > >
> > > > > > > On Mon, Nov 26, 2018 at 8:54 PM Eduard Shangareev <
> > > > > > > eduard.shangareev@gmail.com> wrote:
> > > > > > >
> > > > > > > > Maxim,
> > > > > > > >
> > > > > > > > I have looked through your algorithm of reading partition
consistently.
> > > > > > > > And I have some questions/comments.
> > > > > > > >
> > > > > > > > 1. The algorithm requires heavy synchronization between
> > > > > >
> > > > > > checkpoint-thread
> > > > > > > > and new-approach-rebalance-threads,
> > > > > > > > because you need strong guarantees to not start writing
or reading to
> > > > > > > > chunk which was updated or started reading by the
counterpart.
> > > > > > > >
> > > > > > > > 2. Also, if we have started transferring this chunk
in original
> > > > > >
> > > > > > partition
> > > > > > > > couldn't be updated by checkpoint-threads. They should
wait for
> > > > > >
> > > > > > transfer
> > > > > > > > finishing.
> > > > > > > >
> > > > > > > > 3. If sending is slow and partition is updated then
in worst case
> > > > > > > > checkpoint-threads would create the whole copy of
the partition.
> > > > > > > >
> > > > > > > > So, what we have:
> > > > > > > > -on every page write checkpoint-thread should synchronize
with
> > > > > > > > new-approach-rebalance-threads;
> > > > > > > > -checkpoint-thread should do extra-work, sometimes
this could be as
> > > > > >
> > > > > > huge
> > > > > > > > as copying the whole partition.
> > > > > > > >
> > > > > > > >
> > > > > > > > On Fri, Nov 23, 2018 at 2:55 PM Ilya Kasnacheev <
> > > > > >
> > > > > > ilya.kasnacheev@gmail.com>
> > > > > > > > wrote:
> > > > > > > >
> > > > > > > > > Hello!
> > > > > > > > >
> > > > > > > > > This proposal will also happily break my compression-with-dictionary
> > > > > >
> > > > > > patch
> > > > > > > > > since it relies currently on only having local
dictionaries.
> > > > > > > > >
> > > > > > > > > However, when you have compressed data, maybe
speed boost is even
> > > > > >
> > > > > > greater
> > > > > > > > > with your approach.
> > > > > > > > >
> > > > > > > > > Regards,
> > > > > > > > > --
> > > > > > > > > Ilya Kasnacheev
> > > > > > > > >
> > > > > > > > >
> > > > > > > > > пт, 23 нояб. 2018 г. в 13:08, Maxim Muzafarov
<maxmuzaf@gmail.com>:
> > > > > > > > >
> > > > > > > > > > Igniters,
> > > > > > > > > >
> > > > > > > > > >
> > > > > > > > > > I'd like to take the next step of increasing
the Apache Ignite with
> > > > > > > > > > enabled persistence rebalance speed. Currently,
the rebalancing
> > > > > > > > > > procedure doesn't utilize the network and
storage device throughout
> > > > > >
> > > > > > to
> > > > > > > > > > its full extent even with enough meaningful
values of
> > > > > > > > > > rebalanceThreadPoolSize property. As part
of the previous discussion
> > > > > > > > > > `How to make rebalance faster` [1] and IEP-16
[2] Ilya proposed an
> > > > > > > > > > idea [3] of transferring cache partition
files over the network.
> > > > > > > > > > From my point, the case to which this type
of rebalancing procedure
> > > > > > > > > > can bring the most benefit – is adding
a completely new node or set
> > > > > >
> > > > > > of
> > > > > > > > > > new nodes to the cluster. Such a scenario
implies fully relocation
> > > > > >
> > > > > > of
> > > > > > > > > > cache partition files to the new node. To
roughly estimate the
> > > > > > > > > > superiority of partition file transmitting
over the network the
> > > > > >
> > > > > > native
> > > > > > > > > > Linux scp\rsync commands can be used. My
test environment showed the
> > > > > > > > > > result of the new approach as 270 MB/s vs
the current 40 MB/s
> > > > > > > > > > single-threaded rebalance speed.
> > > > > > > > > >
> > > > > > > > > >
> > > > > > > > > > I've prepared the design document IEP-28
[4] and accumulated all the
> > > > > > > > > > process details of a new rebalance approach
on that page. Below you
> > > > > > > > > > can find the most significant details of
the new rebalance procedure
> > > > > > > > > > and components of the Apache Ignite which
are proposed to change.
> > > > > > > > > >
> > > > > > > > > > Any feedback is very appreciated.
> > > > > > > > > >
> > > > > > > > > >
> > > > > > > > > > *PROCESS OVERVIEW*
> > > > > > > > > >
> > > > > > > > > > The whole process is described in terms
of rebalancing single cache
> > > > > > > > > > group and partition files would be rebalanced
one-by-one:
> > > > > > > > > >
> > > > > > > > > > 1. The demander node sends the GridDhtPartitionDemandMessage
to the
> > > > > > > > > > supplier node;
> > > > > > > > > > 2. When the supplier node receives GridDhtPartitionDemandMessage
and
> > > > > > > > > > starts the new checkpoint process;
> > > > > > > > > > 3. The supplier node creates empty the temporary
cache partition
> > > > > >
> > > > > > file
> > > > > > > > > > with .tmp postfix in the same cache persistence
directory;
> > > > > > > > > > 4. The supplier node splits the whole cache
partition file into
> > > > > > > > > > virtual chunks of predefined size (multiply
to the PageMemory size);
> > > > > > > > > > 4.1. If the concurrent checkpoint thread
determines the appropriate
> > > > > > > > > > cache partition file chunk and tries to
flush dirty page to the
> > > > > >
> > > > > > cache
> > > > > > > > > > partition file
> > > > > > > > > > 4.1.1. If rebalance chunk already transferred
> > > > > > > > > > 4.1.1.1. Flush the dirty page to the file;
> > > > > > > > > > 4.1.2. If rebalance chunk not transferred
> > > > > > > > > > 4.1.2.1. Write this chunk to the temporary
cache partition file;
> > > > > > > > > > 4.1.2.2. Flush the dirty page to the file;
> > > > > > > > > > 4.2. The node starts sending to the demander
node each cache
> > > > > >
> > > > > > partition
> > > > > > > > > > file chunk one by one using FileChannel#transferTo
> > > > > > > > > > 4.2.1. If the current chunk was modified
by checkpoint thread – read
> > > > > > > > > > it from the temporary cache partition file;
> > > > > > > > > > 4.2.2. If the current chunk is not touched
– read it from the
> > > > > >
> > > > > > original
> > > > > > > > > > cache partition file;
> > > > > > > > > > 5. The demander node starts to listen to
new pipe incoming
> > > > > >
> > > > > > connections
> > > > > > > > > > from the supplier node on TcpCommunicationSpi;
> > > > > > > > > > 6. The demander node creates the temporary
cache partition file with
> > > > > > > > > > .tmp postfix in the same cache persistence
directory;
> > > > > > > > > > 7. The demander node receives each cache
partition file chunk one
> > > > > >
> > > > > > by one
> > > > > > > > > > 7.1. The node checks CRC for each PageMemory
in the downloaded
> > > > > >
> > > > > > chunk;
> > > > > > > > > > 7.2. The node flushes the downloaded chunk
at the appropriate cache
> > > > > > > > > > partition file position;
> > > > > > > > > > 8. When the demander node receives the whole
cache partition file
> > > > > > > > > > 8.1. The node initializes received .tmp
file as its appropriate
> > > > > >
> > > > > > cache
> > > > > > > > > > partition file;
> > > > > > > > > > 8.2. Thread-per-partition begins to apply
for data entries from the
> > > > > > > > > > beginning of WAL-temporary storage;
> > > > > > > > > > 8.3. All async operations corresponding
to this partition file still
> > > > > > > > > > write to the end of temporary WAL;
> > > > > > > > > > 8.4. At the moment of WAL-temporary storage
is ready to be empty
> > > > > > > > > > 8.4.1. Start the first checkpoint;
> > > > > > > > > > 8.4.2. Wait for the first checkpoint ends
and own the cache
> > > > > >
> > > > > > partition;
> > > > > > > > > > 8.4.3. All operations now are switched to
the partition file instead
> > > > > > > > > > of writing to the temporary WAL;
> > > > > > > > > > 8.4.4. Schedule the temporary WAL storage
deletion;
> > > > > > > > > > 9. The supplier node deletes the temporary
cache partition file;
> > > > > > > > > >
> > > > > > > > > >
> > > > > > > > > > *COMPONENTS TO CHANGE*
> > > > > > > > > >
> > > > > > > > > > CommunicationSpi
> > > > > > > > > >
> > > > > > > > > > To benefit from zero copy we must delegate
the file transferring to
> > > > > > > > > > FileChannel#transferTo(long, long,
> > > > > > > > > > java.nio.channels.WritableByteChannel) because
the fast path of
> > > > > > > > > > transferTo method is only executed if the
destination buffer
> > > > > >
> > > > > > inherits
> > > > > > > > > > from an internal JDK class.
> > > > > > > > > >
> > > > > > > > > > Preloader
> > > > > > > > > >
> > > > > > > > > > A new implementation of cache entries preloader
assume to be done.
> > > > > >
> > > > > > The
> > > > > > > > > > new implementation must send and receive
cache partition files over
> > > > > > > > > > the CommunicationSpi channels by chunks
of data with validation
> > > > > > > > > > received items. The new layer over the cache
partition file must
> > > > > > > > > > support direct using of FileChannel#transferTo
method over the
> > > > > > > > > > CommunicationSpi pipe connection. The connection
bandwidth of the
> > > > > > > > > > cache partition file transfer must have
the ability to be limited at
> > > > > > > > > > runtime.
> > > > > > > > > >
> > > > > > > > > > Checkpointer
> > > > > > > > > >
> > > > > > > > > > When the supplier node receives the cache
partition file demand
> > > > > > > > > > request it will send the file over the CommunicationSpi.
The cache
> > > > > > > > > > partition file can be concurrently updated
by checkpoint thread
> > > > > >
> > > > > > during
> > > > > > > > > > its transmission. To guarantee the file
consistency Сheckpointer
> > > > > >
> > > > > > must
> > > > > > > > > > use copy-on-write technique and save a copy
of updated chunk into
> > > > > >
> > > > > > the
> > > > > > > > > > temporary file.
> > > > > > > > > >
> > > > > > > > > > (new) Catch-up temporary WAL
> > > > > > > > > >
> > > > > > > > > > While the demander node is in the partition
file transmission state
> > > > > >
> > > > > > it
> > > > > > > > > > must save all cache entries corresponding
to the moving partition
> > > > > >
> > > > > > into
> > > > > > > > > > a new temporary WAL storage. These entries
will be applied later one
> > > > > > > > > > by one on the received cache partition file.
All asynchronous
> > > > > > > > > > operations will be enrolled to the end of
temporary WAL storage
> > > > > >
> > > > > > during
> > > > > > > > > > storage reads until it becomes fully read.
The file-based FIFO
> > > > > > > > > > approach assumes to be used by this process.
> > > > > > > > > >
> > > > > > > > > >
> > > > > > > > > > *RECOVERY*
> > > > > > > > > >
> > > > > > > > > > In case of crash recovery, there is no additional
actions need to be
> > > > > > > > > > applied to keep the cache partition file
consistency. We are not
> > > > > > > > > > recovering partition with the moving state,
thus the single
> > > > > >
> > > > > > partition
> > > > > > > > > > file will be lost and only it. The uniqueness
of it is guaranteed by
> > > > > > > > > > the single-file-transmission process. The
cache partition file will
> > > > > >
> > > > > > be
> > > > > > > > > > fully loaded on the next rebalance procedure.
> > > > > > > > > >
> > > > > > > > > > To provide default cluster recovery guarantee
we must to:
> > > > > > > > > > 1. Start the checkpoint process when the
temporary WAL storage
> > > > > >
> > > > > > becomes
> > > > > > > > > > empty;
> > > > > > > > > > 2. Wait for the first checkpoint ends and
set owning status to the
> > > > > > > > > > cache partition;
> > > > > > > > > >
> > > > > > > > > >
> > > > > > > > > >
> > > > > > > > > >
> > > > > > > > > > [1]
> > > > > > > > > >
> > > > > >
> > > > > > http://apache-ignite-developers.2346864.n4.nabble.com/Rebalancing-how-to-make-it-faster-td28457.html
> > > > > > > > > > [2]
> > > > > > > > > >
> > > > > >
> > > > > > https://cwiki.apache.org/confluence/display/IGNITE/IEP-16%3A+Optimization+of+rebalancing
> > > > > > > > > > [3] https://issues.apache.org/jira/browse/IGNITE-8020
> > > > > > > > > > [4]
> > > > > > > > > >
> > > > > >
> > > > > > https://cwiki.apache.org/confluence/display/IGNITE/IEP-28%3A+Cluster+peer-2-peer+balancing
> > > > > > > > > >

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