Hi Larry,
Please note that applyBatch() takes locks after the call to updateStore() while
flushCacheCoalescing() takes them before updateStore().
UpdateStore() does all the work with persisting your data via CacheStore, so it
is could be quite long & blocking operation for one flushing thread. It seems
that you can have some gain here coalescing with turning off coalesce.
1. There are no changes here b/w 2.3 and 2.1.
2. Could you please clarify your proposal for the fix?
For your use case:
a) if you enable coalescing you will have ONE update (within a batch) to DB
while you perform multiple updates to the same Cache entry. So the DB load is
reduced here but it costs some locking overhead to resolve updates
b) If you disable coalescing you will have MULTIPLE updates to DB while you
perform multiple updates to the same Cache entry. It will reduce locking
overhead but loads your DB more heavily
You can find some balance by tuning batch size, # of flushing threads
with/without coalescing.
Thank you,
Alexey
From: Larry Mark
Sent: Wednesday, November 8, 2017 2:27 AM
To: [email protected]
Cc: Henry Olschofka
Subject: Re: write behind performance impacting main thread. Write behindbuffer
is never full
Alexey,
I dug into this a bit more and it is the perfect storm of the way the write
behind works and the way we are using one of our Caches. We need to keep our
kafka offsets persisted, so we have a cache with the Key being a topic and
partition. When we get a record from that combination we update the value.
When we are very busy we are constantly getting messages, and the contents of
the message gets distributed to many caches, but the offset is to the same
cache with the same key. When that gets flushed to disk the coalesce keeps
locking that key, and is in contention with the main thread trying to update
the key. Turning off coalesce does not seem to help, first of all if I am
reading the code correctly it is still going to take locks in applyBatch after
the call to updateStore and if we have not coalesced we will take the lock on
the same value over and over. Also, because we rewrite that key constantly,
without coalesce the write behind cannot keep up.
Now that we understand what is going on we can work around this.
Two quick questions:
- We are on 2.1, is there anything changed in this area in 2.3 that might make
this better.
- Is this use case of updating the same key unique to us, or is this common
enough that there should be a fix to the coalesce code?
Best,
Larry
On Fri, Nov 3, 2017 at 5:14 PM, Larry Mark <[email protected]> wrote:
Alexey,
With our use case setting the coalesce off will probably make it worse, for at
least some caches we are doing many updates to the same key, one of the reasons
I am setting the batch size to 500.
I will send the cachestore implementation and some logs that show the
phenomenon early next week. Thanks for your help.
Larry
On Fri, Nov 3, 2017 at 12:11 PM, Alexey Popov <[email protected]> wrote:
Hi,
Can you share your cache store implementation?
It could be several reasons for possible performance degradation in
write-behind mode.
Ignite can start flushing your cache values in main() thread if cacheSize
becomes greater than 1,5 x setWriteBehindFlushSize. It is a common case but
it does not look like your case.
WriteBehind implementation could use ReentrantReadWriteLock while you
insert/update the Cache entries in your main thread. WriteBehind background
threads use these locks when they read and flush entries.
Such WriteBehind implementation is used when writeCoalescing is turned on by
setWriteBehindCoalescing(true); BTW, the default value is TRUE. Actually, it
makes sense only when you configure several flush threads
(setWriteBehindFlushThreadCount(X)) to have a real concurrency in multiple
reads and writes.
It is hard to believe that it could slow down your main() thread, but please
check: just add setWriteBehindCoalescing(false) to your config and try your
tests again.
Thanks,
Alexey
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