Hi Anton,
Thanks for sharing your ideas.
I think your approach should work in general. I'll just share my
concerns about possible issues that may come up.
1) Equality of update counters doesn't imply equality of partitions
content under load.
For every update, primary node generates update counter and then update
is delivered to backup node and gets applied with the corresponding
update counter. For example, there are two transactions (A and B) that
update partition X by the following scenario:
- A updates key1 in partition X on primary node and increments counter to 10
- B updates key2 in partition X on primary node and increments counter to 11
- While A is still updating another keys, B is finally committed
- Update of key2 arrives to backup node and sets update counter to 11
Observer will see equal update counters (11), but update of key 1 is
still missing in the backup partition.
This is a fundamental problem which is being solved here:
https://issues.apache.org/jira/browse/IGNITE-10078
"Online verify" should operate with new complex update counters which
take such "update holes" into account. Otherwise, online verify may
provide false-positive inconsistency reports.
2) Acquisition and comparison of update counters is fast, but partition
hash calculation is long. We should check that update counter remains
unchanged after every K keys handled.
3)
Another hope is that we'll be able to pause/continue scan, for
example, we'll check 1/3 partitions today, 1/3 tomorrow, and in three
days we'll check the whole cluster.
Totally makes sense.
We may find ourselves into a situation where some "hot" partitions are
still unprocessed, and every next attempt to calculate partition hash
fails due to another concurrent update. We should be able to track
progress of validation (% of calculation time wasted due to concurrent
operations may be a good metric, 100% is the worst case) and provide
option to stop/pause activity.
I think, pause should return an "intermediate results report" with
information about which partitions have been successfully checked. With
such report, we can resume activity later: partitions from report will
be just skipped.
4)
Since "Idle verify" uses regular pagmem, I assume it replaces hot data
with persisted.
So, we have to warm up the cluster after each check.
Are there any chances to check without cooling the cluster?
I don't see an easy way to achieve it with our page memory architecture.
We definitely can't just read pages from disk directly: we need to
synchronize page access with concurrent update operations and checkpoints.
From my point of view, the correct way to solve this issue is improving
our page replacement [1] mechanics by making it truly scan-resistant.
P. S. There's another possible way of achieving online verify: instead
of on-demand hash calculation, we can always keep up-to-date hash value
for every partition. We'll need to update hash on every
insert/update/remove operation, but there will be no reordering issues
as per function that we use for aggregating hash results (+) is
commutative. With having pre-calculated partition hash value, we can
automatically detect inconsistent partitions on every PME. What do you
think?
[1] -
https://cwiki.apache.org/confluence/display/IGNITE/Ignite+Durable+Memory+-+under+the+hood#IgniteDurableMemory-underthehood-Pagereplacement(rotationwithdisk)
Best Regards,
Ivan Rakov
On 29.04.2019 12:20, Anton Vinogradov wrote:
Igniters and especially Ivan Rakov,
"Idle verify" [1] is a really cool tool, to make sure that cluster is
consistent.
1) But it required to have operations paused during cluster check.
At some clusters, this check requires hours (3-4 hours at cases I saw).
I've checked the code of "idle verify" and it seems it possible to
make it "online" with some assumptions.
Idea:
Currently "Idle verify" checks that partitions hashes, generated this way
while (it.hasNextX()) {
CacheDataRow row = it.nextX();
partHash += row.key().hashCode();
partHash +=
Arrays.hashCode(row.value().valueBytes(grpCtx.cacheObjectContext()));
}
, are the same.
What if we'll generate same pairs updateCounter-partitionHash but will
compare hashes only in case counters are the same?
So, for example, will ask cluster to generate pairs for 64 partitions,
then will find that 55 have the same counters (was not updated during
check) and check them.
The rest (64-55 = 9) partitions will be re-requested and rechecked
with an additional 55.
This way we'll be able to check cluster is consistent even in сase
operations are in progress (just retrying modified).
Risks and assumptions:
Using this strategy we'll check the cluster's consistency ...
eventually, and the check will take more time even on an idle cluster.
In case operationsPerTimeToGeneratePartitionHashes > partitionsCount
we'll definitely gain no progress.
But, in case of the load is not high, we'll be able to check all cluster.
Another hope is that we'll be able to pause/continue scan, for
example, we'll check 1/3 partitions today, 1/3 tomorrow, and in three
days we'll check the whole cluster.
Have I missed something?
2) Since "Idle verify" uses regular pagmem, I assume it replaces hot
data with persisted.
So, we have to warm up the cluster after each check.
Are there any chances to check without cooling the cluster?
[1]
https://apacheignite-tools.readme.io/docs/control-script#section-verification-of-partition-checksums
