We're likely to have something comparable to the current changeset in the
subsequent one. If it's bigger, no big deal, it'll be reallocated, if it's
smaller, no big deal, the next one will be preallocated smaller.
Problem:
```
--- FAIL: TestMemCachedPersist (0.07s)
--- FAIL: TestMemCachedPersist/BoltDBStore (0.07s)
testing.go:894: TempDir RemoveAll cleanup: remove C:\Users\Anna\AppData\Local\Temp\TestMemCachedPersist_BoltDBStore294966711\001\test_bolt_db: The process cannot access the file because it is being used by another process.
```
Solution:
Release the resources occupied by the DB.
b9be892bf9 has made Persist asynchronous which
is very effective in allowing the system to continue processing
blocks/transactions while flushing things to disk. It at the same time is very
dangerous in that if the disk is slow and it takes much time to flush KV set
(more than persisting interval), there might be even bigger new KV set in
MemCachedStore by the time it finishes. Even if the system immediately starts
to flush this new data set it (being bigger) can take more time than the
previous one. And while doing so a new data set will appear in memory,
potentially again bigger than this.
So we can easily end up with the system going out of control, consuming more
and more memory and taking more and more time to persist a single set of
data. To avoid this we need to detect such condition and just wait for Persist
to really finish its job and release the resources.
Real persistent storage guarantees that result of Seek is sorted
by keys. The idea of optimisation is to merge two sorted seek
results into one (memStore+persistentStore), so that
(*MemCachedStore).Seek will return sorted list. The only thing
that remains is to sort items got from (*MemoryStore).Seek.
MemoryStore is used in a MemCachedStore as a persistent layer in tests.
Further commits suppose that persistent storage returns sorted values
from Seek, so sort the result of MemoryStore.Seek.
Benchmark results for 10000 matching items in MemoryStore compared to
master:
name old time/op new time/op delta
MemorySeek-8 712µs ± 0% 3850µs ± 0% +440.52% (p=0.000 n=8+8)
name old alloc/op new alloc/op delta
MemorySeek-8 160kB ± 0% 2724kB ± 0% +1602.61% (p=0.000 n=10+8)
name old allocs/op new allocs/op delta
MemorySeek-8 10.0k ± 0% 10.0k ± 0% +0.24% (p=0.000 n=10+10)
For details on implementation efficiency see the
https://github.com/nspcc-dev/neo-go/pull/2193#discussion_r722993358.
We need several stages to manage state jump process in order not to mess
up old and new contract storage items and to be sure about genesis state data
are properly removed from the storage. Other operations do not require
separate stage and can be performed each time `jumpToStateInternal` is
called.
State jump should be an atomic operation, we can't modify contract
storage items state on-the-fly. Thus, store fresh items under temp
prefix and replase the outdated ones after state sync is completed.
Related
https://github.com/nspcc-dev/neo-go/pull/2019#discussion_r693350460.
We're using batches in wrong way during persist, we already have all changes
accumulated in two maps and then we move them to batch and then this is
applied. For some DBs like BoltDB this batch is just another MemoryStore, so
we essentially just shuffle the changeset from one map to another, for others
like LevelDB batch is just a serialized set of KV pairs, it doesn't help much
on subsequent PutBatch, we just duplicate the changeset again.
So introduce PutChangeSet that allows to take two maps with sets and deletes
directly. It also allows to simplify MemCachedStore logic.
neo-bench for single node with 10 workers, LevelDB:
Reference:
RPS 30189.132 30556.448 30390.482 ≈ 30379 ± 0.61%
TPS 29427.344 29418.687 29434.273 ≈ 29427 ± 0.03%
CPU % 33.304 27.179 33.860 ≈ 31.45 ± 11.79%
Mem MB 800.677 798.389 715.042 ≈ 771 ± 6.33%
Patched:
RPS 30264.326 30386.364 30166.231 ≈ 30272 ± 0.36% ⇅
TPS 29444.673 29407.440 29452.478 ≈ 29435 ± 0.08% ⇅
CPU % 34.012 32.597 33.467 ≈ 33.36 ± 2.14% ⇅
Mem MB 549.126 523.656 517.684 ≈ 530 ± 3.15% ↓ 31.26%
BoltDB:
Reference:
RPS 31937.647 31551.684 31850.408 ≈ 31780 ± 0.64%
TPS 31292.049 30368.368 31307.724 ≈ 30989 ± 1.74%
CPU % 33.792 22.339 35.887 ≈ 30.67 ± 23.78%
Mem MB 1271.687 1254.472 1215.639 ≈ 1247 ± 2.30%
Patched:
RPS 31746.818 30859.485 31689.761 ≈ 31432 ± 1.58% ⇅
TPS 31271.499 30340.726 30342.568 ≈ 30652 ± 1.75% ⇅
CPU % 34.611 34.414 31.553 ≈ 33.53 ± 5.11% ⇅
Mem MB 1262.960 1231.389 1335.569 ≈ 1277 ± 4.18% ⇅
Persist by its definition doesn't change MemCachedStore visible state, all KV
pairs that were acessible via it before Persist remain accessible after
Persist. The only thing it does is flushing of the current set of KV pairs
from memory to peristent store. To do that it needs read-only access to the
current KV pair set, but technically it then replaces maps, so we have to use
full write lock which makes MemCachedStore inaccessible for the duration of
Persist. And Persist can take a lot of time, it's about disk access for
regular DBs.
What we do here is we create new in-memory maps for MemCachedStore before
flushing old ones to the persistent store. Then a fake persistent store is
created which actually is a MemCachedStore with old maps, so it has exactly
the same visible state. This Store is never accessed for writes, so we can
read it without taking any internal locks and at the same time we no longer
need write locks for original MemCachedStore, we're not using it. All of this
makes it possible to use MemCachedStore as normally reads are handled going
down to whatever level is needed and writes are handled by new maps. So while
Persist for (*Blockchain).dao does its most time-consuming work we can process
other blocks (reading data for transactions and persisting storeBlock caches
to (*Blockchain).dao).
The change was tested for performance with neo-bench (single node, 10 workers,
LevelDB) on two machines and block dump processing (RC4 testnet up to 62800
with VerifyBlocks set to false) on i7-8565U.
Reference results (bbe4e9cd7b):
Ryzen 9 5950X:
RPS 23616.969 22817.086 23222.378 ≈ 23218 ± 1.72%
TPS 23047.316 22608.578 22735.540 ≈ 22797 ± 0.99%
CPU % 23.434 25.553 23.848 ≈ 24.3 ± 4.63%
Mem MB 600.636 503.060 582.043 ≈ 562 ± 9.22%
Core i7-8565U:
RPS 6594.007 6499.501 6572.902 ≈ 6555 ± 0.76%
TPS 6561.680 6444.545 6510.120 ≈ 6505 ± 0.90%
CPU % 58.452 60.568 62.474 ≈ 60.5 ± 3.33%
Mem MB 234.893 285.067 269.081 ≈ 263 ± 9.75%
DB restore:
real 0m22.237s 0m23.471s 0m23.409s ≈ 23.04 ± 3.02%
user 0m35.435s 0m38.943s 0m39.247s ≈ 37.88 ± 5.59%
sys 0m3.085s 0m3.360s 0m3.144s ≈ 3.20 ± 4.53%
After the change:
Ryzen 9 5950X:
RPS 27747.349 27407.726 27520.210 ≈ 27558 ± 0.63% ↑ 18.69%
TPS 26992.010 26993.468 27010.966 ≈ 26999 ± 0.04% ↑ 18.43%
CPU % 28.928 28.096 29.105 ≈ 28.7 ± 1.88% ↑ 18.1%
Mem MB 760.385 726.320 756.118 ≈ 748 ± 2.48% ↑ 33.10%
Core i7-8565U:
RPS 7783.229 7628.409 7542.340 ≈ 7651 ± 1.60% ↑ 16.72%
TPS 7708.436 7607.397 7489.459 ≈ 7602 ± 1.44% ↑ 16.85%
CPU % 74.899 71.020 72.697 ≈ 72.9 ± 2.67% ↑ 20.50%
Mem MB 438.047 436.967 416.350 ≈ 430 ± 2.84% ↑ 63.50%
DB restore:
real 0m20.838s 0m21.895s 0m21.794s ≈ 21.51 ± 2.71% ↓ 6.64%
user 0m39.091s 0m40.565s 0m41.493s ≈ 40.38 ± 3.00% ↑ 6.60%
sys 0m3.184s 0m2.923s 0m3.062s ≈ 3.06 ± 4.27% ↓ 4.38%
It obviously uses more memory now and utilizes CPU more aggressively, but at
the same time it allows to improve all relevant metrics and finally reach a
situation where we process 50K transactions in less than second on Ryzen 9
5950X (going higher than 25K TPS). The other observation is much more stable
block time, on Ryzen 9 it's as close to 1 second as it could be.
Balances are to be removed from state.NEP17TransferInfo, so the remnant
fields are NextTransferBatch, NewBatch and a map of LastUpdatedBlocks.
These fields are more staff-related.
Also rename dao.[Get, Put, put]NEP17Balances and STNEP17Balances
preffix.
Also rename NEP17TransferInfo.Trackers to LastUpdatedBlockTrackers
because NEP17TransferInfo.Balances are to be removed.
Everything was wrong here, wrong file used, wrong cleanup procedure, the net
result is this (and some failing tests from time to time):
$ ls -l /tmp/test_bolt_db* | wc -l
30939
Related to #1468, ported from #1475.
We should copy the key to avoid bytes substitution. Otherwise there's a
chance that at the end of dao.Store.Seek(...) execution some keys won't
be the same as the original keys found inside saveToMap function because
storage.Seek can guarantee that provided key and value are only valid
until the next `f` call.
We're constantly checking for transactions there and most of the time this
check is not successful (meaning that the transaction in question is
new). Bloom filter easily reduces the need to search over the DB in 99% of
these cases and gives some 13% increase in single-node TPS.
