I saw the following in the logs for the failed test:
2023.05.16 07:12:12.894051 [ 262 ] {74575ac0-b296-4fdc-bc8e-3476a305e6ea} <Warning> ConnectionPoolWithFailover: Connection failed at try №1, reason: Timeout exceeded while reading from socket (socket (172.16.3.2:9000), receive timeout 2000 ms)
And I think that the culprit is the
test_distributed_replica_max_ignored_errors for which it is normal,
however not for others, and this should not affect other tests.
So fix this by calling SYSTEM RELOAD CONFIG, which should reset error
count.
CI: https://s3.amazonaws.com/clickhouse-test-reports/49380/5abc1a1c68ee204c9024493be1d19835cf5630f7/integration_tests__release__[3_4].html
Signed-off-by: Azat Khuzhin <a.khuzhin@semrush.com>
In clang-16 the behaviour for POD types had been changed in [1], this
does not allows us to use PackedHashMap for some types.
[1]: 277123376c
Note, that I tried to come up with a more generic solution then
enumeratic types, but failed. Though now I think that this is good,
since this shows which types are not allowed for PackedHashMap
Another option is to use -fclang-abi-compat=13.0 but I doubt it is a
good idea.
Signed-off-by: Azat Khuzhin <a.khuzhin@semrush.com>
Use separate helpers that accept/return values, instead of reference,
anyway PackedHashMap is developed for small structure.
v0: fix for keys
v2: fix for values
v3: fix bitEquals
v4: fix for iterating over HashMap
Signed-off-by: Azat Khuzhin <a.khuzhin@semrush.com>
As it turns out, HashMap/PackedHashMap works great even with max load
factor of 0.99. By "great" I mean it least it works faster then
google sparsehash, and not to mention it's friendliness to the memory
allocator (it has zero fragmentation since it works with a continuious
memory region, in comparison to the sparsehash that doing lots of
realloc, which jemalloc does not like, due to it's slabs).
Here is a table of different setups:
settings | load (sec) | read (sec) | read (million rows/s) | bytes_allocated | RSS
- | - | - | - | - | -
HASHED upstream | - | - | - | - | 35GiB
SPARSE_HASHED upstream | - | - | - | - | 26GiB
- | - | - | - | - | -
sparse_hash_map glibc hashbench | - | - | - | - | 17.5GiB
sparse_hash_map packed allocator | 101.878 | 231.48 | 4.32 | - | 17.7GiB
PackedHashMap 0.5 | 15.514 | 42.35 | 23.61 | 20GiB | 22GiB
hashed 0.95 | 34.903 | 115.615 | 8.65 | 16GiB | 18.7GiB
**PackedHashMap 0.95** | **93.6** | **19.883** | **10.68** | **10GiB** | **12.8GiB**
PackedHashMap 0.99 | 26.113 | 83.6 | 11.96 | 10GiB | 12.3GiB
As it shows, PackedHashMap with 0.95 max_load_factor, eats 2.6x less
memory then SPARSE_HASHED in upstream, and it also 2x faster for read!
v2: fix grower
Signed-off-by: Azat Khuzhin <a.khuzhin@semrush.com>
In case you want dictionary optimized for memory, SPARSE_HASHED is not
always gives you what you need.
Consider the following example <UInt64, UInt16> as <Key, Value>, but
this pair will also have a 6 byte padding (on amd64), so this is almost
40% of space wastage.
And because of this padding, even google::sparse_hash_map, does not make
picture better, in fact, sparse_hash_map is not very friendly to memory
allocators (especially jemalloc).
Here are some numbers for dictionary with 1e9 elements and UInt64 as
key, and UInt16 as value:
settings | load (sec) | read (sec) | read (million rows/s) | bytes_allocated | RSS
HASHED upstream | - | - | - | - | 35GiB
SPARSE_HASHED upstream | - | - | - | - | 26GiB
- | - | - | - | - | -
sparse_hash_map glibc hashbench | - | - | - | - | 17.5GiB
sparse_hash_map packed allocator | 101.878 | 231.48 | 4.32 | - | 17.7GiB
PackedHashMap | 15.514 | 42.35 | 23.61 | 20GiB | 22GiB
As you can see PackedHashMap looks way more better then HASHED, and
even better then SPARSE_HASHED, but slightly worse then sparse_hash_map
with packed allocator (it is done with a custom patch to google
sparse_hash_map).
v2: rebase on top of bucket_count fix
Signed-off-by: Azat Khuzhin <a.khuzhin@semrush.com>
In case of you have HashMap with <UInt64, UInt16> as <Key, Value> the
overhead of 38% can be crutial, especially if you have tons of keys.
Signed-off-by: Azat Khuzhin <a.khuzhin@semrush.com>
