dictGetOrNull() relies on IDictionary::hasKeys(), which
RegExpTreeDictionary doesn't implement, so this probably never worked.
If you try to use it, an exception is thrown. The docs shouldn't
indicate that this is supported.
Also fix a markdown hyperlink in the docs.
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>
It does not give significant benefit, but now, you hashed/sparse_hashed
dictionaries can be filled in parallel (#40003), using sharded
dictionaries, and this should be used instead of PREALLOCATE.
Note, that dictionaries, that had been created with PREALLOCATE will
work, but simply ignore this attribute.
Fixes: #41985 (cc @alexey-milovidov)
Reverts: #23979 (cc @kitaisreal)
Signed-off-by: Azat Khuzhin <a.khuzhin@semrush.com>
Right now dictionaries (here I will talk about only
HASHED/SPARSE_HASHED/COMPLEX_KEY_HASHED/COMPLEX_KEY_SPARSE_HASHED)
can load data only in one thread, since it uses one hash table that
cannot be filled from multiple threads.
And in case you have very big dictionary (i.e. 10e9 elements), it can
take a awhile to load them, especially for SPARSE_HASHED variants (and
if you have such amount of elements there, you are likely use
SPARSE_HASHED, since it requires less memory), in my env it takes ~4
hours, which is enormous amount of time.
So this patch add support of shards for dictionaries, number of shards
determine how much hash tables will use this dictionary, also, and which
is more important, how much threads it can use to load the data.
And with 16 threads this works 2x faster, not perfect though, see the
follow up patches in this series.
v0: PARTITION BY
v1: SHARDS 1
v2: SHARDS(1)
v3: tried optimized mod - logical and, but it does not gain even 10%
v4: tried squashing more (max_block_size * shards), but it does not gain even 10% either
v5: move SHARDS into layout parameters (unknown simply ignored)
v6: tune params for perf tests (to avoid too long queries)
Signed-off-by: Azat Khuzhin <a.khuzhin@semrush.com>
