Adding more processors for parallelize_output_from_storages is not a
costless operation (I've experienced some issues in production because
of this), and it is not easy to fix in a normal way, so let's disable it
for now.
Before this patch:
- INSERT INTO input SELECT * FROM numbers(10e6) SETTINGS parallelize_output_from_storages=1, min_insert_block_size_rows=1000
0 rows in set. Elapsed: 3.648 sec. Processed 20.00 million rows, 120.00 MB (5.48 million rows/s., 32.90 MB/s.)
- INSERT INTO input SELECT * FROM numbers(10e6) SETTINGS parallelize_output_from_storages=0, min_insert_block_size_rows=1000
0 rows in set. Elapsed: 1.851 sec. Processed 20.00 million rows, 120.00 MB (10.80 million rows/s., 64.82 MB/s.)
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>
Reasons:
1. The original Gorilla paper proposed a compression schema for pairs of
time stamps and double-precision FP values. ClickHouse's Gorilla
codec only implements compression of the latter and it does not
impose any data type restrictions.
- Data types != Float* or (U)Int* (e.g. Decimal, Point etc.) are
definitely not supposed to be used with Gorilla.
- (U)Int* types are debatable. The paper only considers
integers-stored-as-FP-values, a practical use case for which
Gorilla works well. Standalone integers are not considered which
makes them at least suspicious.
2. Achieve consistency with FPC, another specialized floating-point
timeseries codec, which rejects non-float data.
3. On practical datasets, ZSTD is often "good enough" (**) so it should
be okay to disincentive non-ZSTD codecs a little bit. If needed,
Delta and DoubleDelta codecs are viable alternative for slowly
changing (time-series-like) integer sequences.
Since on-prem and hosted users may still have Gorilla-compressed
non-float data, this combination is only deprecated for now. No warning
or error will be emitted. Users are encouraged to migrate
Gorilla-compressed non-float data to an alternative codec. It is planned
to treat Gorilla-compressed non-float columns as "suspicious" six months
after this commit (i.e. in v23.6). Even then, it will still be possible
to set "allow_suspicious_codecs = true" and read and write
Gorilla-compressed non-float data.
(*) Sec. 4.1.2, "Gorilla restricts the value element in its tuple to a
double floating point type.", https://doi.org/10.14778/2824032.2824078
(**) https://clickhouse.com/blog/optimize-clickhouse-codecs-compression-schema
