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
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>
* impl
* fix style
* make executeQueryWithParallelReplicas similar to executeQuery
* impl for parallel replicas
* cleaner code for remote sorting properties
* update test
* fix
* handle when nodes of old versions participate
* small fixes
* temporary enable for testing
* fix after merge
* Revert "temporary enable for testing"
This reverts commit cce7f8884c.
* review fixes
* add bc test
* Update src/Core/Settings.h
