ARM platforms. This patch resolves this issue and unifies AUX column
processing at x86 and ARM using tempate class SimdProcessor.
The patch also replaces uint16_t mask previously used in column.cpp and
SimProcessor code with a native masks that platform uses, e.g. __m128i
or __m128 on x86 and variety of masks on ARM.
To unify the processing I introduced a new filtering Compare Operator - COMPARE_NULLEQ.
with a 'c1 IS NULL semantics'.
In the joblist code, in addition to sending the lbid of the SCAN
column, we also send the corresponding lbid of the AUX column to PrimProc.
In the primitives processor code in PrimProc, we load the AUX column
block (8192 rows since the AUX column is implemented as a 1-byte
UNSIGNED TINYINT) into memory and then pass it down to the low-level
scanning (vectorized scanning as applicable) routine to build a non-Empty
mask for the block being processed to filter out DELETED rows based on
comparison of the AUX block row to the empty magic value for the AUX column.
The idea is relatively simple - encode prefixes of collated strings as
integers and use them to compute extents' ranges. Then we can eliminate
extents with strings.
The actual patch does have all the code there but miss one important
step: we do not keep collation index, we keep charset index. Because of
this, some of the tests in the bugfix suite fail and thus main
functionality is turned off.
The reason of this patch to be put into PR at all is that it contains
changes that made CHAR/VARCHAR columns unsigned. This change is needed in
vectorization work.
* Fix clang warnings
* Remove vim tab guides
* initialize variables
* 'strncpy' output truncated before terminating nul copying as many bytes from a string as its length
* Fix ISO C++17 does not allow 'register' storage class specifier for outdated bison
* chars are unsigned on ARM, having if (ival < 0) always false
* chars are unsigned by default on ARM and comparison with -1 if always true
data types TEXT, CHAR, VARCHAR, FLOAT and DOUBLE are not yet supported by vectorized path
This patch introduces an example for Google benchmarking suite to measure a perf diff
b/w legacy scan/filtering code and the templated version
2. Set Decimal precision in SimpleColumn::evaluate().
3. Add support for int128_t in ConstantColumn.
4. Set IDB_Decimal::s128Value in buildDecimalColumn().
5. Use width 16 as first if predicate for branching based on decimal width.
