Character Runs – SSE Example 72 CppCon 2020 - Adventures in SIMD Thinking 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 zero zero = _mm_set1_epi8(0) LSB MSBCopyright © 2020 Bob Steagall K E W B 6F 72 64 20 CE BA E1 BD B9 47 72 65 65 6B 20 77 6F 72 64 20 CE BA E1 BD B9 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 zero memory chunk chunk = _mm_loadu_si128((__m128i const*) pSrc) pSrc LSB BD B9 zero memory chunk mask mask = _mm_movemask_epi8(chunk) LSB MSB 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 0000 0000 0001 1111 0000 0000 0000 0000Copyright © 2020 Bob Steagall K E W

ULONG reserved; }; pag_type One byte, signed. Byte 0x00 on the page. This byte defines the page type for the page. Valid page types are: 0x00 Undefined page. You should never see this in a database Each transaction is represented by a pair of bits in a bitmap. Valid values in these two bits are: 00 this transaction is active. 01 this transaction is in limbo. 10 this transaction is dead. 11 this using SQL dialect 3. hdr_read_only 0x200 (bit 9) Database is in read only mode. hdr_backup_mask 0xC00 (bits 10 and 11) Indicates the current backup mode. hdr_shutdown_mask (bit two of two) 0x1080

['1000-01-01', '9999-12-31']。默认的打印形式是’YYYY-MM-DD’。 DATETIME数据类型 DATETIME数据类型 范围: ['1000-01-01 00:00:00', '9999-12-31 00:00:00']。默认的打印形式是’YYYY-MM-DD HH:MM:SS’。 CHAR数据类型 CHAR数据类型 范围: char[(length)]，定长字符串，长度length范围1~255，默认为1。 +--------------------------------+ +--------------------------------+ | 2011-01-01 00:00:00 | | 2011-01-01 00:00:00 | +--------------------------------+ +--------------------------------+ insert into into tbl1 tbl1 select select ** from from empty_tbl empty_tbl;; Query OK Query OK,, 00 rows rows affected affected ((0.02 0.02 sec sec)) Query OK Query OK 0 rows affected 0 rows affected

station_london datetime 2019-05-07 02:00:00 NaN NaN 23.0 2019-05-07 03:00:00 50.5 25.0 19.0 2019-05-07 04:00:00 45.0 27.7 19.0 2019-05-07 05:00:00 NaN 50.4 16.0 2019-05-07 06:00:00 NaN 61.9 NaN Note: The usage station_london datetime 2019-05-07 02:00:00 NaN NaN 23.0 2019-05-07 03:00:00 50.5 25.0 19.0 2019-05-07 04:00:00 45.0 27.7 19.0 2019-05-07 05:00:00 NaN 50.4 16.0 2019-05-07 06:00:00 NaN 61.9 NaN How to create 2019-05-07 02:00:00 NaN NaN 23.0 43. ˓→286 2019-05-07 03:00:00 50.5 25.0 19.0 35. ˓→758 2019-05-07 04:00:00 45.0 27.7 19.0 35. ˓→758 2019-05-07 05:00:00 NaN 50.4 16.0 30. ˓→112 2019-05-07 06:00:00 NaN 61

station_london datetime 2019-05-07 02:00:00 NaN NaN 23.0 2019-05-07 03:00:00 50.5 25.0 19.0 2019-05-07 04:00:00 45.0 27.7 19.0 2019-05-07 05:00:00 NaN 50.4 16.0 2019-05-07 06:00:00 NaN 61.9 NaN Note: The usage station_london datetime 2019-05-07 02:00:00 NaN NaN 23.0 2019-05-07 03:00:00 50.5 25.0 19.0 2019-05-07 04:00:00 45.0 27.7 19.0 2019-05-07 05:00:00 NaN 50.4 16.0 2019-05-07 06:00:00 NaN 61.9 NaN How to create 2019-05-07 02:00:00 NaN NaN 23.0 43. ˓→286 2019-05-07 03:00:00 50.5 25.0 19.0 35. ˓→758 2019-05-07 04:00:00 45.0 27.7 19.0 35. ˓→758 2019-05-07 05:00:00 NaN 50.4 16.0 30. ˓→112 2019-05-07 06:00:00 NaN 61

to_datetime("2015-11-18 15:30:00+05:30") Out[2]: Timestamp('2015-11-18 10:00:00') In [3]: pd.Timestamp("2015-11-18 15:30:00+05:30") Out[3]: Timestamp('2015-11-18 15:30:00+0530', tz='pytz.FixedOffset(330)') timezone) In [4]: pd.to_datetime(["2015-11-18 15:30:00+05:30", "2015-11-18 16:30:00+06:30"]) Out[4]: DatetimeIndex(['2015-11-18 10:00:00', '2015-11-18 10:00:00'], dtype= ˓→'datetime64[ns]', freq=None) New [56]: pd.to_datetime("2015-11-18 15:30:00+05:30") Out[56]: Timestamp('2015-11-18 15:30:00+0530', tz='pytz.FixedOffset(330)') In [57]: pd.Timestamp("2015-11-18 15:30:00+05:30") \\\\\\\\\\\\\\\\\\\\\\\\\\\

˓→'datetime64[ns]', freq=None) The default is set at origin='unix', which defaults to 1970-01-01 00:00:00, which is commonly called ‘unix epoch’ or POSIX time. This was the previous default, so this is 0 0.384316 foo 2013-01-01 00:00:00 1 1.574159 foo 2013-01-01 00:00:01 2 1.588931 foo 2013-01-01 00:00:02 3 0.476720 foo 2013-01-01 00:00:03 4 0.473424 foo 2013-01-01 00:00:04 The default is to infer 0 0.384316 foo 2013-01-01 00:00:00 1 1.574159 foo 2013-01-01 00:00:01 2 1.588931 foo 2013-01-01 00:00:02 3 0.476720 foo 2013-01-01 00:00:03 4 0.473424 foo 2013-01-01 00:00:04 In [32]: df["A"].to_pickle("s1

˓→'datetime64[ns]', freq=None) The default is set at origin='unix', which defaults to 1970-01-01 00:00:00, which is commonly called ‘unix epoch’ or POSIX time. This was the previous default, so this is 0 0.384316 foo 2013-01-01 00:00:00 1 1.574159 foo 2013-01-01 00:00:01 2 1.588931 foo 2013-01-01 00:00:02 3 0.476720 foo 2013-01-01 00:00:03 4 0.473424 foo 2013-01-01 00:00:04 The default is to infer 0 0.384316 foo 2013-01-01 00:00:00 1 1.574159 foo 2013-01-01 00:00:01 2 1.588931 foo 2013-01-01 00:00:02 3 0.476720 foo 2013-01-01 00:00:03 4 0.473424 foo 2013-01-01 00:00:04 In [32]: df["A"].to_pickle("s1