Thank you, Stephen and Reynold.
To Reynold.
The way I see the following is a little different.
> CHAR is an undocumented data type without clearly defined semantics.
Let me describe in Apache Spark User's View point.
Apache Spark started to claim `HiveContext` (and `hql/hiveql` function) at
Apache Spark 1.x without much documentation. In addition, there still
exists an effort which is trying to keep it in 3.0.0 age.
https://issues.apache.org/jira/browse/SPARK-31088
Add back HiveContext and createExternalTable
Historically, we tried to make many SQL-based customer migrate their
workloads from Apache Hive into Apache Spark through `HiveContext`.
Although Apache Spark didn't have a good document about the inconsistent
behavior among its data sources, Apache Hive has been providing its
documentation and many customers rely the behavior.
-
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Types
At that time, frequently in on-prem Hadoop clusters by well-known vendors,
many existing huge tables were created by Apache Hive, not Apache Spark.
And, Apache Spark is used for boosting SQL performance with its *caching*.
This was true because Apache Spark was added into the Hadoop-vendor
products later than Apache Hive.
Until the turning point at Apache Spark 2.0, we tried to catch up more
features to be consistent at least with Hive tables in Apache Hive and
Apache Spark because two SQL engines share the same tables.
For the following, technically, while Apache Hive doesn't changed its
existing behavior in this part, Apache Spark evolves inevitably by moving
away from the original Apache Spark old behaviors one-by-one.
> the value is already fucked up
The following is the change log.
- When we switched the default value of `convertMetastoreParquet`.
(at Apache Spark 1.2)
- When we switched the default value of `convertMetastoreOrc` (at
Apache Spark 2.4)
- When we switched `CREATE TABLE` itself. (Change `TEXT` table to
`PARQUET` table at Apache Spark 3.0)
To sum up, this has been a well-known issue in the community and among the
customers.
Bests,
Dongjoon.
On Mon, Mar 16, 2020 at 5:24 PM Stephen Coy <s...@infomedia.com.au> wrote:
> Hi there,
>
> I’m kind of new around here, but I have had experience with all of all the
> so called “big iron” databases such as Oracle, IBM DB2 and Microsoft SQL
> Server as well as Postgresql.
>
> They all support the notion of “ANSI padding” for CHAR columns - which
> means that such columns are always space padded, and they default to having
> this enabled (for ANSI compliance).
>
> MySQL also supports it, but it defaults to leaving it disabled for
> historical reasons not unlike what we have here.
>
> In my opinion we should push toward standards compliance where possible
> and then document where it cannot work.
>
> If users don’t like the padding on CHAR columns then they should change to
> VARCHAR - I believe that was its purpose in the first place, and it does
> not dictate any sort of “padding".
>
> I can see why you might “ban” the use of CHAR columns where they cannot be
> consistently supported, but VARCHAR is a different animal and I would
> expect it to work consistently everywhere.
>
>
> Cheers,
>
> Steve C
>
> On 17 Mar 2020, at 10:01 am, Dongjoon Hyun <dongjoon.h...@gmail.com>
> wrote:
>
> Hi, Reynold.
> (And +Michael Armbrust)
>
> If you think so, do you think it's okay that we change the return value
> silently? Then, I'm wondering why we reverted `TRIM` functions then?
>
> > Are we sure "not padding" is "incorrect"?
>
> Bests,
> Dongjoon.
>
>
> On Sun, Mar 15, 2020 at 11:15 PM Gourav Sengupta <
> gourav.sengu...@gmail.com> wrote:
>
>> Hi,
>>
>> 100% agree with Reynold.
>>
>>
>> Regards,
>> Gourav Sengupta
>>
>> On Mon, Mar 16, 2020 at 3:31 AM Reynold Xin <r...@databricks.com> wrote:
>>
>>> Are we sure "not padding" is "incorrect"?
>>>
>>> I don't know whether ANSI SQL actually requires padding, but plenty of
>>> databases don't actually pad.
>>>
>>> https://docs.snowflake.net/manuals/sql-reference/data-types-text.html
>>> <https://aus01.safelinks.protection.outlook.com/?url=https:%2F%2Fdocs.snowflake.net%2Fmanuals%2Fsql-reference%2Fdata-types-text.html%23:~:text%3DCHAR%2520%252C%2520CHARACTER%2C(1)%2520is%2520the%2520default.%26text%3DSnowflake%2520currently%2520deviates%2520from%2520common%2Cspace-padded%2520at%2520the%2520end.&data=02%7C01%7Cscoy%40infomedia.com.au%7C5346c8d2675342008b5708d7c9fdff54%7C45d5407150f849caa59f9457123dc71c%7C0%7C0%7C637199965062044368&sdata=BvnZTTPTZBAi8oGWIvJk2fC%2FYSgdvq%2BAxtOj0nVzufk%3D&reserved=0>
>>> :
>>> "Snowflake currently deviates from common CHAR semantics in that strings
>>> shorter than the maximum length are not space-padded at the end."
>>>
>>> MySQL:
>>> https://stackoverflow.com/questions/53528645/why-char-dont-have-padding-in-mysql
>>> <https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F53528645%2Fwhy-char-dont-have-padding-in-mysql&data=02%7C01%7Cscoy%40infomedia.com.au%7C5346c8d2675342008b5708d7c9fdff54%7C45d5407150f849caa59f9457123dc71c%7C0%7C0%7C637199965062044368&sdata=3OGLht%2Fa28GcKhAGwJPXIR%2BMODiIwXGVuNuResZqwXM%3D&reserved=0>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> On Sun, Mar 15, 2020 at 7:02 PM, Dongjoon Hyun <dongjoon.h...@gmail.com>
>>> wrote:
>>>
>>>> Hi, Reynold.
>>>>
>>>> Please see the following for the context.
>>>>
>>>> https://issues.apache.org/jira/browse/SPARK-31136
>>>> <https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fissues.apache.org%2Fjira%2Fbrowse%2FSPARK-31136&data=02%7C01%7Cscoy%40infomedia.com.au%7C5346c8d2675342008b5708d7c9fdff54%7C45d5407150f849caa59f9457123dc71c%7C0%7C0%7C637199965062054364&sdata=pWQ9QhfVY4Uzyc8oIJ1QONQ0zOBAQ2DGSemyBj%2BvFeM%3D&reserved=0>
>>>> "Revert SPARK-30098 Use default datasource as provider for CREATE TABLE
>>>> syntax"
>>>>
>>>> I raised the above issue according to the new rubric, and the banning
>>>> was the proposed alternative to reduce the potential issue.
>>>>
>>>> Please give us your opinion since it's still PR.
>>>>
>>>> Bests,
>>>> Dongjoon.
>>>>
>>>> On Sat, Mar 14, 2020 at 17:54 Reynold Xin <r...@databricks.com> wrote:
>>>>
>>>>> I don’t understand this change. Wouldn’t this “ban” confuse the hell
>>>>> out of both new and old users?
>>>>>
>>>>> For old users, their old code that was working for char(3) would now
>>>>> stop working.
>>>>>
>>>>> For new users, depending on whether the underlying metastore char(3)
>>>>> is either supported but different from ansi Sql (which is not that big of
>>>>> a
>>>>> deal if we explain it) or not supported.
>>>>>
>>>>> On Sat, Mar 14, 2020 at 3:51 PM Dongjoon Hyun <dongjoon.h...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hi, All.
>>>>>>
>>>>>> Apache Spark has been suffered from a known consistency issue on
>>>>>> `CHAR` type behavior among its usages and configurations. However, the
>>>>>> evolution direction has been gradually moving forward to be consistent
>>>>>> inside Apache Spark because we don't have `CHAR` offically. The following
>>>>>> is the summary.
>>>>>>
>>>>>> With 1.6.x ~ 2.3.x, `STORED PARQUET` has the following different
>>>>>> result.
>>>>>> (`spark.sql.hive.convertMetastoreParquet=false` provides a fallback
>>>>>> to Hive behavior.)
>>>>>>
>>>>>> spark-sql> CREATE TABLE t1(a CHAR(3));
>>>>>> spark-sql> CREATE TABLE t2(a CHAR(3)) STORED AS ORC;
>>>>>> spark-sql> CREATE TABLE t3(a CHAR(3)) STORED AS PARQUET;
>>>>>>
>>>>>> spark-sql> INSERT INTO TABLE t1 SELECT 'a ';
>>>>>> spark-sql> INSERT INTO TABLE t2 SELECT 'a ';
>>>>>> spark-sql> INSERT INTO TABLE t3 SELECT 'a ';
>>>>>>
>>>>>> spark-sql> SELECT a, length(a) FROM t1;
>>>>>> a 3
>>>>>> spark-sql> SELECT a, length(a) FROM t2;
>>>>>> a 3
>>>>>> spark-sql> SELECT a, length(a) FROM t3;
>>>>>> a 2
>>>>>>
>>>>>> Since 2.4.0, `STORED AS ORC` became consistent.
>>>>>> (`spark.sql.hive.convertMetastoreOrc=false` provides a fallback to
>>>>>> Hive behavior.)
>>>>>>
>>>>>> spark-sql> SELECT a, length(a) FROM t1;
>>>>>> a 3
>>>>>> spark-sql> SELECT a, length(a) FROM t2;
>>>>>> a 2
>>>>>> spark-sql> SELECT a, length(a) FROM t3;
>>>>>> a 2
>>>>>>
>>>>>> Since 3.0.0-preview2, `CREATE TABLE` (without `STORED AS` clause)
>>>>>> became consistent.
>>>>>> (`spark.sql.legacy.createHiveTableByDefault.enabled=true` provides a
>>>>>> fallback to Hive behavior.)
>>>>>>
>>>>>> spark-sql> SELECT a, length(a) FROM t1;
>>>>>> a 2
>>>>>> spark-sql> SELECT a, length(a) FROM t2;
>>>>>> a 2
>>>>>> spark-sql> SELECT a, length(a) FROM t3;
>>>>>> a 2
>>>>>>
>>>>>> In addition, in 3.0.0, SPARK-31147 aims to ban `CHAR/VARCHAR` type in
>>>>>> the following syntax to be safe.
>>>>>>
>>>>>> CREATE TABLE t(a CHAR(3));
>>>>>> https://github.com/apache/spark/pull/27902
>>>>>> <https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fapache%2Fspark%2Fpull%2F27902&data=02%7C01%7Cscoy%40infomedia.com.au%7C5346c8d2675342008b5708d7c9fdff54%7C45d5407150f849caa59f9457123dc71c%7C0%7C0%7C637199965062054364&sdata=lhwUP5TcTtaO%2BLUTmx%2BPTjT0ASXPrQ7oKLL0N6EG0Ug%3D&reserved=0>
>>>>>>
>>>>>> This email is sent out to inform you based on the new policy we voted.
>>>>>> The recommendation is always using Apache Spark's native type
>>>>>> `String`.
>>>>>>
>>>>>> Bests,
>>>>>> Dongjoon.
>>>>>>
>>>>>> References:
>>>>>> 1. "CHAR implementation?", 2017/09/15
>>>>>>
>>>>>> https://lists.apache.org/thread.html/96b004331d9762e356053b5c8c97e953e398e489d15e1b49e775702f%40%3Cdev.spark.apache.org%3E
>>>>>> <https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Flists.apache.org%2Fthread.html%2F96b004331d9762e356053b5c8c97e953e398e489d15e1b49e775702f%2540%253Cdev.spark.apache.org%253E&data=02%7C01%7Cscoy%40infomedia.com.au%7C5346c8d2675342008b5708d7c9fdff54%7C45d5407150f849caa59f9457123dc71c%7C0%7C0%7C637199965062064358&sdata=6hkno6zKTkcIrO%2FJo4hTYihsYvNynMuWcxhzL0fZR68%3D&reserved=0>
>>>>>> 2. "FYI: SPARK-30098 Use default datasource as provider for CREATE
>>>>>> TABLE syntax", 2019/12/06
>>>>>>
>>>>>> https://lists.apache.org/thread.html/493f88c10169680191791f9f6962fd16cd0ffa3b06726e92ed04cbe1%40%3Cdev.spark.apache.org%3E
>>>>>> <https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Flists.apache.org%2Fthread.html%2F493f88c10169680191791f9f6962fd16cd0ffa3b06726e92ed04cbe1%2540%253Cdev.spark.apache.org%253E&data=02%7C01%7Cscoy%40infomedia.com.au%7C5346c8d2675342008b5708d7c9fdff54%7C45d5407150f849caa59f9457123dc71c%7C0%7C0%7C637199965062064358&sdata=QJnEU3mvUJff53Gw8F%2FAbxzd%2F8ZA1hhuoQwicX4ZXyI%3D&reserved=0>
>>>>>>
>>>>>
>>>
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