For sure. There's another reason I feel char is not that important and it's more important to be internally consistent (e.g. all data sources support it with the same behavior, vs one data sources do one behavior and another do the other). char was created at a time when cpu was slow and storage was expensive, and being able to pack things nicely at fixed length was highly useful. The fact that it was padded was initially done for performance, not for the padding itself. A lot has changed since char was invented, and with modern technologies (columnar, dictionary encoding, etc) there is little reason to use a char data type for anything. As a matter of fact, Spark internally converts char type to string to work with.
I see two solutions really. 1. We require padding, and ban all uses of char when it is not properly padded. This would ban all the native data sources, which are the primarily way people are using Spark. This leaves only char support for tables going through Hive serdes, which are slow to begin with. It is basically Dongjoon and Wenchen's suggestion. This turns char support into a compatibility feature only for some Hive tables that cannot be converted into Spark native data sources. This has confusing end-user behavior because depending on whether that Hive table is converted into Spark native data sources, we might or might not support char type. An extension to the above is to introduce padding for char type across the board, and make char type a first class data type. There are a lot of work to introduce another data type, especially for one that has virtually no usage ( https://trends.google.com/trends/explore?geo=US&q=hive%20char,hive%20string ) and its usage will likely continue to decline in the future (just reason from first principle based on the reason char was introduced in the first place). Now I'm assuming it's a lot of work to do char properly. But if it is not the case (e.g. just a simple rule to insert padding at planning time), then maybe it's worth doing it this way. I'm totally OK with this too. What I'd oppose is to just ban char for the native data sources, and do not have a plan to address this problem systematically. 2. Just forget about padding, like what Snowflake and MySQL have done. Document that char(x) is just an alias for string. And then move on. Almost no work needs to be done... On Mon, Mar 16, 2020 at 5:54 PM, Dongjoon Hyun < dongjoon.h...@gmail.com > wrote: > > Thank you for sharing and confirming. > > > We had better consider all heterogeneous customers in the world. And, I > also have experiences with the non-negligible cases in on-prem. > > > > Bests, > Dongjoon. > > > On Mon, Mar 16, 2020 at 5:42 PM Reynold Xin < rxin@ databricks. com ( > r...@databricks.com ) > wrote: > > >> −User >> >> >> >> char barely showed up (honestly negligible). I was comparing select vs >> select. >> >> >> >> >> >> On Mon, Mar 16, 2020 at 5:40 PM, Dongjoon Hyun < dongjoon. hyun@ gmail. com >> ( dongjoon.h...@gmail.com ) > wrote: >> >>> Ur, are you comparing the number of SELECT statement with TRIM and CREATE >>> statements with `CHAR`? >>> >>> > I looked up our usage logs (sorry I can't share this publicly) and trim >>> has at least four orders of magnitude higher usage than char. >>> >>> We need to discuss more about what to do. This thread is what I expected >>> exactly. :) >>> >>> > BTW I'm not opposing us sticking to SQL standard (I'm in general for >>> it). I was merely pointing out that if we deviate away from SQL standard >>> in any way we are considered "wrong" or "incorrect". That argument itself >>> is flawed when plenty of other popular database systems also deviate away >>> from the standard on this specific behavior. >>> >>> >>> Bests, >>> Dongjoon. >>> >>> On Mon, Mar 16, 2020 at 5:35 PM Reynold Xin < rxin@ databricks. com ( >>> r...@databricks.com ) > wrote: >>> >>> >>>> BTW I'm not opposing us sticking to SQL standard (I'm in general for it). >>>> I was merely pointing out that if we deviate away from SQL standard in any >>>> way we are considered "wrong" or "incorrect". That argument itself is >>>> flawed when plenty of other popular database systems also deviate away >>>> from the standard on this specific behavior. >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> On Mon, Mar 16, 2020 at 5:29 PM, Reynold Xin < rxin@ databricks. com ( >>>> r...@databricks.com ) > wrote: >>>> >>>>> I looked up our usage logs (sorry I can't share this publicly) and trim >>>>> has at least four orders of magnitude higher usage than char. >>>>> >>>>> >>>>> >>>>> >>>>> On Mon, Mar 16, 2020 at 5:27 PM, Dongjoon Hyun < dongjoon. hyun@ gmail. >>>>> com >>>>> ( dongjoon.h...@gmail.com ) > wrote: >>>>> >>>>>> 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 ( >>>>>> 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 >>>>>> ( 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 < scoy@ infomedia. com. au ( >>>>>> 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. hyun@ gmail. >>>>>>>> com ( >>>>>>>> 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. sengupta@ >>>>>>>> gmail. >>>>>>>> com ( gourav.sengu...@gmail.com ) > wrote: >>>>>>>> >>>>>>>> >>>>>>>>> Hi, >>>>>>>>> >>>>>>>>> >>>>>>>>> 100% agree with Reynold. >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> Regards, >>>>>>>>> Gourav Sengupta >>>>>>>>> >>>>>>>>> >>>>>>>>> On Mon, Mar 16, 2020 at 3:31 AM Reynold Xin < rxin@ databricks. com ( >>>>>>>>> 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. hyun@ >>>>>>>>>> gmail. com >>>>>>>>>> ( 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 < rxin@ databricks. com ( >>>>>>>>>>> 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. hyun@ >>>>>>>>>>>> gmail. com >>>>>>>>>>>> ( 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 >>>>>>>>>>>>> ) >>>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> This email contains confidential information of and is the copyright of >>>>>>> Infomedia. 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