I agree that Spark can define the semantic of CHAR(x) differently than the SQL standard (no padding), and ask the data sources to follow it. But the problem is, some data sources may not be able to skip padding, like the Hive serde table.
On the other hand, it's easier to require padding for CHAR(x). Even if some data sources don't support padding, Spark can simply do the padding at the read time, using the `rpad` function. However, if CHAR(x) is rarely used, maybe we should just ban it and only keep it for Hive compatibility, to save our work. VARCHAR(x) is a different story as it's a commonly used data type in databases. It has a length limitation which can help the backed engine to make better decisions when dealing with it. Currently Spark just treats VARCHAR(x) as string type, which works fine in most cases, but different data sources may have different behaviors during writing. For example, pgsql JDBC data source fails the writing if length limitation is hit, Hive serde table simply truncate the chars exceeding length limitation, Parquet data source writes whatever string it gets. We can just document that, the underlying data source may or may not enforce the length limitation of VARCHAR(x). Or we can make VARCHAR(x) a first-class data type, which requires a lot more changes (type coercion, type cast, etc.). Before we make a final decision, I think it's reasonable to ban CHAR/VARCHAR in non-Hive-serde tables in 3.0, so that we don't introduce silent result changing here. Any ideas are welcome! Thanks, Wenchen On Tue, Mar 17, 2020 at 11:29 AM Stephen Coy <s...@infomedia.com.au.invalid> wrote: > I don’t think I can recall any usages of type CHAR in any situation. > > Really, it’s only use (on any traditional SQL database) would be when you > *want* a fixed width character column that has been right padded with > spaces. > > > On 17 Mar 2020, at 12:13 pm, Reynold Xin <r...@databricks.com> wrote: > > 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 <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.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 <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 <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.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 >>>>>>> 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> >>>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>> >>>>>>>> This email contains confidential information of and is the >>>>>>>> copyright of Infomedia. It must not be forwarded, amended or disclosed >>>>>>>> without consent of the sender. If you received this message by mistake, >>>>>>>> please advise the sender and delete all copies. 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