Stephen, For the 3rd topic, you mentioned that "If the boolean expressions are monotonous (have no NOT), then the UNKNOWN value can be the same as FALSE ", as UNKNOWN means it could be TRUE as well, does it a proper way to handle it just as FALSE?
Aljoscha, I agree with you, Table can only be transformed from Tuple/Case Class DataSet now, and Tuple/Case Class does not allow null field value, so read files from data source to Row DataSet is necessary for NULL value handling. -----Original Message----- From: Aljoscha Krettek [mailto:aljos...@apache.org] Sent: Friday, November 27, 2015 6:41 PM To: dev@flink.apache.org Subject: Re: The null in Flink Oh, this is probably the Jira for what I mentioned: https://issues.apache.org/jira/browse/FLINK-2988 > On 27 Nov 2015, at 11:02, Aljoscha Krettek <aljos...@apache.org> wrote: > > Hi, > just some information. The Table API code generator already has preliminary > support for generating code that is NULL-aware. So for example if you have > expressions like 1 + NULL the result would also be null. > > I think one of the missing pieces is a way to get data that contains null > values into the system. For example, right now the expected way to read csv > files is via tuples and they don’t support null values. I think we need a way > to directly read CSV files into a Row DataSet (or Table). > > Cheers, > Aljoscha >> On 26 Nov 2015, at 12:31, Stephan Ewen <se...@apache.org> wrote: >> >> Hi! >> >> Thanks for the good discussion! Here are some thoughts from my side: >> >> 1) >> I would agree with Chengxiang that it helps to have as much NULL >> handling in the table API as possible, since most SQL constructs will >> be permitted there are well. >> >> 2) >> A question that I have is whether we want to actually follow the SQL >> standard exactly. There is a lot of criticism on NULL in the SQL >> standard, and there have been many good proposals for more meaningful >> semantics (for example differentiate between the meanings "value >> missing", "value unknown", "value not applicable", etc). >> >> Going with the SQL way is easiest and makes SQL addition on top of >> the table API much easier. Also, there is only one type of NULL, >> meaning that null-values can be encoded efficiently in bitmaps. >> Further more, the fact that the Table API users have the power of a >> programming language at hand (rather than the limited set of SQL >> operators), they should be able to easily define their own constants >> for special meanings like "value not applicable" or so. >> >> Just curious if anyone has experience with some of the other >> null-sematic proposals that have been around. >> >> 3) >> One comment concerning the three-value-logic for boolean expressions: >> >> A while back, I worked on a SQL engine, and we were able to not >> implement three-value logic with trick. If I recall correctly, it was like >> this: >> >> If the boolean expressions are monotonous (have no NOT), then the >> UNKNOWN value can be the same as FALSE. So the query planner had to >> rewrite all expression trees to have no NOT, which means pushing the >> NOT down into the leaf comparison operations (for example push NOT into == >> to become !=). >> These leaf comparison operators needed to be NULL aware to return >> FALSE on comparisons with NULL. >> >> >> Greetings, >> Stephan >> >> >> On Thu, Nov 26, 2015 at 6:41 AM, Li, Chengxiang >> <chengxiang...@intel.com> >> wrote: >> >>> Thanks, Timo. >>> We may put the NULL related function support to SQL API, but for >>> Scalar expression and Boolean expression, it already been supported >>> in Table API, without NULL value handling support, query with Scalar >>> expression and Boolean expression would fail while encounter NULL value. >>> >>> Thanks >>> Chengxiang >>> >>> -----Original Message----- >>> From: Timo Walther [mailto:twal...@apache.org] >>> Sent: Wednesday, November 25, 2015 7:33 PM >>> To: dev@flink.apache.org >>> Subject: Re: The null in Flink >>> >>> Hi Chengxiang, >>> >>> I totally agree that the Table API should fully support NULL values. >>> The Table API is a logical API and therefore we should be as close >>> to ANSI SQL as possible. Rows need to be nullable in the near future. >>> >>> 2. i, ii, iii and iv sound reasonable. But v, vi and vii sound to >>> much like SQL magic. I think all other SQL magic (DBMS specific >>> corner cases) should be handled by the SQL API on top of the Table API. >>> >>> Regards, >>> Timo >>> >>> >>> On 25.11.2015 11:31, Li, Chengxiang wrote: >>>> Hi >>>> In this mail list, there are some discussions about null value >>>> handling >>> in Flink, and I saw several related JIRAs as well(like FLINK-2203, >>> FLINK-2210), but unfortunately, got reverted due to immature design, >>> and no further action since then. I would like to pick this topic up >>> here, as it's quite an important part of data analysis and many features >>> depend on it. >>> Hopefully, through a plenary discussion, we can generate an >>> acceptable solution and move forward. Stephan has explained very >>> clearly about how and why Flink handle "Null values in the >>> Programming Language APIs", so I mainly talk about the second part >>> of "Null values in the high-level >>> (logical) APIs ". >>>> >>>> 1. Why should Flink support Null values handling in Table API? >>>> i. Data source may miss column value in many cases, if no Null >>> values handling in Table API, user need to write an extra ETL to >>> handle missing values manually. >>>> ii. Some Table API operators generate Null values on their own, >>> like Outer Join/Cube/Rollup/Grouping Set, and so on. Null values >>> handling in Table API is the prerequisite of these features. >>>> >>>> 2. The semantic of Null value handling in Table API. >>>> Fortunately, there are already mature DBMS standards we can follow >>>> for >>> Null value handling, I list several semantic of Null value handling here. >>> To be noted that, this may not cover all the cases, and the >>> semantics may vary in different DBMSs, so it should totally open to discuss. >>>> I, NULL compare. In ascending order, NULL is smaller than any >>> other value, and NULL == NULL return false. >>>> ii. NULL exists in GroupBy Key, all NULL values are grouped as >>>> a >>> single group. >>>> iii. NULL exists in Aggregate columns, ignore NULL in >>>> aggregation >>> function. >>>> iv. NULL exists in both side Join key, refer to #i, >>> NULL == NULL return false, no output for NULL Join key. >>>> v. NULL in Scalar expression, expression within >>> NULL(eg. 1 + NULL) return NULL. >>>> vi. NULL in Boolean expression, add an extra result: >>> UNKNOWN, more semantic for Boolean expression in reference #1. >>>> vii. More related function support, like COALESCE, >>>> NVL, >>> NANVL, and so on. >>>> >>>> 3. NULL value storage in Table API. >>>> Just set null to Row field value. To mark NULL value in serialized >>> binary record data, normally it use extra flag for each field to >>> mark whether its value is NULL, which would change the data layout >>> of Row object. So any logic that access serialized Row data directly >>> should updated to sync with new data layout, for example, many >>> methods in RowComparator. >>>> >>>> Reference: >>>> 1. Nulls: Nothing to worry about: >>> http://www.oracle.com/technetwork/issue-archive/2005/05-jul/o45sql-0 >>> 97727.html >>> . >>>> 2. Null related functions: >>>> https://oracle-base.com/articles/misc/null-related-functions >>>> >>>> -----Original Message----- >>>> From: ewenstep...@gmail.com [mailto:ewenstep...@gmail.com] On >>>> Behalf Of Stephan Ewen >>>> Sent: Thursday, June 18, 2015 8:43 AM >>>> To: dev@flink.apache.org >>>> Subject: Re: The null in Flink >>>> >>>> Hi! >>>> >>>> I think we actually have two discussions here, both of them important: >>>> >>>> -------------------------------------------------------------- >>>> 1) Null values in the Programming Language APIs >>>> -------------------------------------------------------------- >>>> >>>> Fields in composite types may simply be null pointers. >>>> >>>> In object types: >>>> - primitives members are naturally non-nullable >>>> - all other members are nullable >>>> >>>> => If you want to avoid the overhead of nullability, go with >>>> primitive >>> types. >>>> >>>> In Tuples, and derives types (Scala case classes): >>>> - Fields are non-nullable. >>>> >>>> => The reason here is that we initially decided to keep tuples as a >>>> very >>> fast data type. Because tuples cannot hold primitives in Java/Scala, >>> we would not have a way to make fast non-nullable fields. The >>> performance of nullable fields affects the key-operations, especially on >>> normalized keys. >>>> We can work around that with some effort, but have not one it so far. >>>> >>>> => In Scala, the Option types is a natural way of elegantly working >>> around that. >>>> >>>> >>>> -------------------------------------------------------------- >>>> 2) Null values in the high-level (logial) APIs >>>> -------------------------------------------------------------- >>>> >>>> This is mainly what Ted was referring to, if I understood him correctly. >>>> >>>> Here, we need to figure out what form of semantical null values in >>>> the >>> Table API and later, in SQL. >>>> >>>> Besides deciding what semantics to follow here in the logical APIs, >>>> we >>> need to decide what these values confert to/from when switching >>> between logical/physical APIs. >>>> >>>> >>>> >>>> >>>> >>>> >>>> On Mon, Jun 15, 2015 at 10:07 AM, Ted Dunning >>>> <ted.dunn...@gmail.com> >>> wrote: >>>> >>>>> On Mon, Jun 15, 2015 at 8:45 AM, Maximilian Michels >>>>> <m...@apache.org> >>>>> wrote: >>>>> >>>>>> Just to give an idea what null values could cause in Flink: >>>>> DataSet.count() >>>>>> returns the number of elements of all values in a Dataset (null >>>>>> or >>>>>> not) while #834 would ignore null values and aggregate the >>>>>> DataSet without >>>>> them. >>>>> Compare R's na.action. >>>>> >>>>> http://www.ats.ucla.edu/stat/r/faq/missing.htm >>>>> >>> >>> >
