Yea I don't see why this needs to be per table config. If the user wants to configure it per table, can't they just declare the data type on a per table basis, once we have separate types for timestamp w/ tz and w/o tz?
On Thu, Jun 1, 2017 at 4:14 PM, Michael Allman <mich...@videoamp.com> wrote: > I would suggest that making timestamp type behavior configurable and > persisted per-table could introduce some real confusion, e.g. in queries > involving tables with different timestamp type semantics. > > I suggest starting with the assumption that timestamp type behavior is a > per-session flag that can be set in a global `spark-defaults.conf` and > consider more granular levels of configuration as people identify solid use > cases. > > Cheers, > > Michael > > > > On May 30, 2017, at 7:41 AM, Zoltan Ivanfi <z...@cloudera.com> wrote: > > Hi, > > If I remember correctly, the TIMESTAMP type had UTC-normalized local time > semantics even before Spark 2, so I can understand that Spark considers it > to be the "established" behavior that must not be broken. Unfortunately, > this behavior does not provide interoperability with other SQL engines of > the Hadoop stack. > > Let me summarize the findings of this e-mail thread so far: > > - Timezone-agnostic TIMESTAMP semantics would be beneficial for > interoperability and SQL compliance. > - Spark can not make a breaking change. For backward-compatibility > with existing data, timestamp semantics should be user-configurable on a > per-table level. > > Before going into the specifics of a possible solution, do we all agree on > these points? > > Thanks, > > Zoltan > > On Sat, May 27, 2017 at 8:57 PM Imran Rashid <iras...@cloudera.com> wrote: > >> I had asked zoltan to bring this discussion to the dev list because I >> think it's a question that extends beyond a single jira (we can't figure >> out the semantics of timestamp in parquet if we don't k ow the overall goal >> of the timestamp type) and since its a design question the entire community >> should be involved. >> >> I think that a lot of the confusion comes because we're talking about >> different ways time zone affect behavior: (1) parsing and (2) behavior when >> changing time zones for processing data. >> >> It seems we agree that spark should eventually provide a timestamp type >> which does conform to the standard. The question is, how do we get >> there? Has spark already broken compliance so much that it's impossible to >> go back without breaking user behavior? Or perhaps spark already has >> inconsistent behavior / broken compatibility within the 2.x line, so its >> not unthinkable to have another breaking change? >> >> (Another part of the confusion is on me -- I believed the behavior change >> was in 2.2, but actually it looks like its in 2.0.1. That changes how we >> think about this in context of what goes into a 2.2 release. SPARK-18350 >> isn't the origin of the difference in behavior.) >> >> First: consider processing data that is already stored in tables, and >> then accessing it from machines in different time zones. The standard is >> clear that "timestamp" should be just like "timestamp without time zone": >> it does not represent one instant in time, rather it's always displayed the >> same, regardless of time zone. This was the behavior in spark 2.0.0 (and >> 1.6), for hive tables stored as text files, and for spark's json formats. >> >> Spark 2.0.1 changed the behavior of the json format (I believe >> with SPARK-16216), so that it behaves more like timestamp *with* time >> zone. It also makes csv behave the same (timestamp in csv was basically >> broken in 2.0.0). However it did *not* change the behavior of a hive >> textfile; it still behaves like "timestamp with*out* time zone". Here's >> some experiments I tried -- there are a bunch of files there for >> completeness, but mostly focus on the difference between >> query_output_2_0_0.txt vs. query_output_2_0_1.txt >> >> https://gist.github.com/squito/f348508ca7903ec2e1a64f4233e7aa70 >> >> Given that spark has changed this behavior post 2.0.0, is it still out of >> the question to change this behavior to bring it back in line with the sql >> standard for timestamp (without time zone) in the 2.x line? Or, as reynold >> proposes, is the only option at this point to add an off-by-default feature >> flag to get "timestamp without time zone" semantics? >> >> >> Second, there is the question of parsing strings into timestamp type. >> I'm far less knowledgeable about this, so I mostly just have questions: >> >> * does the standard dictate what the parsing behavior should be for >> timestamp (without time zone) when a time zone is present? >> >> * if it does and spark violates this standard is it worth trying to >> retain the *other* semantics of timestamp without time zone, even if we >> violate the parsing part? >> >> I did look at what postgres does for comparison: >> >> https://gist.github.com/squito/cb81a1bb07e8f67e9d27eaef44cc522c >> >> spark's timestamp certainly does not match postgres's timestamp for >> parsing, it seems closer to postgres's "timestamp with timezone" -- though >> I dunno if that is standard behavior at all. >> >> thanks, >> Imran >> >> On Fri, May 26, 2017 at 1:27 AM, Reynold Xin <r...@databricks.com> wrote: >> >>> That's just my point 4, isn't it? >>> >>> >>> On Fri, May 26, 2017 at 1:07 AM, Ofir Manor <ofir.ma...@equalum.io> >>> wrote: >>> >>>> Reynold, >>>> my point is that Spark should aim to follow the SQL standard instead of >>>> rolling its own type system. >>>> If I understand correctly, the existing implementation is similar to >>>> TIMESTAMP WITH LOCAL TIMEZONE data type in Oracle.. >>>> In addition, there are the standard TIMESTAMP and TIMESTAMP WITH >>>> TIMEZONE data types which are missing from Spark. >>>> So, it is better (for me) if instead of extending the existing types, >>>> Spark would just implement the additional well-defined types properly. >>>> Just trying to copy-paste CREATE TABLE between SQL engines should not >>>> be an exercise of flags and incompatibilities. >>>> >>>> Regarding the current behaviour, if I remember correctly I had to force >>>> our spark O/S user into UTC so Spark wont change my timestamps. >>>> >>>> Ofir Manor >>>> >>>> Co-Founder & CTO | Equalum >>>> >>>> Mobile: +972-54-7801286 | Email: ofir.ma...@equalum.io >>>> >>>> On Thu, May 25, 2017 at 1:33 PM, Reynold Xin <r...@databricks.com> >>>> wrote: >>>> >>>>> Zoltan, >>>>> >>>>> Thanks for raising this again, although I'm a bit confused since I've >>>>> communicated with you a few times on JIRA and on private emails to explain >>>>> that you have some misunderstanding of the timestamp type in Spark and >>>>> some >>>>> of your statements are wrong (e.g. the except text file part). Not sure >>>>> why >>>>> you didn't get any of those. >>>>> >>>>> >>>>> Here's another try: >>>>> >>>>> >>>>> 1. I think you guys misunderstood the semantics of timestamp in Spark >>>>> before session local timezone change. IIUC, Spark has always assumed >>>>> timestamps to be with timezone, since it parses timestamps with timezone >>>>> and does all the datetime conversions with timezone in mind (it doesn't >>>>> ignore timezone if a timestamp string has timezone specified). The session >>>>> local timezone change further pushes Spark to that direction, but the >>>>> semantics has been with timezone before that change. Just run Spark on >>>>> machines with different timezone and you will know what I'm talking about. >>>>> >>>>> 2. CSV/Text is not different. The data type has always been "with >>>>> timezone". If you put a timezone in the timestamp string, it parses the >>>>> timezone. >>>>> >>>>> 3. We can't change semantics now, because it'd break all existing >>>>> Spark apps. >>>>> >>>>> 4. We can however introduce a new timestamp without timezone type, and >>>>> have a config flag to specify which one (with tz or without tz) is the >>>>> default behavior. >>>>> >>>>> >>>>> >>>>> On Wed, May 24, 2017 at 5:46 PM, Zoltan Ivanfi <z...@cloudera.com> >>>>> wrote: >>>>> >>>>>> Hi, >>>>>> >>>>>> Sorry if you receive this mail twice, it seems that my first attempt >>>>>> did not make it to the list for some reason. >>>>>> >>>>>> I would like to start a discussion about SPARK-18350 >>>>>> <https://issues.apache.org/jira/browse/SPARK-18350> before it gets >>>>>> released because it seems to be going in a different direction than what >>>>>> other SQL engines of the Hadoop stack do. >>>>>> >>>>>> ANSI SQL defines the TIMESTAMP type (also known as TIMESTAMP WITHOUT >>>>>> TIME ZONE) to have timezone-agnostic semantics - basically a type that >>>>>> expresses readings from calendars and clocks and is unaffected by time >>>>>> zone. In the Hadoop stack, Impala has always worked like this and >>>>>> recently >>>>>> Presto also took steps >>>>>> <https://github.com/prestodb/presto/issues/7122> to become standards >>>>>> compliant. (Presto's design doc >>>>>> <https://docs.google.com/document/d/1UUDktZDx8fGwHZV4VyaEDQURorFbbg6ioeZ5KMHwoCk/edit> >>>>>> also contains a great summary of the different semantics.) Hive has a >>>>>> timezone-agnostic TIMESTAMP type as well (except for Parquet, a major >>>>>> source of incompatibility that is already being addressed >>>>>> <https://issues.apache.org/jira/browse/HIVE-12767>). A TIMESTAMP in >>>>>> SparkSQL, however, has UTC-normalized local time semantics (except for >>>>>> textfile), which is generally the semantics of the TIMESTAMP WITH TIME >>>>>> ZONE >>>>>> type. >>>>>> >>>>>> Given that timezone-agnostic TIMESTAMP semantics provide standards >>>>>> compliance and consistency with most SQL engines, I was wondering whether >>>>>> SparkSQL should also consider it in order to become ANSI SQL compliant >>>>>> and >>>>>> interoperable with other SQL engines of the Hadoop stack. Should SparkSQL >>>>>> adapt this semantics in the future, SPARK-18350 >>>>>> <https://issues.apache.org/jira/browse/SPARK-18350> may turn out to >>>>>> be a source of problems. Please correct me if I'm wrong, but this change >>>>>> seems to explicitly assign TIMESTAMP WITH TIME ZONE semantics to the >>>>>> TIMESTAMP type. I think SPARK-18350 would be a great feature for a >>>>>> separate >>>>>> TIMESTAMP WITH TIME ZONE type, but the plain unqualified TIMESTAMP type >>>>>> would be better becoming timezone-agnostic instead of gaining further >>>>>> timezone-aware capabilities. (Of course becoming timezone-agnostic would >>>>>> be >>>>>> a behavior change, so it must be optional and configurable by the user, >>>>>> as >>>>>> in Presto.) >>>>>> >>>>>> I would like to hear your opinions about this concern and about >>>>>> TIMESTAMP semantics in general. Does the community agree that a >>>>>> standards-compliant and interoperable TIMESTAMP type is desired? Do you >>>>>> perceive SPARK-18350 as a potential problem in achieving this or do I >>>>>> misunderstand the effects of this change? >>>>>> >>>>>> Thanks, >>>>>> >>>>>> Zoltan >>>>>> >>>>>> --- >>>>>> >>>>>> List of links in case in-line links do not work: >>>>>> >>>>>> - SPARK-18350: https://issues.apache.org/jira/browse/SPARK-18350 >>>>>> - Presto's change: https://github.com/prestodb/presto/issues/7122 >>>>>> - Presto's design doc: https://docs.google.com/document/d/ >>>>>> 1UUDktZDx8fGwHZV4VyaEDQURorFbbg6ioeZ5KMHwoCk/edit >>>>>> >>>>>> <https://docs.google.com/document/d/1UUDktZDx8fGwHZV4VyaEDQURorFbbg6ioeZ5KMHwoCk/edit> >>>>>> >>>>>> >>>>>> >>>>> >>>> >>> >> >
