Hi Michael, To answer this I think we should distinguish between the long-term fix and the short-term fix.
If understand the replies correctly, everyone agrees that the desired long-term fix is to have two separate SQL types (TIMESTAMP [WITH|WITHOUT] TIME ZONE). Because of having separate types, mixing them as you described can not happen (unless a new feature intentionally allows that). Of course, conversions are still needed, but there are many examples from different database systems that we can follow. Since having two separate types is a huge effort, for a short term solution I would suggest allowing the single existing TIMESTAMP type to allow both semantics, configurable per table. The implementation of timezone-agnostic semantics could be similar to Hive. In Hive, just like in Spark, a timestamp is UTC-normalized internally but it is shown as a local time when it gets displayed. To achieve timezone-agnostic behavior, Hive still uses UTC-based timestamps in memory and adjusts on-disk data to/from this internal representation if needed. When the on-disk data is UTC-normalized as well, it matches this internal representation, so the on-disk value directly corresponds to the UTC instant of the in-memory representation. When the on-disk data is supposed to have timezone-agnostic semantics, the on-disk value is made to match the local time value of the in-memory timestamp, so the value that ultimately gets displayed to the user has timezone-agnostic semantics (although the corresponding UTC value will be different depending on the local time zone). So instead of implementing a separate in-memory representation for timezone-agnostic timestamps, the desired on-disk semantics are simulated on top of the existing representation. Timestamps are adjusted during reading/writing as needed. Implementing this workaround takes a lot less effort and simplifies some scenarios as well. For example, the situation that you described (union of two queries returning timestamps of different semantics) does not have to be handled explicitly, since the in-memory representation are the same, including their interpretation. Semantics only matter when reading/writing timestamps from/to disk. A disadvantage of this workaround is that it is not perfect. In most time zones, there is an hour skipped by the DST change every year. Timezone-agnostic timestamps from that single hour can not be emulated this way, because they are invalid in the local timezone, so there is no UTC instant that would ultimately get displayed as the desired timestamp. But that only affects ~0.01% of all timestamps and adapting this workaround would allow interoperability with 99.99% of timezone-agnostic timestamps written by Impala and Hive instead of the current situation in which 0% of these timestamps are interpreted correctly. Please let me know if some parts of my description were unclear and I will gladly elaborate on them. Thanks, Zoltan On Fri, Jun 2, 2017 at 9:41 PM Michael Allman <mich...@videoamp.com> wrote: > Hi Zoltan, > > I don't fully understand your proposal for table-specific timestamp type > semantics. I think it will be helpful to everyone in this conversation if > you can identify the expected behavior for a few concrete scenarios. > > Suppose we have a Hive metastore table hivelogs with a column named ts > with the hive timestamp type as described here: > https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Types#LanguageManualTypes-timestamp. > This table was created by Hive and is usually accessed through Hive or > Presto. > > Suppose again we have a Hive metastore table sparklogs with a column named > ts with the Spark SQL timestamp type as described here: > http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.types.TimestampType$. > This table was created by Spark SQL and is usually accessed through Spark > SQL. > > Let's say Spark SQL sets and reads a table property called > timestamp_interp to determine timestamp type semantics for that table. > Consider a dataframe df defined by sql("SELECT sts as ts FROM sparklogs > UNION ALL SELECT hts as ts FROM hivelogs"). Suppose the timestamp_interp > table property is absent from hivelogs. For each possible value of > timestamp_interp set on the table sparklogs, > > 1. does df successfully pass analysis (i.e. is it a valid query)? > 2. if it's a valid dataframe, what is the type of the ts column? > 3. if it's a valid dataframe, what are the semantics of the type of the ts > column? > > Suppose further that Spark SQL sets the timestamp_interp on hivelogs. Can > you answer the same three questions for each combination of > timestamp_interp on hivelogs and sparklogs? > > Thank you. > > Michael > > > On Jun 2, 2017, at 8:33 AM, Zoltan Ivanfi <z...@cloudera.com> wrote: > > Hi, > > We would like to solve the problem of interoperability of existing data, > and that is the main use case for having table-level control. Spark should > be able to read timestamps written by Impala or Hive and at the same time > read back its own data. These have different semantics, so having a single > flag is not enough. > > Two separate types will solve this problem indeed, but only once every > component involved supports them. Unfortunately, adding these separate SQL > types is a larger effort that is only feasible in the long term and we > would like to provide a short-term solution for interoperability in the > meantime. > > Br, > > Zoltan > > On Fri, Jun 2, 2017 at 1:32 AM Reynold Xin <r...@databricks.com> wrote: > >> 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 >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >> >
