Hi Philip, You're welcome. Just a small correction: Hive's SORT BY should be DataSet.groupBy(key).sortGroup(key) in Flink. This ensures sorted grouped records within the reducer that follows. No need to set the parallelism to 1.
Best, Max On Mon, Oct 19, 2015 at 1:28 PM, Philip Lee <philjj...@gmail.com> wrote: > Thanks, Max! > > I really appreciate about the way you answered. > As you remarked on ORDER BY, in order to user this function on flink > I have to set parallelism to 1 in SortPartiton for a total ordering in on > place? > > I just want to make sure about SORT BY as well. > As a reminder, this function on Hive is just sorting ordering within a > reducer. > If I apply [groupBy] function before [sortPartiton], > it will be same way of SORT BY? It does not need to set paralleism to 1, > right? > > Best Regards, > Philip > > > On Mon, Oct 19, 2015 at 1:17 PM, Philip Lee <philjj...@gmail.com> wrote: >> >> Dear all, >> >> Actually, last night I sent the email to flink committer about proper >> translation from Hive to Flink. >> I got the answer from flink mailing-list people about it. >> I am pretty sure these two mails will really help you. >> >> I will take a note by following this contents on our google docs. >> This note will also help big-benchmark people later. >> >> Regards, >> Philip >> >> >> >> ---------- Forwarded message ---------- >> From: Maximilian Michels <m...@apache.org> >> Date: Mon, Oct 19, 2015 at 1:01 PM >> Subject: Re: Hi, Flink people, a question about translation from HIVE >> Query to Flink fucntioin by using Table API >> To: "user@flink.apache.org" <user@flink.apache.org> >> >> >> Hi Philip, >> >> Thank you for your questions. I think you have mapped the HIVE >> functions to the Flink ones correctly. Just a remark on the ORDER BY. >> You wrote that it produces a total order of all the records. In this >> case, you'd have do a SortPartition operation with parallelism set to >> 1. This is necessary because we need to have all records in one place >> to perform a sort on them. >> >> Considering your reduce question: There is no fundamental >> advantage/disadvantage of using GroupReduce over Reduce. It depends on >> your use case which one is more convenient or efficient. For the >> regular reduce, you just get two elements and produce one. You can't >> easily keep state between the reduces other than in the value itself. >> The GroupReduce, on the other hand, may produce none, one, or multiple >> elements per grouping and keep state in between emitting values. Thus, >> GroupReduce is a more powerful operator and can be seen as a superset >> of the Reduce operator. I would advise you to use the one you find >> easiest to use. >> >> Best regards, >> Max >> >> On Sun, Oct 18, 2015 at 9:16 PM, Philip Lee <philjj...@gmail.com> wrote: >> > Hi, Flink people, a question about translation from HIVE Query to Flink >> > fucntioin by using Table API. In sum up, I am working on some benchmark >> > for >> > flink >> > >> > I am Philip Lee majoring in Computer Science in Master Degree of TUB. , >> > I >> > work on translation from Hive Query of Benchmark to Flink codes. >> > >> > As I stuided it, I have a few of questions. >> > >> > First of all, if there are people who do no know Hive functions, let me >> > briefly explan. >> > >> > ORDER BY: it just guarntees total order in the output. >> > SORT BY: it only guarntess ordering of the rows within a reducer. >> > GROUP BY: this is just groupBy function in SQL. >> > DISTRIBUTE BY: all rows with the same distributed by columns will go to >> > the >> > same reducer. >> > CLUSTER BY: this is just consisted of Distribute By the same column + >> > Sort >> > By the same column. >> > >> > I just want to check that the flink functions I use are equal to Hive >> > one. >> > < Hive SQL Query = Flink functions > >> > >> > ORDER BY = sortPartition(,) >> > SORT BY= groupBy(`col).sortPartition(,) >> > GROUP BY: this is just groupBy function. >> > DISTRIBUTE BY = groupBy(`col) >> > CLUSTER BY = groupBy(`col).sortPartition(,) >> > >> > I do not see much difference between groupBy and distributed by if I >> > apply >> > it to flink function. >> > If this is hadoop version, we could say mapper is distribute by on >> > hadoop. >> > However, I am not much sure what could be DISTRIBUTE BY on flink. I >> > tried to >> > guess groupBy on Flink could be the function which is to distribute the >> > rows >> > by the specified key. >> > >> > Please feel free to correct what I suggested. >> > >> > >> > Secondly, I just want to make sure the difference between reduce >> > function >> > and reduceGroup. I guess there must be a trade-off between two >> > functinos. I >> > know reduceGroup is invoked with an Iterator, but which case is more >> > proper >> > and benifical to use reduceGroup function rather than reduce function? >> > >> > Best Regards, >> > Philip >> > >> > -- >> > >> > ========================================================== >> > >> > Hae Joon Lee >> > >> > >> > Now, in Germany, >> > >> > M.S. Candidate, Interested in Distributed System, Iterative Processing >> > >> > Dept. of Computer Science, Informatik in German, TUB >> > >> > Technical University of Berlin >> > >> > >> > In Korea, >> > >> > M.S. Candidate, Computer Architecture Laboratory >> > >> > Dept. of Computer Science, KAIST >> > >> > >> > Rm# 4414 CS Dept. KAIST >> > >> > 373-1 Guseong-dong, Yuseong-gu, Daejon, South Korea (305-701) >> > >> > >> > Mobile) 49) 015-251-448-278 in Germany, no cellular in Korea >> > >> > ========================================================== >> >> >> >> -- >> >> ========================================================== >> >> Hae Joon Lee >> >> >> Now, in Germany, >> >> M.S. Candidate, Interested in Distributed System, Iterative Processing >> >> Dept. of Computer Science, Informatik in German, TUB >> >> Technical University of Berlin >> >> >> In Korea, >> >> M.S. Candidate, Computer Architecture Laboratory >> >> Dept. of Computer Science, KAIST >> >> >> Rm# 4414 CS Dept. KAIST >> >> 373-1 Guseong-dong, Yuseong-gu, Daejon, South Korea (305-701) >> >> >> Mobile) 49) 015-251-448-278 in Germany, no cellular in Korea >> >> ========================================================== > > > > > -- > > ========================================================== > > Hae Joon Lee > > > Now, in Germany, > > M.S. Candidate, Interested in Distributed System, Iterative Processing > > Dept. of Computer Science, Informatik in German, TUB > > Technical University of Berlin > > > In Korea, > > M.S. Candidate, Computer Architecture Laboratory > > Dept. of Computer Science, KAIST > > > Rm# 4414 CS Dept. KAIST > > 373-1 Guseong-dong, Yuseong-gu, Daejon, South Korea (305-701) > > > Mobile) 49) 015-251-448-278 in Germany, no cellular in Korea > > ==========================================================
