Hi, I run the new code pull from master branch, and compare with another stream job which run hudi 0.4.5 on maven. Both running per 10 minutes. The roll-back worked. Top is 0.4.5, bottom is 0.4.6 [image: Screen Shot 2019-05-08 at 1.06.17 PM.png] [image: Screen Shot 2019-05-08 at 1.06.54 PM.png] And about log, I rewrite the log.trace to log.error to avoid log explode with trace. And There is nothing in variable:
> 19/05/07 06:30:36 ERROR HoodieSparkSQLWriter: insert failed with 1 errors : > 19/05/07 06:30:36 ERROR HoodieSparkSQLWriter: Printing out the top 100 > errors > ....spark log.... > 19/05/07 06:30:36 ERROR HoodieSparkSQLWriter: Global error : > .....spark log.... > 19/05/07 07:10:40 ERROR HoodieSparkSQLWriter: insert failed with 1 errors : > 19/05/07 07:10:40 ERROR HoodieSparkSQLWriter: Printing out the top 100 > errors Thanks, Jun On Sat, May 4, 2019 at 11:31 AM Vinoth Chandar <[email protected]> wrote: > No worries. This just landed on master, you can give it a shot. You ll also > end up picking up interval tree based filtering for global index, which > will speed things along a lot. Fyi > > Have a good holiday! > > Thanks > Vinoth > > On Fri, May 3, 2019 at 7:19 PM Jun Zhu <[email protected]> wrote: > > > Hi team, > > i will try that, thank you so much, sorry for late reply, just have a > > holiday in china😅. > > Thanks > > Jun > > > > On Wed, May 1, 2019 at 7:08 PM Vinoth Chandar <[email protected]> wrote: > > > > > Hi Jun, > > > > > > I was able to track that the HoodieSparkSQLWriter (common path for > > > streaming sink and batch datasource) ends up calling > > > DataSourceUtils.createHoodieClient, which creates the client as follows > > > > > > return new HoodieWriteClient<>(jssc, writeConfig); > > > > > > There is a third parameter that denotes whether the writer needs to > > > rollback inflights. For e.g, DeltaStreamer invokes > > > > > > HoodieWriteClient client = new HoodieWriteClient<>(jssc, hoodieCfg, > > true); > > > > > > While I trace down why we had this difference, could you try changing > > this > > > one line here, and add third "true" argument and give it a shot. > > > > > > > > > https://github.com/apache/incubator-hudi/blob/b34a204a527a156406908686e54484a0c3d8a3d7/hoodie-spark/src/main/java/com/uber/hoodie/DataSourceUtils.java#L148 > > > > > > > > > Thanks > > > Vinoth > > > > > > On Tue, Apr 30, 2019 at 11:16 PM [email protected] < > [email protected]> > > > wrote: > > > > > > > > > > > Hi Jun, > > > > You had mentioned that you are seeing the log message > > > > "insert failed with 1 errors" > > > > Did you see any exception stack traces before this message. You can > > also > > > > take a look at spark UI to see if stdout/stderr of failed tasks (if > > > > present). > > > > Also, it looks like if you also enable "trace" level logging, you > would > > > > see exceptions getting logged at the end. So, enabling "trace" level > > > > logging is another way to debug what is happening. > > > > '''log.error(s"$operation failed with ${errorCount} errors :"); > > > > if (log.isTraceEnabled) { > > > > log.trace("Printing out the top 100 errors") ....... > > > > ''' > > > > Balaji.V > > > > > > > > On Tuesday, April 30, 2019, 8:17:57 AM PDT, Vinoth Chandar < > > > > [email protected]> wrote: > > > > > > > > Hi Jun, > > > > > > > > Basically you are saying streaming path leaves some inflights > behind.. > > > let > > > > me see if I can reproduce it. If you have a simple test case, please > > > share > > > > > > > > Thanks > > > > Vinoth > > > > > > > > On Tue, Apr 30, 2019 at 1:04 AM Jun Zhu <[email protected]> > > > > wrote: > > > > > > > > > Hi Vinoth, > > > > > In spark streaming log I find "2019-04-30 03:26:11 ERROR > > > > > HoodieSparkSQLWriter:182 - insert failed with 1 errors :"(no > continue > > > > error > > > > > logs) , during which commit end with inflight and not cleaned. > > > > > Just for feedback, we can dedup data correctly in batch way. Should > > add > > > > > more logic for exception handling if using spark stream I think. > > > > > Regards, > > > > > Jun > > > > > > > > > > > > > > > On Tue, Apr 30, 2019 at 2:46 AM Vinoth Chandar <[email protected]> > > > > wrote: > > > > > > > > > > > Another option to try would be setting the > > > > > > spark.sql.hive.convertMetastoreParquet=false, if you are querying > > via > > > > the > > > > > > Hive table registered by Hudi. > > > > > > > > > > > > On Sat, Apr 27, 2019 at 7:02 PM Jun Zhu > <[email protected] > > > > > > > > > wrote: > > > > > > > > > > > > > Thanks for explanation vinoth, code was same list in > > > > > > > https://github.com/apache/incubator-hudi/issues/639, with > > setting > > > > > table > > > > > > > format to `.option(DataSourceWriteOptions.STORAGE_TYPE_OPT_KEY, > > > > > > > DataSourceWriteOptions.MOR_STORAGE_TYPE_OPT_VAL)`. > > > > > > > And the result data was stored on aws s3. > > > > > > > I will try more on > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > `spark.sparkContext.hadoopConfiguration.setClass("mapreduce.input.pathFilter.class", > > > > > > > classOf[com.uber.hoodie.hadoop.HoodieROTablePathFilter], > > > > > > > classOf[org.apache.hadoop.fs.PathFilter]);` from the > phenomenon, > > > the > > > > > > > config did not take effects maybe. > > > > > > > > > > > > > > On Sat, Apr 27, 2019 at 12:09 AM Vinoth Chandar < > > [email protected] > > > > > > > > > > wrote: > > > > > > > > > > > > > > > Hi, > > > > > > > > > > > > > > > > >>The duplicates was found in inflight commit parquet files. > > > > > Wondering > > > > > > if > > > > > > > > this was expected? > > > > > > > > Spark shell should not even be reading in-flight parquet > files. > > > Can > > > > > you > > > > > > > > double check if the spark access is properly configured? > > > > > > > > http://hudi.apache.org/querying_data.html#spark > > > > > > > > > > > > > > > > Inflight should be rolled back at the start of the next > > > > commit/delta > > > > > > > > commit.. Not sure why there are so many inflight delta > commits. > > > > > > > > If you can give a reproducible case, happy to debug it more.. > > > > > > > > > > > > > > > > Only complete instants are archived.. So yes, inflight is not > > > > > > archived.. > > > > > > > > > > > > > > > > Hope that helps > > > > > > > > > > > > > > > > Thanks > > > > > > > > Vinoth > > > > > > > > > > > > > > > > On Fri, Apr 26, 2019 at 2:09 AM Jun Zhu > > > <[email protected] > > > > > > > > > > > > > wrote: > > > > > > > > > > > > > > > > > Hi Vinoth, > > > > > > > > > Some continue question about this thread. > > > > > > > > > Here is what I found after running a few days: > > > > > > > > > in .hoodie folder, due to retain policy maybe, there is an > > > > > obviously > > > > > > > > > line(list in the end of email). Before it the cleaned > commit > > > was > > > > > > > > archived, > > > > > > > > > find duplication when query inflight commit correspond > > > partition > > > > by > > > > > > > > > spark-shell. After the line, all behave normal, global > dedup > > > > works. > > > > > > > > > The duplicates was found in inflight commit parquet files. > > > > > Wondering > > > > > > if > > > > > > > > > this was expected? > > > > > > > > > Q: > > > > > > > > > 1. The inflight commit should be turned to roll back > status > > in > > > > > next > > > > > > > > > writes. Is it normal that so many inflight commit did not > > make > > > > it? > > > > > Or > > > > > > > > can I > > > > > > > > > config a retain policy to turn inflight to roll_back in > > another > > > > > way? > > > > > > > > > 2. Did commit retain policy do not archive inflight commit? > > > > > > > > > > > > > > > > > > 2019-04-23 20:23:47 378 > > > > 20190423122339.deltacommit.inflight > > > > > > > > > > > > > > > > > > 2019-04-23 20:43:53 378 > > > > 20190423124343.deltacommit.inflight > > > > > > > > > > > > > > > > > > 2019-04-23 22:14:04 378 > > > > 20190423141354.deltacommit.inflight > > > > > > > > > > > > > > > > > > 2019-04-23 22:44:09 378 > > > > 20190423144400.deltacommit.inflight > > > > > > > > > > > > > > > > > > 2019-04-23 22:54:18 378 > > > > 20190423145408.deltacommit.inflight > > > > > > > > > > > > > > > > > > 2019-04-23 23:04:09 378 > > > > 20190423150400.deltacommit.inflight > > > > > > > > > > > > > > > > > > 2019-04-23 23:24:30 378 > > > > 20190423152421.deltacommit.inflight > > > > > > > > > > > > > > > > > > *2019-04-23 23:44:34 378 > > > > > 20190423154424.deltacommit.inflight* > > > > > > > > > > > > > > > > > > *2019-04-24 00:15:46 2991 20190423161431.clean* > > > > > > > > > > > > > > > > > > 2019-04-24 00:15:21 870536 20190423161431.deltacommit > > > > > > > > > > > > > > > > > > 2019-04-24 00:25:19 2991 20190423162424.clean > > > > > > > > > > > > > > > > > > 2019-04-24 00:25:09 875825 20190423162424.deltacommit > > > > > > > > > > > > > > > > > > 2019-04-24 00:35:26 2991 20190423163429.clean > > > > > > > > > > > > > > > > > > 2019-04-24 00:35:18 881925 20190423163429.deltacommit > > > > > > > > > > > > > > > > > > 2019-04-24 00:46:14 2991 20190423164428.clean > > > > > > > > > > > > > > > > > > 2019-04-24 00:45:44 888025 20190423164428.deltacommit > > > > > > > > > > > > > > > > > > Thanks, > > > > > > > > > Jun > > > > > > > > > > > > > > > > > > On 2019/04/18 14:29:23, Vinoth Chandar <[email protected]> > > > wrote: > > > > > > > > > > Hi Jun,> > > > > > > > > > > > > > > > > > > > > Responses below.> > > > > > > > > > > > > > > > > > > > > >>1. Some file inflight may never reach commit?> > > > > > > > > > > yes. the next attempt at writing will first issue a > > rollback > > > to > > > > > > clean > > > > > > > > up> > > > > > > > > > > such partial/leftover files first, before it begins the > new > > > > > > commit.> > > > > > > > > > > > > > > > > > > > > >>2. In occasion which inflight and parquet file > generated > > by > > > > > > > inflight > > > > > > > > > still> > > > > > > > > > > exists, the global dedup will not dedup based on such > kind > > > > > file?> > > > > > > > > > > even if not rolled back, we check for the inflight > parquet > > > > files > > > > > > > > against> > > > > > > > > > > the committed timeline, which it wont be a part of. So > > should > > > > be > > > > > > > safe.> > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >>3. In occasion which inflight and parquet file > generated > > by > > > > > > > inflight > > > > > > > > > still> > > > > > > > > > > exists, the correct query result will be decided by read > > > > > config(I> > > > > > > > > > > mean mapreduce.input.pathFilter.class> > > > > > > > > > > in sparksql)> > > > > > > > > > > yes. the filtering should work as well. its the same > > > technique > > > > > used > > > > > > > by> > > > > > > > > > > writer.> > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >>4. Is there any way we can use> > > > > > > > > > > > > > > > > > > > > >> > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > spark.sparkContext.hadoopConfiguration.setClass("mapreduce.input.pathFilter.class",> > > > > > > > > > > > > > > > > > > > > > classOf[com.uber.hoodie.hadoop.HoodieROTablePathFilter],> > > > > > > > > > > > classOf[org.apache.hadoop.fs.PathFilter]);> > > > > > > > > > > > > > > > > > > > > in spark thrift server when start it?> > > > > > > > > > > > > > > > > > > > > I am not familiar with the Spark thrift server myself. > Any > > > > > pointers > > > > > > > > where > > > > > > > > > I> > > > > > > > > > > can learn more?> > > > > > > > > > > Two suggestions :> > > > > > > > > > > - You can check if you can add this to the Hadoop > > > configuration > > > > > xml > > > > > > > > > files> > > > > > > > > > > and see if it gets picked up by Spark?> > > > > > > > > > > - Alternatively, you can set the spark config mentioned > > here> > > > > > > > > > > http://hudi.apache.org/querying_data.html#spark-rt-view > > > (works > > > > > for > > > > > > > ro > > > > > > > > > view> > > > > > > > > > > also), which should be doable I am assuming at this > thrift > > > > > server> > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > Thanks> > > > > > > > > > > Vinoth> > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > On Wed, Apr 17, 2019 at 12:08 AM Jun Zhu > > > > > <[email protected] > > > > > > > > > > > > > > > > wrote:> > > > > > > > > > > > > > > > > > > > > > Hi,> > > > > > > > > > > > Link: > > https://github.com/apache/incubator-hudi/issues/639> > > > > > > > > > > > Sorry , failed open > > > > > > > > > https://lists.apache.org/[email protected]> > > > > > > > > > > > .> > > > > > > > > > > > I have some follow up questions for issue 639:> > > > > > > > > > > >> > > > > > > > > > > > So, the sequence of events is . We write parquet files > > and > > > > then > > > > > > > upon> > > > > > > > > > > > > successful writing of all attempted parquet files, we > > > > > actually > > > > > > > make > > > > > > > > > the> > > > > > > > > > > > > commit as completed. (i.e not inflight anymore). So > > this > > > is > > > > > > > normal. > > > > > > > > > This> > > > > > > > > > > > is> > > > > > > > > > > > > done to prevent queries from reading partially > written > > > > > parquet > > > > > > > > > files..> > > > > > > > > > > > >> > > > > > > > > > > >> > > > > > > > > > > > Does that mean:> > > > > > > > > > > > 1. Some file inflight may never reach commit?> > > > > > > > > > > > 2. In occasion which inflight and parquet file > generated > > by > > > > > > > inflight > > > > > > > > > still> > > > > > > > > > > > exists, the global dedup will not dedup based on such > > kind > > > > > > file?> > > > > > > > > > > > 3. In occasion which inflight and parquet file > generated > > by > > > > > > > inflight > > > > > > > > > still> > > > > > > > > > > > exists, the correct query result will be decided by > read > > > > > > config(I> > > > > > > > > > > > mean mapreduce.input.pathFilter.class> > > > > > > > > > > > in sparksql)> > > > > > > > > > > > 4. Is there any way we can use> > > > > > > > > > > >> > > > > > > > > > > > >> > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > spark.sparkContext.hadoopConfiguration.setClass("mapreduce.input.pathFilter.class",> > > > > > > > > > > > > > > > > > > > > > > > classOf[com.uber.hoodie.hadoop.HoodieROTablePathFilter],> > > > > > > > > > > > > classOf[org.apache.hadoop.fs.PathFilter]);> > > > > > > > > > > >> > > > > > > > > > > > in spark thrift server when start it?> > > > > > > > > > > >> > > > > > > > > > > > Best,> > > > > > > > > > > > --> > > > > > > > > > > > [image: vshapesaqua11553186012.gif] < > https://vungle.com/ > > > > > > > > *Jun > > > > > > > > Zhu*> > > > > > > > > > > > Sr. Engineer I, Data> > > > > > > > > > > > ＋86 18565739171> > > > > > > > > > > >> > > > > > > > > > > > [image: in1552694272.png] < > > > > > > https://www.linkedin.com/company/vungle > > > > > > > >> > > > > > > > > > > > [image:> > > > > > > > > > > > fb1552694203.png] <https://facebook.com/vungle> > > > > [image:> > > > > > > > > > > > tw1552694330.png] <https://twitter.com/vungle> > > > [image:> > > > > > > > > > > > ig1552694392.png] <https://www.instagram.com/vungle>> > > > > > > > > > > > Units 3801, 3804, 38F, C Block, Beijing Yintai Center, > > > > Beijing, > > > > > > > > China> > > > > > > > > > > >> > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > -- > > > > > > > [image: vshapesaqua11553186012.gif] <https://vungle.com/> > *Jun > > > Zhu* > > > > > > > Sr. Engineer I, Data > > > > > > > ＋86 18565739171 > > > > > > > > > > > > > > [image: in1552694272.png] < > > https://www.linkedin.com/company/vungle > > > > > > > > > > > [image: > > > > > > > fb1552694203.png] <https://facebook.com/vungle> [image: > > > > > > > tw1552694330.png] <https://twitter.com/vungle> [image: > > > > > > > ig1552694392.png] <https://www.instagram.com/vungle> > > > > > > > Units 3801, 3804, 38F, C Block, Beijing Yintai Center, Beijing, > > > China > > > > > > > > > > > > > > > > > > > > > > > > > > > > -- > > > > > [image: vshapesaqua11553186012.gif] <https://vungle.com/> *Jun > Zhu* > > > > > Sr. Engineer I, Data > > > > > ＋86 18565739171 > > > > > > > > > > [image: in1552694272.png] <https://www.linkedin.com/company/vungle > > > > > > > [image: > > > > > fb1552694203.png] <https://facebook.com/vungle> [image: > > > > > tw1552694330.png] <https://twitter.com/vungle> [image: > > > > > ig1552694392.png] <https://www.instagram.com/vungle> > > > > > Units 3801, 3804, 38F, C Block, Beijing Yintai Center, Beijing, > China > > > > > > > > > > > > > > -- > > [image: vshapesaqua11553186012.gif] <https://vungle.com/> *Jun Zhu* > > Sr. Engineer I, Data > > ＋86 18565739171 > > > > [image: in1552694272.png] <https://www.linkedin.com/company/vungle> > > [image: > > fb1552694203.png] <https://facebook.com/vungle> [image: > > tw1552694330.png] <https://twitter.com/vungle> [image: > > ig1552694392.png] <https://www.instagram.com/vungle> > > Units 3801, 3804, 38F, C Block, Beijing Yintai Center, Beijing, China > > > -- [image: vshapesaqua11553186012.gif] <https://vungle.com/> *Jun Zhu* Sr. Engineer I, Data ＋86 18565739171 [image: in1552694272.png] <https://www.linkedin.com/company/vungle> [image: fb1552694203.png] <https://facebook.com/vungle> [image: tw1552694330.png] <https://twitter.com/vungle> [image: ig1552694392.png] <https://www.instagram.com/vungle> Units 3801, 3804, 38F, C Block, Beijing Yintai Center, Beijing, China
