I attempted to install Hive yesterday. The experience was similar to other attempts at installing Hive: it took a few hours and at the end of the process, I didn't have a working setup. The latest stable release would not run. I never discovered the cause, but similar StackOverflow questions suggest it might be a Java incompatibility issue. Since I didn't want to downgrade or install an additional Java version, I attempted to use the latest alpha as well. This appears to have worked, although I couldn't figure out how to get it to use the metastore_db from Spark.
After turning my attention back to Spark, I determined the issue. After much troubleshooting, I discovered that if I performed a COUNT(*) using the same JOINs, the problem query worked. I removed all the columns from the SELECT statement and added them one by one until I found the culprit. It's a text field on one of the tables. When the query SELECTs this column, or attempts to filter on it, the query hangs and never completes. If I remove all explicit references to this column, the query works fine. Since I need this column in the results, I went back to the ETL and extracted the values to a dimension table. I replaced the text column in the source table with an integer ID column and the query worked without issue. On the topic of Hive, does anyone have any detailed resources for how to set up Hive from scratch? Aside from the official site, since those instructions didn't work for me. I'm starting to feel uneasy about building my process around Spark. There really shouldn't be any instances where I ask Spark to run legal ANSI SQL code and it just does nothing. In the past 4 days I've run into 2 of these instances, and the solution was more voodoo and magic than examining errors/logs and fixing code. I feel that I should have a contingency plan in place for when I run into an issue with Spark that can't be resolved. Thanks everyone. On Sat, Aug 12, 2023 at 2:18 PM Mich Talebzadeh <mich.talebza...@gmail.com> wrote: > OK you would not have known unless you went through the process so to > speak. > > Let us do something revolutionary here 😁 > > Install hive and its metastore. You already have hadoop anyway > > https://cwiki.apache.org/confluence/display/hive/adminmanual+installation > > hive metastore > > > https://data-flair.training/blogs/apache-hive-metastore/#:~:text=What%20is%20Hive%20Metastore%3F,by%20using%20metastore%20service%20API > . > > choose one of these > > derby hive mssql mysql oracle postgres > > Mine is an oracle. postgres is good as well. > > HTH > > Mich Talebzadeh, > Solutions Architect/Engineering Lead > London > United Kingdom > > > view my Linkedin profile > <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> > > > https://en.everybodywiki.com/Mich_Talebzadeh > > > > *Disclaimer:* Use it at your own risk. Any and all responsibility for any > loss, damage or destruction of data or any other property which may arise > from relying on this email's technical content is explicitly disclaimed. > The author will in no case be liable for any monetary damages arising from > such loss, damage or destruction. > > > > > On Sat, 12 Aug 2023 at 18:31, Patrick Tucci <patrick.tu...@gmail.com> > wrote: > >> Yes, on premise. >> >> Unfortunately after installing Delta Lake and re-writing all tables as >> Delta tables, the issue persists. >> >> On Sat, Aug 12, 2023 at 11:34 AM Mich Talebzadeh < >> mich.talebza...@gmail.com> wrote: >> >>> ok sure. >>> >>> Is this Delta Lake going to be on-premise? >>> >>> Mich Talebzadeh, >>> Solutions Architect/Engineering Lead >>> London >>> United Kingdom >>> >>> >>> view my Linkedin profile >>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> >>> >>> >>> https://en.everybodywiki.com/Mich_Talebzadeh >>> >>> >>> >>> *Disclaimer:* Use it at your own risk. Any and all responsibility for >>> any loss, damage or destruction of data or any other property which may >>> arise from relying on this email's technical content is explicitly >>> disclaimed. The author will in no case be liable for any monetary damages >>> arising from such loss, damage or destruction. >>> >>> >>> >>> >>> On Sat, 12 Aug 2023 at 12:03, Patrick Tucci <patrick.tu...@gmail.com> >>> wrote: >>> >>>> Hi Mich, >>>> >>>> Thanks for the feedback. My original intention after reading your >>>> response was to stick to Hive for managing tables. Unfortunately, I'm >>>> running into another case of SQL scripts hanging. Since all tables are >>>> already Parquet, I'm out of troubleshooting options. I'm going to migrate >>>> to Delta Lake and see if that solves the issue. >>>> >>>> Thanks again for your feedback. >>>> >>>> Patrick >>>> >>>> On Fri, Aug 11, 2023 at 10:09 AM Mich Talebzadeh < >>>> mich.talebza...@gmail.com> wrote: >>>> >>>>> Hi Patrick, >>>>> >>>>> There is not anything wrong with Hive On-premise it is the best data >>>>> warehouse there is >>>>> >>>>> Hive handles both ORC and Parquet formal well. They are both columnar >>>>> implementations of relational model. What you are seeing is the Spark API >>>>> to Hive which prefers Parquet. I found out a few years ago. >>>>> >>>>> From your point of view I suggest you stick to parquet format with >>>>> Hive specific to Spark. As far as I know you don't have a fully >>>>> independent >>>>> Hive DB as yet. >>>>> >>>>> Anyway stick to Hive for now as you never know what issues you may be >>>>> facing using moving to Delta Lake. >>>>> >>>>> You can also use compression >>>>> >>>>> STORED AS PARQUET >>>>> TBLPROPERTIES ("parquet.compression"="SNAPPY") >>>>> >>>>> ALSO >>>>> >>>>> ANALYZE TABLE <TABLE_NAME> COMPUTE STATISTICS FOR COLUMNS >>>>> >>>>> HTH >>>>> >>>>> Mich Talebzadeh, >>>>> Solutions Architect/Engineering Lead >>>>> London >>>>> United Kingdom >>>>> >>>>> >>>>> view my Linkedin profile >>>>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> >>>>> >>>>> >>>>> https://en.everybodywiki.com/Mich_Talebzadeh >>>>> >>>>> >>>>> >>>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for >>>>> any loss, damage or destruction of data or any other property which may >>>>> arise from relying on this email's technical content is explicitly >>>>> disclaimed. The author will in no case be liable for any monetary damages >>>>> arising from such loss, damage or destruction. >>>>> >>>>> >>>>> >>>>> >>>>> On Fri, 11 Aug 2023 at 11:26, Patrick Tucci <patrick.tu...@gmail.com> >>>>> wrote: >>>>> >>>>>> Thanks for the reply Stephen and Mich. >>>>>> >>>>>> Stephen, you're right, it feels like Spark is waiting for something, >>>>>> but I'm not sure what. I'm the only user on the cluster and there are >>>>>> plenty of resources (+60 cores, +250GB RAM). I even tried restarting >>>>>> Hadoop, Spark and the host servers to make sure nothing was lingering in >>>>>> the background. >>>>>> >>>>>> Mich, thank you so much, your suggestion worked. Storing the tables >>>>>> as Parquet solves the issue. >>>>>> >>>>>> Interestingly, I found that only the MemberEnrollment table needs to >>>>>> be Parquet. The ID field in MemberEnrollment is an int calculated during >>>>>> load by a ROW_NUMBER() function. Further testing found that if I hard >>>>>> code >>>>>> a 0 as MemberEnrollment.ID instead of using the ROW_NUMBER() function, >>>>>> the >>>>>> query works without issue even if both tables are ORC. >>>>>> >>>>>> Should I infer from this issue that the Hive components prefer >>>>>> Parquet over ORC? Furthermore, should I consider using a different table >>>>>> storage framework, like Delta Lake, instead of the Hive components? Given >>>>>> this issue and other issues I've had with Hive, I'm starting to think a >>>>>> different solution might be more robust and stable. The main condition is >>>>>> that my application operates solely through Thrift server, so I need to >>>>>> be >>>>>> able to connect to Spark through Thrift server and have it write tables >>>>>> using Delta Lake instead of Hive. From this StackOverflow question, it >>>>>> looks like this is possible: >>>>>> https://stackoverflow.com/questions/69862388/how-to-run-spark-sql-thrift-server-in-local-mode-and-connect-to-delta-using-jdbc >>>>>> >>>>>> Thanks again to everyone who replied for their help. >>>>>> >>>>>> Patrick >>>>>> >>>>>> >>>>>> On Fri, Aug 11, 2023 at 2:14 AM Mich Talebzadeh < >>>>>> mich.talebza...@gmail.com> wrote: >>>>>> >>>>>>> Steve may have a valid point. You raised an issue with concurrent >>>>>>> writes before, if I recall correctly. Since this limitation may be due >>>>>>> to >>>>>>> Hive metastore. By default Spark uses Apache Derby for its database >>>>>>> persistence. *However it is limited to only one Spark session at >>>>>>> any time for the purposes of metadata storage.* That may be the >>>>>>> cause here as well. Does this happen if the underlying tables are >>>>>>> created >>>>>>> as PARQUET as opposed to ORC? >>>>>>> >>>>>>> HTH >>>>>>> >>>>>>> Mich Talebzadeh, >>>>>>> Solutions Architect/Engineering Lead >>>>>>> London >>>>>>> United Kingdom >>>>>>> >>>>>>> >>>>>>> view my Linkedin profile >>>>>>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> >>>>>>> >>>>>>> >>>>>>> https://en.everybodywiki.com/Mich_Talebzadeh >>>>>>> >>>>>>> >>>>>>> >>>>>>> *Disclaimer:* Use it at your own risk. Any and all responsibility >>>>>>> for any loss, damage or destruction of data or any other property which >>>>>>> may >>>>>>> arise from relying on this email's technical content is explicitly >>>>>>> disclaimed. The author will in no case be liable for any monetary >>>>>>> damages >>>>>>> arising from such loss, damage or destruction. >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> On Fri, 11 Aug 2023 at 01:33, Stephen Coy >>>>>>> <s...@infomedia.com.au.invalid> wrote: >>>>>>> >>>>>>>> Hi Patrick, >>>>>>>> >>>>>>>> When this has happened to me in the past (admittedly via >>>>>>>> spark-submit) it has been because another job was still running and had >>>>>>>> already claimed some of the resources (cores and memory). >>>>>>>> >>>>>>>> I think this can also happen if your configuration tries to claim >>>>>>>> resources that will never be available. >>>>>>>> >>>>>>>> Cheers, >>>>>>>> >>>>>>>> SteveC >>>>>>>> >>>>>>>> >>>>>>>> On 11 Aug 2023, at 3:36 am, Patrick Tucci <patrick.tu...@gmail.com> >>>>>>>> wrote: >>>>>>>> >>>>>>>> Hello, >>>>>>>> >>>>>>>> I'm attempting to run a query on Spark 3.4.0 through the Spark >>>>>>>> ThriftServer. The cluster has 64 cores, 250GB RAM, and operates in >>>>>>>> standalone mode using HDFS for storage. >>>>>>>> >>>>>>>> The query is as follows: >>>>>>>> >>>>>>>> SELECT ME.*, MB.BenefitID >>>>>>>> FROM MemberEnrollment ME >>>>>>>> JOIN MemberBenefits MB >>>>>>>> ON ME.ID <http://me.id/> = MB.EnrollmentID >>>>>>>> WHERE MB.BenefitID = 5 >>>>>>>> LIMIT 10 >>>>>>>> >>>>>>>> The tables are defined as follows: >>>>>>>> >>>>>>>> -- Contains about 3M rows >>>>>>>> CREATE TABLE MemberEnrollment >>>>>>>> ( >>>>>>>> ID INT >>>>>>>> , MemberID VARCHAR(50) >>>>>>>> , StartDate DATE >>>>>>>> , EndDate DATE >>>>>>>> -- Other columns, but these are the most important >>>>>>>> ) STORED AS ORC; >>>>>>>> >>>>>>>> -- Contains about 25m rows >>>>>>>> CREATE TABLE MemberBenefits >>>>>>>> ( >>>>>>>> EnrollmentID INT >>>>>>>> , BenefitID INT >>>>>>>> ) STORED AS ORC; >>>>>>>> >>>>>>>> When I execute the query, it runs a single broadcast exchange >>>>>>>> stage, which completes after a few seconds. Then everything just >>>>>>>> hangs. The >>>>>>>> JDBC/ODBC tab in the UI shows the query state as COMPILED, but no >>>>>>>> stages or >>>>>>>> tasks are executing or pending: >>>>>>>> >>>>>>>> <image.png> >>>>>>>> >>>>>>>> I've let the query run for as long as 30 minutes with no additional >>>>>>>> stages, progress, or errors. I'm not sure where to start >>>>>>>> troubleshooting. >>>>>>>> >>>>>>>> Thanks for your help, >>>>>>>> >>>>>>>> Patrick >>>>>>>> >>>>>>>> >>>>>>>> This email contains confidential information of and is the >>>>>>>> copyright of Infomedia. It must not be forwarded, amended or disclosed >>>>>>>> without consent of the sender. If you received this message by mistake, >>>>>>>> please advise the sender and delete all copies. Security of >>>>>>>> transmission on >>>>>>>> the internet cannot be guaranteed, could be infected, intercepted, or >>>>>>>> corrupted and you should ensure you have suitable antivirus protection >>>>>>>> in >>>>>>>> place. By sending us your or any third party personal details, you >>>>>>>> consent >>>>>>>> to (or confirm you have obtained consent from such third parties) to >>>>>>>> Infomedia’s privacy policy. >>>>>>>> http://www.infomedia.com.au/privacy-policy/ >>>>>>>> >>>>>>>
