Hi, I am confining myself to Hive tables. As I stated it before I have not tried it in Spark. So I stand corrected.
Let us try this simple test in Hive -- Create table hive> *create table testme(col1 int);*OK --insert a row hive> *insert into testme values(1);* Loading data to table test.testme OK -- Add a new column to testme hive> *alter table testme add columns (new_col varchar(30));*OK Time taken: 0.055 seconds -- Expect one row here hive> *select * from testme;*OK 1 NULL -- *Add a new row including values for new_col. This should work*hive> *insert into testme values(1,'London');*Loading data to table test.testme OK hive> *select * from testme;*OK 1 NULL 1 London Time taken: 0.074 seconds, Fetched: 2 row(s) -- Now update the new column hive> update testme set col2 = 'NY'; FAILED: SemanticException [Error 10297]: Attempt to do update or delete on table test.testme that does not use an AcidOutputFormat or is not bucketed So this is Hive. You can add new rows including values for the new column but cannot update the null values. Will this work for you? HTH Dr Mich Talebzadeh LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* http://talebzadehmich.wordpress.com On 10 April 2016 at 19:34, Maurin Lenglart <mau...@cuberonlabs.com> wrote: > Hi, > So basically you are telling me that I need to recreate a table, and > re-insert everything every time I update a column? > I understand the constraints, but that solution doesn’t look good to me. I > am updating the schema everyday and the table is a couple of TB of data. > > Do you see any other options that will allow me not to move TB of data > everyday? > > Thanks for you answer > > From: Mich Talebzadeh <mich.talebza...@gmail.com> > Date: Sunday, April 10, 2016 at 3:41 AM > To: maurin lenglart <mau...@cuberonlabs.com> > Cc: "user@spark.apache.org" <user@spark.apache.org> > Subject: Re: alter table add columns aternatives or hive refresh > > I have not tried it on Spark but the column added in Hive to an existing > table cannot be updated for existing rows. In other words the new column is > set to null which does not require the change in the existing file length. > > So basically as I understand when a column is added to an already table. > > 1. The metadata for the underlying table will be updated > 2. The new column will by default have null value > 3. The existing rows cannot have new column updated to a non null value > 4. New rows can have non null values set for the new column > 5. No sql operation can be done on that column. For example select * > from <TABLE> where new_column IS NOT NULL > 6. The easiest option is to create a new table with the new column and > do insert/select from the existing table with values set for the new column > > HTH > > Dr Mich Talebzadeh > > > > LinkedIn * > https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw > <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* > > > > http://talebzadehmich.wordpress.com > > > > On 10 April 2016 at 05:06, Maurin Lenglart <mau...@cuberonlabs.com> wrote: > >> Hi, >> I am trying to add columns to table that I created with the “saveAsTable” >> api. >> I update the columns using sqlContext.sql(‘alter table myTable add >> columns (mycol string)’). >> The next time I create a df and save it in the same table, with the new >> columns I get a : >> “ParquetRelation >> requires that the query in the SELECT clause of the INSERT >> INTO/OVERWRITE statement generates the same number of columns as its >> schema.” >> >> Also thise two commands don t return the same columns : >> 1. sqlContext.table(‘myTable’).schema.fields <— wrong result >> 2. sqlContext.sql(’show columns in mytable’) <—— good results >> >> It seems to be a known bug : >> https://issues.apache.org/jira/browse/SPARK-9764 (see related bugs) >> >> But I am wondering, how else can I update the columns or make sure that >> spark take the new columns? >> >> I already tried to refreshTable and to restart spark. >> >> thanks >> >> >
