Your solution works in hive, but not in spark, even if I use hive context.
I tried to create a temp table and then this query:
- sqlContext.sql("insert into table myTable select * from myTable_temp”)
But I still get the same error.
thanks
From: Mich Talebzadeh
<mich.talebza...@gmail.com<mailto:mich.talebza...@gmail.com>>
Date: Sunday, April 10, 2016 at 12:25 PM
To: "user @spark" <user@spark.apache.org<mailto:user@spark.apache.org>>
Subject: Re: alter table add columns aternatives or hive refresh
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
http://talebzadehmich.wordpress.com<http://talebzadehmich.wordpress.com/>
On 10 April 2016 at 19:34, Maurin Lenglart
<mau...@cuberonlabs.com<mailto: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<mailto:mich.talebza...@gmail.com>>
Date: Sunday, April 10, 2016 at 3:41 AM
To: maurin lenglart <mau...@cuberonlabs.com<mailto:mau...@cuberonlabs.com>>
Cc: "user@spark.apache.org<mailto:user@spark.apache.org>"
<user@spark.apache.org<mailto: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
http://talebzadehmich.wordpress.com<http://talebzadehmich.wordpress.com/>
On 10 April 2016 at 05:06, Maurin Lenglart
<mau...@cuberonlabs.com<mailto: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
