I have an oracle table which has 80 columns and id partitioned on state column. My requirement is to create a hive table with similar schema of oracle table and partitioned on state.
I tried using sqoop -create-hive-table option. But keep getting an error
ERROR sqoop.Sqoop: Got exception running Sqoop: java.lang.IllegalArgumentException: Partition key state cannot be a column to import.
I understand that in Hive the partitioned column should not be in table definition, but then how do I get around the issue?
I do not want to manually write create table command, as I have 50 such tables to import and would like to use sqoop.
Any suggestion or ideas?
Thanks
There is a turn around for this.
Below is the procedure i fallow :
On Oracle run query to get the schema for a table and store it to a file.
Move that file to Hadoop
On Hadoop create a shell script which constructs a HQL file.
That hql file contains "Hive create table statement along with columns". For this we can use the above file(Oracle schema file copied to hadoop).
For this script to run u need to just pass Hive database name,table name, partition column name,path, etc.. depending on u r customization level.At the end of this shell script add "hive -f HQL filename".
If everything is ready it just takes couple of mins for each table creation.
Related
I have a Hive Parquet table which I am creating using Spark 2.3 API df.saveAstable. There is a separate Hive process that alters the same parquet table to add columns (based on requirements).
However, next time when I try to read the same parquet table into Spark dataframe, the new column which was added to the parquet table using Hive Alter Table command is not showing up in the df.printSchema output.
Based on initial analysis, it seems that there might be some conflict, and Spark is using its own schema instead of reading the Hive metastore.
Hence, I tried the below options :
Changing the spark setting:
spark.sql.hive.convertMetastoreParquet=false
and Refreshing the spark catalog:
spark.catalog.refreshTable("table_name")
However, the above two options are not solving the problem.
Any suggestions or alternatives would be super helpful.
This sounds like a bug described in SPARK-21841. JIRA description also contains the idea for a possible workaround:
...Interestingly enough it appears that if you create the table
differently like:
spark.sql("create table mydb.t1 select ip_address from mydb.test_table limit 1")
Run your alter table on mydb.t1 val t1 = spark.table("mydb.t1")
Then it works properly...
To fix this solution, you have to use the same alter command used in hive to spark-shell as well.
spark.sql("alter table TABLE_NAME add COLUMNS (col_A string)")
Maybe this is an easy question but, I am having a difficult time resolving the issue. At this time, I have an pseudo-distributed HDFS that contains recordings that are encoded using protobuf 3.0.0. Then, using Elephant-Bird/Hive I am able to put that data into Hive tables to query. The problem that I am having is partitioning the data.
This is the table create statement that I am using
CREATE EXTERNAL TABLE IF NOT EXISTS test_messages
PARTITIONED BY (dt string)
ROW FORMAT SERDE
"com.twitter.elephantbird.hive.serde.ProtobufDeserializer"
WITH serdeproperties (
"serialization.class"="path.to.my.java.class.ProtoClass")
STORED AS SEQUENCEFILE;
The table is created and I do not receive any runtime errors when I query the table.
When I attempt to load data as follows:
ALTER TABLE test_messages_20180116_20180116 ADD PARTITION (dt = '20171117') LOCATION '/test/20171117'
I receive an "OK" statement. However, when I query the table:
select * from test_messages limit 1;
I receive the following error:
Failed with exception java.io.IOException:java.lang.IllegalArgumentException: FieldDescriptor does not match message type.
I have been reading up on Hive table and have seen that the partition columns do not need to be part of the data being loaded. The reason I am trying to partition the date is both for performance but, more so, because the "LOAD DATA ... " statements move the files between directories in HDFS.
P.S. I have proven that I am able to run queries against hive table without partitioning.
Any thoughts ?
I see that you have created EXTERNAL TABLE. So you cannot add or drop partition using hive. you need to create a folder using hdfs or MR or SPARK. EXTERNAL table can only be read by hive but not managed by HDFS. You can check the hdfs location '/test/dt=20171117' and you will see that folder has not been created.
My suggestion is create the folder(partition) using "hadoop fs -mkdir '/test/20171117'" then try to query the table. although it will give 0 row. but you can add the data to that folder and read from Hive.
You need to specify a LOCATION for an EXTERNAL TABLE
CREATE EXTERNAL TABLE
...
LOCATION '/test';
Then, is the data actually a sequence file? All you've said is that it's protobuf data. I'm not sure how the elephantbird library works, but you'll want to double check that.
Then, your table locations need to look like /test/dt=value in order for Hive to read them.
After you create an external table over HDFS location, you must run MSCK REPAIR TABLE table_name for the partitions to be added to the Hive metastore
I have some data in hdfs.
This data was migrated from a PostgreSQL database by using Sqoop.
The data has the following hadoopish format, like _SUCCESS, part-m-00000, etc.
I need to create a Hive table based on this data and then I need to export this table to a single tab-separated file.
As far as I know, I can create a table this way.
create external table table_name (
id int,
myfields string
)
location '/my/location/in/hdfs';
Then I can save the table as tsv file:
hive -e 'select * from some_table' > /home/myfile.tsv
I don't know how to load data from hdfs into a Hive table.
Moreover, should I manually define the structure of a table using create or is there any automated way when all columns are created automatically?
I don't know how to load data from hdfs into Hive table
You create a table schema over a hdfs directory like you're doing.
should I manually define the structure of a table using create or is there any automated way when all columns are created automatically?
Unless you didn't tell sqoop to create the table, you must do it manually.
export this table into a single tab-separated file.
A query might work, or unless sqoop set the delimiter to \t, then you need to create another table from the first specifying such column separator. And then, you don't even need to query the table, just run hdfs dfs -getMerge on the directory
I have to create a hive table from data present in oracle tables.
I'm doing a sqoop, thereby converting the oracle data into HDFS files. Then I'm creating a hive table on the HDFS files.
The sqoop completes successfully and the files also get generated in the HDFS target directory.
Then I run the create table script in hive. The tables gets created. But it is an empty table, no data is seen in the hive table.
Has anyone faced a similar problem?
Hive default delimiter is ctrlA, if you don't specify any delimiter it will take default delimiter. Add below line in your hive script .
row format delimited fields terminated by '\t'
Your Hive script and your expectation is wrong. You are trying to create a partitioned table on the data that you have already imported, partitions won't work that way. If your query has no partition in it then you can able to see data.
Basically If you want partitioned table , you can't create on the under lying data like you have tried above. If you want hive partition load the data from intermediate table or that sqoop directory to your partitioned table to get Hive partitions.
In hive, create external table by CTAS is a semantic error, why?
The table created by CTAS is atomic, while external table means data will not be deleted when dropping table, they do not seem to conflict.
In Hive when we create a table(NOT external) the data will be stored in /user/hive/warehouse.
But during External hive table creation the file will be anywhere else, we are just pointing to that hdfs directory and exposing the data as hive table to run hive queries etc.
This SO answer more precisely Create hive table using "as select" or "like" and also specify delimiter
Am I missing something here?
Try this...You should be able to create an external table with CTAS.
CREATE TABLE ext_table LOCATION '/user/XXXXX/XXXXXX'
AS SELECT * from managed_table;
I was able to create one. I am using 0.12.
i think its a semantic error because it misses the most imp parameter of external table definition viz. the External Location of the data file! by definition, 1. External means the data is outside hive control residing outside the hive data warehouse dir. 2. if table is dropped data remains intact only table definition is removed from hive metastore. so,
i. if CTAS is with managed table, the new ext table will have file in warehouse which will be removed with drop table making #2 wrong
ii. if CTAS is with other external table, the 2 tables will point to same file location.
CTAS creates a managed hive table with the new name using the schema and data of the said table.
You can convert it to an external table using:
ALTER TABLE <TABLE_NAME> SET TBLPROPERTIES('EXTERNAL'='TRUE');