I have external table with complex datatype,(map(string,array(struct))) and I'm able to select and query this external table without any issue.
However if I am trying to load this data to a managed table, it runs forever. Is there any best approach to load this data to managed table in hive?
CREATE EXTERNAL TABLE DB.TBL(
id string ,
list map<string,array<struct<ID:string,col:boolean,col2:string,col3:string,col4:string>>>
) LOCATION <path>
BTW, you can convert table to managed (though this may not work on cloudera distribution due warehouse dir restriction):
use DB;
alter table TBLSET TBLPROPERTIES('EXTERNAL'='FALSE');
If you need to load into another managed table, you can simply copy files into it's location.
--Create managed table (or use existing one)
use db;
create table tbl_managed(id string,
list map<string,array<struct<ID:string,col:boolean,col2:string,col3:string,col4:string>>> ) ;
--Check table location
use db;
desc formatted tbl_managed;
This will print location along with other info, use it to copy files.
Copy all files from external table location into managed table location, this will work most efficiently, much faster than insert..select:
hadoop fs -cp external/location/path/* managed/location/path
After copying files, table will be selectable. You may want to analyze table to compute statistics:
ANALYZE TABLE db_name.tablename COMPUTE STATISTICS [FOR COLUMNS]
Related
Is it even possible to add a partition to an existing table in Athena that currently is without partitions? If so, please also write syntax for doing so in the answer.
For example:
ALTER TABLE table1 ADD PARTITION (ourDateStringCol = '2021-01-01')
The above command will give the following error:
FAILED: SemanticException table is not partitioned but partition spec exists
Note: I have done a web-search, and variants exist for SQL server, or adding a partition to an already partitioned table. However, I personally could not find a case where one could successfully add a partition to an existing non-partitioned table.
This is extremely similar to:
SemanticException adding partiton Hive table
However, the answer given there requires re-creating the table.
I want to do so without re-creating the table.
Partitions in Athena are based on folder structure in S3. Unlike standard RDBMS that are loading the data into their disks or memory, Athena is based on scanning data in S3. This is how you enjoy the scale and low cost of the service.
What it means is that you have to have your data in different folders in a meaningful structure such as year=2019, year=2020, and make sure that the data for each year is all and only in that folder.
The simple solution is to run a CREATE TABLE AS SELECT (CTAS) query that will copy the data and create a new table that can be optimized for your analytical queries. You can choose the table format (Parquet, for example), the compression (SNAPPY, for example), and also the partition schema (per year, for example).
I am new to HDFS and HIVE. I got some introduction of both after reading some books and documentation. I have a question regarding creation of a table in HIVE for which file is present in HDFS.
I have this file with 300 fields in HDFS. I want to create a table accessing this file in HDFS. But I want to make use of say 30 fields from this file.
My questions are
1. Does hive create a separate file directory?
2. Do I have to create hive table first and import data from HDFS?
3. Since I want to create a table with 30 columns out of 300 columns, Does hive create a file with only those 30 columns?
4. Do I have to create a separate file with 30 columns and import into HDFS and then create hive table pointing to HDFS directory?
My questions are
Does hive create a separate file directory?
YES if you create a hive table (managed/external) and load the data using load command.
NO if you create an external table and point to the existing file.
Do I have to create hive table first and import data from HDFS?
Not Necessarily you can create a hive external table and point to this existing file.
Since I want to create a table with 30 columns out of 300 columns, Does hive create a file with only those 30 columns?
You can do it easily using hiveQL. follow the below steps (note: this is not the only approach):
create a external table with 300 column and point to the existing
file.
create another hive table with desired 30 columns and insert data to this new table from 300 column table using "insert into
table30col select ... from table300col". Note: hive will create the
file with 30 columns during this insert operation.
Do I have to create a separate file with 30 columns and import into HDFS and then create hive table pointing to HDFS directory?
Yes this can be an alternative.
I personally like solution mentioned in question 3 as I don't have to recreate the file and I can do all of that in hadoop without depending on some other system.
You have several options. One is to have Hive simply point to the existing file, i.e. create an external HIVE table:
CREATE EXTERNAL TABLE ... LOCATION '<your existing hdfs file>';
This table in Hive will, obviously, match exactly your existing table. You must declare all 300 columns. There will be no data duplication, there is only one one file, Hive simply references the already existing file.
A second option would be to either IMPORT or LOAD the data into a Hive table. This would copy the data into a Hive table and let Hive control the location. But is important to understand that neither IMPORT nor LOAD do not transform the data, so the result table will have exactly the same structure layout and storage as your original table.
Another option, which I would recommend, is to create a specific Hive table and then import the data into it, using a tool like Sqoop or going through an intermediate staging table created by one of the methods above (preferably external reference to avoid an extra copy). Create the desired table, create the external reference staging table, insert the data into the target using INSERT ... SELECT, then drop the staging table. I recommend this because it lets you control not only the table structure/schema (ie. have only the needed 30 columns) but also, importantly, the storage. Hive has a highly columnar performant storage format, namely ORC, and you should thrive to use this storage format because will give you tremendous query performance boost.
I have created a hive table with dynamic partitioning on a column. Is there a way to directly load the data from files using "LOAD DATA" statement? Or do we have to only depend on creating a non-partitioned intermediate table and load file data to it and then inserting data from this intermediate table to partitioned table as mentioned in Hive loading in partitioned table?
No, the LOAD DATA command ONLY copies the files to the destination directory. It doesn't read the records of the input file, so it CANNOT do partitioning based on record values.
If your input data is already split into multiple files based on partitions, you could directly copy the files to table location in HDFS under their partition directory manually created by you (OR just point to their current location in case of EXTERNAL table) and use the following ALTER command to ADD the partition. This way you could skip the LOAD DATA statement altogether.
ALTER TABLE <table-name>
ADD PARTITION (<...>)
No other go, if we need to insert directly, we'll need to specify partitions manually.
For dynamic partitioning, we need staging table and then insert from there.
The below table returns no data while running a select statement
CREATE EXTERNAL TABLE foo (
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\073'
LINES TERMINATED BY '\n'
LOCATION '/user/data/CSV/2016/1/27/*/part-*';
I need my hive to point to a dynamic folder so as a mapreduce job puts a part file in a folder and hive loads into the table.
Is there any way the location be made dynamic like
/user/data/CSV/*/*/*/*/part-*
or just /user/data/CSV/* would do fine ?
(The same code works fine when created as internal table and loaded with the file path - hence there is no issues due to formatting)
First of, your table definition is missing columns. Second, external table location always points to folder, not particular files. Hive will consider all files in the folder to be data for the table.
If you have data that is generated e.g. on a daily basis by some external process you should consider partitioning your table by date. Then you need to add a new partition to the table when the data is available.
Hive does not iterate through multiple folders -
Hence for the above scenario
I ran a command line argument that iterates through these multiple folders and cat (print to the console) all the part files and then put it to a desired location.(that Hive points to)
hadoop fs -cat /user/data/CSV/*/*/*/*/part-* | hadoop fs -put - <destination folder>
This line
LOCATION '/user/data/CSV/2016/1/27/*/part-*';
Does not look correct, I don't think that the table can created from multiple locations. Have you tried just importing by a single location to confirm this?
Could also be the delimiter you're using is not correct. If you are using a CSV file to import your data try delimitating by ','.
You can use an alter table statement to change the locations. In the example below partitions are based on dates where data is stored in time dependent file locations. If I want to search many days I have to add an alter table statement for each location. This idea may extend to your situation quite well. You create a script to generate the create table statement as below using some other technology such as python.
CREATE EXTERNAL TABLE foo (
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\073'
LINES TERMINATED BY '\n'
;
alter table foo add partition (date='20160201') location /user/data/CSV/20160201/data;
alter table foo add partition (date='20160202') location /user/data/CSV/20160202/data;
alter table foo add partition (date='20160203') location /user/data/CSV/20160203/data;
alter table foo add partition (date='20160204') location /user/data/CSV/20160204/data;
You can use as many add and drop statements you need to define your locations. Then your table can find data held in many locations in HDFS rather than having all your files in one location.
You may also be able to leverage a
create table like
statement. To create a schema like you have in another table. Then alter the table to point at the files you want.
I know this isn't exactly what you want and is more of a work around. Good luck!
Is there a way to dump all the schema / data of an impala database so I can recreate in a new database instance?
Something akin to what mysqldump does?
Yes ,
you can take all data from impala warehouse ( usually /user/hive/warehouse)
use dictcp to copy from one cluster to other cluster in same location
Fire show create table to get schema of each table and just change location to destination location
Since there is no DUMP command (or something similar):
http://www.cloudera.com/content/cloudera/en/documentation/cloudera-impala/latest/topics/impala_shell_commands.html
I think the best solution will be to use only external tables in one database.
That way, you can know where your data is saved, and potentially copy it in another place.
CREATE EXTERNAL TABLE table_name(one_field INT, another_field BIGINT,
another_field1 STRING)
COMMENT 'This is an external table'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\054'
STORED AS TEXTFILE
LOCATION '<my_hdfs_location>';