I have created an external data source and CSV file format.I am creating an external table susing cetas script
create external table test
With (location='test/data/',
Data_source=test_datasource,
File_format=csv_format)
As select * from dimcustomer
But when I run this is query this is generating many files with extension .text.deflated .
Can we generate only one file and can we give the name to the file which we generate.
Input appreciated .I am creating this external table to export synapse data to data lake container.
Tried creating an external table
Related
I want to transfer data from csv file which is in an azure blob storage with the correct data types to SQL server table.
How can I get the structure for the table in the CSV file? ( I mean like when we do script table to new query in SSMS).
Note that the CSV file is not available on premise.
If your target table is already created in SSMS, copy activity will take care of the schema of source and target tables.
This is my sample csv file from blob:
In the sink I have used a table from Azure SQL database. For you, you can create SQL server dataset by SQL server linked service.
You can see the schema of csv and target tables and their mapping.
Result:
if your target table is not created in SSMS, you can use dataflows and can define the schema that you want in the Projection.
Create a data flow and in the sink give our blob csv file. In the projection of sink, we can give the datatypes that we want for the csv file.
As our target table is not created before, check on edit in the dataset and give the name for the table.
In the sink, give this dataset (SQL server dataset in your case) and make sure you check on the Recreate table in the sink Settings, so that a new table with that name will be created.
Execute this Dataflow, your target table will be created with your user defined data types.
Environment:
MS Azure:
Blob Container, multiple csv files saved in a folder. This is my source.
Azure Sql Database. This is my target
Goal:
Use Azure Data Factory and build a pipeline to "copy" all files from the container and store them in their respective tables in the Azure Sql database by automatically creating those tables.
How do I do that? I tried following this but I just end up having tables incorrectly created in the database, where table is created with a single column having same name as the table name.
I believe I followed the instructions from that link pretty must as they are.
My CSV file is as follows, one column contains the table name.
The previous steps will not be repeated,it is the same as the link.
At Step3 inside the Foreach activity, we should add a Lookup activity to query the table name from the source dataset.
We can declare a String type variable tableName pervious, then set the value via expression #activity('Lookup1').output.firstRow.tableName.
At sink setting of the Copy activity, we can key in #variables('tableName').
ADF will auto create the table for us.
The debug result is as follows:
Facing issue on creating hive table on top of parquet file. Can someone help me on the same.? I have read many articles and followed the guidelines but not able to load a parquet file in Hive Table.
According "Using Parquet Tables in Hive" it is often useful to create the table as an external table pointing to the location where the files will be created, if a table will be populated with data files generated outside of Hive.
hive> create external table parquet_table_name (<yourParquetDataStructure>)
STORED AS PARQUET
LOCATION '/<yourPath>/<yourParquetFile>';
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.
At my firm, I see these two commands used frequently, and I'd like to be aware of the differences, because their functionality seems the same to me:
1
create table <mytable>
(name string,
number double);
load data inpath '/directory-path/file.csv' into <mytable>;
2
create table <mytable>
(name string,
number double);
location '/directory-path/file.csv';
They both copy the data from the directory on HDFS into the directory for the table on HIVE. Are there differences that one should be aware of when using these? Thank you.
Yes, they are used for different purposes at all.
load data inpath command is use to load data into hive table. 'LOCAL' signifies that the input file is on the local file system. If 'LOCAL' is omitted then it looks for the file in HDFS.
load data inpath '/directory-path/file.csv' into <mytable>;
load data local inpath '/local-directory-path/file.csv' into <mytable>;
LOCATION keyword allows to point to any HDFS location for its storage, rather than being stored in a folder specified by the configuration property hive.metastore.warehouse.dir.
In other words, with specified LOCATION '/your-path/', Hive does not use a default location for this table. This comes in handy if you already have data generated.
Remember, LOCATION can be specified on EXTERNAL tables only. For regular tables, the default location will be used.
To summarize,
load data inpath tell hive where to look for input files and LOCATION keyword tells hive where to save output files on HDFS.
References:
https://cwiki.apache.org/confluence/display/Hive/GettingStarted
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL
Option 1: Internal table
create table <mytable>
(name string,
number double);
load data inpath '/directory-path/file.csv' into <mytable>;
This command will remove content at source directory and create a internal table
Option 2: External table
create table <mytable>
(name string,
number double);
location '/directory-path/file.csv';
Create external table and copy the data into table. Now data won't be moved from source. You can drop external table but still source data is available.
When you drop an external table, it only drops the meta data of HIVE table. Data still exists at HDFS file location.
Have a look at this related SE questions regarding use cases for both internal and external tables
Difference between Hive internal tables and external tables?