consider 2000 year data.
test.csv
country_code,product_code,rpt_period
us,crd,2000
us,pcl,2000
us,mtg,2000
in,crd,2000
in,pcl,2000
in,mtg,2000
now i am appending newly generated 2001 records to test.csv. after appending new data to test.csv my data looks like below.
append.csv
country_code,product_code,rpt_period
us,crd,2000
us,pcl,2000
us,mtg,2000
in,crd,2000
in,pcl,2000
in,mtg,2000
us,crd,2001
us,pcl,2001
us,mtg,2001
in,crd,2001
in,pcl,2001
in,mtg,2001
Below scenarios are possible in the hive? If yes, please answer questions.
How to create schema for Partition table Foo using this data?. and also I
want partition columns as country_code and product_code.
For instance, i want to load (from test.csv file records) to table Foo? using hive LOAD DATA comand ?
How to load append.csv (only 2001 records) to table Foo. this also needs to be done using hive LOAD DATA command
Thanks.
Yes, All the scenarios you have mentioned are possible with Hive.
Create temp table and load all the data you have and the you can create partitioned table with 2 columns you have mentioned.
For 2 and 3: Just the load command will work. If your intention is to load into partitioned table you have to go via creating temp table and insert into partitioned table.
Let me know this is what you want else update your question.
Related
We can not use create or replace table statement for partitioned tables in BigQuery. I can export the table to GCS but BigQuery generates then multiple JSON files that can not be imported into a table in once. Is there a safe way to drop a column from a partitioned table? I use BigQuery's web interface.
Renaming a column is not supported by the Cloud Console, the classic BigQuery web UI, the bq command-line tool, or the API. If you attempt to update a table schema using a renamed column, the following error is returned: BigQuery error in update operation: Provided Schema does not match Table project_id:dataset.table.
There are two ways to manually rename a column:
Using a SQL query: choose this option if you are more concerned about simplicity and ease of use, and you are less concerned about costs.
Recreating the table: choose this option if you are more concerned about costs, and you are less concerned about simplicity and ease of use.
If you want to drop a column you can either:
Use a SELECT * EXCEPT query that excludes the column (or columns) you want to remove and use the query result to overwrite the table or to create a new destination table
You can also remove a column by exporting your table data to Cloud Storage, deleting the data corresponding to the column (or columns) you want to remove, and then loading the data into a new table with a schema definition that does not include the removed column(s). You can also use the load job to overwrite the existing table
There is a guide published for Manually Changing Table Schemas.
edit
In order to change a Partitioned table to a Non-partitioned table, you can use the Console to query your data and overwrite your current table or copy to a new one. As an example, I have a table in BigQuery partitioned by _PARTITIONTIME. I used the following query to create a non-partitioned table,
SELECT *, _PARTITIONTIME as pt FROM `project.dataset.table`
With the above code, you will query the data among all table's partitions and create an extra column to show which partition it came from. Then, before executing it, there are two options, save the view in a new non-partitioned table or overwrite the current table:
Creating a new table go to: More(under the query editor) > Query Settings > Check the box "Set a destination table for query results" > Choose your project, dataset and write your new table's name > Under Destination table write preference check Write if empty.
Overwriting the current table: More(under the query editor) > Query Settings > Check the box "Set a destination table for query results" > Choose the same project and dataset for your current table > Write the same table's name as the one you want to overwrite > Under Destination table write preference check Overwrite table.
credit
The question is how to let Google BigQuery automatically create partitioned tables on the daily base (one day -> one table, etc.)?
I've used the following command in the command line to create the table:
bq mk --time_partitioning_type=DAY testtable1
The table1 appeared in the dataset, but how to create tables for every day automatically?
From the partitioned table documentation, you need to run the command to create the table only once. After that, you specify the partition to which you want to write as the destination table of the query, such as testtable1$20170919.
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.
Can anyone please suggest how to create partition table in Big Query ?.
Example: Suppose I have one log data in google storage for the year of 2016. I stored all data in one bucket partitioned by year , month and date wise. Here I want create table with partitioned by date.
Thanks in Advance
Documentation for partitioned tables is here:
https://cloud.google.com/bigquery/docs/creating-partitioned-tables
In this case, you'd create a partitioned table and populate the partitions with the data. You can run a query job that reads from GCS (and filters data for the specific date) and writes to the corresponding partition of a table. For example, to load data for May 1st, 2016 -- you'd specify the destination_table as table$20160501.
Currently, you'll have to run several query jobs to achieve this process. Please note that you'll be charged for each query job based on bytes processed.
Please see this post for some more details:
Migrating from non-partitioned to Partitioned tables
There are two options:
Option 1
You can load each daily file into separate respective table with name as YourLogs_YYYYMMDD
See details on how to Load Data from Cloud Storage
After tables created, you can access them either using Table wildcard functions (Legacy SQL) or using Wildcard Table (Standar SQL). See also Querying Multiple Tables Using a Wildcard Table for more examples
Option 2
You can create Date-Partitioned Table (just one table - YourLogs) - but you still will need to load each daily file into respective partition - see Creating and Updating Date-Partitioned Tables
After table is loaded you can easily Query Date-Partitioned Tables
Having partitions for an External Table is not allowed as for now. There is a Feature Request for it:
https://issuetracker.google.com/issues/62993684
(please vote for it if you're interested in it!)
Google says that they are considering it.
I am trying to update only one column of the 1 million records in a table based on the value in the CSV file.
Sample CSV file:
1,Apple
2,Orange
3,Mango
The first column in the file is the PK I will use to filter the record and the second column is the new value of the column in the table I want to update.The PK in the CSV file may or may not exist in the DB though. I was thinking of creating a script to make a million update statements based on the file. I would like to know if there are any better way on how could I do this?
personally i would
load the CSV file into a new table using sqlldr
make sure the correct indexes are on the new and existing table
write ONE update statement to update the existing table from the new
one
I would:
Create an external table using the csv
Update existing table from the new external table in just one update