I am trying to convert the following Hive query to BigQuery with little luck. The idea is to remove the records from the specified partition and insert new records into the partition without touching other partitions. I have seen Google's documentation on using a DML statement to add rows to an ingestion-time partitioned table, but this isn't what I'm trying to accomplish.
INSERT OVERWRITE TABLE mytable PARTITION (integer_id = 100) select tmp.*, NULL as value from (select * from mytable2) as tmp;
Any help would be greatly appreciated!
I have a query that I want to execute daily that's to be partitioned by the date it's executed. The results of this query should be appended to a the same table.
My idea was ideally having something similar to the CREATE TABLE IF NOT EXISTS command for adding data by a new partition every day to the existing table if the partition doesn't already exist, but I can't figure out how I'd be able to integrate this in my query.
My query:
CREATE TABLE IF NOT EXISTS db_name.table_name
WITH (
external_location = 's3://my-query-results-location/',
format = 'PARQUET',
parquet_compression = 'SNAPPY',
partitioned_by = ARRAY['date_executed'])
AS
SELECT
{columns_that_I_am_selecting_here_including_'date_executed'}
What this does is create a new table for the first day it's executed but nothing happens for subsequent days, I'm assuming because of the CREATE TABLE IF NOT EXISTS validating that the table already exists and not proceeding with the logic.
Is there a way to modify my query to create a table for the first day executed and append the results by a new partition for each subsequent day?
I'm quite sure ALTER TABLE table_name ADD [IF NOT EXISTS] PARTITION would not apply to my use case here as I'm running a CTAS query.
You can simply use INSERT INTO existing_table SELECT....
Presumably your table is already partitioned, so include that partition column in the SELECT and Amazon Athena will automatically put the data in the correct directory.
For example, you might include hte column like this: SELECT ... CURRENT_DATE as date_executed
See: INSERT INTO - Amazon Athena
I have table A as truncate and load for every month file and table B will be append
So table A will be file to table in hive
Table B will be tableA Insert and append data
Issue here is table B is straight move select stmt from table A , and chances are it can be inserted with duplicate/ same data
How should I write a select query to insert data from Table A
Both tables will have file-date as the column
Left join A and B is giving wrong counts in this insert tables
And hive is not working for not exists code
Issue Is:
Append table script : partitioned by yearmonth
Insert into table dist.t2
Select
Person_sk,
Np_id,
Yearmonth,
Insert_date
File_date
From table raw.ma
Data in Table raw.ma —this is truncate and reload
File1 data:201902
File2data:201903
File3data:201904
File4data: if 201902 data gets loaded to table — this should not duplicate the file1 data.. it should either not get inserted or should overwrite that partition
Here I need a filter or where condition to append data into dist.t2
Can you please help with this ??
I tried alter drop table partition in hive, but it’s failing in the spark framework
Please help with avoiding duplicate entries insert
I have a user table in Hive of the form:
User:
Id String,
Name String,
Col1 String,
UpdateTimestamp Timestamp
I'm inserting data in this table from a file which has the following format:
I/U,Timestamp when record was written to file, Id, Name, Col1, UpdateTimestamp
e.g. for inserting a user with Id 1:
I,2019-08-21 14:18:41.002947,1,Bob,stuff,123456
and updating col1 for the same user with Id 1:
U,2019-08-21 14:18:45.000000,1,,updatedstuff,123457
The columns which are not updated are returned as null.
Now simple insertion is easy in hive using load in path in a staging table and then ignoring the first two fields from the stage table.
However, how would I go about the update statements? So that my final row in hive looks like below:
1,Bob,updatedstuff,123457
I was thinking to insert all rows in a staging table and then perform some sort of merge query. Any ideas?
Typically with a merge statement your "file" would still be unique on ID and the merge statement would determine whether it needs to insert this as a new record, or update values from that record.
However, if the file is non-negotiable and will always have the I/U format, you could break the process up into two steps, the insert, then the updates, as you suggested.
In order to perform updates in Hive, you will need the users table to be stored as ORC and have ACID enabled on your cluster. For my example, I would create the users table with a cluster key, and the transactional table property:
create table test.orc_acid_example_users
(
id int
,name string
,col1 string
,updatetimestamp timestamp
)
clustered by (id) into 5 buckets
stored as ORC
tblproperties('transactional'='true');
After your insert statements, your Bob record would say "stuff" in col1:
As far as the updates - you could tackle these with an update or merge statement. I think the key here is the null values. It's important to keep the original name, or col1, or whatever, if the staging table from the file has a null value. Here's a merge example which coalesces the staging tables fields. Basically, if there is a value in the staging table, take that, or else fall back to the original value.
merge into test.orc_acid_example_users as t
using test.orc_acid_example_staging as s
on t.id = s.id
and s.type = 'U'
when matched
then update set name = coalesce(s.name,t.name), col1 = coalesce(s.col1, t.col1)
Now Bob will show "updatedstuff"
Quick disclaimer - if you have more than one update for Bob in the staging table, things will get messy. You will need to have a pre-processing step to get the latest non-null values of all the updates prior to doing the update/merge. Hive isn't really a complete transactional DB - it would be preferred for the source to send full user records any time there's an update, instead of just the changed fields only.
You can reconstruct each record in the table using you can use last_value() with the null option:
select h.id,
coalesce(h.name, last_value(h.name, true) over (partition by h.id order by h.timestamp) as name,
coalesce(h.col1, last_value(h.col1, true) over (partition by h.id order by h.timestamp) as col1,
update_timestamp
from history h;
You can use row_number() and a subquery if you want the most recent record.
I have a sql statement to insert data into a table for archiving, but I need a merge statement to run on a monthyl basis to update the new table(2) with any data that changed in the old table(1) that should now be moved into archive.
Part of the issue is to remove the moved data from the old table. My insert is not doing that, but I need to have it to where the saved data is purged from the original table.
Is there a single sql statement that will move data out of one table into another in this way? Or does it need to be a two step operation?
the initial statement moved data depending on age and a few other relative factors.
insert is:
INSERT /*+ append */
INTO tab1
SELECT *
FROM tab2
WHERE (Postingdate < TO_DATE ('2001/07/01', 'yyyy/mm/dd')
OR jobname IS NULL)
AND STATUS <> '45';
All help appreciated...
The merge statement will let you do this in one statement by adding a delete statement in the update clause. See Oracle Documentation on Merge.
I think you should try this with a partition table. My idea is to create table which have range partition on date:
create table(id number primary key,name varchar,J_date date )
partition by range(J_date)(PARTITION one_mnth VALUES LESS THAN(sysdate-30)),
partition by range(J_date)(PARTITION one_mnth VALUES LESS THAN(maxvalue)));
then move that partition in to another table and and truncate that partition