How should I import data in BigQuery on a daily basis when I have potential duplicated row ?
Here is a bit of context. I'm updating data on a daily basis from a spreadsheet to BigQuery. I'm using Google App Script with a simple WRITE_APPEND method.
Sometimes I'm importing data I've already imported the day before. So I'm wondering how I can avoid this ?
Can I build a sql query in order to clean my table from duplicate row every day ? Or is this possible to detect duplicate even before importing them (with some specific command in my job definition for example...) ?
thanks !
Step 1: Have a sheet with data to be imported
Step 2: Set up your spreadsheet as a federated data source in BigQuery.
Step 3: Use DML to load data into an existing table
(requires #standardSql)
#standardSQL
INSERT INTO `fh-bigquery.tt.test_import_native` (id, data)
SELECT *
FROM `fh-bigquery.tt.test_import_sheet`
WHERE id NOT IN (
SELECT id
FROM `fh-bigquery.tt.test_import_native`
)
WHERE id NOT IN (...) ensures that only rows with new ids are loaded into the table.
As far as I know, the answer provided by Felipe Hoffa is the most effective way to avoid duplicate rows since Bigquery do not normalize data when loading data. The reason is that Bigquery performs best with denormalized data [1]. To better understand it, I’d recommend you to have a look in this SO thread.
I also would like to suggest using SQL aggregate or analytic function to clean the duplicate rows in a Bigquery table, as Felipe Hoffa's or Jordan Tigani's answer in this SO question.
If you have a large-size partitioned table, and only want to remove duplicates in a given range without scanning through (cost-saving) and replacing the whole table.
use the MERGE SQL below:
-- WARNING: back up the table before this operation
-- FOR large size timestamp partitioned table
-- -------------------------------------------
-- -- To de-duplicate rows of a given range of a partition table, using surrage_key as unique id
-- -------------------------------------------
DECLARE dt_start DEFAULT TIMESTAMP("2019-09-17T00:00:00", "America/Los_Angeles") ;
DECLARE dt_end DEFAULT TIMESTAMP("2019-09-22T00:00:00", "America/Los_Angeles");
MERGE INTO `your_project`.`data_set`.`the_table` AS INTERNAL_DEST
USING (
SELECT k.*
FROM (
SELECT ARRAY_AGG(original_data LIMIT 1)[OFFSET(0)] k
FROM `your_project`.`data_set`.`the_table` AS original_data
WHERE stamp BETWEEN dt_start AND dt_end
GROUP BY surrogate_key
)
) AS INTERNAL_SOURCE
ON FALSE
WHEN NOT MATCHED BY SOURCE
AND INTERNAL_DEST.stamp BETWEEN dt_start AND dt_end -- remove all data in partiion range
THEN DELETE
WHEN NOT MATCHED THEN INSERT ROW
credit: https://gist.github.com/hui-zheng/f7e972bcbe9cde0c6cb6318f7270b67a
Related
I was wondering if there is a way to disallow duplicates from BigQuery?
Based on this article I can deduplicate a whole or a partition of a table.
To deduplicate a whole table:
CREATE OR REPLACE TABLE `transactions.testdata`
PARTITION BY date
AS SELECT DISTINCT * FROM `transactions.testdata`;
To deduplicate a table based on partitions defined in a WHERE clause:
MERGE `transactions.testdata` t
USING (
SELECT DISTINCT *
FROM `transactions.testdata`
WHERE date=CURRENT_DATE()
)
ON FALSE
WHEN NOT MATCHED BY SOURCE AND date=CURRENT_DATE() THEN DELETE
WHEN NOT MATCHED BY TARGET THEN INSERT ROW
If there is no way to disallow duplicates then is this a reasonable approach to deduplicate a table?
BigQuery doesn't have a mechanism like constraints that can be found in traditional DBMS. In other words, you can't set a primary key or anything like that because BigQuery is not focused on transactions but in fast analysis and scalability. You should think about it as a Data Lake and not as a database with uniqueness property.
If you have an existing table and need to de-duplicate it, the mentioned approaches will work. If you need your table to have unique rows by default and want to programmatically insert unique rows in your table without resorting to external resources, I can suggest you a workaround:
First insert your data into an temporary table
Then, run a query in your temporary table and save the results into your actual table. This step could be programmatically done in some different ways:
Using the approach you mentioned as a scheduled query
Using a bq command such as bq query --use_legacy_sql=false --destination_table=<dataset.actual_table> 'select distinct * from <dataset.temporary_table>' that will query the distinct values in your temporary table and load the results into the target table pointed in the --destination_table attribute. Its important to mention that this approach will also work for partitioned tables.
Finally, drop the temporary table. Like the previous step, this step could be done either using a scheduled query or bq command.
I hope it helps
Need help on below issue.
I need to delete rows from a table having huge amount of data getting inserted on daily basis, I have written a procedure which deletes the rows based on a column having index on it which to me should be enough but my collegue suggested me to use a date column as well to delete the data as this will use date parition (Parition is based on date).
My doubt is which delete statement would be faster to delete the data.
E.g
1. Column name :- FILE_NAME (Having index)
delete from table_name where column_name1=file_name
2. Column name1 :- FILE_NAME (HHaving index) and column name2:- TXN_DATE (no index, Partition is on this column)
delete from table_name where column_name1=file_name and txn_date=date_value
Please advise.
Thanks
Yes, your colleague is right. The second query will be quicker.
The process is called partition pruning. Using the column, based on which partitions are created will automatically hit only the necessary partitions where the data is available.
You can also directly reference the partition if you can determine the name of the partition for the date_value, as
DELETE FROM table_name
PARTITION (partition_date_value)
WHERE column_name1=file_name;
References:
Examples for DELETE on Oracle Database SQL Language Reference
Partition Pruning
Another Partition Pruning website
If file name is a index that actually improves the navigation on your table, i think it would be faster to use the first one.
I'm currently using Oracle 11g and let's say I have a table with the following columns (more or less)
Table1
ID varchar(64)
Status int(1)
Transaction_date date
tons of other columns
And this table has about 1 Billion rows. I would want to update the status column with a specific where clause, let's say
where transaction_date = somedatehere
What other alternatives can I use rather than just the normal UPDATE statement?
Currently what I'm trying to do is using CTAS or Insert into select to get the rows that I want to update and put on another table while using AS COLUMN_NAME so the values are already updated on the new/temporary table, which looks something like this:
INSERT INTO TABLE1_TEMPORARY (
ID,
STATUS,
TRANSACTION_DATE,
TONS_OF_OTHER_COLUMNS)
SELECT
ID
3 AS STATUS,
TRANSACTION_DATE,
TONS_OF_OTHER_COLUMNS
FROM TABLE1
WHERE
TRANSACTION_DATE = SOMEDATE
So far everything seems to work faster than the normal update statement. The problem now is I would want to get the remaining data from the original table which I do not need to update but I do need to be included on my updated table/list.
What I tried to do at first was use DELETE on the same original table using the same where clause so that in theory, everything that should be left on that table should be all the data that i do not need to update, leaving me now with the two tables:
TABLE1 --which now contains the rows that i did not need to update
TABLE1_TEMPORARY --which contains the data I updated
But the delete statement in itself is also too slow or as slow as the orginal UPDATE statement so without the delete statement brings me to this point.
TABLE1 --which contains BOTH the data that I want to update and do not want to update
TABLE1_TEMPORARY --which contains the data I updated
What other alternatives can I use in order to get the data that's the opposite of my WHERE clause (take note that the where clause in this example has been simplified so I'm not looking for an answer of NOT EXISTS/NOT IN/NOT EQUALS plus those clauses are slower too compared to positive clauses)
I have ruled out deletion by partition since the data I need to update and not update can exist in different partitions, as well as TRUNCATE since I'm not updating all of the data, just part of it.
Is there some kind of JOIN statement I use with my TABLE1 and TABLE1_TEMPORARY in order to filter out the data that does not need to be updated?
I would also like to achieve this using as less REDO/UNDO/LOGGING as possible.
Thanks in advance.
I'm assuming this is not a one-time operation, but you are trying to design for a repeatable procedure.
Partition/subpartition the table in a way so the rows touched are not totally spread over all partitions but confined to a few partitions.
Ensure your transactions wouldn't use these partitions for now.
Per each partition/subpartition you would normally UPDATE, perform CTAS of all the rows (I mean even the rows which stay the same go to TABLE1_TEMPORARY). Then EXCHANGE PARTITION and rebuild index partitions.
At the end rebuild global indexes.
If you don't have Oracle Enterprise Edition, you would need to either CTAS entire billion of rows (followed by ALTER TABLE RENAME instead of ALTER TABLE EXCHANGE PARTITION) or to prepare some kind of "poor man's partitioning" using a view (SELECT UNION ALL SELECT UNION ALL SELECT etc) and a bunch of tables.
There is some chance that this mess would actually be faster than UPDATE.
I'm not saying that this is elegant or optimal, I'm saying that this is the canonical way of speeding up large UPDATE operations in Oracle.
How about keeping in the UPDATE in the same table, but breaking it into multiple small chunks?
UPDATE .. WHERE transaction_date = somedatehere AND id BETWEEN 0000000 and 0999999
COMMIT
UPDATE .. WHERE transaction_date = somedatehere AND id BETWEEN 1000000 and 1999999
COMMIT
UPDATE .. WHERE transaction_date = somedatehere AND id BETWEEN 2000000 and 2999999
COMMIT
This could help if the total workload is potentially manageable, but doing it all in one chunk is the problem. This approach breaks it into modest-sized pieces.
Doing it this way could, for example, enable other apps to keep running & give other workloads a look in; and would avoid needing a single humungous transaction in the logfile.
Consider this Table:
Table: ORDER
Columns: id, order_num, order_date, order_status
This table has 1 million records. I want to update the order_status to value of '5', for a bunch (about 10,000) of order_num's that i will be reading from a input text file.
My SQL could be:
(A) update ORDER set order_status=5 where order_num in ('34343', '34454', '454545',...)
OR
(B) update ORDER set order_status=5 where order_num='34343'
I can loop over this update several times until I have covered my 10,000 order updates.
(Also note that i have few Child Tables of ORDER like ORDER_ITEMS, where similar status must be updated and information audited)
My problem is here is:
How can i Audit this update in a separate ORDER_AUDIT Table:
Order_Num: 34343 - Updated Successfully
Order_Num: 34454 - Order Not Found
Order_Num: 454545 - Updated Successfully
Order_Num: 45457 - Order Not Found
If i go for batch update as in (A), I cannot Audit at Order Level.
If i go for Single Order at at time update as in (B), I will have to loop 10,000 times - that may be quite slow - but I can Audit at Order level in this case.
Is there any other way?
First of all, build an external table over your "input text file". That way you can run a simple single UPDATE statement:
update ORDER
set order_status=5
where order_num in ( select col1 from ext_table order by col1)
Neat and efficient. (Sorting the sub-query is optional: it may improve the performance of the update but the key point is, we can treat external tables like regular tables and use the full panoply of the SELECT syntax on them.) Find out more.
Secondly use the RETURNING clause to capture the hits.
update ORDER
set order_status=5
where order_num in ( select col1 from ext_table order by col1)
returning order_num bulk collect into l_nums;
l_nums in this context is a PL/SQL collection of type number. The RETURNING clause will give you all the ORDER_NUM values for updated rows only. Find out more.
If you declare the type for l_nums as a SQL nested table object you can use it in further SQL statements for your auditing:
insert into order_audit
select 'Order_Num: '||to_char(t.column_value)||' - Updated Succesfully'
from table ( l_nums ) t
/
insert into order_audit
select 'Order_Num: '||to_char(col1)||' - Order Not Found'
from ext_table
minus
select * from table ( l_nums )
/
Notes on performance:
You don't say how many of the rows you have in the input text file will match. Perhaps you don't know (actually on re-reading it's not clear whether 10,000 is the number of rows in the file or the number of matching rows). Pl/SQL collections use private session memory, so very large collections can blow the PGA. However, you should be able to cope with ten thousand NUMBER instances without blinching.
My solution does require you to read the external table twice. This shouldn't be a problem. And it will certainly be way faster than dynamically assembling one hundred IN clauses of a thousand numbers and looping over each.
Note that update is often the slowest bulk operation known to man. There are ways of speeding them up, but those methods can get quite involved. However, if this is something you'll want to do often and performance becomes a sticking point you should read this OraFAQ article.
Use MERGE. Firstly load data into a temporary table called ORDER_UPD_TMP with only one column id. You can do it using SQLDeveloper import feature. Then use MERGE in order to udpate your base table:
MERGE INTO ORDER b
USING (
SELECT order_id
FROM ORDER_UPD_TMP
) e
ON (b.id = e.id)
WHEN MATCHED THEN
UPDATE SET b.status = 5
You can also update with a different status when records don't match. Check the documentation for more details:
http://docs.oracle.com/cd/B28359_01/server.111/b28286/statements_9016.htm
I think the best way will be:
to import your file to the database first
then do few SQL UPDATE/INSERT queries in one transaction to update status for all orders and create audit records.
I'm making some tests to avoid doublons during insert. I have noticed rows[].insertId could permit to avoid doublons but it's seems the bq command line have no such parameter. I have tried with --undefok but with no effect.
bq --apilog= --show_build_data insert --insert_id=201603210850 --template_suffix=_20160520 --dataset_id=mydataset --undefok=insert_id MYTEMPLATE.table myjson.json
have I missing something ?
AFAIK the insert_id is only taken into account on streaming insert bases, not load jobs.
And it's not a switch, it's a value of the row being ingested.
https://cloud.google.com/bigquery/streaming-data-into-bigquery#before_you_begin
Manually removing duplicates
You can use the following manual process to ensure that no duplicate rows exist after you are done streaming.
1) Add the insertID as a column in your table schema and include the insertID value in the data for each row.
2) After streaming has stopped, perform the following query to check for duplicates:
SELECT max(count) FROM(
SELECT <id_column>, count(*) as count
FROM <table>
GROUP BY id_column)
If the result is greater than 1, duplicates exist.
3) To remove duplicates, perform the following query. You should specify a destination table, allow large results, and disable result flattening.
SELECT *
FROM (
SELECT
*,
ROW_NUMBER()
OVER (PARTITION BY <id_column>)
row_number,
FROM <table>
)
WHERE row_number = 1
Notes about the duplicate removal query:
The safer strategy for the duplicate removal query is to target a new table. Alternatively, you can target the source table with write disposition WRITE_TRUNCATE.
The duplicate removal query adds a row_number column with the value 1 to the end of the table schema. You can select by specific column names to omit this column.
For querying live data with duplicates removed, you can also create a view over your table using the duplicate removal query. Be aware that query costs against the view will be calculated based on the columns selected in your view, which can result in large bytes scanned sizes.