I am still learning Oracle SQL, and I've been trying to find the best way to update/insert records in an OracleSQL table with the data from a CSV file.
So far, I've figured out how to load the csv into a temporary table using External Tables in Oracle, but I'm having difficulty finding a detailed guide on how to update/insert (UPSERT) the loaded data into an existing table.
What is the best way to do this, when I have 30+ fields in the table? For example, is it best to read the csv line by line with something like pandas and update each record one by one, or is it best to do it with a sql script using something like a merge statement? Not all records in the csv have a value for the primary key, in which case I need to insert rather than update. Thanks for the help!
That looks like a MERGE, indeed.
Data from external table would then be used to
update values in existing rows
create new rows in the target table
Pandas and row-by-row processing? I wouldn't do that. If you already have a powerful database, then use its capabilities. Row-by-row is usually slow-by-slow and there's rarely some benefit in doing it that way.
Related
Am syncing my mongo DB data to snowflake on a daily basis using a node js script. So if a row is already existing in snowflake, then I want to replace that row with the new data, or if the row doesn't exist in snowflake then I want to insert a new row.
Also, I want to do this for a lot of data.
So is there any way to do bulk upsert in snowflake? If not, then what will be the optimal way to achieve this?
The table may have millions of rows and possibly go to billions in the future.
This is a typical use case for a merge statement. You can see the documentation for merge here: https://docs.snowflake.com/en/sql-reference/sql/merge.html
Using a merge statement for billions of rows can lead to some high-churn tables so it isn't ideal. It could be better if you can append to the table only and figure out the latest record with a select statement.
You can bulk copy your data into a staging table then use MERGE feature in snowflake.
I'm new to DB/postgres SQL.
Scenario:
Need to load an csv file into postgres DB. This CSV data needs to loaded into multiple tables according DB schema. I'm looking for better design using python script.
My thought:
1. Load CSV file to intermediate table in postgres
2. Write a trigger on intermediate table to insert data into multiple tables on event of insert
3. Trigger includes truncate data at end
Any suggestions for better design/other ways without any ETL tools, and also any info on modules in Python 3.
Thanks.
Rather than using a trigger, use an explicit INSERT or UPDATE statement. That is probably faster, since it is not invoked per row.
Apart from that, your procedure is fine.
we have a table in Azure Data Warehouse with 17 billion records. Now we have a scenario where we have to delete records from this table based on some where condition. We are writing Spark in Scala language in Azure Databricks notebooks.
We searched for different options to do this in Spark, but all suggested to first read the entire table, delete records from this and then overwrite the entire table in Data Warehosue. However this approach will not work in our case due to huge number of records in our table.
Can you please suggest how we can achieve this functionality using spark/scala?
1) checked if we can call stored procedure through spark/scala code in azure databricks but Spark do not support stored procedures.
2) Tried reading the entire table first to delete the records but it goes into never ending loop.
Is possible to create view with select clause as per your requirement, then using of the view
I am moving data around within Impala, not my design, and I have lost some data. I need to copy the data from the parquet tables back to their original non-parquet tables. Originally, the developers had done this with a simple one liner in a script. Since I don't know anything about databases and especially about Impala I was hoping you could help me out. This is the one line that is used to translate to a parquet table that I need to be reversed.
impalaShell -i <ipaddr> use db INVALIDATE METADATA <text_table>;
CREATE TABLE <parquet_table> LIKE <text_table> STORED AS PARQUET TABLE;
INSERT OVERWRITE <parquet_table> SELECT * FROM <text_table>;
Thanks.
Have you tried simply doing
CREATE TABLE <text_table>
AS
SELECT *
FROM <parquet_table>
Per the Cloudera documentation, this should be possible.
NOTE: Ensure that your does not exist or use a table name that does not already exist so that you do not accidentally overwrite other data.
I have a .csv file that gets pivoted into 6 million rows during a SSIS package. I have a table in SQLServer 2005 of 25 million + rows. The .csv file has data that duplicates data in the table, is it possible for rows to get updated if it already exists or what would be the best method to achieve this efficiently?
Comparing 6m rows against 25m rows is not going to be too efficient with a lookup or a SQL command data flow component being called for each row to do an upsert. In these cases, sometimes it is most efficient to load them quickly into a staging table and use a single set-based SQL command to do the upsert.
Even if you do decide to do the lookup - split the flow into two streams, one which inserts and the other which inserts into a staging table for an update operation.
If you don't mind losing the old data (ie. the latest file is all that matters, not what's in the table) you could erase all the records in the table and insert them again.
You could also load into a temporary table and determine what needs to be updated and what needs to be inserted from there.
You can use the Lookup task to identify any matching rows in the CSV and the table, then pass the output of this to another table or data flow and use a SQL task to perform the required Update.