- #Snowflake tasks how to#
- #Snowflake tasks update#
You updated two records in the NATION table, so there are four rows in the stream. In the following figure, notice the METADATA$ISUPDATE column contains TRUE. Run the following command: select * from nation_table_changes Next, let’s look at what the stream captured. In the following figure, notice the updated values in the N_COMMENT and UPDATE_TIMESTAMP columns for N_NATIONKEY 2 and N_NATIONKEY 3. View the data in the NATION table by running the following command: select * from nation where n_nationkey in (1, 2,3)
#Snowflake tasks update#
To start, run the following transaction to update two records: begin update nation set n_comment = 'New comment for Brazil', update_timestamp = current_timestamp()::timestamp_ntz where n_nationkey = 2 update nation set n_comment = 'New comment for Canada', update_timestamp = current_timestamp()::timestamp_ntz where n_nationkey = 3 commit Then, you’ll delete data and set up automatic processing. First, you’ll update some data and then manually process it.
Now, let’s automate the stream and have it run on a schedule.
#Snowflake tasks how to#
The root task needs to be enabled before the child tasks using the task RESUME command.Part 1 of this two-part post demonstrated how to build a Type 2 Slowly Changing Dimension (SCD) using Snowflake’s Stream functionality to set up a stream and insert data. Once the root task finishes running, which in this scenario creates a Snowflake table if not already existing, the child tasks which call the bulk load stored procedure run. In the following example, you have a root task s3_load_task_root, and 3 child tasks. You can learn more about the Snowflake tree of tasks here. Multiple child tasks can also call the same stored procedure and you can pass parameters, such as the bucket prefix. Alternatively you can choose to assign each child task a different warehouse. Each child task launched will create its own session which would allow multiple queries to run in parallel on a single warehouse. The other option would be to have multiple tasks running in parallel, which is doable when you create a tree of tasks. While you can call multiple SQL queries within one stored procedure, they can only be run sequentially versus async. bucket\northamerica\*, bucket\europe\*, etc. If you have a scenario where you need to bulk load a high volume of files and your COPY INTO command is taking a significantly long time to complete, it may be beneficial for you to run multiple COPY INTO commands in parallel, especially if you are able to easily group files under common prefixes, e.g. This implementation works great if you’re able to efficiently load all the files you need in one copy into command. CREATE OR REPLACE TASK s3_to_snowflake_task The following example will create an S3 task that runs every 2 hours and calls a simple stored procedure which executes the copy into command. Once your copy into statement has been generated and validated, a Snowflake task can be used to bulk load data on a schedule. In the following example, when run, the command will copy the sales.json file from an S3 bucket stage, load the contents into the house_sales table and delete the file after successful load. Copy Into is an easy to use and highly configurable command that gives you the option to specify a subset of files to copy based on a prefix, pass a list of files to copy, validate files before loading, and also purge files after loading. Once secure access to your S3 bucket has been configured, the COPY INTO command can be used to bulk load data from your “S3 Stage” into Snowflake.
0 kommentar(er)
