#Azure data studio mysql update#

This end condition tests to see whether the output of the inner 'Copy data' activity is less than the limit that was set in the beginning. This is the value in Figure 9: users range').output.rowsCopied, 100)įigure 9: Setting the End Condition for the 'Until' Function Move back out into the 'Until' function and set the end condition.

#Azure data studio mysql update#

This is taking the output from the 'Lookup' activity and setting it directly to the 'offset_start' variable.įigure 8: 'Set variable' Activity to Update the offset_start variable Here is the value used in Figure 8: last_id')._id) This allows the next set of results to pick up from where the previous left off.

Add a 'Set variable' activity to update the 'offset_start' variable to the last seen ID.

Here is what I am using in Figure 7: selectįigure 7: A Lookup Activity to Retrieve the Last ID from the Results Select the same source as before, as well as the 'Query' option.Īdd a SQL statement that retrives the last ID from the set of results. Where id > 6: Adding a Recurring 'Copy data' Activity Here is what I am using in this example: select Note - this is for a MySQL database and the SQL query will likely change for a different source SQL database. Under the 'Source' tab, choose the 'Query' option next to 'Use query.'Įnter a SQL query to select the range of data using the limit and offset variables created in step 5. Select the sink dataset (Not pictured, but ParquetOutput for my example). Select the source dataset (MySqlTable for my example). This area will be used to run tasks within the 'Until' function.Ĭreate a 'Copy data' activity (under the 'Move & Transform' dropdown). These will be used within the 'Until' function to keep track of the current index while copying the data chunk by chunk.įigure 5: Initializing the limit and offset variablesĬlick the pencil icon in the 'Activities' box.

Initialize the limit and offset variables.

Refer to Figure 2 for a sample of adf_ers (mock data generated from ).įigure 2: First Ten Rows and Metadata of adf_ersįigure 3: Creating a Pipeline in the ADF Studio I am using a MySQL table (via Amazon Aurora) called 'MySqlTable' that points to adf_ers for the source and a Parquet file in Azure Blob Storage called 'ParquetOutput' for the sink. The example in this article uses a small table, but there are results provided at the end from a larger production workload on tables with hundreds of millions of records.Įnsure you have 'source' and 'sink' datasets defined. This approach works well for tables that have a unique and incrementing integer column for the primary key. This means there are no more rows in the source table to copy.

Since the row range is set to a size of 1,000, the “Until” function will stop after a set of results returning less than 1,000 rows has been processed. For example, if you are copying 1,000 rows at a time from a table, the first iteration would copy rows from indexes 1 - 1,000, then the second iteration would copy rows 1,001 - 2,000, and so on. The “Until” function is a special type of loop that continues to run until a condition is met.Įach iteration copies the rows from the given row range and writes them to a sink. This strategy uses an ADF “ Until” function that loops through a table at a specific row range at a time. This strategy only uses ADF native functions without requiring external services, tools, or code. The major benefits of this approach are avoiding server-side timeouts and controlling the size of batch copies. This article walks through an ingestion strategy called 'Iterative Range Copy' to bulk load SQL tables using Azure Data Factory (ADF).