I have registered a dataset after an Azure Databricks ETL operation. When it is registered as an AzureML Dataset, one of the columns is rendered as a timestamp. I know the schema has been inferred properly as the Dataset->Explore blade renders it properly:
However, when using Dataset.get_by_name(ws,<name>).to_pandas_dataframe(), the timestamp column is rendered as all None:
How do I mention the schema so that it is rendered properly while Getting the Dataset.get_by_name()
This may be a bug, but my guess is that the Explore tab is only looking at the first 1000 rows, which have no issues, but that there may be a malformatted value in a row after the 1st 1000.
Can you:
confirm the column complete for all rows in Databricks?
what is the source file format?
what is the column type for that column in the registered Tabular Dataset, can you confirm that it isn't a string?
Related
I am replicating my data from Azure SQl DB TO Azure SQL DB. I have some tables with date columns and some tables with just the ID columns which are assigning primary key. While performing incremental load in ADF, I can select date as watermark column for the tables which have date column and id as watermark column for the tables which has id column, But the issue is my id has guid values, So can I i take that as my watermark column ? and if yes while copy activity process it gives me following error in ADF
Please see the image for above reference
How can I overcome this issue. Help is appreciated
Thank you
Gp
I have tried dynamic mapping https://martinschoombee.com/2022/03/22/dynamic-column-mapping-in-azure-data-factory/ from here but it does not work it still gives me same error.
Regarding your question about watermak:
A watermark is a column that has the last updated time stamp or an incrementing key
So GUID column would not be a good fit.
Try to find a date column, or an integer identity which is ever incrementing, to use as watermark.
Since your source is SQL server, you can also use change data capture.
Links:
Incremental loading in ADF
Change data capture
Regards,
Chen
The watermark logic takes advantange of the fact that all the new records which are inserted after the last watermark saved should only be considered for copying from source A to B , basically we are using ">=" operator to our advantage here .
In case of guid you cannot use that logic as guid cann surely be unique but not ">=" or "=<" will not work.
I have a delimited file separated by hashes that looks somewhat like this,
value#value#value#value#value#value##value
value#value#value#value##value#####value#####value
value#value#value#value###value#value####value##value
As you can see, when separated by hashes, there are more columns in the 2nd and 3rd rows than there is in the first. I want to be able to ingest this into a database using a ADF Data Flow after some transformations. However, whenever I try to do any kind of mapping, I always only see 7 columns (the number of columns in the first row).
Is there any way to get all of the values? As many columns as there are in the row with most number of items? I do not mind the nulls.
Note: I do not have a header row for this.
Azure Data Factory directly will not be able to Import schema -row with the maximum number of column. Hence, it is important to make sure you have same number of columns in your file.
You can use Azure functions to validate your file and update it to get equal number of columns in all rows.
You could give it a try to have a local file with row with the maximum number of column and import the schema from the file, else you have to go for Azure Functions where you have to convert the file and then trigger the pipeline.
The excel consist of 62 columns and 7 columns are fixed and rest of them have weeks as in year(week1 to week 52)
I have used a data flow task to unpivot the 53 columns into rows with 2 extra columns year and value.
The problem is that I have the 52 week column names keep changing on every week data load and how to I handle this change in column names in data flow. For a single run it gives the exact output
What you'll want to do here is to implement late-binding of your schema, or what ADF refers to as "schema drift". Instead of setting a hardened "early binding" schema in your Source projection, leave the dataset schema and projection empty.
Next, add a Derived Column after your source and call it "Projection". This is where you'll build your projection using rules to account for your evolving schema.
Build out your canonical model with the column names for your entire year using byName('columnname'). That will tell ADF to look for the existence of the column in single quotes from your source data while also providing a schema that you can use to build out your pivot table.
If you need to cast the values, wrap byName() inside of a casting function, i.e. toString(), toDate(), etc.
I am new to databricks notebooks and dataframes. I have a requirement to load few columns(out of many) in a table of around 14million records into a dataframe. once the table is loaded, I need to create a new column based on values present in two columns.
I want to write the logic for the new column along with the select command while loading the table into dataframe.
Ex:
df = spark.read.table(tableName)
.select(columnsList)
.withColumn('newColumnName', 'logic')
will it have any performance impact? is it better to first load the table for the few columns into the df and then perform the column manipulation on the loaded df?
does the table data gets loaded all at once or row by row into the df? if row by row, then by including column manipulation logic while reading the table, am I causing any performance degradation?
Thanks in advance!!
This really depends on the underlying format of the table - is it backed by Parquet or Delta, or it's an interface to the actual database, etc. In general, Spark is trying to read only necessary data, and if, for example, Parquet is used (or Delta), then it's easier because it's column-oriented file format, so data for each column is placed together.
Regarding the question on the reading - Spark is lazy by default, so even if you put df = spark.read.table(....) as separate variable, then add .select, and then add .withColumn, it won't do anything until you call some action, for example .count, or write your results. Until that time, Spark will just check that table exists, your operations are correct, etc. You can always call .explain on the resulting dataframe to see how Spark will perform operations.
P.S. I recommend to grab a free copy of the Learning Spark, 2ed that is provided by Databricks - it will provide you a foundation for development of the code for Spark/Databricks
How does spark structured streaming let the sink know that a new row is an update of an existing row when run in an update mode? Does it look at all the values of all columns of the new row and an existing row for an equality match or does it compute some sort of hash?
Reading the documentation, we see some interesting information about update mode (bold formatting added by me):
Update Mode - Only the rows that were updated in the Result Table since the last trigger will be written to the external storage (available since Spark 2.1.1). Note that this is different from the Complete Mode in that this mode only outputs the rows that have changed since the last trigger. If the query doesn’t contain aggregations, it will be equivalent to Append mode.
So, to use update mode there needs to be some kind of aggregation otherwise all data will simply be added to the end of the result table. In turn, to use aggregation the data need to use one or more coulmns as a key. Since a key is needed it is easy to know if a row has been updated or not - simply compare the values with the previous iteration of the table (the key tells you which row to compare with). In aggregations that contains a groupby, the columns being grouped on are the keys.
Simple aggregations that return a single value will not require a key. However, since only a single value is returned it will update if that value is changed. An example here could be taking the sum of a column (without groupby).
The documentation contains a picture that gives a good understanding of this, see the "Model of the Quick Example" from the link above.