lineage | Family Tree Data Expression Engine | Data Visualization library

I'm assuming that each row contains the same category (e.g. order, family, species etc):

In spark there are two types of operations: transformations and actions. A transformation on a dataframe will return another dataframe and an action on a dataframe will return a value.

Transformations are lazy, so when a transformation is performed spark will add it to the DAG and execute it when an action is called.

Suppose, you read a file into a dataframe, then perform a filter, join, aggregate, and then count. The count operation which is an action will actually kick all the previous transformation.

If we call another action(like show) the whole operation is executed again which can be time consuming. So, if we want not to run the whole set of operation again and again we can cache the dataframe.

Few pointers you can consider while caching:

1. Cache only when the resulting dataframe is generated from significant transformation. If spark can regenerate the cached dataframe in few seconds then caching is not required.
2. Cache should be performed when the dataframe is used for multiple actions. If there are only 1-2 actions on the dataframe then it is not worth saving that dataframe in memory.

This was a fun problem to investigate :-)

Your issue is a classic problem of recursion, which is a kinda hard concept the first time you see it.

As you don't know how many recursions there will be, a long format is better.

Here, the recursive function will call itself as long as there are links to parse. The escape condition is based on the number of remaining links. However, I added a max_r value to avoid being stuck in an infinite loop, in the case you have an item linking to itself (directly or not).

The initiation loop (if(r==0)) is only here to prepare the long format, where a single item can be on multiple rows: there is a source item, a current item and a current recursion number. This should be externalized to simplify the function (then you start at r=1) if you don't care to change your dataset format.

You just need to add an equal sign :

Terraform uses this state to create plans and make changes to your infrastructure. Prior to any operation, Terraform does a refresh to update the state with the real infrastructure. In this case, you only change the resource name and keep the existing resource_group name. Terraform will require to import the existing infrastructure into the state.

Warning: Terraform expects that each remote object it is managing will be bound to only one resource address, which is normally guaranteed by Terraform itself having created all objects. If you import existing objects into Terraform, be careful to import each remote object to only one Terraform resource address.

You will import the state with the command terraform import azurerm_resource_group.test_b . Once you have imported the existing infrastructure, terraform will try to add the resource azurerm_resource_group.test_b according to the latest state.

You can only generate lineage within a job, using DataStage. That is, you can answer questions "show where data flows to" and "show where data comes from" within the context of the one job. You can access this functionality by right-click on the stage about which you're interested in asking the question.

Beyond that, you can generate data lineage more formally using the Information Governance Catalog tool. If you are not using shared metadata resources, and not generating operational metadata when running jobs, then the lineage report will be based on design data only.

If you share the table definitions you use in your jobs into the common metadata repository (from the Repository menu in DataStage Designer), then you will get better lineage results in IGC. If you generate operational metadata when running your jobs then these operational metadata will also be available in lineage reports.

Don't forget that DataStage jobs are not included in lineage by default. You need to mark at least the jobs of interest as "include for lineage" in the Administration page of IGC.

Can you check here :

https://www.programcreek.com/scala/org.apache.spark.SparkEnv

I think you have to set the value like

To everyone who is getting this Problem in the Future:

Just flash the following File:

https://forum.xda-developers.com/t/tool-a320fl-f-y-repartition-script-for-vendor-support.3951105/

Lets assume the data returned looks like this mockup (in the question it is strangely formatted):