I need to export some database of arround 180k objects to JSON files so I can retain data structure in certain way that suits me for later import to other database. However because of amount of data, I wanto to separate and group data based on some atribute value from database records itself. So all records that have attribute1=value1, I want to go to value1.json, value2.json and so on.
However I still haven't figured out how to do this kind of job. I am using RepositoryItemReader and JsonFileWriter.
I started by filtering data on that attribute and running separate exports, just to verify that works, however I need to do this so I can automate whole process and let it work.
Can this be done?
There are several ways to do that. Here are a couple of options:
Option 1: parallel steps
You start by creating a tasklet that calculates the distinct values of the attribute you want to group items by, and you put this information in the job execution context.
After that, you create a flow with a chunk-oriented step for each value. Each chunk-oriented step would process a distinct value and generate an output file. The item reader and writer would be step-scoped bean and dynamically configured with the information from the job execution context.
Option 2: partitioned step
Here, you would implement a Partitioner that creates a partition for each distinct value. Each worker step would then process a distinct value and generate an output file.
Both options should perform equally in your use-case. However, option 2 is easier to implement and configure in my opinion.
Related
I have my settings in my ADF Sink to Clear the folder but Partitioned via an ID
But this sink already has other partitions in that exists that I do not want to remove.
If an ID comes in, I just want to clear that specific folder/partition but it is actually clearing the full folder versus just partition. Am I missing a setting?
To overwrite only the partitions that appear in new data and keep the rest of the old partition data, you can make use of the pre commands present in the settings tab of the dataflow sink. Look at the following demonstration.
The following is my initial data which I have partitioned based on id.
Now let's say the following is the new data that you are going to write. Here, according to the requirement, you want to overwrite the partitions that are present and keep the rest as it is.
First, we need to get the distinct key column values (id in my case). Then use them in the pre commands of sink settings to remove files only from these partitions.
Take the above data (the 2nd image data) as dataflow1 source. Apply derived column transformation to add a new column with constant value say 'xxx' (to group based on this column and apply collect() aggregate function).
Group by this new column and use the aggregate as distinct(collect(id)).
Now for sink, choose as Cache, check write to activity output. When you run this dataflow in the pipeline, the debug output would be:
Send this array value to a parameter created in another dataflow where you make necessary changes and overwrite partitions. Give the following dynamic content
#activity('Data flow1').output.runStatus.output.sink1.value[0].val
Now in this second dataflow, the source is the same data used in first dataflow. For sink, instead of selecting clear the folder option, scroll down where you can find pre/post commands sections where you give the following dynamic content:
concat('rm /output/id=',toString($parts),'/*')
Now when you run this pipeline, it successfully executes and runs the overwrites only the required partitions, whereas keeps the other partitions.
The following is a sample partition data (id=2) to show that the data is overwritten (only one part file with required data will be available).
Why do not you specify the filename and write it to 1 single file.
I have simple pipeline that has a Copy activity to populate a table. That task is based on a query and will only ever return 1 row.
The problem I am having is that I want to reuse the value from one of the columns (batch number) to set a variable so that at the end of the pipeline I can use a Stored Procedure to log that the batch was processed. I would rather avoid running the query a second time in a lookup task so can I make use of the data already being returned?
I have tried duplicating the column in the Copy activity and then mapping that to something like #BatchNo but that fails and have even tried to add a Set Variable task but can't figure out how to take a single column #{activity('Populate Aleprstw').output} does not error but not sure what that will actually do in this case.
Thanks and sorry if its a silly question.
Cheers
Mark
I always do it like this:
Generate a batch number (usually with a proc)
Use a lookup to grab it into a variable
Use the batch number in all activities (might be multiple copes, procs etc.)
Write the batch completion
From your description it seems you have the batch embedded in the data copy from the start which is not typical.
If you must do it this way, is there really an issue with running a lookup again?
Copy activity doesn't return data like that, so you won't be able to capture the results that way. With this design, running the query again in a Lookup is the best option.
Is the query in the Source running on the same Server as the Sink? If so, you could collapse the entire operation into a Stored Procedure that returns the data point you are trying to capture.
I have a use-case for which I could use spring batch job which I could design in following ways.
1) First Way:
Step1 (Chunk oriented step): Read from the file —> filter, validate and transform the read row into DTO (data transfer object), if there are any errors, store errors in DTO itself —> Check if any of the DTOs has errors , if not write to Database. If yes, write to an error file.
However, problem with this way is - I need this entire JOB in transaction boundary. So if there is a failure in any of the chunks then I don’t want to write to DB and want to rollback all successful writes till that point in DB. Above way forces me to write rollback logic for all successful writes if there is a failure in any of the chunks.
2) Second way
Step 1 (Chunk oriented step): Read items from the file —> filter, validate and transform the read row in DTO (data transfer object). This does store the errors in the DTO object itself.
Step 2 (Tasklet): Read entire list (and not chunks) of DTOs created from step 1 —> Check if any of the DTOs has errors populated in it. If yes, then abort the writing to DB and fail the JOB.
In second way, I get all benefits of chunk processing and scaling. At the same time I have created transaction boundary for entire job.
PS: In both ways in their first step there won’t be any step failure, if there is failure; errors are stored in DTO object itself. Thus, DTO object is always created.
Question is - Since I am new to Spring batch, is it a good pattern to go with second way. And is there a way that I can share data between steps so that entire List of DTOs is available to second step (in second way above) ?
In my opinion, trying to process the entire file in a single transaction (ie a transaction at the job level) is not the way to go. I would proceed in two steps:
Step 1: process the input and writes errors to the file
Step 2: this step is conditioned by step1. If no errors has been detected in step 1, then save data to the db.
This approach does not require to write data to the database and roll it back if there are errors (as suggested by option 1 in your description). It only writes to the database when everything is ok.
Moreover, this approach does not require holding a list of items in-memory as suggested by option 2, which could be inefficient in terms of memory usage and performs poorly if the file is big.
I want to take data from 2 databases and copy(coalesce) it into 1 using Data factory.
The issue is: It seems that multiple inputs is not allowed for copy activities.
So i resorted to having 2 different datasets which are exact copies but with a different name... and then putting 2 different activities into the 1 pipeline which use their specific output dataset.
It just seems odd and wrong to do it this way.
Can i have some help.
This is what my diagram currently looks like:
Is there no way of just copying data from 2 seperate databases (which have the same structure but different data) to the 1 database?
The short answer is yes. But you need to work within the constraints of how ADF handles this.
A couple of things to help...
You'll always need at least 2 activities to do this when using the copy type activity. Microsoft of course charges per activity execution in ADF, so they aren't going to allow you to take shortcuts having many inputs and output per single copy activity (single charge).
The approach you show above is ok and to pass the ADF validation as you've found you simply need to have the output datasets created separately and called different things. Even if they still refer to the same underlying target table etc. This is really only a problem for the copy activity. What you could do is land the data firstly into separate staging tables in the Azure target database just for the copy (1:1). Then have a third downstream activity that executes a stored procedure that does the union of tables. In this case you could have 2 inputs to 1 output in the activity if you want to have that level of control in ADF.
Like this:
Final point, if you don't want the activities to execute in parallel you could chain the datasets to enforce a fake dependency or add a simple 'delay' clause to one of the copy operations. A delay on an activity would be simpler than provisioning a time slice offset.
Hope this helps
I am using Spring-batch 3.0.4 stable. While submitting a job I add some specific parameters to its execution, say, a tag. Jobs information is persisted in the DB.
Later on I will need to retrieve all the executions marked with a particular tag.
Currently I see 2 options:
Get all job instances with org.springframework.batch.core.explore.JobExplorer#findJobInstancesByJobName. For each instance get all available executions with org.springframework.batch.core.explore.JobExplorer#getJobExecutions. Filter the resulting collection of executions checking its JobParameters.
Write my own JdbcTemplate-based DAO implementation to run the select query.
While the former option seems pretty inefficient, the latter one suggests writing extra code to deal with the Spring-specific database tables structure.
Is there any option I am missing here?