Is it possible to overwrite a record in the target if specific conditions are met using spark without reading the target into a dataframe? For example, I know we can do this if both sets of data are loaded into dataframes, but I would like to know if there is a way to perform this action without loading the target into a dataframe. Basically, a way to specify overwrite/update conditions.
I am guessing no, but I figured I would ask before I dive into this project. I know we have the write options of append and overwrite. What I really want is, if a few specific columns already exist in the data target, then overwrite it and fill in the other columns with the new data. For example:
File1:
id,name,date,score
1,John,"1-10-17",35
2,James,"1-11-17",43
File2:
id,name,date,score
3,Michael,"1-10-17",23
4,James,"1-11-17",56
5,James,"1-12-17",58
I would like the result to look like this:
id,name,date,score
1,John,"1-10-17",35
3,Michael,"1-10-17",23
4,James,"1-11-17",56
5,James,"1-12-17",58
Basically, Name and Date columns act like primary keys in this scenario. I want updates to occur based on those two columns matching, otherwise make a new record. As you can see id 4 overwrites id 2, but id 5 appends because the date column did not match. Thanks ahead guys!
Related
We are evaluating NEsper. Our focus is to monitor data quality in an enterprise context. In an application we are going to log every change on a lot of fields - for example in an "order". So we have fields like
Consignee name
Consignee street
Orderdate
....and a lot of more fields. As you can imagine the log files are going to grow big.
Because the data is sent by different customers and is imported in the application, we want to analyze how many (and which) fields are updated from "no value" to "a value" (just as an example).
I tried to build a test case with just with the fields
order reference
fieldname
fieldvalue
For my test cases I added two statements with context-information. The first one should just count the changes in general per order:
epService.EPAdministrator.CreateEPL("create context RefContext partition by Ref from LogEvent");
var userChanges = epService.EPAdministrator.CreateEPL("context RefContext select count(*) as x, context.key1 as Ref from LogEvent");
The second statement should count updates from "no value" to "a value":
epService.EPAdministrator.CreateEPL("create context FieldAndRefContext partition by Ref,Fieldname from LogEvent");
var countOfDataInput = epService.EPAdministrator.CreateEPL("context FieldAndRefContext SELECT context.key1 as Ref, context.key2 as Fieldname,count(*) as x from pattern[every (a=LogEvent(Value = '') -> b=LogEvent(Value != ''))]");
To read the test-logfile I use the csvInputAdapter:
CSVInputAdapterSpec csvSpec = new CSVInputAdapterSpec(ais, "LogEvent");
csvInputAdapter = new CSVInputAdapter(epService.Container, epService, csvSpec);
csvInputAdapter.Start();
I do not want to use the update listener, because I am interested only in the result of all events (probably this is not possible and this is my failure).
So after reading the csv (csvInputAdapter.Start() returns) I read all events, which are stored in the statements NewEvents-Stream.
Using 10 Entries in the CSV-File everything works fine. Using 1 Million lines it takes way to long. I tried without EPL-Statement (so just the CSV import) - it took about 5sec. With the first statement (not the complex pattern statement) I always stop after 20 minutes - so I am not sure how long it would take.
Then I changed my EPL of the first statement: I introduce a group by instead of the context.
select Ref,count(*) as x from LogEvent group by Ref
Now it is really fast - but I do not have any results in my NewEvents Stream after the CSVInputAdapter comes back...
My questions:
Is the way I want to use NEsper a supported use case or is this the root cause of my failure?
If this is a valid use case: Where is my mistake? How can I get the results I want in a performant way?
Why are there no NewEvents in my EPL-statement when using "group by" instead of "context"?
To 1), yes
To 2) this is valid, your EPL design is probably a little inefficient. You would want to understand how patterns work, by using filter indexes and index entries, which are more expensive to create but are extremely fast at discarding unneeded events.
Read:
http://esper.espertech.com/release-7.1.0/esper-reference/html_single/index.html#processingmodel_indexes_filterindexes and also
http://esper.espertech.com/release-7.1.0/esper-reference/html_single/index.html#pattern-walkthrough
Try the "previous" perhaps. Measure performance for each statement separately.
Also I don't think the CSV adapter is optimized for processing a large file. I think CSV may not stream.
To 3) check your code? Don't use CSV file for large stuff. Make sure a listener is attached.
I am pretty new to Pentaho so my query might sound very novice.
I have written a transformation in which am using CSV file input step and table input step.
Steps I followed:
Initially, I created a parameter in transformation properties. The
parameter birthdate doesn't have any default value set.
I have used this parameter in postgresql query in table input step
in the following manner:
select * from person where EXTRACT(YEAR FROM birthdate) > ${birthdate};
I am reading the CSV file using CSV file input step. How do I assign the birthdate value which is present in my CSV file to the parameter which I created in the transformation?
(OR)
Could you guide me the process of assigning the CSV field value directly to the SQL query used in the table input step without the use of a parameter?
TLDR;
I recommend using a "database join" step like in my third suggestion below.
See the last image for reference
First idea - Using Table Input as originally asked
Well, you don't need any parameter for that, unless you are going to provide the value for that parameter when asking the transformation to run. If you need to read data from a CSV you can do that with this approach.
First, read your CSV and make sure your rows are ok.
After that, use a select values to keep only the columns to be used as parameters.
In the table input, use a placeholder (?) to determine where to place the data and ask it to run for each row that it receives from the source step.
Just keep in ming that the order of columns received by the table input (the columns out of the select values) is the same order that it will be used for the placeholders (?). This should not be a problem with your question that uses only one placeholder, but keep that in mind as you ramp up using Pentaho.
Second idea, using a Database Lookup
This is another approach where you can't personalize the query made to the database and may experience a better performance because you can set a "Enable cache" flag and if you don't need to use a function on your where clause this is really recommended.
Third idea, using a Database Join
That is my recommended approach if you need a function on your where clause. It looks a lot like the Table Input approach but you can skip the select values step and select what columns to use, repeat the same column a bunch of times and enable a "outer join" flag that returns the rows without result from the query
ProTip: If you feel the transformation running too slow, try to use multiple copies from the step (documentation here) and obviously make sure the table have the appropriate indexes in place.
Yes there's a way of assigning directly without the use of parameter. Do as follows.
Use Block this step until steps finish to halt the table input step till csv input step completes.
Following is how you configure each step.
Note:
Postgres query should be select * from person where EXTRACT(YEAR
FROM birthdate) > ?::integer
Check Execute for each row and Replace variables in in Table input step.
Select only the birthday column in CSV input step.
I have this kind of data:
I need to transpose this data into something like this using Talend:
Help would be much appreciated.
dbh's suggestion should work indeed, but I did not try it.
However, I have another solution which doesn't require to change input format and is not too complicated to implement. Indeed the job has only 2 transformation components (tDenormalize and tMap).
The job looks like the following:
Explanation :
Your input is read from a CSV file (could be a database or any other kind of input)
tDenormalize component will Denormalize your column value (column 2), based on value on id column (column 1), separating fields with a specific delimiter (";" in my case), resulting as shown in 2 rows.
tMap : split the aggregated column into multiple columns, by using java's String.split() method and spreading the resulting array into multiple columns. The tMap should like like this:
Since Talend doesn't accept to store Array objects, make sure to store the splitted String in Object format. Then, cast that object into Array on the right side of the Map.
That approach should give you the expected result.
IMPORTANT:
tNormalize might shuffle the rows, meaning for bigger input, you might encounter unsorted output. Make sure to sort it if needed or use tDenormalizeSortedRow instead.
tNormalize is similar to an aggregation component meaning it scans the whole input before processing, which results into possible performance issues with particularly big inputs (tens of millions of records).
Your input is probably wrong (you have 5 entries with 1 as id, and 6 entries with 2 as id). 6 columns are expected meaning you should always have 6 lines per id. If not, then you should implement dbh's solution, and you probably HAVE TO add a column with a key.
You can use Talend's tPivotToColumnsDelimited component to achieve this. You will most likely need an additional column in your data to represent the field name.
Like "Identifier, field name, value "
Then you can use this component to pivot the data and write a file as output. If you need to process the data further, read the resulting file with tFileInoutDelimited .
See docs and an example at
https://help.talend.com/display/TalendOpenStudioComponentsReferenceGuide521EN/13.43+tPivotToColumnsDelimited
What is the best way to delete from a table using Talend?
I'm currently using a tELTJDBCoutput with the action on Delete.
It looks like Talend always generate a DELETE ... WHERE EXISTS (<your generated query>) query.
So I am wondering if we have to use the field values or just put a fixed value of 1 (even in only one field) in the tELTmap mapping.
To me, putting real values looks like it useless as in the where exists it only matters the Where clause.
Is there a better way to delete using ELT components?
My current job is set up like so:
The tELTMAP component with real data values looks like:
But I can also do the same thing with the following configuration:
Am I missing the reason why we should put something in the fields?
The following answer is a demonstration of how to perform deletes using ETL operations where the data is extracted from the database, read in to memory, transformed and then fed back into the database. After clarification, the OP specifically wants information around how this would differ for ELT operations
If you need to delete certain records from a table then you can use the normal database output components.
In the following example, the use case is to take some updated database and check to see which records are no longer in the new data set compared to the old data set and then delete the relevant rows in the old data set. This might be used for refreshing data from one live system to a non live system or some other usage case where you need to manually move data deltas from one database to another.
We set up our job like so:
Which has two tMySqlConnection components that connect to two different databases (potentially on different hosts), one containing our new data set and one containing our old data set.
We then select the relevant data from the old data set and inner join it using a tMap against the new data set, capturing any rejects from the inner join (rows that exist in the old data set but not in the new data set):
We are only interested in the key for the output as we will delete with a WHERE query on this unique key. Notice as well that the key has been selected for the id field. This needs to be done for updates and deletes.
And then we simply need to tell Talend to delete these rows from the relevant table by configuring our tMySqlOutput component properly:
Alternatively you can simply specify some constraint that would be used to delete the records as if you had built the DELETE statement manually. This can then be fed in as the key via a main link to your tMySqlOutput component.
For instance I might want to read in a CSV with a list of email addresses, first names and last names of people who are opting out of being contacted and then make all of these fields a key and connect this to the tMySqlOutput and Talend will generate a DELETE for every row that matches the email address, first name and last name of the records in the database.
In the first example shown in your question:
you are specifically only selecting (for the deletion) products where the SOME_TABLE.CODE_COUNTRY is equal to JS_OPP.CODE_COUNTRY and SOME_TABLE.FK_USER is equal to JS_OPP.FK_USER in your where clause and then the data you send to the delete statement is setting the CODE_COUNTRY equal to JS_OPP.CODE_COUNTRY and FK_USER equal to JS_OPP.CODE_COUNTRY.
If you were to put a tLogRow (or some other output) directly after your tELTxMap you would be presented with something that looks like:
.----------+---------.
| tLogRow_1 |
|=-----------+------=|
|CODE_COUNTRY|FK_USER|
|=-----------+------=|
|GBR |1 |
|GBR |2 |
|USA |3 |
'------------+-------'
In your second example:
You are setting CODE_COUNTRY to an integer of 1 (your database will then translate this to a VARCHAR "1"). This would then mean the output from the component would instead look like:
.------------.
|tLogRow_1 |
|=-----------|
|CODE_COUNTRY|
|=-----------|
|1 |
|1 |
|1 |
'------------'
In your use case this would mean that the deletion should only delete the rows where the CODE_COUNTRY is equal to "1".
You might want to test this a bit further though because the ELT components are sometimes a little less straightforward than they seem to be.
I have a large file contain > 10 million line. I want to get dupplicate line using MapReduce.
How can I solve this problem?
Thanks for help
You need to make use of the fact that the default behaviour of MapReduce is to group values based on a common key.
So the basic steps required are:
Read in each line of you file to you mapper, probably using something like the TextInputFormat.
Set the output Key (Text object) to the value of each line. The contents of the value doesn't really matter. You can just set it to a NullWritable if you want.
In the reduce check the number of values grouped for each key. If you have more than one value you know you have a duplicate.
If you just want the duplicate values, write out the keys that have multiple values.