Showing posts with label Datastage. Show all posts
Showing posts with label Datastage. Show all posts

Friday, 11 November 2016

DataStage Partitioning #2


Keyless partition
    Round Robin
    Entire
    Same
    Random

Round Robin
The first record goes to the first processing node, the second to the second processing node, and so on. When DataStage reaches the last processing node in the system, it starts over. This method is useful for resizing partitions of an input data set that are not equal in size. The round robin method always creates approximately equal-sized partitions. This method is the one normally used when DataStage initially partitions data.
Example: Assume 8 nodes are allocated to store the records then 1st record will go into the first node and the 2nd record will go into the second node ……8th record will go into the eighth node and the 9th record will go into the first node and so on….

Entire
Send all rows down all partitions.
Example: Assume 8 nodes are allocated, then in all the 8 nodes all the records will be passed.

Same
Preserve the same partitioning.
Example: Two stages in a job (Sort and Dataset). In sort stage you have done “Hash” partition and in the dataset you have given “Same” partition. In the dataset the data will be preserved with the hash partition.

Random
DataStage uses a random algorithm to choose where the rows goes. The result of Random is that you cannot know where a row will end up.


Application Execution: Parallel jobs can be executed in two ways
> Sequential
> Parallel
In the first slot of the below figure sequential execution is shown. Parallel job can be executed in two processing, SMP and MPP, in the second and third slot of the below figure it is shown.




Ref - www.ibm.com


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Thursday, 10 November 2016

DataStage Partitioning #1


Partitioning mechanism divides a portion of data into smaller segments, which is then processed independently by each node in parallel. It helps make a benefit of parallel architectures like SMP, MPP, Grid computing and Clusters.

Partition is logical. Partition is to divide memory or mass storage into isolated sections. Memory space will be split into many partitions to have high parallelism. In DOS systems, you can partition a disk, and each partition will behave like a separate disk drive.


Note:
In hash partitioning no specified space will be allocated to a partition in the memory. The partition space is allocated depending upon the data.



Why Partition?
•    Ability to run multiple operating systems, or multiple versions of an operating system, on the same server
•    Ability to improve workload balancing and distribution by managing processor allocations across applications and users on the server
•    Ability to leverage hardware models such as “Capacity on Demand” and "Pay as You Grow.”

Types of partition
  • Hash
  • Modulus
  • DB2
  • Auto
  • Random
  • Range
  • Round Robin
  • Entire
  • Same

Auto

DataStage inserts partitioners as necessary to ensure correct result. Generally chooses Round Robin or Same. Since Datastage has limited awareness of data and business rules, best practice is to explicitly specify partitioning as per requirement when processing requires groups of related records.

Key based partition
  • Hash
  • Modulus
  • DB2
  • Range

Hash
Determines partition based on key value(s). One or more keys with different data type are supported. DataStage’s internal algorithm applied to key values determines the partition. All key values are converted to characters before the algorithm is applied.
Example: Key is State. All “CA” rows go into one partition; all “MA” rows go into one partition. Two rows of the same state never go into different partitions.

Modulus

Partition based on modulus of key divided by the number of partitions. Key is an Integer type. ( partition=MOD(key_value/number of partition) )
Example: Key is OrderNumber (Integer type). Rows with the same order number will all go into the same partition.

DB2
Matches DB2 EEE partitioning, DB2 published its hashing algorithm and DataStage copies that.
Example: This partition is used when loading data into the DB2 table. It takes the partition key from the loading DB2 table and inserts the records effectively. If the partition key is defined in the DB2 database then it takes that Partition key otherwise it defaults to primary key.

Range
The partition is chosen based on a range map, which maps ranges of values to specified partitions. This is similar to Hash, but partition mapping is user-determined and partitions are ordered. Range partitioning requires processing the data twice which makes it hard to find a reason for using it.

This figure gives the clear view of Key based Partitioning and repartitioning.



DataStage's parallel technology operates by a divide-and-conquer technique, splitting the largest integration jobs into subsets ("partition parallelism") and flowing these subsets concurrently across all available processors ("pipeline parallelism"). This combination of pipeline and partition parallelism delivers true linear scalability (defined as an increase in performance proportional to the number of processors) and makes hardware the only mitigating factor to performance.

                However, downstream processes may need data partitioned differently. Consider a transformation that is based on customer last name, but the enriching needs to occur on zip code - for house-holding purposes - with loading into the warehouse based on customer credit card number (more on parallel database interfaces below). With dynamic data re-partitioning, data is re-partitioned on-the-fly between processes - without landing the data to disk - based on the downstream process data partitioning needs.


Ref - www.ibm.com



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Monday, 7 November 2016

Modify Stage - Drop Columns

Sunday, 6 November 2016

Export the jobs from DS windows client



Datastage jobs Export/Import are occasional activity (Deployment time :-)) for a developer But it becomes very tedious if the job list are long or it's daily routine to export or import jobs.

So I have written a batch script (windows script) which we can execute from the Client Machine (where datastage clients are installed) and automate this process.



DSjobExportClient.bat :
Export Script read the job name from the file (ExportJobList.txt) and exports the jobName.dsx from the project to the export base location and maintain the folder structure specified in the ExportJobList.txt file. File "ExportJobList.txt" and “Export.properties” should be updated before running the export script.

Copy the above file on any location of your DS windows client machine and update the "ExportJobList.txt" and “Export.properties” files.
-    Export.properties
-    ExportJobList.txt
-    ExportJobs.bat   



Export.properties :


ExportJobList.txt :


DsExportJobsClient.bat :




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Tuesday, 11 October 2016

5 Tips For Better DataStage Design #16


1. Use 4-node configuration file for unit testing/system testing the job.
2. If there are multiple jobs to be run for the same module. Archive the source files in the after job routine of the last job.
3. Check whether the file exists in the landing directory before moving the sequential file. The ‘mv’ command will move the landing directory if the file is not found.

4. Ensure that the unix files created by any Datastage job is created by the same unix user who has run the job.
5. Make sure that the Short Job Description is filled using ‘Description Annotation’ and it contains the job name as part of the description. Don’t use Annotation for putting the job description.





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Saturday, 8 October 2016

#2 DataStage Solutions to Common Warnings/Error - Null Handling


Warnings/Errors Related to Null Handling -



1.1       When checking operator: When binding output interface field “XXXXX” to field “XXXXX”: Converting a nullable source to a non-nullable result

Cause: This can happen when reading from oracle database or in any processing stage where input column is defined as nullable and metadata in datastage is defined as non-nullable.

Resolution: Convert a nullable field to non  nullable. Need to apply available null functions in datastage or in the query.


1.2       APT_CombinedOperatorController(1),0: Field 'XXXXX' from input dataset '0' is NULL. Record dropped.

Cause: This can happen when there is no null handling mentioned on column and the same column is used in constraints/Stage Varibales.

Resolution:  Provide Null handling function to the column mentioned in constraint/Stage variable.


http://www.datagenx.net/2016/09/datastage-solutions-to-common.html


1.3       Fatal Error: Attempt to setIsNull() on the accessor interfacing to non-nullable field "XXXX".

Cause: This can happen when the column in source is nullable but in DB2 stage its mentioned as Non Nullable

Resolution: Change the Nullable field for the column to “Yes” instead of “No” i.e.


1.4       Exporting nullable field without null handling properties

Cause: This can happen when the columns are mentioned as nullable in sequential file stage and no representation for null values was specified.

Resolution: Specify Null field value in Format tab of sequential file stage.






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Wednesday, 28 September 2016

DS_PXDEBUG - DataStage Parallel Debugging Variable





* Controlled with an environment variable, not exposed on GUI.  DS_PXDEBUG set to activate feature (e.g. DS_PXDEBUG=1)
* Warning logged when job run with this debug feature on.
* Debug collected under a new project-level directory "Debugging" on the server. Subdirectories on a per-job basis, named after the job (created as required). For multi-instance jobs jobs run with a non-empty invocation ID, the directory will be "<jobname>.<invocationID>".
* Internally turns on Osh environment variable APT_MSG_FILELINE so that warnings/errors issued by Osh have source filename & linenumber attached.
* Internally turns on Osh environment variable APT_ENGLISH_MESSAGES so that unlocalised copies of PX-originated error / warning messages are issued in addition to the localised copy (where available).

* Internally turns on Osh environment variables APT_PM_PLAYER_TIMING APT_PM_PLAYER_MEMORY APT_RECORD_COUNTS for more reporting from playes
* Places content of jobs RT_SC<jobnum> directory in the debug location (includes job parameter file, Osh script, parent shell script, any osh and compile scripts associated with transformers). These will be in the same characterset as the original files.
* Places content of jobs RT_BP<jobnum>.O directory in the debug location. Includes library file binaries for PX transformers (plus possibly binaries associated with any Server portions of the job).
* Dump of environment varaible values at startup (same as in the log) placed in a named file in the debug location.
* Dump of osh command options placed in a named file in the debug location. Note that this is as issued from the Server wrapper code. Particularly in the case of Windows, it may not represent exactly what is received by the Osh command line, due to the action of the OshWrapper program,  and interpretation of quotes and backslash-escapes.
* Copy of received raw osh output messages in a named file in the debug location. These will typically be in the host characterset, even though on an NLS system Orchestrate will be originating them in UTF8.
* Copy of PX configuration file placed in the debug location. This will be in the same characterset as the original file.
* This new feature collects together and enhances a number of debug features already exposed with other environment variables. In order to minimise code impact risk, the original features will not be removed at this stage.
* The exception is the "dump of raw osh output messages"; it was previously placed in the &COMO& directory. If the old and the new debug options are both enabled, the new one will take precedence and there will not be a copy in &COMO&. Again this decision has been taken to minimise code change.

Contributed by Christ Thornton 2/2/2007





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Monday, 19 September 2016

#1 DataStage Solutions to Common Warnings/Errors - Datatype


Warnings/Errors Related to Datatype

This Warnings/Errors described in this section are based on Data type like data type mismatch, Column length variations.
Few common Warnings/Errors we get based on data type are :

1.1    Conversion error calling conversion routine decimal_from_string data may have been lost
Cause:    This can happen when the input is in incorrect format while converting into target data type or is contains null value, so that the conversion function is not able to convert it to target data type
Resolution:Check for the correct date format or decimal format and also null values in the date or decimal fields before passing to StringToDecimal functions.
    Similar issue can come for the datastage StringToDate, DateToString,DecimalToString  conversion functions as well




1.2    Possible truncation of input string when converting from a higher length string to lower length stringCause:    This can happen when the input is having length which is more than the length given in output of the same stage
Resolution: Change the length of the specified column in specified stage by giving same length in output as of it is in input.
This can happen in stages like Merge, Sort, Join, Lookup etc

1.3    APT_CombinedOperatorController,0: Numeric string expected for input column 'XXXXX’. Use default value. Cause:    This can happen when the input data type and output data type are different and the type conversion is not handled in transformer.
Resolution: Type conversion function should be applied based on target data type.
Ex:    Input data type = Char, Output data type= BigInt
In this case, the direct mapping with out any type conversion will give this message. Need to provide the type conversion function

Note:
i. The Log normally doesn’t show this message as Warning/ Errror, it will be mentioned as “Info”  
ii. When this happen the records will not be inserted into the table/file.
iii. The stage name will not mentioned in the log, to get the stage name where this issue is happening, need to include 1 Environment Variable in job properties. i.e. $APT_DISABLE_COMBINATION and set it to “True”
   
1.4    Reading the WVARCHAR database column XXXXX into a VARCHAR column can cause data loss or corruption due to character set conversionsCause:    This can happen when the data type is supposed to be Unicode but it’s not mentioned in stage.
Resolution:Change the data type for the column to Varchar along with “Unicode” instead of Varchar alone.  i.e. select Unicode from the Drop down provided in Extended column.

1.5    Schema reconciliation detected a type mismatch for field SSSSS. When moving data from field type CHAR(min=100,max=100) into INT64Cause:    This can happen when the data type is supposed to be Char  but it’s mentioned as BigInt in stage.
Resolution:Change the data type for the column to Char with length 100 instead of BigInt in corresponding stage.






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Monday, 29 August 2016

Modify Stage - What's been promised


Modify stage, one of the most un-used stage in DataStage but very useful in terms of performance tuning. It is advisable to Developers not to use transformer stage to just Trimming or NULL handling but if and only if in the case when they are aware and comfortable with the syntax and derivations supported by modify stage as there is no drop down or right click options to help us with functions/synatx.
http://buff.ly/2bqOV7Z

The definition of Modify Stage as IBM documented -

"The Modify stage alters the record schema of its input data set. The modified data set is then output. You
can drop or keep columns from the schema, or change the type of a column.
The Modify stage is a processing stage. It can have a single input link and a single output link."


http://www.ibm.com/support/knowledgecenter/SSZJPZ_11.3.0/com.ibm.swg.im.iis.ds.parjob.dev.doc/topics/c_deeref_Modify_Stage.html
The operations offered by Modify Stage is -
1. Dropping of Columns
2. Keeping Columns
3. Create Duplicate Columns
4. Rename Columns
5. Change Null-ability of Columns
6. Change Data Type of Columns


Stage is supporting only 1 input stage and 1 output stage.

All these operations are easily done in other stages such as copy, transformer etc. But why Modify stage is required or can say, we should use this?

Answer of this Datastage problem is simple - Performance Tuning of jobs

** Why not to use Transformer -
Cause, Whenever we call the transformer functions, data processed to and through C++ code (transformer implementation) which cause the performance letancy(delay). This delay is negligible for less no of records than higher no. So, Prefer the Modify stage when no of records are high to process.

Keep looking for this place as we are going to learn lot of tips on Modify stage.


Get this Article as PDF - http://bit.ly/2fdroHR and  http://bit.ly/2fdrDCr



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Monday, 22 August 2016

5 Tips For Better DataStage Design #15



1. Stage variable does not accept null value. Hence no null able column should be directly mapped to stage variable without null handling.

2. Use of SetNull() function in stage variables should be avoided because it causes compilation error.

http://www.datagenx.net/2016/08/5-tips-for-better-datastage-design-15.html

3. If input links are not already partitioned on join key then they should be hash partitioned on the join key in join stage. In case of multiple join key it is recommended to partition on one key and sort by the other keys.

4. If there is a need to do the repartition on an input link then we need to clear the preserve partitioning flag in the previous stage. Otherwise it will generate warning in job log.

5. If database table has less volume of data as a reference then it is good to use lookup stage.

6. It is always advisable to avoid Transformation stage. Because the Transformation stage is not written in DataStage native language, instead it is written in c. So every time you compile a job it embeds the c code with the native code in the executable file, which degrades the performance of the job.





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Wednesday, 3 August 2016

#3 How to Copy DataSet from One Server to Another Server

This post is third and last of How to Copy DataSet from One Server to Another Server Series

We have generated a populated a dataset and identified the files which we need to move to another server serverB from serverA

Continue.......

4. Reading the dataset on another server

This is the most crucial step, Now all 4 files are moved on serverB or the common location which can be accessible from serverB.

For my case, common dir is my home - /home/users/atul


A. Change the default.apt file
We need to change the fastname in default.apt (config file) which we copied from the serverA, [ NOT the default.apt for serverB]

Open the file in any text editor or vi and change as below screen shot -


Temporarily create the "resource disk" and "resource scratchdisk" location if not existing as defined in above config file.

B. Copy the dataset data files 

Move the dataset data file from common directory to "resource disk" as defined in config file.

cp ~/dummy.ds.* /opt/IBM/InformationServer/Server/DataSets/


Now, all files locations are like -

Config file and Dataset descriptor file - my home dir or common dir
Dataset data files - /opt/IBM/InformationServer/Server/DataSets/


Design a job which will read thess dataset files and populate data into sequential file or any other output.


Job Paramaters -
APT_CONFIG_FILE = /home/users/atul/default.apt

DataSet Properties
DataSet File - /home/users/atul/dummy.ds

That is all, you can read the copied dataset on serverB, you can populate this data to some other output such as seq file, table so that you can avoid the use of copied default.apt config file which is not for serverB.

Try it out, let me know if you have any question.




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Monday, 1 August 2016

#2 How to Copy DataSet from One Server to Another Server


This post is second part of How to Copy DataSet from One Server to Another Server

Continue.......

 After generating the dummy dataset, next step is to identify the files which we need to copy.

2. Files which we need to move

a. APT_CONFIG_FILE - configuration file which used in dataset
b. DataSet Descriptor file - *.ds file, in our case it is dummy.ds
c. DataSet Data files - Actual data files which stored in RESOURCE DISK location

So let's get all the path which we need to access -

APT_CONFIG_FILE = /opt/IBM/InformationServer/Server/Configulations/default.apt
RESOURCE DISK = /opt/IBM/InformationServer/Server/DataSets
DATASET LOC = /home/users/atul/dummy.ds



Use commands or any FTP tool to copy these files in a shared location which can be accessible from another server (serverB)

For my case, I have stored all of them into my linux home direcory which is common in both server.

So I have executed these commands to copy all the required files into my home directory.


cp  /opt/IBM/InformationServer/Server/Configulations/default.apt ~
cp  /opt/IBM/InformationServer/Server/DataSets/dummy.ds.* ~
cp  /home/users/atul/dummy.ds ~


Now, my home directory is having these files -


You can copy these 4 files on serverB where you want to move your dataset. I am not doing the same as my home directory is common for both server.

3. Why we need these files only

Config file was used by datastage to create dataset ( descriptot file, data files, data file location)
So, we needed - config file, dataset descriptor file and dataset data files.





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Saturday, 30 July 2016

#1 How to Copy DataSet from One Server to Another Server



Hi Guys...
I've been asked so many times that how can we move/copy one dataset from one server to another So here is the way which I follow.

At very first step, Analyze if you can avoid this by using some other way like creating sequential file and ftp Or load the data into temporary table which can be accessible on another server, if using datastage packs then via mqs, xml or json formats etc. Why I am suggesting these solutions coz these are easy to design and guaranteed the data quality at other end.

If above solutions are not possible, please follow the below steps -

Points I am going to cover here -
1. Generating a dummy dataset
2. Files which we need to move
3. Why we need these files only
4. Reading the dataset on another server

http://www.datagenx.net/2016/06/datastage-quiz-1.html

 

1. Generating a dummy dataset

I have created a dummy job which is generating a dataset with default APT_Config_file which has 2 nodes.

http://www.datagenx.net/2015/12/how-to-use-universe-shell-uvsh-in.html





Here, I am generating 10 dummy rows with the help of Row Generator stage and storing them into a datasset.

a. Config File - I am using the default config file (replaced the server name in "fastname" with serverA)

APT_CONFIG_FILE = /opt/IBM/InformationServer/Server/Configulations/default.apt

http://www.datagenx.net/2016/06/5-tips-for-better-datastage-design-14.html

check out the "resource disk" location in config file, we need it for further processing

RESOURCE DISK = /opt/IBM/InformationServer/Server/DataSets

b. dataset location - I have created this dataset in my home dir named dummy.ds

DATASET LOC = /home/atul/dummy.ds


Keep looking for next post........





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Friday, 22 July 2016

DataStage job got Queued



Today, when I was working on my DEV server ( DS V11.5), the very first time I have faced this 'job queued' status so I waited for some time but my jobs were still in 'queue' so googled this.
I was surprise to know :P that IBM introduced this feature from V9.1 and here I was totally unaware of that.
So sharing what I have found on Developer's GOD - Google   :-)

              Courtesy - www.ibm.com


IBM introduced this feature as Workload Management (WLM) where it is capable to manage the priority and order of DataStage jobs which basically derived from many points such as Max no of running jobs, Memory and Server load etc.
You can manage all these configutations setting under Operation Console. More information you can find on this IBM Knowledge Base Link


I have resolved my issue by resetting the value of WLMON (=0) in DSODBConfig.cfg file.



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Friday, 24 June 2016

DataStage Quiz #2

http://www.datagenx.net/2016/06/datastage-quiz-1.html

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Tuesday, 21 June 2016

DataStage Quiz #1

http://www.datagenx.net/2016/06/datastage-quiz-1.html

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Thursday, 16 June 2016

5 Tips For Better DataStage Design #14



1. The use of Lookup stage depends upon the volume of data.Sparse lookup type should be used when primary input data volume is small.If the reference data volume is more, Lookup Stage should be avoided.

2. Use of ORDER BY clause in the database is good as compared to use of sort stage.



3. In Dtatastage Administrator, Tuned the 'Project Tunable' for better performance.

4. For Funnel, the use of this stage reduces the performance of a job. Funnel Stage should be run in continuous mode.

5. If the hash file is used only for lookup then "enable Preload to memory". This will improve the performance.






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Thursday, 19 May 2016

5 Tips For Better DataStage Design #13



1. The query used in the database should be in such a way that required number of rows are fetched. Do not extract the columns which are not required.

2. For parallel jobs, sequential File should not be read using same partitioning.


http://www.datagenx.net/2016/05/5-tips-for-better-datastage-design-13.html


3. For huge amount of data, use of sequential file stage is not a good practice. This stage also should not be used for intermediate storage between jobs. It degrades the performance of the job.

4. The number of lookups in a job design should be minimum. Join stage is a good alternative to lookup stage.

5. For parallel jobs, proper portioning method is to be used for better job performance and accurate flow of data.





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Friday, 6 May 2016

DS Fatal Error: Destination "APT_TRinput0Rec0" is already bound


Fatal Error: Destination "APT_TRinput0Rec0" is already bound - Transformer Stage Error


Solutions:
* Check if the output stage is having identical column names
* Check if RCP is enabled in input links

If yes,
Rename the target output name accordingly
or Disable the RCP>





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Tuesday, 3 May 2016

Otherwise Constraint - A Quick DataStage Recipe


Recipe:

How to use "Otherwise" constraint in Transformer Stage


www.datagenx.net

How To:

To use "Otherwise" constraint in Transformer stage, Order of link is important.
Typically link with "Otherwise" constraint should be last in Transformer stage link order




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