Disable auto insertion of Partition and Sort

Partitioner insertion and sort insertion each make writing a flow easier by alleviating the need for a user to think about either partitioning or sorting data. By examining the requirements of operators in the flow, the parallel engine can insert partitioners, collectors and sorts as necessary within a data flow.

However, there are some situations where these features can be a avoided or not needed.
If data is pre-partitioned and pre-sorted, and the InfoSphere DataStage job is unaware of this, you could disable automatic partitioning and sorting for the whole job by setting the following environment variables while the job runs:

Order of temporary disk space uses by Sort stage

The Sort stage uses temporary disk space when performing a sort. It looks in the following locations, in the
following order, for this temporary space.

1. Scratch disks in the disk pool sort (you can create these pools in the configuration file).
2. Scratch disks in the default disk pool (scratch disks are included here by default).

Things need to consider while developing a Datastage job

Datasets
Datasets are the best when storing the results intermediately. Datasets will keep the partitions and sort order if set. This will save re-partitioning, sorting and would make the job more robust.

Performance of the job can be improved if:
1) Unnecessary column are removed from the up and down stream links.
2) Removing these unnecessary columns will help reducing the memory consumption.
3) Always specify the list of columns in the select statement when reading from database. This will not bring unnecessary column data in the job which will save memory and network consumption.
4) Use RCP very carefully.
5) Understand the data-type before using them in the job. Do the data profiling before bringing data in the job.

Performance Tunings in DataStage

JOB LEVEL

• Parametrize all the inputs needed for the jobs; avoid hard coding of parameters like username, password, directory paths etc.
• By using the environment variable ‘APT_CONFIG_FILE’ as a job parameter, user can dynamically change the number of nodes being used for processing a particular job.
• For reading/writing data from large tables/files, make use of the environment variable ‘APT_BUFFER_MAXIMUM_MEMORY’. It can be used to change the memory buffer size being used for each stage.
• It is recommended to set the environment variable $APT_DUMP_SCORE to a value of 1. When this environment variable is set, an entry is placed in the WebSphere DataStage job log showing the actual runtime structure (processes, their associated internal operators, datasets, nodes, etc) used to execute the job flow.

DataStage Scenario - Design5 - job1

DataStage Scenario Problem -->  DataStage Scenario - Problem5

  

Solution Design :

a) Job Design :

Below is the design which can achieve the output as we needed. Here, we are reading seq file as a input, then data is passing through a Sort and Transformer stage to achieve the output.

DataStage Scenario - Design4 - job1

DataStage Scenario Problem -->  DataStage Scenario - Problem4

Solution Design :

a) Job Design :

Below is the design which can achieve the output as we needed. Here, we are reading seq file as a input, then data is passing through a Sort and Transformer stage to achieve the output.

DataStage Scenario - Design3 - job1

DataStage Scenario Problem -->  DataStage Scenario - Problem3

Solution Design :

a) Job Design :

Below is the design which can achieve the output as we needed. Here, we are reading seq file as a input, then data is passing through a Sort and Transformer stage to achieve the output.

DataStage Scenario - Design2 - job3

DataStage Scenario Problem -->  DataStage Scenario - Problem2

Solution Design :


a) Job Design :

Datastage Common Errors and Solutions

1.     While running ./NodeAgents.sh start command... getting the following error: “LoggingAgent.sh process stopped unexpectedly”

SOL:   needs to kill LoggingAgentSocketImpl

              Ps –ef |  grep  LoggingAgentSocketImpl   (OR)

              PS –ef |               grep Agent  (to check the process id of the above)

2.     Warning: A sequential operator cannot preserve the partitioning of input data set on input port 0

SOL:    Clear the preserve partition flag before Sequential file stages.

List of Environment Variables in DataStage

General Job Administration

APT_CHECKPOINT_DIR
APT_CLOBBER_OUTPUT
APT_CONFIG_FILE
APT_DISABLE_COMBINATION
APT_EXECUTION_MODE
APT_ORCHHOME
APT_STARTUP_SCRIPT
APT_NO_STARTUP_SCRIPT
APT_STARTUP_STATUS
APT_THIN_SCORE

Why Entire partition is used in LOOKUP stage ?

Entire partition has all data across the nodes So while matching(in lookup) the records all data should be present across all nodes.

DataStage Scenario - Problem2

Goal : To get the Unique and Duplicates values from Input Data

 

Input :

There is a input file which contains duplicates data, Suppose :

14 design tips for better performance in Datastage

1) Avoid unnecessary type conversions: set the OSH_PRINT_SCHEMAS environment variable to verify that run time schemas match the job design column definitions. If you are using stage variables on a Transformer stage, ensure that their data types match the expected result types.

2) Use Transformer stages sparingly and wisely. Transformer stages can slow down your job. Do not have multiple stages where the functionality could be incorporated into a single stage, and use other stage types to perform simple transformation operations

datastage4you QAnT - April2013 - 2

Click here ---> datastage4you QAnT - April2013 - 1

#14. DataStage Server – starting the server
a) DB2
su - db2inst1 -c "db2start“
b) WAS Server
/opt/IBM/WebSphere/AppServer/bin/startServer.sh server1 -user wasadmin -password PASSWORD
c) NODE
/opt/IBM/InformationServer/ASBNode/bin/NodeAgents.sh start
d) Engine
/opt/IBM/InformationServer/Server/DSEngine/bin/uv -admin -start

datastage4you QAnT - April2013 - 1

 #1. Use the Data Set Management utility, which is available in the Tools menu of the DataStage Designer or the DataStage Manager, to examine the schema, look at row counts, and delete a Parallel Data Set. You can also view the data itself.

#2. Use Sort stages instead of Remove duplicate stages. Sort stage has got more grouping options and sort indicator options.

#3. for quick checking if DS job is running on Server or not from UNIX
ps -ef | grep 'DSD.RUN'

Difference Between The Continuous Funnel And Sort Funnel

# Continuous Funnel combines the records of the input data in no guaranteed order. It takes one record from each input link in turn. If data is not available on an input link, the stage skips to the next link rather than waiting.

# Sort Funnel combines the input records in the order defined by the value(s) of one or more key columns and the order of the output records is determined by these sorting keys.

Sort stage to remove duplicate

1)what is the advantage of using sort stage over remove duplicate stage in removing duplicates.
2) Is there any way in which we can specify which record to retain(like retaining the last record or retaining the first) when we remove duplicate using transform stage, similarly in sort stage also.

Ans : 

1)The advantage of using sort stage over remove duplicate stage is that sort stage allows us to capture the duplicate records whereas remove duplicate stage does not.

2) Using a sort stage we can only retain the first record.
Normally we go for retaining last when we sort a particular field in ascending order and try to get the last rec. The same can be done using sort stage by sorting in descending order to retain the first record.