1. APT_CONFIG_FILE is the file using which DataStage determines the configuration file (one can have many configuration files for a project) to be used. In fact, this is what is generally used in production. However, if this environment variable is not defined then how DataStage determines which file to use? 1. If the APT_CONFIG_FILE environment variable is not defined then DataStage look for default configuration file (config.apt) in following path: 1. Current working directory. 2. INSTALL_DIR/etc, where INSTALL_DIR ($APT_ORCHHOME) is the top level directory of DataStage installation. 2. What are the different options a logical node can have in the configuration file? 1. fastname The fastname is the physical node name that stages use to open connections for high volume data transfers. The attribute of this option is often the network name. Typically, you can get this name by using Unix command uname -n. 2. pools Name of the pools to which the node is assigned to. Based on the characteristics of the processing nodes you can group nodes into set of pools. 1. A pool can be associated with many nodes and a node can be part of many pools. 2. A node belongs to the default pool unless you explicitly specify a pool list for it, and omit the default pool name () from the list. 3. A parallel job or specific stage in the parallel job can be constrained to run on a pool (set of processing nodes). 1. In case job as well as stage within the job are constrained to run on specific processing nodes then stage will run on the node which is common to stage as well as job. 3. resource resourceresource_type location [{pools disk_pool_name}] | resource resource_type value . resource_type can be canonicalhostname (Which takes quotedethernet name of a node in clusterthat is unconnectedto Conductor node by the hight speed network.)or disk (To read/write persistent data to this directory.) or scratchdisk (Quoted absolute path name of a directory on a file system where intermediate data will be temporarily stored. It is local to the processing node.) or RDBMS Specific resourses(e.g. DB2, INFORMIX, ORACLE, etc.) 3. How datastage decides on which processing node a stage should be run? 1. If a job or stage is not constrained to run on specific nodes then parallel engine executes a parallel stage on all nodes defined in the default node pool. (Default Behavior) 2. If the node is constrained then the constrained processing nodes are choosen while executing the parallel stage. (Refer to 2.2.3 for more detail). 4. When configuring an MPP, you specify the physical nodes in your system on which the parallel engine will run your parallel jobs. This is called Conductor Node. For other nodes, you do not need to specify the physical node. Also, You need to copy the (.apt) configuration file only to the nodes from which you start parallel engine applications. It is possible that conductor node is not connected with the high-speed network switches. However, the other nodes are connected to each other using a very high-speed network switches. How do you configure your system so that you will be able to achieve optimized parallelism? 1. Make sure that none of the stages are specified to be run on the conductor node. 2. Use conductor node just to start the execution of parallel job. 3. Make sure that conductor node is not the part of the default pool. 5. Although, parallelization increases the throughput and speed of the process, why maximum parallelization is not necessarily the optimal parallelization? 1. Datastage createsoneprocess for every stage for each processing node. Hence, if the hardware resource is not available to support the maximum parallelization, the performance of overall system goes down. For example, suppose we have a SMP system with three CPU and a Parallel job with 4 stage. We have 3 logical node (one corresponding to each physical node (say CPU)). Now DataStage will start 3*4 = 12 processes, which has to be managed by a single operating system. Significant time will be spent in switching context and scheduling the process. 6. Since we can have different logical processing nodes, it is possible that some node will be more suitable for some stage while other nodes will be more suitable for other stages. So, when to decide which node will be suitable for which stage? 1. If a stage is performing a memory intensive task then it should be run on a node which has more disk space available for it. E.g. sorting a data is memory intensive task and it should be run on such nodes. 2. If some stage depends on licensed version of software (e.g. SAS Stage, RDBMS related stages, etc.) then you need to associate those stages with the processing node, which is physically mapped to the machine on which the licensed software is installed. (Assumption: The machine on which licensed software is installed is connected through other machines using high speed network.) 3. If a job containsstages,which exchange large amounts of data then they should be assigned to nodes where stages communicate by either shared memory (SMP) or high-speed link (MPP) in most optimized manner. 7. Basically nodes are nothing but set of machines (specially in MPP systems). You start the execution of parallel jobs from the conductor node. Conductor nodes creates a shell of remote machines (depending on the processing nodes) and copies the same environment on them. However, it is possible to create a startup script which will selectively change the environment on a specific node. This script has a default name of startup.apt. However, like main configuration file, we can also have many startup configuration files. The appropriate configuration file can be picked up using the environment variable APT_STARTUP_SCRIPT. What is use of APT_NO_STARTUP_SCRIPT environment variable? 1. Using APT_NO_STARTUP_SCRIPT environment variable, you can instruct Parallel engine not to run the startup script on the remote shell. 8. What are the generic things one must follow while creating a configuration file so that optimal parallelization can be achieved? 1. Consider avoiding the disk/disks that your input files reside on. 2. Ensure that the different file systems mentioned as the disk and scratchdisk resources hit disjoint sets of spindles even if theyre located on a RAID (Redundant Array of Inexpensive Disks) system. 3. Know what is real and what is NFS: 1. Real disks are directly attached, or are reachable over a SAN (storage-area network -dedicated, just for storage, low-level protocols). 2. Never use NFS file systems for scratchdisk resources, remember scratchdisk are also used for temporary storage of file/data during processing. 3. If you use NFS file system space for disk resources, then you need to know what you are doing. For example, your final result files may need to be written out onto the NFS disk area, but that doesnt mean the intermediate data sets created and used temporarily in a multi-job sequence should use this NFS disk area. Better to setup a final disk pool, and constrain the result sequential file or data set to reside there, but let intermediate storage go to local or SAN resources, not NFS. 4. Know what data points are striped (RAID) and which are not. Where possible, avoid striping across data points that are already striped at the spindle level.

DataStage EE environment variables The default environment variables settings are provided during the Datastage installation (common for all users).

Users have a few options to override the default settings with Datastage client applications: With Datastage Administrator - project-wide defaults for general environment variables, set per project in the Projects tab under Properties -> General Tab -> Environment... With Datastage Designer - settings at the job level in Job Properties With Datastage Director - settings per run, overrides all other settings and is very useful for testing and debuging.

The Datastage environment variables are grouped and each variable falls into one of categories. Basically the default values set up during an installation are resonable and in most cases there is no need to modify them. Setting environment variables for parallel execution in Datastage Administrator

Environment variables overview Listed below are only environment variables that are candidates to adjustment in real-life project deployments. Please refer to the datastage help for details on variables not listed here. General variables LD_LIBRARY_PATH - specifies the location of dynamic libraries on Unix PATH - Unix shell search path TMPDIR - temporary directory Parallel properties APT_CONFIG_FILE - the parallel job configuration file. It points to the active configuration file on the server. Please refer to Datastage EE configuration guide for more details on creating a config file. APT_DISABLE_COMBINATION - prevents operators (stages) from being combined into one process. Used mainly for benchmarks. APT_ORCHHOME - home path for parallel content. APT_STRING_PADCHAR - defines a pad character which is used when a varchar is converted to a fixed length string Operator specificThe operator specific variables under parallel properties are stage specific settings and usually set during an installation. The settings apply to the supported parallel database engines (DB2, Oracle, Sas and Teradata). APT_DBNAME - default DB2 database name to use APT_RDBMS_COMMIT_ROWS - RDBMS commit interval ReportingThe reporting variables control logging options and take True/False values only. APT_DUMP_SCORE - shows operators, datasets, nodes, partitions, combinations and processes used in a job. APT_RECORD_COUNTS - helps detect and analyze load imbalance. It prints the number of records consumed by getRecord() and produced by putRecord() OSH_PRINT_SCHEMAS - shows unformatted metadata for all stages (interface schema) and datasets (record schema). OSH_PRINT_SCHEMAS environment variable should be set to verify that runtime schemas match the job design column definitions (especially from Oracle). OSH_DUMP - shows an OSH script and produces a verbose description of a step before executing it APT_NO_JOBMON - disables performance statistics and process metadata reporting in Designer. Compiler APT_COMPILER - path to the C++ compiler needed to compile transformer stages

Datastage EE configuration file The Datastage EE configuration file is a master control file (a textfile which sits on the server side) for Enterprise Edition jobs which describes the parallel system resources and architecture. The configuration file provides hardware configuration for supporting such architectures as SMP (Single machine with multiple CPU , shared memory and disk), Grid , Cluster or MPP (multiple CPU, mulitple nodes and dedicated memory per node).

The configuration file defines all processing and storage resources and can be edited with any text editor or within Datastage Manager. The main outcome from having the configuration file is to separate software and hardware configuration from job design. It allows changing hardware and software resources without changing a job design. Datastage EE jobs can point to different configuration files by using job parameters, which means that a job can utilize different hardware architectures without being recompiled.

The Datastage EE configuration file is specified at runtime by a $APT_CONFIG_FILE variable. Configuration file structureDatastage EE configuration file defines number of nodes, assigns resources to each node and provides advanced resource optimizations and configuration. The configuration file structure and key instructions: node - a node is a logical processing unit. Each node in a configuration file is distinguished by a virtual name and defines a number and speed of CPUs, memory availability, page and swap space, network connectivity details, etc. fastname defines node's hostname or IP address pool - defines resource allocation. Pools can overlap accross nodes or can be independent. resource (resources) names of disk directories accessible to each node. The resource keyword is followed by the type of resource that a given resource is restricted to, for instance resource disk, resource scratchdisk, resource sort, resource bigdata Sample configuration filesConfiguration file for a simple SMPA basic configuration file for a single machine, two node server (2-CPU) is shown below. The file defines 2 nodes (dev1 and dev2) on a single etltools-dev server (IP address might be provided as well instead of a hostname) with 3 disk resources (d1 , d2 for the data and temp as scratch space).

The configuration file is shown below: {node "dev1"{fastname "etltools-dev"pool ""resource disk "/data/etltools-tutorial/d1" { }resource disk "/data/etltools-tutorial/d2" { }resource scratchdisk "/data/etltools-tutorial/temp" { }}

node "dev2"{fastname "etltools-dev"pool ""resource disk "/data/etltools-tutorial/d1" { }resource scratchdisk "/data/etltools-tutorial/temp" { }}}Configuration file for a cluster / MPP / gridThe sample configuration file for a cluster or a grid computing on 4 machines is shown below.The configuration defines 4 nodes (etltools-prod[1-4]), node pools (n[1-4]) and s[1-4), resource pools bigdata and sort and a temporary space. {node "prod1"{fastname "etltools-prod1"pool "" "n1" "s1""tutorial2" "sort"resource disk "/data/prod1/d1" {}resource disk "/data/prod1/d2" {"bigdata"}resource scratchdisk "/etltools-tutorial/temp" {"sort"}}

node "prod2"{fastname "etltools-prod2"pool "" "n2" "s2""tutorial1"resource disk "/data/prod2/d1" {}resource disk "/data/prod2/d2" {"bigdata"}resource scratchdisk "/etltools-tutorial/temp" {}}

node "prod3"{fastname "etltools-prod3"pool "" "n3" "s3""tutorial1"resource disk "/data/prod3/d1" {}resource scratchdisk "/etltools-tutorial/temp" {}}

node "prod4"{fastname "etltools-prod4"pool "n4" "s4""tutorial1"resource disk "/data/prod4/d1" {}resource scratchdisk "/etltools-tutorial/temp" {}}}Validate configuration fileThe easiest way to validate the configuration file is to export APT_CONFIG_FILE variable pointing to the newly created configuration file and then issue the following command: orchadmin check