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For so many of my customers, SAP Data Services is a relatively new tool. A typical DS project is mainly focused on ensuring the solution works for the business and is launched on time. Unfortunately, many of these projects fail to utilize some of the built-in features of Data Services to help simplify how code is managed in the solution. This is an architecture gap that adds hidden costs to owning and operating the solution.

In this article, I outline the framework for managing Data Services code that I have taught to dozens of the largest customers in the Americas. Ideally, you should implement this during the blueprint phase so that you provide your developers with the tools and processes to create better code the first time. However, you can still benefit from this framework even if you are approaching the testing phase of a Go-Live.

The elements of this framework include:

1. Implement multiple central repositories

In Data Services, a “central repository” is a different type of repository used only for version control of objects. This is comparable to version control systems like CTS+, Visual SourceSafe, Apache Subversion, BusinessObjects LCM, etc. You can check in new code, check it out to work on it, check in new versions, and get copies of specific versions.

Many customers do not use central repositories. Instead, they create their code in a local repository, export the code to an “.atl” file, and import it into the test or production local repository. You can save backups of the .atl file and keep track of them in a number of ways…even Apple Time Machine and Dropbox can keep track of multiple versions of a file through time. However, this is likely not a scalable or trustworthy solution for enterprise IT.

If you want to learn how to work with a central repository, the Data Services Tutorial Chapter 12 “Multi-user Development” does a fantastic job at demonstrating all the techniques. The “Using a Central Repo” Wiki Page also captures some of the basic techniques. But neither will tell you why, or discuss how you should set up your landscape or processes.

[Note: There are two different types of central repositories: non-secure and secure. Secure central repositories allow only specific users permissions on specific objects and provide an audit trail of who changed which objects. Non-secure central repositories lack these features. Due to this gap, I never recommend the use of non-secure central repositories. In this article, whenever I refer to a central repository, I am talking about secure central repositories. Chapters 23-24 in the Data Services Designer Guide discuss these differences.]

This is how I recommend for you to configure up your secure central repositories.

  • Development Central – a central repository that can be accessed by developers and testers.  Developers create content in their local repositories and check in this content into the development central repository. Each logical set of code should be checked in with the same label (e.g. “0.1.2.1a”) so that they can be easily identified and grouped together.

    During a test cycle, a tester logs into a local repository dedicated to testing and connects to the development central repository.  The tester gets all objects to be tested from the development central repository.  The tester deactivates the connection to the development central repository and then connects to the test central repository.

  • Test Central – a central repository that can be accessed by testers and production administrators.  During the test cycle, testers check in development objects before and after testing, labeling them appropriately (e.g. “0.1.2.1pretest” and “0.1.2.1passed”).  Thus, the test central repository contains only objects that have been promoted from development to test and have passed testing.
  • Production Central – a central repository that can be accessed only by production administrators.  When testers certify that the code can be migrated to production, a production administrator logs into a production local repository.  The administrator activates a connection to the test central repository and gets a copy of all objects to be promoted to production (e.g. “0.1.2.1passed”).  The administrator deactivates the test central repository and then activates the production central repository.  All objects that were promoted into production are then checked into the production central repository (e.g. “0.1.2.1prod”).  Thus, the production central repository contains only objects that have been successfully put into production.

Remember, central repositories are only for version control, storing your code, and helping you migrate it. You never run batch jobs or launch real-time services from a central repo — only from a local repo.

This tiered approach plan looks like this:

Slide1.jpg

The repositories themselves are just database schemas — you can put them in any supported database. Check the Product Availability Matrix for Data Services to see which databases are supported. However, I would recommend for you to group them together within the same physical database within your specific tier. For instance:

  • Dev database — dev local repositories, dev central repository, and dev CMS database. Co-located with the dev Data Services hardware.
  • Test database — test local repository and test central repository, and test CMS database. Co-located with test Data Services hardware.
  • Prod database — prod local repository and prod central repository, and prod CMS database. Co-located with prod Data Services hardware.

1.1 Additional best practices for central repositories

  • Security — Set up group-based permissions for repository authentication and for individual objects. Refer to the Designer Guide section 24.1.1, Management Console Guide section 3.3.1, and Administrator’s Guide section 4.1.
  • Checking out datastores — Using the security features of secure central repositories, make sure that only specific groups have read+write permissions on datastores. Everyone always has permissions to edit datastores in their local repository, but it would be disorganized to let all of them check in these datastore changes to the central repository. Thus, you should have administrators create your datastores and check them into your local repository. Anyone can get them from the central repo but only administrators have permissions to check them out, modify them, and check in their changes. For more info on defining datastores, see below “3. Defining datastore configurations“.
  • Backup — These repositories contain most of your investment in your DS solution! Make sure to back up these databases regularly as you would with any other database. Too often I see no backups taken on the development central repository because “it’s not part of the productive tier.” This is a terrible business decision! What if your development central repository database crashes and your developers lose everything?
  • Designer performance — DS Designer requires a database connection to the local and central repositories. I always meet people who complain about Designer being too slow. Okay, but you are using Designer on your laptop in the Toronto airport from a VPN connection to your Seattle network hub and the repo database is in your Chicago datacenter. Designer performs numerous small transactions that each require network round-trips — if the connection is slow, Designer is going to be slow to save anything to your local repository or interact with a central repository.

    Are you regularly using a thick-client Windows app like Designer from remote locations? Maybe you should think about putting Designer on Citrix Presentation Server — check the Installation Guide for Windows section 6.6. Additionally, Designer 4.1 introduces the ability to use DS under multiple Windows Terminal Server users.

  • Concurrent usage — I often hear issues about developers connected to the same central repo who have their Designer hang up on them whenever their colleagues do anything (“Get Latest Version”, “Check-Out by Label”, etc.). To protect the code from being corrupted by multiple people trying to do multiple things at the same time, Designer performs table locking on certain central repo tables. While one user has an exclusive table lock on a central repo table, any other users trying to interact with the same table will be queued until the first user’s exclusive table lock is released. How to work around this? Simple — don’t keep your connection to the central repo active all the time. There’s a Designer option that allows you to activate a central repo connection automatically, and you should disable this option. Only activate your central repo connection when you need to get code from or check code into the central repo.

2. Define substitution parameters & multiple substitution parameter configurations

Substitution Parameters are such a handy feature, but I seldom see them used to their full potential! If you know C++, they are similar to compiler directives. They are static values that never change during code execution (so we don’t call them variables). They are called “substitution” parameters because their values get substituted into the code by the optimizer when you run the job. They can thus change the run-time behavior of your code.

Often I see many programmers use a script block at the beginning of a job to set global variable values. These global variables are then used to control the logic or mappings later in the job. However, in 90% of these cases the global variables NEVER CHANGE during runtime. So now you have several problems in your code:

  • You hid your global variable declarations in a script somewhere in your job. How do you expect other people to understand what you did in your code?
  • A global variable is specific to one job only. Other jobs do not inherit global variable names, types, or values. So if you have 100 jobs that use a variable named $START_DATE, you have to declare $START_DATE in every one of those 100 jobs.
  • Global variables have no way of being set quickly en masse. You can override them individually at run-time, but this introduces the risk of human error.

Substitution parameters fix all of these global variable short-comings. They are defined for an entire repository, not per individual job. Their values are controlled at a repository level, so you don’t have to include scripts to set them. They cannot change through run-time, so they don’t have the risk of being modified erroneously. Lastly, they don’t just have one default value — you can set up multiple substitution parameter configurations for your repository so that you have multiple different sets of run-time values.

Here are some common uses for substitution parameters:

  • File paths and file names — tell jobs where to find files in a specific staging area or target location. If you always set flat file and XML file sources and targets to use substitution parameters instead of hard-coded paths, you can change all file locations at once globally instead of having to find every single object, drill into it, and change the path. This is also used to specify reference data locations.
  • Control logic — tell the same job how to run differently if a different substitution parameter value is found. You can use this to set up one job that does both initial loading and delta loading. You can have a conditional block evaluate a parameter named [$$IS_DELTA] and decide whether to process the “delta” workflow or the “initial” workflow. This lets you have fewer jobs and simplifies your life!
  • Transform options — tell transforms to behave in a specific way. This is often used in Data Quality transform options to set country-specific options, engine options, performance parameters, or rules. However, you can use them in most of the transforms and mappings to override hard-coded values with your own substitution parameters.

Substitution Parameter Configurations are helpful because they let you set multiple different sets of substitution parameters. You can use this to set up multiple configurations for:

  • Dev / Test / Prod
  • Initial vs. Delta
  • Enabling verbose debug code in your own script blocks or custom functions
  • Specifying multiple file paths, e.g. fileshares in Chicago, L.A., Shanghai, Wrocław, and San Leopoldo.

Substitution Parameters are not objects that can be checked into a central repository, since they aren’t actually code objects. As such, there is a specific way to move them between local repositories. You must export them to an ATL file and import them into another local repository. Please refer to the example below:

Slide2.jpg

This is an additional step to include in your migration plans from Dev -> Test -> Production. However, it is relatively quick procedure for an administrator.

3. Define datastore configurations

Datastore mistake 1: In many customer environments, I log into a local repository and see several datastores named similarly (“HANA_TARGET_DEV”, “HANA_TARGET_QA”, and “HANA_TARGET_PROD”). Or maybe I see many SAP datastores named after their SIDs (“BWD”, “BWQ”, “BWP). If you make this mistake, you need to go through the following unnecessary steps:

  • If you move a job from development to test, you have to edit every single dataflow and delete every single table object, replacing the table objects from datastore “HANA_TARGET_DEV” with the ones from “HANA_TARGET_QA”.
  • This increases the risk of human error — what if you pick the wrong table by mistake?
  • This increases the number of table objects to maintain — you have to import the same table object 3 different times, one from each different datastore.
  • You risk having differences in the table metadata from the different development/test/production datastores. Don’t you want to ensure that the code is always the same?

Datastore mistake 2: Since this gets to be so time-consuming, many developers realize that they can just reuse one datastore from dev to test to production. So you see a datastore named “HANA_TARGET_DEV” or “BWD” in a production local repository. In this case, the administrators just explain how they change the hostname, username, and password of the datastore when they move it to test or production. Though this sounds simple, you still run the risk that you must change more than just username/password. In the case of an SAP ECC source datastore, are the transport file paths the same between your different ECC sources?

The solution to both of these mistakes? Datastore configurations.

Datastore configurations are very powerful. They allow you to have a  single datastore that can connect to multiple different sources. They  work very similar to substitution parameter configurations: at run-time,  the optimizer selects a single configuration, and this connection  information is used for the entire execution of the job and cannot be  modified. You set them up in the datastore editor…the Data Services Wiki shows a good example.

I would strongly urge you to avoid the two mistakes  above by starting your project with the following principles:

  1. Give datastores meaningful names that describe their data domain. Do NOT name them after a specific tier (dev/test/prod) or a specific region (AMER/EMEA/APJ) or a specific database (“DB2”, “HANA”, “SYBASEIQ”) or a specific SAP SID (ECD/ECQ/ECP). Just name them after their data: “SALES”, “VENDOR”, “MATERIALS”, “VERTEX”, “BANKING”. This is important because you cannot rename a datastore once it is defined.
  2. Set up multiple datastore configurations inside of each datastore. Multiple datastore configurations should be used when the same metadata exists in multiple systems. If the metadata is different between two systems, they belong in separate datastores.
  3. If you have Dev/Test/Prod tiers, make sure to set up separate datastore configurations for Dev/Test/Prod in your development local repositories. No, you don’t have to know the correct usernames/passwords for the test or production systems (and in fact, this would be a serious risk!). Get them set up anyway! When testers and production administrators go into production, the only thing they will need to change will be the username and password. This helps avoid the risk of human error during a critical Go-Live.

For advanced users, you can even use datastore configurations to move from one database platform to another without having to re-develop all your code.

3.1 Use aliases to map table owners (optional)

If you are using database sources or targets, these tables always have an owner name or schema name (e.g. “SCOTT”.”TIGER”). In the Data Services Designer interface, these owner names exist but are not usually very obvious to the user.

This is usually a problem that manifests itself when you migrate from Dev to Test or Test to Production. Let’s say you developed your dataflow and used a source table named “SQLDEVUSR1″.”EMPLOYEES”. The username “SQLDEVUSR1” is the table owner. You also set up a second datastore configuration for the Test environment, and the username is “SQLTESTUSR5”. When you run the job and set the Test datastore to be default, the job crashes at this dataflow with a “TABLE NOT FOUND” error. Why? It connected to the database specified in the Test datastore configuration as username “SQLTESTUSR5” and tried to find a table named “SQLDEVUSR1″.”EMPLOYEES”.  This is a design problem, not a Data Services error.

Instead, you need to tell Data Services how to interpret the name “SQLDEVUSR1” differently depending on which datastore configuration is active. There is a feature called “Aliases” in each database datastore that lets you control this!

You can create one or more aliases in each database datastore to automatically change the table owner name defined in the dataflow with the table owner name of your choice. At runtime, the optimizer does a search and replace through the code for any objects from that datastore and maps an alias named ‘SQLDEVUSR1″ to be “SQLTESTUSER5”.

Here’s another example:

Slide4.jpg

This is a little-known feature, but it saves you a ton of time if you have many developers who connected to various sandbox databases when developing the code. You can simply set up multiple aliases to search for various (and possibly incorrect) owner names and map them to what their “real” owner names should be within your official Dev/Test/Production datastore configurations.

4. Define system configurations to map together combinations of substitution parameters & datastore configurations

At this point, you have done the following:

  • Created substitution parameters
  • Created multiple substitution parameter configurations to control various aspects of run-time behavior
  • Created datastores
  • Created multiple datastore configurations to connect to different sources of data that have identical metadata

Your setup might look like this:

Slide3.jpg

The final step is to create system configurations. These are combinations of datastore configurations and substitution parameters that let you set up job execution profiles that can be quickly and easily set at run-time. The optimizer then chooses only that combination of configurations for the execution of the entire job. If you have never defined a system configuration in a specific local repository, you will never see it as a drop-down option when you try to run a job. However, after you configure system configurations, you will now see a convenient drop-down box that shows the names of your various system configurations:

If we use the example above with the 3 datastores with 3 different configurations and the 6 different substitution parameter configurations, you can now create system configurations as combinations of these. Here is how you might set up your system configurations:
Slide5.jpg

After this, when you run or schedule a job, you would see a drop-down with your 6 different system configuration names:

  • DevInit
  • DevDelta
  • TestInit
  • TestDelta
  • ProdInit
  • ProdDelta

To be honest, this isn’t a very good example. Why would you want your production local repository to have the ability to easily execute jobs in your Dev landscape? Thus, you would probably want to set up system configurations that specifically correspond to the substitution parameter configurations and datastore configurations that you really want to use when you actually run the jobs.  So in thisexample you would probably want to set up your production local repository system configurations to only include “ProdInit” and “ProdDelta” so you never make the mistake of selecting one of the Dev or Test configs.

What if you don’t select a system configuration at run-time? Each datastore has a “default” datastore configuration. Likewise, there is also a default substitution parameter configuration. If no substitution parameter configuration is selected, the optimizer selects the default datastore configuration for each datastore and the default substitution parameter configuration for that repository.

Similar to substitution parameter configurations, system configurations cannot be checked into a central repository. They can be migrated in the same way you saw above with exporting substitution parameters to an ATL file. However, this is probably not necessary — system configurations are very quick to define, and you probably only want to create them for the environment that you want to run in (e.g. “ProdInit” and “ProdDelta”).


Let me know if this framework makes sense. If you see weird errors, search the KBase or file a SAP Support Message to component EIM-DS.

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32 Comments

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  1. Arne Weitzel

    Great overview. We were using the same setup of central repoitories at one customer site. In addition we were using a local repository for emergency fixes: this was required in order to be able to apply / test emergency fixes while at the same time other developments for scheduled releases were using the central dev and/or test repositories. We also setup a process to manually re-synch the emergency central repo with the dev and test central repo.

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  2. Abhay Mhatre

    Hi Scott, very good tips on some of the critical aspects of DS projects.

    We have DS D/T/P setup similar to what you have depicted in the DS landscape diagram. However for the code migration I was exporting it to ATL file and move it across D/T/P tiers.

    I tried to connect to Central Repository in Dev from Local Test repository to get the latest version of code, but it returns error (Cannot retrieve <version> from the repository) during connecting to it (Dev Central repo). Is there any specific configuration to do, to enable the code migration approach your recommend?

    I use DB2 database with DS repositories. I setup the connection (ODBC) to DB2 database to Central repository in Dev and that’s working fine (can views tables in the repository from DB2 Control Center).

    Thanks,

    Abhay

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    1. Scott Broadway Post author

      “Cannot retrieve <version> from the repository” means that the database you are connecting to isn’t a DS repository.  Go to Repository Manager and do a Check Version of your Dev Central repo. Repo Man needs to be able to tell you that it is a secure central repository of a specific version.

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      1. Abhay Mhatre

        Since the database name for Dev Central Repo and Test Local repo were same, it caused the issue. I updated the entry in AL_MACHINE_INFO table with alias for the Development DS repo database and that resolve above issue. Now I am able to “GET” objects in Local Test repository from Central Repository in Development.

        Thanks,

        Abhay

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        1. Scott Broadway Post author

          Not sure I understand you.  You really should not be editing anything in AL_MACHINE_INFO…

          The database names for the Dev Central Repo and the Test Local Repo should be different…you shouldn’t keep them together in the same DB2 database because of this very reason.  In MSSQL you would put them in a different database and in Oracle you would put them in a different schema.  It was throwing you the error about “cannot retrieve version from the repository” because it was logging into the local repository and expecting to find a central repository.  You should NEVER mix these two up for fear of corrupting one or both.

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          1. Abhay Mhatre

            I think you are mixing up. I have completely two separate Db2 databases (running on separate instances) one for Dev Central Repo and other for the Test Local repo but they happen to have the same name ‘DS_REPO’. I don’t think the DS will support the same database/schema configured for local and central repo at same time in two environment.

            I am with you on not altering the systems tables, so can you eplain exactly how did you configure the repositories in Test/Dev to enable “GET” the code into Test Local Repo from Central Repository in Dev?

            Another question for you, in your scenario, does the database names for Dev Central Repo and Test Loal Repo are same or different? How are they configured in Dev/Test CMC (what’s the Connection String value – it’s auto filled by CMC).

            Thanks,

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            1. Scott Broadway Post author

              Same database name is ok — but you obviously need to catalog them as different aliases on the local DB2 client.  You bring up a good point — be sure to catalog DB2 local repos and central repos with consistent alias naming across all of your DS Designer and job server machines.  The same would be true for Oracle TNSNames.ora entries for Oracle customers.

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            2. Scott Broadway Post author

              “how did you configure the repositories in Test/Dev to enable “GET” the code into Test Local Repo from Central Repository in Dev”

              This is basic central repo operations.  You log into your Test Local Repo from Designer.  You activate the Dev Central Repo connection. You do a “get by label” or “get latest version” or “get object and dependents” or “get by label with dependents” or “with filtering” or whatever.  This puts the Dev Central Repo code into your Test Local Repo.  Go through the DS tutorial…it teaches you all of these steps in detail.

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  3. Arun Kumar

    Scott,

    Instead of ATL for moving to different repo, if we use EXPORT option will substitution parameters and datastore config work? Logically, i think it should work right?

    Arun

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    1. Scott Broadway Post author

      Yes, that might work, but the whole point of my article is that you should stop migrating code with ATL files or direct local repo-to-local repo exports. I mention how to manually migrate substitution parameters and system configurations separately because those should be migrated separately since they are not objects that can be checked into a central repo…they are unique to a local repo.

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  4. Mirambek F

    Hi Scott,

    This is really good article. Our company also uses SAP Dataservices 4.0. We want to migrate to new DataServices 4.1. and install it in new server. I installed everything correctly and upgraded database from 4.0 and 4.1. Once I am trying to open through data designer it tries to connect previous server database where 4.0 installed. I correctly configured and registered in SAP Boe new database server. I beleive this is problem with some tables where previous server is hardcoded. Can you give some advice where look at?

    Thanks!

    (0) 
    1. Scott Broadway Post author

      Can you please post this question in the forum instead?  Or open a support message on this issue?  This blog post isn’t really the correct place to discuss it.

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  5. Paul Laman

    Good article. Most of the features you mention I already use, but I do not quit get the advantage of having multiple central repositories. In out DTAP setup we have one for DTA and P has none. This means that we label stuff in development as we want to push it to test. If defects are to be fixed, we do so and label the whole set again. Once the tests are successful we pull the whole lot (by label) to acceptance for a final test. Again, if needed we re-iterate, but if it passes acceptance testing, we pull an atl file, name the file by the label in the central repo and import that in production. I don’t see the advantage of putting it again in it’s own central repository. When will you get objects from it, anyway?
    The advantage of an atl file is that you can create a package around it that also includes other bits and pieces of the solution like (database scripts), documentation, config files, etc. which can be rolled out as a whole. You even might automate this…

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  6. Frederic PROVOT

    Hi Scott,

    Thanks for this documentation. Something’s not clear about the alias chapter: You tell that the alias is used to change the user name of the configuration. But this user name is already defined in the connection “user name” field (for oracle db). Or should we set a local (say dev) accessible username, then in the alias field set the real user name to be used when deployed on another (prod) plateform?

    As there’s no screenshot, I’m not sure about what should be set in the alias field (and not more with the designer manual that says : “

    From the datastore editor, you can also create multiple aliases for a datastore

    then map datastore configurations to each alias“)

    Regards

    (0) 
    1. Scott Broadway Post author

      The alias changes the owner name, not the user name.  When you import the table from some databases, the owner name is also the user name; in other databases, the owner name is the schema name.

      You would use multiple aliases if you have the problem of multiple owners that you want to map to the same table.  Let’s say that your dev repo datastore is rather dirty, and contains the following tables (I’ve seen this many times):

      USER1.TABLE1

      USER2.TABLE1

      TMP.TABLE1

      When you run a job, which table owner is it actually gonna use?  The answer: the table that you dropped into the dataflow as a source.  This is why aliases come in handy.  You would create 3 aliases and map them to your desired owner name:

                      Datastore Config 1             

      Alias         Owner                            

      USER1      DEVSCHEMA

      USER2      DEVSCHEMA

      TMP          DEVSCHEMA

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      1. Rv Awl

        What is the need to even use same table (which is finally going to be populated & used) by three different users ? say for Table1, can’t a single user be defined for accessing this table at DB level, corresponding to which DF and DS config can be created with no need of Aliases…

        USER1.TABLE1, USER2.TABLE1, TMP.TABLE1 – for these 3, USER_DWDEV1.TABLE1 can be defined.

        However, in higher environs, where a different database is used, table can be owned by other User, there in I guess, Aliases would be helpful, just a thought 😛

        Regards,

        Rv

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  7. Peeyush Upadhyay

    Hi Scott,

    I have different setup (CMC, IPS, BODS) for development & test environments. IS it possible to setup a user from test env. (different CMC) to connect to development central repository?

    I guess not, but if you can confirm that would be great. How would you recommend the development & test teams migrate code fro deployment (in a system with different CMS)

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  8. Alexey Tarasov

    When we talk about central repo we mean one repo for project team or one repo for all developers in company? Of course in company we have many different projects and project teams.

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  9. Matthew Giroux

    Excellent overview.

    It does raise two important features really needed though: 

    1) The ability to easily “rollback” or undo a code migration.

    2) The ability to automate migrations via scripting or some other API.

    For the first one I suppose a labeling technique prior to check-out in the production central repository would serve as the “undo” point.  For the second one – I don’t think anything exists…  hopefully there are plans to improve this area.

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  10. Matthew Giroux

    Okay,

    I have to gripe about this.  Creating local repositories for every developer is ridiculous.  It is a maintenance nightmare to just even add one developer to the landscape (create the local repo, provision user to database, create user in CMS, provision security, etc, etc, etc…)

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  11. Mitchell Gold

    Hi Scott – we are going to follow your strategy regarding the implementation of multiple central repositories.  We have multiple local repositories for our developers, as well as multiple local repositories for our different test regions, and we have 1 local repository for production.  We have recently created 3 central repositories (Dev, Test, and Prod).  My questions revolve around how to get started.

    1 – should we ‘initialize’ the Prod Central Repository by logging in to the Prod Local Repository, connecting to the Prod Central Repository, and checking everything in to the Prod Central Repository?

    2 – when a developer needs to modify an existing job, does he log into his local repository, connect to the Prod Central Repository, and check out the job?

    Thanks

    (0) 
    1. Akhilesh Sengupta

      Hi Mitchell,

      The option 1 is correct but the code should go through mutliple central repositories.

      You can follow the below steps:

      1. Develop the code in Developer’s local repo and promote it to developer’s central repo.

      2. Inform Test team to take this code into their local repo. The code should be tested here and once everything goes fine they should promote this code to their central repo.

      3. Prod implemtnation team should follow the above step (by taking the code from test team’s central repo)

      NOTE – The above approach holds good for most of the projects where SDLC is followed but this process becomes an issue in case of production hot fix becasue the latest code remains in the production central repo and developers take the latest code from their central repo in case of future changes. There is a very high risk of version mismatch between the code available in prod and the one on which developers start their changes.

      This should be tracked as it may cause a major problem.

      Please let me know if you have any doubt in this.

      Thanks,

      Akhilesh

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      1. Mitchell Gold

        Thanks Aklilesh.  What about my 2nd question?  When a devleloper needs to work on an existing job that is in production, where should he get it from? (remember, We have 3 central repositories (Dev, Test, and Prod).

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  12. Akhilesh Sengupta

    Thanks Scott..you explained it very nicely.  I am following most of it except the substitutional parameters part. I was actually looking for some good contents on substitutional parameters.

    Thanks again!!

    Akhilesh

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  13. Shashi Prakash

    Hi Scott,

    Thanks for the wonderful post. I have a query. In my project we have many developers who checkin code to Central repo and from Central repo we move the code to local test repo. Is there a way to identify list of the jobs/objects which has been checked-in to Central repo or list of jobs/objects which has been imported to a local repo. Any metadata tables where I could get this details.

    Many thanks in Advance.

    Thanks

    Shashi

    (0) 
  14. CC Gao

    Hi Scott,

    Thanks a lot for this post! It is really helpful for the migration. I followed the method of using one datastore with multiple configurations which did bring convenience to the development and migration. However, I found there is a little pain thing. At this point, I have two configurations, one is config_dev which is for dev and another one config_test which is  is for test environment.If there is no need to edit on the DataStore, there is no any problem since I don’t need to check in and check out the DataStore ( just check in Objects and Dependence) But if   I checked out the objects and dependences with filtering (include DataStore) in the dev to edit some, and then check in .When I switch to Test, check out the things to local repo in test, I found I have to manually change the default configuration to test one.  So, I wonder if there any auto way that the DataStore could remember/recognize which configuration should be as default in that environment? Please let me know, thanks again!

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