Meta Change Queue

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------=_NextPart_01C4D079.2EB43A00 Content-Location: file:///C:/8F4C2305/MetaChangeQueue_files/pres.xml Content-Transfer-Encoding: quoted-printable Content-Type: text/xml; charset="utf-8" ------=_NextPart_01C4D079.2EB43A00 Content-Location: file:///C:/8F4C2305/MetaChangeQueue_files/slide0001.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Meta Change Queue

2004 LISA XVIII - Atlanta, GA

Meta Change Queue: Tracking Changes to People, Places and Things

Jon Finke

Rensselaer Polytechnic Institute

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2004 LISA XVIII - Atlanta, GA

When Dinosaurs Roamed….

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Content-Type: text/html; charset="us-ascii" Meta Change Queue

2004 LISA XVIII - Atlanta, GA

Import Table

Simon

ID Card

System

Import Trigger

Import Table

Push Changes

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2004 LISA XVIII - Atlanta, GA

Startup Loads

<= span class=3DBB style=3D'position:absolute;left:-5.34%'>•Seed the = queue with desired info

–Ensures load process= ing works

<= span class=3DB1B style=3D'position:absolute;left:-4.41%'>–Saves development of alternate load methods

•Bulk Prio= rity Queue

<= span class=3DB1B style=3D'position:absolute;left:-4.57%'>–Allow “real time” requests to jump ahead of batch processin= g.

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2004 LISA XVIII - Atlanta, GA

Single Sign On

Simon

LDAP

Kerberos

(AFS)

Windows

2000

Trouble

Ticket

The Other

LDAP

Web

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2004 LISA XVIII - Atlanta, GA

Futures

•Batch Priority Queue

•Better Error Handling

•XML Interfaces

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2004 LISA XVIII - Atlanta, GA

Takeaways

•Index a key column and set to null after processing.

<= span class=3DBB style=3D'position:absolute;left:-5.57%'>•Import ta= ble with trigger works well with Oracle target systems.

•Batch (low priority) queue is important.

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2004 LISA XVIII - Atlanta, GA

Meta Change Queue: Tracking Changes to People, Places and Things

Jon Finke

Rensselaer Polytechnic Institute

finkej@rpi.edu

? Questions ?

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2004 LISA XVIII - Atlanta, GA

Web Page Examples

•List of Tables

•Table Details

•List of Listeners

•Listener Details

•Listener Details with Recent Activity

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2004 LISA XVIII - Atlanta, GA

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	We now have lots of different systems, serving many different functions, run, by different departments, with products from a number of different vendors, = with different operating systems. One thing that the shared, is that they could all can benefit from a good flo= w of information.

	This is simplified diagram, and a lot is missing – but even this is pretty complicated.

	White arrows represent “people” data, orange dashed arrows are financial, and so on.

	Our administration database, Banner, feeds some people information into Simon, our account management database, and the place where most of the Meta Cha= nge Queue project, actually lives. From Simon, we feed information to our ID card system, voice mail,= a bunch of different directory systems.&nb= sp; We also get information from other systems such as space managemen= t, and push that along as needed. Some of these systems are Oracle, others are non Oracle but still relational such as SQL-SERVER, and others are specialized.

	Lets talk about the types of data a bit more


	Lets look at some of the different types of data we need to move around.
	•The biggest category for us, is the inf= ormation about people. This starts w= ith basic information like a person’s name, and then moves on to direct= ory information such as phone numbers, addresses of different types, etc. We also like to know a person’s status – this is a topic for another paper.
	•We also have account information –= ; email and computer, but lately, this has been including telephone and voicemail accounts.
	•Another type of data is dealing with the physical site. Buildi= ngs, floors, rooms, other facilities and so on.
	•We also are interested in financial inf= ormation – we have a 6 part budget number, with each part requiring validation. Personally I wo= nder why an enterprise with 2000 staff members has over 6000 active funds, but= I am not an accountant.
	•Lastly, we have our organizational stru= cture – our department names, where they fit into the enterprise, abbreviations and so on.


	Besides the types of data, there are a number of issues that must be understood a= bout each type of data.

	•Perhaps the hardest issue with enterpri= se data, is identifying who “OWNS” the data, and where the authoritati= ve or official source of that data element resides. Some cases are easy, others take= a bit of discussion, as the obvious choice may turn out to be wrong. It took about 8 months of bi-wee= kly meetings to agree on the official source, or “system of record̶= 1; for people data. Although I fought hard, I ended up getting it.
	•The object of this exercise, is to move= data from the system of record, to the systems that need it. One that that is important to understand, is how quickly does that have to take place. In some cases, waiting weeks is = not a problem, like when they are building a new building (although modular buildings can throw that off), while in other cases, we need the change to happen in as close to real time as possible.
	•We also need to understand how we can g= et the data into the target system, while still respecting the business rules of that system.
	•We also need to consider mechanical iss= ues like what do you do when the target system is down, and do you want to push the data in from the source, or pull it from the target?
	•And finally, just exactly what informat= ion do you want to load? We have something like 50 different data elements associated with a person. Most systems only care about a m= uch smaller subset. We also wan= t to deal with systems that don’t want all people, but just some of them, say just employees.


	This project was a re-implementation of some existing queuing procedures I had= in place, so a lot of the design work and interface definitions were already= in place, but I did have some things I wanted to address in the rewrite.

	•You may recall from one of the earlier = slides, that there were a lot of boxes and arrows. But things didn’t start th= at way, they evolved to that level. One of the hassles with the earlier work, is that adding a new “listener”, was a big hassle. This involved changes to the tab= le definition, updating procedures to set the new fields, and this would hit problems with the table being in use by the existing systems and “locked” by Oracle. A big hassle.
	•Along with adding new systems that want= to listen, we had a sudden surge in the number of different data elements th= at we wanted to track. Adding = new tables to monitor was even more hassle than adding new listeners.<= br>
	•We were also hitting the problem of hav= ing lots of new things that not everyone wanted.
	•We also had to ensure that the solution= had a very low impact on the systems involved.= I had to convince the DBA’s of the systems that I did not ru= n, that this approach would not put an excessive, or even a noticeable load = on their applications.
	•And since I was playing in the code any= way, tying this in to our existing monitoring systems and making that as close= to automatic as possible was highly desirable.


	We ended up with a list of tables (data types), which in some cases we broke down = into subtypes. This was really d= riven by identifying the primary key of a data element. For example, a phone nu= mber change would be marked as a change to the “Person” table with= a subtype of “telephone”.

	We then identified all of the systems that were interested in these changes, and = we called them all “listeners” (even if we ended up pushing the changes to them).

	Armed with this list of tables and subtypes, and the list of listeners, we were able to define a matrix indicating who wants what.

	This was actually realized with a table


	I think Peter has this same slide next door, but anyway…

	Back in the olden days, when dinosaurs roamed&nb= sp; the earth, and white coated operators fed round tapes to the spinn= ing tape drives, moving enterprise data between systems was easy – there was only one system. = And you couldn’t have the data anyway.

	Things have changed, they got worse.


	When a change is detected, we insert a number of records, one for each listener.=
	•we start with table name and subtype if applicable. This tells the processing routines where to look for the data.
	•Next up is information that defines wha= t record actually changed. An import= ant thing to note here, is that we do NOT store the data that changed, but on= ly a key to find the most current record.&nbs= p; In designing the processing routines, it is very important that systems only be interested in the final data values and not intermediate values.
	•Last is the name of the listener. We actually store this twice. The first copy is used by the selection routines.After a record is processed, the field is set to NULL.= This column is indexed by Oracle= , and null values are NOT in the index. As a result, the index is very small, containing just the records = that are pending processing, and looking for work can be very low cost! This allows for frequent polling – small number of seconds has little impact on the database server performance. The second hel= ps with statistical analysis.
	•We also include some other columns for housekeeping purposes – they are described in the paper.


	Lets back up a step – how do we detect changes in the first place?
	•In systems that I have been deve= loping recently, I generally make all access to a database table via a single database package, This give= s me a handy place to “detect” changes – since they are being = done via a single interface. Thi= s also lets me decide if a change is worth notice – updating a comment on = an entry, or a “last checked date” field might not count as a “change” to the data element, although certainly is an update from a database sense.

	•In many existing applications, a= s well as systems provided by other vendors, this first option is not available. But with many da= tabase systems, you can define a trigger on a table that will detect when a reco= rd is inserted, deleted or changed. These triggers can attempt to do some of the checking to determine= if the change is of interest, and make the appropriate entries.

	•For those of you old enough to r= emember the days when dinosaurs walked the earth, we would actually get tapes of = data from other systems – we would then do a brute force, line by line comparison with our own copy of the data and generate the change list. We still do a lot more of this t= han I would care to admit, but on the other hand, it does work. We have managed to eliminate the= tape step at least.
	•We also have manual interfaces &= #8211; a web page with a button that says “Refresh” or “Export” that just sends a “change” record through. Since what we send= is the fact that a record has “changed” and not the actual change itself, this works ok. This= is useful for testing, and in tweaking those records that hit some oddball problem the first time through.


	One of the interface methods we use, involves a “listener”. This is a program, possibly JAVA= or C# that polls the database looking for changed records, and when it sees one, makes the appropriate updates in the Active Directory or LDAP or whatever= we are updating. T= his is how we generally feed non Oracle systems.

	For these types of setups, I write a listener specific PL/SQL interface that = has a Get_Changes routine that gives the next change, and an Ack_Change routi= ne that the program uses to mark the change as processed. There are also “get_detail” routines that allow the program to get specifics= on the record as needed. In po= int of fact, the listener specific interface isn’t all that specific ̵= 1; several use the same interface and just specify the listener name in the calling routines. This was also a g= ood place to build in the run time monitoring. These programs are generally run= ning in a polling mode, looking for changes every few seconds.


	Another approach that we use when we are sending to another oracle system, involv= es creating an import table on the target system. Although we had originally plann= ed to have a process on the target system poll that table for changes, we ended= up writing a trigger on insertions into the import table, that makes all the appropriate business decisions, and enters the data directly into the application. We have used t= his approach on 3 different vendor systems, with the a vendor engineer writing that trigger. This keeps the vendor happy, and provides us with a clean interface.

	These systems are generally updated once a day – although we are queuing changes all day for them, we have a cron job that runs each morning that takes all queue changes, and pushes them out. We could of course make this hap= pen more frequently if we needed to.

	We are able to use this same mechanism to do on demand update from web pages. Our ID card office uses this frequently to handle last minute changes.


	Either interface approach is handy with startup situations. Simply write a short SQL script<= span style=3D'mso-spacerun:yes'> to mark every record of interest= as “changed” for the new listener, and then they all flow into t= he new system. This gives the = system a good test and you don’t need to develop an initial load procedure= .

	One of the aspects of being from a university, is that you do get big blocks of changes, like when a new semester starts and you need to process a few thousand new students. This= can often take a pretty substantial amount of time, and the ability to have a “bulk” queue that runs at a lower priority would be handy. We don’t want interactive = users to get stuck behind a long list of new user creates or something. We plan on adding this option so= on.


	We like the idea of having single signon available to all of users. Despite the appearance to our us= ers, we have a lot of different authentication systems in place. Some of these are due to migrati= on issues say from our AFS Kerberos server to a Kerberos V5 server, whereas others are driven more by divisional politics.

	This is actually a different project from general Meta Change Queue, but is using many of the same design concepts. We have the advantage of only one “table” of interest,= but we complicate things with some public key encryption so that each “listener” uses it’s own private key to decrypt the password before applying it to that particular authentication base.


	There are a few things that I plan on adding to this project – although i= t is certainly in production with close to a million changes in the table ( I suppose I should flush that able one of these days).
	•The first thing I need to add, is the b= atch priority. Some things just don’t have to go through as quickly as other things. If I have a user waiting for a r= esponse, I want to give it to them quickly. (Oddly enough, our old 3033 mainframe had the same concept).
	•When we put out voicemail system online= , we used this queuing approach to control the configuration. Sometimes these actions fail, an= d a better feedback mechanism would be handy.
	•Another project is using an XML based (= SOAP) interface to load configuration information to our VOIP phone system. While this is generally interact= ive, it is possible to overload the interface, and it will simply shut down fo= r a minute to rest. As we allow= end users to generate configuration change requests, we are going to have to = be able to queue these requests if we can’t handle them right away. Ideally we would be able to writ= e this as a pretty generic “listener”.


	Ok, what do I think are some of the important things for you to take away from this presentation?

	•When using a database, use a flag colum= n to indicate pending work, create an index on that column, and set that colum= n to Null when processed. The in= dex will be very small, and the overhead from frequent polling by listeners w= ill be trivial.
	•An Import table with a trigger provides= a nice demarkation between your world and the vendor world. This allows for both batch loads= as well as real time loads as needed.
	•A low priority queue for big update is = really nice. In the single signon example, bringing a new authentication server online involves “replaying” a lot of past password changes, which generates a= lot of public key decryption and encryption.= While all of this is processing, normal user password changes get stuck in that queue. I can’t take that service down for the 6 hours or so it takes to catc= h up on 6,000 password changes.


	I could solve this problem using a relational database, such as Oracle.

	Ok – is anyone here surprised that I came to that conclusion?

	How about this,


	Good Morning