addresses - SCN Wiki

Report
&
Address Cleansing
Integration of REST web services
Chris Twirbutt & Forrest Horner
2010 November
1
Melissadata REST & SAP Integration
•
•
•
•
Background of Sacramento County
Address cleansing choices
Architecture to facilitate REST web services
Example SAP screens
2
Background Sacramento County CA
• Sacramento County, California
– About 1.3 million citizens
– Multiple jurisdictions, Cities, Districts, etc
• SAP IS-U/CCS Utility Billing System (aka: “FOCUS”)
–
–
–
–
Implemented in July 1999 (on v1.1b)…now vECC6
Bill for ~300K customers, ~$300m revenue
Waste Water, Solid Waste, Drainage, Potable Water
…have a SAP tech support staff of: ~8
3
Address Cleansing Choices
#1 – Load Postal CD’s to SAP Regional Structure
…but alas, it doesn’t fix “bad” addresses…only flags
address is not good.
#2 – Periodic Batch Cleansing
Is re-active management…and bad addresses
percolate through downstream data systems.
#3 – Web Service Address Cleansing
Is pro-active,but must put in a technical architecture.
4
Postal CD Data – Does Not “Fix”
Postal CD comes from the Post Master
The USPS web site
http://www.usps.com/ncsc/addressinfo/addressinfomenu.htm
The web site has the postal AIS technical guide in PDF format
and order forms for ordering the postal CD.
Note: RSADRLSM02 is the
program that loads the data
Connection Object
Note: SAP function
module
ADDR_CHECK is what
SAP uses for verifying
if an BP or CO
address is valid
against the postal
structures
Business Partner
5
Periodic Batch Cleanse (Monthly)
ADRC table
File dump
Data
Extract
SAP Production
Cleansing Service
Address
Corrections
File
Data Upload and update
This fixes after the fact…after bad mail has already gone out…and after other
downstream data systems have already consumed the bad address
6
Melissadata – Web Service
Has many different address cleansing products
Meldata provides back a ‘corrected’ address.
SAP Production / SYSID:“PPW”
Web
Service
FOCUS
RFC
Web Hit
Counter
Dev / Test
Systems
Address
Cache
An “architecture” had to be built in SAP.
The SAP production system acts as a “service buss”.
7
Overview of MelData Web Services
Mellisadata (Henceforth referred to as “Meldata”) offers numerous
services and Suites of Services.
Sac County procured the “Data Quality Web Services Suite” , which is a
“Multiplatform toolkit to verify, correct and standardize address, phone,
email and names at point-of-entry.”
This Suite consists of 7 services, 5 of which we have enabled (
And two of which we are currently using (
):
• Address Check
• RBDI (Residential/Business Delivery Indicator)
• GeoCode
• IP Location
• Email
• Name Check
• Phone Check
)
8
Usage allowance
We subscribed to 600K hits per year for each enabled service.
The following is a screen-print from MelData’s side which tracks our usage:
9
Technical Implementation
SOAP vs. REST
Overall, more companies such as Google and Amazon are using REST-based Web Services.
REST stands for “Representational State Transfer”.
We won’t go into fine detail of SOAP vs. REST but rather, why we chose REST.
A nice robust link on the technical differences between SOAP and REST:
http://www.taranfx.com/rest-vs-soap-using-http-choosing-the-right-webservice-protocol
We utilize the REST-based Web Service Interface because:
• SOAP required more JAVA skills than were available in-house, for example:
• SOAP had many data-type binding issues which had to be worked out by contacting
MelData tech support and having them make changes to the WSDL, etc.
• REST was 10 times easier to code for in ABAP as it didn’t have to deal with Proxy
generation (data binding issues), and other JAVA-related issues.
• Developers who simply understand HTTP and XML can start building Web services right
away, without needing any toolkits beyond what they normally use for Internet application
development.
10
BASIS/Config. Requirement
(for SAP to communicate with the Web Service)
-Using SSL (https) to access external web site recommended.
- Public web servers have SSL certificates signed by known public Certificate Authority
- Web browsers come with built-in store of known public CA’s to verify certificate
server is presenting.
-The SAP Web Application Server (Web AS) ships with very few CAs, so it won’t know
the CA the remote site had its SSL cert signed by. For example, Melissadata uses
“GoDaddy”.
-Navigate to the secure URL using a web browser, not SAP initially.
-While there, click the “lock” icon and download the security certificates in base 64
format. This incl. both the root and class 2 certificates.
-There may be multiple cert’s, called a chain. Download cert for each part of chain.
-Load the security certificate into SAP (STRUST)
-Import certificates into SAP anonymous &client standard PSE’s (Personal Security Environment)
-Still In STRUST, export the certificates (to load them into the db).
-Restart ICM.
-Set up a “hosts file” entry, if needed (i.e. DNS is turned off, which it is in PPW) to
Melissadata so the SAP server “knows” who Melissadata is.
11
Architecture Overview
Custom(‘Z’) Naming Convention
Begin all Web-Service-related Z-table names with “ZWS_”
12
Architecture Overview (cont’d)
• All requests for address scrubs are done thru PPW (Production), regardless of source
system. This is done by RFC and ensures that all hits are tracked in one place (Production).
• PPW architected with several control & tracking tables:
• A master table(ZWS_MELDATA) which stores/caches all detailed request history
• A table to configure how many hits allowed per day (ZWS_HITS_MAX)
• A table to store actual hits per day counts (ZWS_HITS_DAILY)
13
Architecture (Cont’d)
PPW contains (cont’d):
• table (ZWS_MELDATACODES) stores all possible return codes (and descriptions)
Note on Return Codes:
• 1st Char:
•Address Scrub return Codes begin with “A”
•and GeoCoding Return Codes begin with “G”.
• 2nd Char:
•“S” (Success – Corrected address returned)
•or “E” (Error – Not able to return a fully corrected address)
14
Architecture (Cont’d)
• Communication to PPW from all systems:
-An RFC interface was added (By BASIS) to all the systems that "points to" PPW. This
interface is named "PPW_SCRUB".
-A user and role was created in PPW to perform the scrub function. The user is
"adrscrub", and the role is "z_address_scrub".
• Communication to Web Service from SAP/ABAP:
A user-friendly wrapper/RFC/FM was developed called:
ZMELISSADATA_ADDRESS_CLEANSE
This FM/RFC currently performs both Address Check and Geocoding.
This FM uses SAP’s OO methods contained in Class/Interface “IF_HTTP_CLIENT”,
For example:
CALL METHOD http_client=>create_by_url (sets up the URL to be called)
CALL METHOD http_client->send
(makes a call to the URL)
CALL METHOD http_client->receive
(receives data back from URL)
The result comes back in XML format and is parsed by the FM (could have been done using
XLST Transformation, but in our case using custom code to parse the XML result into Z-Table
fields (this was prior to our knowledge of XLST Transformations).
15
Example of how to call the RFC
IN
OUT
Note: Input has incorrect Street name
(“AVE” vs. “RD”) and incorrect zip (95826
Instead of 95827)
Output has corrected Street Name
And Zip Code (+4).
“Address Key” required for GeoCoding (see next slide)
16
Example of Geo-Coding
IN
OUT
17
Table Entry Created (in ZWS_MELDATA)
MANDT
COUNTER ALL TIME
020
9910
SOURCE SYSTEM
PPW
SOURCE USER
TWIRBUTTC
CALLING PROGRAM
SAPLSEUJ
CALLING TXN
SE37
CALLED ON DATE
06/14/2010
CALLED ON TIME
14:06:45
CALLED BY
ADRSCRUB
URL CALLED
https://addresscheck.melissadata.net/v2/REST/Service.svc/doAddressCheck?id=1234567890&a1=9700%20GOETHE%20AVE&ctry=US&opt=true&a2=&ste=C&city=SA
CRAMENTO&state=CA&zip=95826
RETURN CODE
AS01,AS12
BACKGROUND FLAG
IN HOUSE NUM
9700
IN STREET
GOETHE AVE
IN SUITE
C
IN CITY
SACRAMENTO
IN STATE
CA
IN ZIP
95826
OUT PARSED HOUSE
9700
OUT PARSED STR
Goethe
OUT PARSED STSUF
Rd
OUT PARSED SUIT1
Ste
OUT PARSED SUIT2
C
OUT ADDRESS1
9700 Goethe Rd
OUT ADDRESS2
OUT SUITE
Ste C
OUT CITY
Sacramento
OUT STATE
CA
OUT ZIP
95827
OUT PLUS4
3558
OUT RESULT CODES
AS01,AS12
HOUSESTREETMATCH
CITY MATCH
X
STATE MATCH
X
ZIP MATCH
ADRC NUM
OUT ADDRESSKEY
95827355875
GEO RESULT CODES
GS01
GEO LAT
38.554697
GEO LONG
-121.335972
18
ZWS_MELDATA: Field Definitions
MANDT
COUNTER_ALL_TIME
020
9910
# of hits made to the Web Service (all-time)
SOURCE_SYSTEM
PPW
Calling System
SOURCE_USER
TWIRBUTTC
Self-explanatory
CALLING_PROGRAM
SAPLSEUJ
“
CALLING_TXN
SE37
“
CALLED_ON_DATE
06/14/2010
“
CALLED_ON_TIME
14:06:45
“
CALLED_BY
ADRSCRUB
RFC User id (set up by BASIS)
URL_CALLED
https://addresscheck.melissadata.net/v2/REST/Service.svc/doAddressCheck?id=1234567890&a1=0970%20GOETHE%20AVE&ctry
=US&opt=true&a2=&ste=C&city=SACRAMENTO&state=CA&zip=95826
RETURN_CODE
AS01,AS12
MelData Return Codes
BACKGROUND_FLAG
Background/Foreground indicator
IN_HOUSE_NUM
9700
Input House #
IN_STREET
GOETHE AVE
Input Street
IN_SUITE
C
Input Suite (opt’l)
IN_CITY
SACRAMENTO
Input City
IN_STATE
CA
Input State
IN_ZIP
95826
Input Zip
Note:
Key fields are MANDT & COUNTER_ALL_TIME
Index exists on URL_CALLED since we CACHE based on this one field (each unique URL represents a unique AddressCheck).
Continued on next Slide…
19
Field Definitions (cont’d)
OUT PARSED HOUSE
OUT PARSED STR
OUT PARSED STSUF
OUT PARSED SUIT1
OUT PARSED SUIT2
OUT ADDRESS1
OUT ADDRESS2
OUT SUITE
OUT CITY
OUT STATE
OUT ZIP
OUT PLUS4
OUT RESULT CODES
HOUSESTREETMATCH
CITY MATCH
STATE MATCH
ZIP MATCH
ADRC NUM
OUT ADDRESSKEY
GEO RESULT CODES
GEO LAT
GEO LONG
9700
Goethe
Rd
Ste
C
9700 Goethe Rd
Ste C
Sacramento
CA
95827
3558
AS01,AS12
X
X
95827355875
GS01
38.554697
-121.335972
Parsed Address fields returned from MelData
“
“
“
“
“
“
“
“
“
“
“
MelData Return Code(s)
Did INPUT House # and Street match the OUTPUT House # and Street ?
Did INPUT City match the OUTPUT City ?
Did INPUT State match the OUTPUT State?
Did INPUT Zip
match the OUTPUT Zip ?
ADRC Record # scrubbed (if applicable)
Meldata address key returned (needed to GeoCode)
GeoCode Return Code(s)
GeoCode Lat Result
GeoCode Long Result
20
Find Billing Account by Address lookup
Via GeoCoding
Step 1 – Meldata address check
-- Cache result in table ZWS_MELDATA
Step 2- Update sap address if necessary and possible (if address scrub was successful)
Step 3 – Using MelData Address key (from Successful scrub only), get lat long
Step 4 – Using haversine formula, get nearest parcel (ZPSD_APN) and VKONT (ZGIS_APN_DATA)
(Using FM “Z_RFC_LAT_LONG_IN_PARCEL_OUT”)
IN
OUT
Since Geo-Coding is not always down to the rooftop level, enter As much info of the
Address as possible (in this case House # and 1st letter of Street)
21
Secret Sauce: HAVERSINE Formula
http://en.wikipedia.org/wiki/Haversine_formula
SAP ABAP program code:
Assume that U = North Pole, and V and W are
our pair of X,Y coordinates (note the variable
names above do not match that of the program
code in this presentation)
a,b, and c are the “Great Circle”* distances
between U,V and W.
* A great circle of a sphere is a circle that
runs along the surface of that sphere so as
to cut it into two equal halves. The great
circle therefore has both the same
circumference and the same center as the
sphere. It is the largest circle that can be
drawn on a given sphere.
Great circles serve as the analogue of
"straight lines" in spherical geometry.
22
Other FOCUS Applications:
This stand alone screen
demonstrates the architecture.
Link to Youtube demo of this
screen:
http://www.youtube.com/watc
h?v=7txYpWmKWCg
Link to SAP Developer Network
(SDN) SAP/Meldata WiKi:
http://wiki.sdn.sap.com/wiki/di
splay/stage/Address%20Cleansi
ng%20via%20REST%20Web%20
Service%20Consumption
23
Other FOCUS Applications:
E.g. Lifeline Entry (for Non-FOCUS Accts.)
New Button
New Sub-screen
For scrubbed address
Results.
24
Future Integration
•
Managed update, with clerical review of suggested Address fixes.
•Fully Automated update of our Address from Mel-Data.
• Business Partner mailing address update
• Physical location addresses
• To
be continued……
A myriad of other opportunities will present themselves:
Finding duplicate customers, locations.
Finding missing locations: address exists, but we aren’t billing.
Geocoding & Work Order proximity based routing.
•Regional MADD (Master Address Database)
Effort by Cities, and public safety, police, fire, etc to standardize addresses.
Lessons Learned:
•Must have an architecture to facilitate & manage the web service “hits”
•Must involve your BASIS team to “open up” the interface
•Mass cleansing (thousands at a time) needs “404” error handling
•Melissadata tech team were great to collaborate with!...an essential aspect.
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