Creating a Single View: Data Design and Loading Strategies

Enterprise Architect, MongoDB Buzz Moschetti buzz.moschetti@mongodb.com #ConferenceHashTag Creating a Single View Part 2: Data Design & Loading Strategies

Who Is Talking To You? • Yes, I use “Buzz” on my business cards • Former Investment Bank Chief Architect at JPMorganChase and Bear Stearns before that • Over 27 years of designing and building systems • Big and small • Super-specialized to broadly useful in any vertical • “Traditional” to completely disruptive • Advocate of language leverage and strong factoring • Inventor of perl DBI/DBD • Still programming – using emacs, of course

What Is He Going To Talk About? Historic Challenges New Strategy for Success Technical examples and tips Overview & Data Analysis Data Design & Loading Strategies Securing Your Deployment ç Ω Creating A Single View Part 1 Part 2 Part 3

It’s 2014: Why is this still hard to do? • Business / Technical / Information Challenges • Missteps in evolution of data transfer technology A X

We wish this “just worked” A Query objects from A with great performance Query objects from B with great performance X Query objects from merged A and B with great performance B

…but Beware The Blue Arrow! A X • Extracting many tables into many files • Some tables require more than one file to capture representation • Encoding/formatting clever tricks • Reconciliation • Different extracts for different consumers • Different extracts for different versions of data to same consumer

Loss of fidelity exposed class Product { String productName; List<Features> ff; Date introDate; List<Date> versDates; int[] unitBundles; //… } widget1,,3,,good texture,retains value,,,20142304,102.3,201401 widget2,XS,6,,,,not fragile,,,20132304,73,87653 widget3,XT,,,4,,dense,shiny,mysterious,,,19990304,73,87653,, widget4,,,3,4,,,,,,20040101,,999999,, AORM

What happened to XML? class Product { String productName; List<Features> ff; Date introDate; List<Date> versDates; int[] unitBundles; //… } <product> <name>widget1</name> <features> <feature> <text>good texture</text> <type>A</type> </feature> </features> <introDate>20140204</introDate> <versDates> <versDate>20100103</versDate> <versDate>20100601</versDate> </versDates> <unitBundles>1,3,9</unitBun… ç Ω

XML: Created More Issues Than Solved <product> <name>widget1</name> <features> <feature> <text>good texture</text> <type>A</type> </feature> </features> <introDate>20140204</introDate> <versDates> <versDate>20100103</versDate> <versDate>20100601</versDate> </versDates> <unitBundles>1,3,9</unitBun… • No native handling of arrays • Attribute vs. nested tag rules/conventions widely variable • Generic parsing (DOM) yields a tree of Nodes of Strings – not very friendly • SAX is fast but too low level

… and it eventually became this <p name=“widget1” ftxt1=“good texture” ftyp1=“A” idt=“20140203” … <p name=“widget2” ftxt1=“not fragile” ftyp1=“A” idt=“20110117” … <p name=“widget3” ftxt1=“dense” idt=“20140203” … <p name=“widget4” idt=“20140203” versD=“20130403,20130104,20100605” … • Short, cryptic, conflated tag names • Everything is a string attribute • Mix of flattened arrays and delimited strings • Irony: org.xml.sax.Attributes easier to deal with than rest of DOM

Schema Change Challenges: Multiplied & Concentrated! X Alter table(s) split() more data A Alter table(s) Extract more data LOE = x1 Alter table(s) split() more data Alter table(s) split() more data B Alter table(s) Extract more data LOE = x2 C Alter table(s) Extract more data LOE = x3 LOE = xn 1 n å + f (n) where f() is nonlinear wrt n

SLAs & Security: Tough to Combine A B User 1 entitled to see X User 2 entitled to see Y User 1 entitled to see Z User 2 entitled to see V X Entitlements managed per- system/per-application here…. …are lost in the low-fidelity transfer of data…. …and have to be reconstituted here …somehow…

Solving The Problem with mongoDB

Overall Strategy For Success • Let the source systems entities drive the data design, not the physical database • Capture data in full fidelity • Perform cross-ref and additional logic at the single point of view, not in transit

Don’t forget the power of the API class Product { String productName; List<Features> ff; Date introDate; List<Date> versDates; int[] unitBundles; //… } If you can, avoid files altogether! Haskell ç Ω

But if you are creating files: emit JSON class Product { String productName; List<Features> ff; Date introDate; List<Date> versDates; int[] unitBundles; //… } { “name”: “widget1”, “features”: [ { “text”: “good texture”, “type”: “A” } ], “introDate”: “20140204”, “versDates”: [ “20100103”, “20100601” ], “unitBundles”: [1,3,7,9] // … } ç Ω

Let The Feeding System Express itself A B C { “name”: “widget1”, “features”: [ { “text”: “good texture”, “type”: “A” } ] } { “myColors”: [“red”,”blue”], “myFloats”: [ 3.14159, 2.71828 ], “nest”: { “as”: { “deep”: true }}} } { “myBlob”: { “$binary”: “aGVsbG8K”}, “myDate”: { “$date”: “20130405” } }

What if you forgot something? { “name”: “widget1”, “features”: [ { “text”: “good texture”, “type”: “A” } ], “introDate”: “20140204”, “versDates”: [ “20100103”, “20100601” ], “versMinorNum”: [1,3,7,9] // … } { “name”: “widget1”, “features”: [ { “text”: “good texture”, “type”: “A” } ], “coverage”: [ “NY”, “NJ” ], “introDate”: “20140204”, “versDates”: [ “20100103”, “20100601” ], “versMinorNum”: [1,3,7,9] // … } ç Ω

The Joy (and value) of mongoDB A Alter table(s) Extract more data LOE = .25x1 B Alter table(s) Extract more data LOE = .25x2 C Alter table(s) Extract more data LOE = .25x3 LOE =O(1)

Helpful Hint: Use the APIs curs.execute("select A.did, A.fullname, B.number from contact A left outer join phones B on A.did = B.did order by A.did") for q in curs.fetchall(): if q[0] != lastDID: if lastDID != None: coll.insert(contact) contact = { "did": q[0], "name": q[1]} lastDID = q[0] if q[2] is not None: if 'phones' not in contact: contact['phones'] = [] contact['phones'].append({"number”:q[2]}) if lastDID != None: coll.insert(contact) { "did" : ”D159308", "phones" : [ {"number”: "1-666-444-3333”}, {"number”: "1-999-444-3333”}, {"number”: "1-999-444-9999”} ], "name" : ”Buzz" } ç Ω

Helpful Hint: Declare Types Use mongoDB conventions for dates and binary data: {“dateA”: {“$date”:“2014-05-16T09:42:57.112-0000”}} {“dateB”: {“$date”:1400617865438}} {“someBlob”: { "$binary" : "YmxhIGJsYSBibGE=", "$type" : "00" }

Helpful Hint: Keep the file flexible Use CR-delimited JSON: { “name”: “buzz”, “locale”: “NY”} { “name”: “steve”, “locale”: “UK”} { “name”: “john”, “locale”: “NY”} …instead of a giant array: records = [ { “name”: “buzz”, “locale”: “NY”}, { “name”: “steve”, “locale”: “UK”}, { “name”: “john”, “locale”: “NY”}, ]

Helpful Hint: A quick sidebar on jq $ cat myData { "name": "dave", “type”: “mobile”, "phones": [ { "type": "mobile", "number": "2123455634", "dnc": false }, { "type": "mobile", "number": "6173455634" }, { "type": "land", "number": "2023455634" } ] } { "name": "bob", “type”: “WFH”, "phones": [ { "type": ”land", "number": "70812342342", "dnc": false }, { "type": "land", "number": "7083455634" } ] } (another 99,998 rows)

Helpful Hint: jq is JSON awk/sed/grep $ jq -c '.phones[] | select(.dnc == false and .type == “mobile” )' myData {"dnc":false,"number":"2123455634","type":"mobile"} {"dnc":false,"number":"70812342342","type":"mobile"} … $ jq [expression above] | wc –l 32433 $ gzip –c –d myData.gz | jq [expression above] | wc –l 32433 http://stedolan.github.io/jq/

Helpful Hint: Don’t be afraid of metadata Use a version number in each document: { “v”: 1, “name”: “buzz”, “locale”: “NY”} { “v”: 1, “name”: “steve”, “locale”: “UK”} { “v”: 2, “name”: “john”, “region”: “NY”} …or get fancier and use a header record: { “vers”: 1, “creator”: “ID”, “createDate”: …} { “name”: “buzz”, “locale”: “NY”} { “name”: “steve”, “locale”: “UK”} { “name”: “john”, “locale”: “NY”}

Helpful Hints: Use batch ID { “vers”: 1, “batchID”: “B213W”, “createDate”:…} { “name”: “buzz”, “locale”: “NY”} { “name”: “steve”, “locale”: “UK”} { “name”: “john”, “locale”: “NY”}

Now that we have the data… You’re well on your way to a single view consolidation…but first: – Data Work • Cross-reference important keys • Potential scrubbing/cleansing – Software Stack Work

You’ve Built a Great Data Asset; leverage it!

DON’T Build This! Giant Glom Of GUI-biased code http://yourcompany/yourapp

Build THIS! http://yourcompany/yourapp Data Access Layer Object Constructon Layer Basic Functional Layer Portal Functional Layer GUI adapter Layer Web Service Layer Other Regular Performance Applications Higher Performance Applications Special Generic Applications

What Is Happening Next? Access Control Data Protection Auditing Overview & Data Analysis Data Design & Loading Strategies ç Ω Creating A Single View Part 1 Part 2 Securing Your Deployment Part 3

Enterprise Architect, MongoDB Buzz Moschetti buzz.moschetti@mongodb.com #ConferenceHashTag Q&A

Creating a Single View: Data Design and Loading Strategies

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