Webinar: Transitioning from SQL to MongoDB

Transitioning from SQL to MongoDB
Joe Drumgoole
Director of Developer Advocacy, EMEA
@jdrumgoole
Joe.Drumgoole@mongodb.com
V1.2

Before We Begin
• This webinar is being recorded
• Use The Chat Window for
• Technical assistance
• Q&A
• MongoDB Team will answer quick questions in real time
• “Common” questions will be reviewed at the end of the
webinar

Who is your Presenter?
• Programmer
• Developer Manager
• Entrepreneur
• Geek
• Some time pre-sales guy

MongoDB: The New Default Database
Document
Data Model
Open-
Source
Fully Featured
High Performance
Scalable
{
name: “John Smith”,
pfxs: [“Dr.”,”Mr.”],
address: “10 3rd St.”,
phone: {
home: 1234567890,
mobile: 1234568138 }
}

6
It’s a JSON Database
{u'_id': ObjectId('58511bfbb26a8803b6b4d56c'),
u'batchID': 108,
u'member': {u'chapters': [{u'id': 1775736,
u'name': u'London MongoDB User Group',
u'urlname': u'London-MongoDB-User-Group'},
{u'id': 1780459,
u'name': u'Stockholm MongoDB User Group',
u'urlname': u'Stockholm-MongoDB-User-Group'},
{u'id': 3478392,
u'name': u'Dublin MongoDB User Group',
u'urlname': u'DublinMUG'},
u'urlname': u'Mannheim-MongoDB-User-Group'}],
u'city': u'Dublin',
u'country': u'Ireland',
u'events_attended': 13,
u'is_organizer': True,
u'join_time': datetime.datetime(2013, 10, 30, 17, 5, 31),
u'last_access_time': datetime.datetime(2016, 12, 13, 15, 45, 27),
u'location': {u'coordinates': [-6.25, 53.33000183105469],
u'type': u'Point'},
u'member_id': 99473492,
u'member_name': u'Joe Drumgoole',
u'photo_thumb_url': u'http://photos2.meetupstatic.com/photos/member/e/5/0/1/thumb_255178625.jpeg'},
u'timestamp': datetime.datetime(2016, 12, 14, 10, 16, 27, 607000)}
Typed
Hierarchical, with lists and maps
Geo-Spatial

Functions of a Database
• Durable data storage
• Structural representation of data
• CRUD operations
• Authentication and authorization
• Programmer Efficiency?

What Are Your Developers Doing All Day?
1964 - IMS
1977 - Oracle
1984 - dBASE
1991 - MySQL
2009 - MongoDB

The Challenge is Product Development
1976 2016
Business Data
Goals
Process Payroll Monthly Process real-time billing to the minute for 1m
customers
Release
Schedule
Semi-Annually Monthly
Application/Code COBOL, Fortran, Algol, PL/1,
assembler, proprietary tools
Python, Java, Node.js, Ruby, PHP, Perl, Scala,
Erlang and the rest
Tools None Apache, LAMP, Mean, Eclipse, Intellij,
Sourceforge etc.
Database I/VSAM, early RDBMS RDBMS, NoSQL

Rectangles are 1976. Maps and Lists are 2016
{ customer_id : 1,
first_name : "Mark",
last_name : "Smith",
city : "San Francisco",
phones: [ {
type : “work”,
number: “1-800-555-1212”
},
{ type : “home”,
number: “1-800-555-1313”,
DNC: true
},
number: “1-800-555-1414”,
DNC: true
}
]
}

AnActual Code Example
Let’s compare and contrast RDBMS/SQL to MongoDB
development using Java over the course of a few weeks.
Some ground rules:
1. Observe rules of Software Engineering 101: Assume separation of application,
Data Access Layer, and database implementation
2. Data Access Layer must be able to
a. Expose simple, functional, data-only interfaces to the application
• No ORM, frameworks, compile-time bindings, special tools
b. Exploit high performance features of database
3. Focus on core data handling code and avoid distractions that require the same
amount of work in both technologies
a. No exception or error handling
b. Leave out DB connection and other setup resources
4. Day counts are a proxy for progress, not actual time to complete indicated task
5. Don’t expect to cut and paste this code 

The Task: Saving and Fetching Contact data
Map m = new HashMap();
m.put(“name”, “Joe D”);
m.put(“id”, “K1”);
Start with this simple,
flat shape in the Data
Access Layer:
id = save(Map m)
And assume we
save it in this way:
Map m = fetch(String id)
And assume we
fetch one by primary
key in this way:
Brace yourself…..

MongoDBSQL
DDL: create table contact ( … )
init()
{
contactInsertStmt = connection.prepareStatement
(“insert into contact ( id, name ) values ( ?,? )”);
fetchStmt = connection.prepareStatement
(“select id, name from contact where id = ?”);
}
save(Map m)
{
contactInsertStmt.setString(1, m.get(“id”));
contactInsertStmt.setString(2, m.get(“name”));
contactInsertStmt.execute();
}
Map fetch(String id)
{
Map m = null;
fetchStmt.setString(1, id);
rs = fetchStmt.execute();
if(rs.next()) {
m = new HashMap();
m.put(“id”, rs.getString(1));
m.put(“name”, rs.getString(2));
}
return m;
}
Day 1: Initial efforts for both technologies
DDL: none
{
Map m = null;
c = collection.find(eq( “id”, id ));
if( c.hasNext())
m = (Map) c.next();
}
return m;
}
save( Map m )
{
collection.insert(Document( m ));
}
{“name” : ”Joe D”,
“id” : ”K1” }

Day 2: Add simple fields
m.put(“id”, “K1”);
m.put(“title”, “Mr.”);
m.put(“hireDate”, new Date(2011, 11, 1));
• Capturing title and hireDate is part of adding a new
business feature
• It was pretty easy to add two fields to the structure
• …but now we have to change our persistence code

SQL Day 2 (changes in bold)
DDL: alter table contact add title varchar(8);
alter table contact add hireDate date;
init()
{
(“insert into contact ( id, name, title, hiredate ) values
( ?,?,?,? )”);
(“select id, name, title, hiredate from contact where id =
?”);
}
save(Map m)
{
contactInsertStmt.setString(3, m.get(“title”));
contactInsertStmt.setDate(4, m.get(“hireDate”));
}
{
Map m = null;
if(rs.next()) {
m = new HashMap();
m.put(“title”, rs.getString(3));
m.put(“hireDate”, rs.getDate(4));
}
return m;
}
Consequences:
1. Code release schedule linked
to database upgrade (new
code cannot run on old
schema)
2. Issues with case sensitivity
starting to creep in (many
RDBMS are case insensitive
for column names, but code is
case sensitive)
3. Changes require careful mods
in 4 places
4. Beginning of technical debt

MongoDB Day 2
save( Map m )
{
}
{
Map m = null;
if( c.hasNext())
m = (Map) c.next();
}
return m;
}
Advantages:
1. Zero time and money spent on
overhead code
2. Code and database not physically
linked
3. New material with more fields can
be added into existing collections;
backfill is optional
4. Names of fields in database
precisely match key names in
code layer and directly match on
name, not indirectly via positional
offset
5. No technical debt is created
✔ NO
CHANGE

Day 3: Add list of phone numbers
m.put(“id”, “K1”);
m.put(“title”, “Mr.”);
m.put(“hireDate”, new Date(2011, 11, 1));
n1.put(“type”, “work”);
n1.put(“number”, “1-800-555-1212”));
list.add(n1);
n2.put(“type”, “home”));
n2.put(“number”, “1-866-444-3131”));
list.add(n2);
m.put(“phones”, list);
• It was still pretty easy to add this data to the structure
• .. but meanwhile, in the persistence code …
REALLY brace yourself…

SQL Day 3 changes: Option 1: Assume just 1
work and 1 home phone number
DDL: alter table contact add work_phone varchar(16);
alter table contact add home_phone varchar(16);
init()
{
(“insert into contact ( id, name, title, hiredate,
work_phone, home_phone ) values ( ?,?,?,?,?,? )”);
(“select id, name, title, hiredate, work_phone,
home_phone from contact where id = ?”);
}
save(Map m)
{
for(Map onePhone : m.get(“phones”)) {
String t = onePhone.get(“type”);
String n = onePhone.get(“number”);
if(t.equals(“work”)) {
contactInsertStmt.setString(5, n);
} else if(t.equals(“home”)) {
contactInsertStmt.setString(6, n);
}
}
}
{
Map m = null;
if(rs.next()) {
m = new HashMap();
Map onePhone;
onePhone = new HashMap();
onePhone.put(“type”, “work”);
onePhone.put(“number”, rs.getString(5));
list.add(onePhone);
onePhone = new HashMap();
onePhone.put(“type”, “home”);
list.add(onePhone);
}
This is just plain bad….

SQL Day 3 changes: Option 2:
Proper approach with multiple phone numbers
DDL: create table phones ( … )
init()
{
(“insert into contact ( id, name, title, hiredate )
values ( ?,?,?,? )”);
c2stmt = connection.prepareStatement(“insert into
phones (id, type, number) values (?, ?, ?)”;
(“select id, name, title, hiredate, type, number from
contact, phones where phones.id = contact.id and
contact.id = ?”);
}
save(Map m)
{
startTrans();
for(Map onePhone : m.get(“phones”)) {
c2stmt.setString(1, m.get(“id”));
c2stmt.setString(2, onePhone.get(“type”));
c2stmt.setString(3, onePhone.get(“number”));
c2stmt.execute();
}
endTrans();
}
{
Map m = null;
int i = 0;
List list = new ArrayList();
while (rs.next()) {
if(i == 0) {
m = new HashMap();
}
Map onePhone = new HashMap();
onePhone.put(“type”, rs.getString(5));
list.add(onePhone);
i++;
}
return m;
}
This took time and money

SQL Day 5: Zero or More Entries
init()
{
(“insert into contact ( id, name, title, hiredate )
values ( ?,?,?,? )”);
c2stmt = connection.prepareStatement(“insert into
phones (id, type, number) values (?, ?, ?)”;
(“select A.id, A.name, A.title, A.hiredate, B.type,
B.number from contact A left outer join phones B on
(A.id = B. id) where A.id = ?”);
}
Whoops! And it’s also wrong!
We did not design the query accounting
for contacts that have no phone number.
Thus, we have to change the join to an
outer join.
But this ALSO means we have to change
the unwind logic
This took more time and
money!
while (rs.next()) {
if(i == 0) {
// …
}
String s = rs.getString(5);
if(s != null) {
Map onePhone = new HashMap();
onePhone.put(“type”, s);
list.add(onePhone);
}
}
…but at least we have a DAL…
right?

MongoDB Day 3
Advantages:
overhead code
2. No need to fear fields that are
“naturally occurring” lists
containing data specific to the
parent structure and thus do not
benefit from normalization and
referential integrity
3. Safe from “Zero or More” entities
save( Map m )
{
}
{
Map m = null;
if( c.hasNext())
m = (Map) c.next();
}
return m;
}
✔ NO
CHANGE

By Day 14, our structure looks like this:
n4.put(“geo”, “US-EAST”);
n4.put(“startupApps”, new String[] { “app1”, “app2”, “app3” } );
list2.add(n4);
n4.put(“geo”, “EMEA”);
n4.put(“startupApps”, new String[] { “app6” } );
n4.put(“useLocalNumberFormats”, false):
list2.add(n4);
m.put(“preferences”, list2)
n6.put(“optOut”, true);
n6.put(“assertDate”, someDate);
seclist.add(n6);
m.put(“attestations”, seclist)
m.put(“security”, mapOfDataCreatedByExternalSource);

SQL Day 14
Error: Could not fit all the code into this space.
But very likely, among other things:
• n4.put(“startupApps”,new
String[]{“app1”,“app2”,“app3”});
was implemented as a single semi-colon delimited string or we
had to create another table and change the DAL
• m.put(“security”, anotherMapOfData);
was implemented by flattening it out and storing a subset of
fields or as a blob

MongoDB Day 14 – and every other day
Advantages:
overhead code
2. Persistence is so easy and flexible
and backward compatible that the
persistor does not upward-
influence the shapes we want to
persist i.e. the tail does not wag
the dog
save( Map m )
{
}
{
Map m = null;
if( c.hasNext())
m = (Map) c.next();
}
return m;
}
✔ NO
CHANGE

But what if we must do a join?
Both RDBMS and MongoDB will have a PhoneTransactions table/collection
{ customer_id : 1,
first_name : "Mark",
last_name : "Smith",
city : "San Francisco",
phones: [ {
type : “work”,
number: “1-800-555-1212”
},
number: “1-800-555-1313”,
DNC: true
},
number: “1-800-555-1414”,
DNC: true
}
]
}
{ number: “1-800-555-1212”,
target: “1-999-238-3423”,
duration: 20
}
{ number: “1-800-555-1212”,
target: “1-444-785-6611”,
duration: 243
}
{ number: “1-800-555-1414”,
target: “1-645-331-4345”,
duration: 132
}
{ number: “1-800-555-1414”,
target: “1-990-875-2134”,
duration: 71
}
PhoneTransactions

SQL JoinAttempt #1
select A.id, A.lname, B.type, B.number, C.target, C.duration
from contact A, phones B, phonestx C
where A.id = B.id and B.number = C.number
id | lname | type | number | target | duration
-----+--------------+------+----------------+----------------+----------
g9 | Moschetti | home | 1-900-555-1212 | 1-222-707-7070 | 23
g10 | Kalan | work | 1-999-444-9999 | 1-222-907-7071 | 7
g9 | Moschetti | work | 1-800-989-2231 | 1-987-707-7072 | 9
g9 | Moschetti | home | 1-900-555-1212 | 1-222-707-7071 | 7
g9 | Moschetti | home | 1-777-999-1212 | 1-222-807-7070 | 23
g9 | Moschetti | home | 1-777-999-1212 | 1-222-807-7071 | 7
g10 | Kalan | work | 1-999-444-9999 | 1-222-907-7070 | 23
g9 | Moschetti | home | 1-900-555-1212 | 1-222-707-7072 | 9
g10 | Kalan | work | 1-999-444-9999 | 1-222-907-7072 | 9
g9 | Moschetti | home | 1-777-999-1212 | 1-222-807-7072 | 9
How to turn this into a list of names –
each with a list of numbers, each of those with a list of target
numbers?

SQL UnwindAttempt #1
Map idmap = new HashMap();
ResultSet rs = fetchStmt.execute();
while (rs.next()) {
String id = rs.getString(“id");
String nmbr = rs.getString("number");
List tnum;
Map snum;
if((snum = (List) idmap.get(id)) == null) {
snum = new HashMap();
idmap.put(did, snum);
}
if((tnum = snum.get(nmbr)) == null) {
tnum = new ArrayList();
snum.put(number, tnum);
}
Map info = new HashMap();
info.put("target", rs.getString("target"));
info.put("duration", rs.getInteger("duration"));
tnum.add(info);
}
// idmap[“g9”][“1-900-555-1212”] = ({target:1-222-707-7070,duration:23…)

SQL JoinAttempt #2
select A.id, A.lname, B.type, B.number, C.target, C.duration
from contact A, phones B, phonestx C
where A.id = B.id and B.number = C.number order by A.id, B.number
id | lname | type | number | target | duration
-----+--------------+------+----------------+----------------+----------
g10 | Kalan | work | 1-999-444-9999 | 1-222-907-7072 | 9
g10 | Kalan | work | 1-999-444-9999 | 1-222-907-7070 | 23
g10 | Kalan | work | 1-999-444-9999 | 1-222-907-7071 | 7
g9 | Moschetti | home | 1-777-999-1212 | 1-222-807-7072 | 9
g9 | Moschetti | home | 1-777-999-1212 | 1-222-807-7070 | 23
g9 | Moschetti | home | 1-777-999-1212 | 1-222-807-7071 | 7
g9 | Moschetti | work | 1-800-989-2231 | 1-987-707-7072 | 9
g9 | Moschetti | home | 1-900-555-1212 | 1-222-707-7071 | 7
g9 | Moschetti | home | 1-900-555-1212 | 1-222-707-7072 | 9
g9 | Moschetti | home | 1-900-555-1212 | 1-222-707-7070 | 23
“Early bail out” from cursor is now possible –
but logic to construct list of source and target numbers is similar

SQL is about Disassembly
String s = “select A, B, C, D, E, F from T1,T2,T3
where T1.col = T2.col and T2.col2 = T3.col2 and X =
Y and X2 != Y2 and G > 10 and G < 100 and TO_DATE(‘
…”;
ResultSet rs = execute(s);
while(ResultSet.next()) {
if(new column1 value from T1) {
set up new Object;
}
set up new Object2
}
set up new Object3
}
populate maps, lists and scalars
}
Design a Big Query
including business logic
to grab all the data up
front
Throw it at the engine
Disassemble Big
Rectangle into usable
objects with logic implicit
in change in column
values

MongoDB is aboutAssembly
Cursor c = coll1.find({“X”:”Y”});
while(c.hasNext()) {
populate maps, lists and scalars;
Cursor c2 = coll2.find(logic+key from c);
while(c2.hasNext()) {
Cursor c3 = coll3.find(logic+key from c2);
while(c3.hasNext()) {
}
}
DIY:
OR assemble usable objects incrementally with
explicit calls to $lookup and $graphLookup

MongoDB ”Join”
db.contacts.aggregate([
{$unwind: "$phones"}
,{$lookup: {
from: "phonestx”,
localField: "phones.number”,
foreignField: "number",
as:"TX"}}
]);
{
"customer_id" : 1,
"first_name" : "Mark",
"last_name" : "Smith",
"city" : "San Francisco",
"phones" : {
"type" : "home",
"number" : "1-800-555-1414",
"DNC" : true
},
"TX" : [
{
"number" : "1-800-555-1414",
"target" : "1-645-331-4345",
"duration" : 132
},
{
"number" : "1-800-555-1414",
"target" : "1-990-875-2134",
"duration" : 71
}
]
}

But what about “real” queries?
• MongoDB query language is a physical map-of-
map based structure, not a String
• Operators (e.g. AND, OR, GT, EQ, etc.) and arguments are
keys and values in a cascade of Maps
• No grammar to parse, no templates to fill in, no whitespace,
no escaping quotes, no parentheses, no punctuation
• Same paradigm to manipulate data is used to
manipulate query expressions
• …which is also, by the way, the same paradigm
for working with MongoDB metadata and
explain()

33
Mongo Shell
JD10Gen:mugalyser jdrumgoole$ mongo
MongoDB shell version: 3.2.7
connecting to: test
MongoDB Enterprise > use MUGS
switched to db MUGS
MongoDB Enterprise > show collections
attendees
audit
groups
members
past_events
upcoming_events
MongoDB Enterprise >
MongoDB Enterprise > db.members.find( { "batchID" : 108, "member.member_name" : "Joe Drumgoole" }).pretty()
{
"_id" : ObjectId("58511bfbb26a8803b6b4d56c"),
"member" : {
"city" : "Dublin",
"events_attended" : 13,
"last_access_time" : ISODate("2016-12-13T15:45:27Z"),
"country" : "Ireland",
"member_id" : 99473492,
"chapters" : [
{
"urlname" : "London-MongoDB-User-Group",
"name" : "London MongoDB User Group",
"id" : 1775736
…

MongoDB Query Examples
SQL CLI select * from contact A, phones B where
A.did = B.did and B.type = 'work’;
MongoDB CLI db.contact.find({"phones.type”:”work”});
SQL in Java String s = “select * from contact A, phones B where
A.did = B.did and B.type = 'work’”;
ResultSet rs = execute(s);
MongoDB via
Java driver
Cursor c = contact.find(eq( “phones.type”, “work” ));
Find all contacts with at least one work phone

MongoDB Query Examples
SQL select A.did, A.lname, A.hiredate, B.type, B.number from contact A
left outer join phones B on (B.did = A.did) where b.type = 'work' or
A.hiredate > '2014-02-02'::date
Java db.contacts.find( or( eq( “phones.type’ : ”work” ),
gt( “hiredate”, Date( 2014, 2, 2 ))
CLI db.contacts.find(
{ $or : [ { “phones.type” : “work” },
{ “hiredate” : new Date("2014-02-02T00:00:00.000Z")]})
Find all contacts with at least one work phone or
hired after 2014-02-02

…and before you ask…
Yes, MongoDB query expressions
support
1. Sorting
2. Cursor size limit
3. Projection (asking for only parts of the rich
shape to be returned)
4. Aggregation (“GROUP BY”) functions

Maybe even MORE powerful than SQL…?
> db.results.values.aggregate([
{$match: { runnum:23, timeSeriesPath: "CDSSpread.12M//1909468128” }
,{$project: { timeSeriesPath: "$timeSeriesPath", values: foml }}
,{$unwind: {path: "$values", idx: "v_idx"}}
,{$match: {values: {$gt: 60}, {$or: [ {idx: 0}, {idx: {$size: . . .}
,{$group: {_id: {a: "$timeSeriesPath", b: term: "$idx"},
n: {$sum:1}, max: {$max: "$values"}, min: {$min: "$values"}},
sdev: {$stdDevPop: "$values"}}
,{$lookup: { from: ”deskLimits", localField: ”instID", foreignField:
”instID", as: ”inst"}}
,{$match: {maxDeskLimit: {$gt: {$cond: [ {$gt: [2, $max]}, 2, $max]}}}}
,{$group: {_id: "$deskID", total: {$sum: “$max”}}}
]);

What is an Aggregation Pipeline
Match
Project
Join
Graph
Sort
View

39
Aggregation Pipeline
$match
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{}
{★ds}
{★ds}
{★ds}

40
$match $project
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{}
{★ds}
{★ds}
{★ds}
{=d+s}

41
$match $project
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{}
{★ds}
{★ds}
{★ds}
{★}
{★}
{★}
{=d+s}

42
$match $project $lookup
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{}
{★ds}
{★ds}
{★ds}
{★}
{★}
{★}
{★}
{★}
{★}
{★}
{=d+s}

43
$match $project $lookup
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{}
{★ds}
{★ds}
{★ds}
{★}
{★}
{★}
{★}
{★}
{★}
{★}
{=d+s}
{★[]}
{★[]}
{★}

44
$match $project $lookup $group
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{★ds}
{}
{★ds}
{★ds}
{★ds}
{★}
{★}
{★}
{★}
{★}
{★}
{★}
{=d+s}
{
Σ λ σ}
{
Σ λ σ}
{
Σ λ σ}
{★[]}
{★[]}
{★}

Aggregation Pipeline Stages
• $match
Filter documents
• $geoNear
Geospherical query
• $project
Reshape documents
• $lookup
Left-outer equi joins
• $unwind
Expand documents
• $group
Summarize documents
• $sample
Randomly selects a subset of
documents
• $sort
Order documents
• $skip
Jump over a number of documents
• $limit
Limit number of documents
• $redact
Restrict documents
• $out
Sends results to a new collection

The Fundamental Change with mongoDB
RDBMS designed in era when:
• CPU and disk was slow &
expensive
• Memory was VERY expensive
• Network? What network?
• Languages had limited means to
dynamically reflect on their types
• Languages had poor support for
richly structured types
Thus, the database had to
• Act as combiner-coordinator of
simpler types
• Define a rigid schema
• (Together with the code) optimize
at compile-time, not run-time
In mongoDB, the
data is the schema!

MongoDB and the Rich Map Ecosystem
Generic comparison of two
records
Map expr = new HashMap();
expr.put("myKey", "K1");
DBObject a = collection.findOne(expr);
expr.put("myKey", "K2");
DBObject b = collection.findOne(expr);
List<MapDiff.Difference> d = MapDiff.diff((Map)a, (Map)b);
Getting default values for a thing
on a certain date and then
overlaying user preferences (like
for a calculation run)
Map expr = new HashMap();
expr.put("myKey", "DEFAULT");
expr.put("createDate", new Date(2013, 11, 1));
DBObject a = collection.findOne(expr);
expr.clear();
expr.put("myKey", "user1");
DBObject b = otherCollectionPerhaps.findOne(expr);
MapStack s = new MapStack();
s.push((Map)a);
s.push((Map)b);
Map merged = s.project();
Runtime reflection of Maps and Lists enables generic powerful utilities
(MapDiff, MapStack) to be created once and used for all kinds of shapes,
saving time and money

Lastly: ACLI with teeth
> db.contact.find({"SeqNum": {"$gt”:10000}}).explain();
{
"cursor" : "BasicCursor",
"n" : 200000,
//...
"millis" : 223
}
Try a query and show the
diagnostics
> for(v=[],i=0;i<3;i++) {
… n = i*50000;
… expr = {"SeqNum": {"$gt”: n}};
… v.push( [n, db.contact.find(expr).explain().millis)] }
Run it 3 times with smaller and
smaller chunks and create a
vector of timing result pairs
(size,time)
> v
[ [ 0, 225 ], [ 50000, 222 ], [ 100000, 220 ] ]
Let’s see that vector
> load(“jStat.js”)
> jStat.stdev(v.map(function(p){return p[1];}))
2.0548046676563256
Use any other javascript you
want inside the shell
> for(i=0;i<3;i++) {
… expr = {"SeqNum": {"$gt":i*1000}};
… db.foo.insert(db.contact.find(expr).explain()); }
Party trick: save the explain()
output back into a collection!

And There is More – Compass and Atlas
Compass
Atlas

50
What Does This Add Up To?
Relational
NoSQL
Expressive Query
Language
Strong
Consistency
Secondary Indexes
Flexibility
Scalability
Performance

Webinar: Transitioning from SQL to MongoDB

More Related Content

What's hot (20)

Viewers also liked (20)

Similar to Webinar: Transitioning from SQL to MongoDB (20)

More from MongoDB (20)

Recently uploaded (20)

Webinar: Transitioning from SQL to MongoDB

Editor's Notes