Until recently, agile business had to choose between the benefits of JSON-based NoSQL databases and the benefits of SQL-based querying. NoSQL provides schema flexibility, high performance, and elastic scaling, while SQL provides expressive, independent data access. Recent convergence allows developers and organizations to have the best of both worlds. Developers need to deliver apps that readily evolve, perform, and scale, all to match changing business needs. Organizations need rapid access to their operational data, using standard analytical tools, for insight into their business. In this session, you will learn the ways that SQL can be applied to NoSQL databases (N1QL, SQL++, ODBC, JDBC, and others), and what additional features are needed to deal with JSON documents. SQL for JSON, JSON data modeling, indexing, and tool integration will be covered.

1. Querying NoSQL with SQL
HAVINGYour JSON Cake and SELECTing it too
Matthew D. Groves, @mgroves

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Agenda
Why NoSQL?
JSON Data Modeling
SQL for JSON
Ecosystem and convergence

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Where am I?
• KCDC (Kansas City Developer Conference)
• http://kcdc.info

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Who am I?
• Matthew D. Groves
• DeveloperAdvocate for Couchbase
• @mgroves onTwitter
• Podcast and blog: http://crosscuttingconcerns.com
• “I am not an expert, but I am an enthusiast.”
– Alan Stevens

6. Querying NoSQL with SQL
HAVINGYour JSON Cake and
SELECTing it too
Matthew D. Groves, @mgroves

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Major Enterprises Across Industries are Adopting NoSQL
CommunicationsTechnology
Travel & Hospitality Media &
Entertainment
E-Commerce &
DigitalAdvertising
Retail & Apparel
Games & GamingFinance &
Business Services

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Why NoSQL?

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NoSQL Landscape
Document
• Couchbase
• MongoDB
• DynamoDB
• DocumentDB
Graph
• OrientDB
• Neo4J
• DEX
• GraphBase
Key-Value
• Couchbase
• Riak
• BerkeleyDB
• Redis Wide Column
• Hbase
• Cassandra
• Hypertable

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NoSQL Landscape
• Get by key(s)
• Set by key(s)
• Replace by key(s)
• Delete by key(s)
• Map/Reduce
Document
• Couchbase
• MongoDB
• DynamoDB
• DocumentDB

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Why NoSQL? Scalability

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Why NoSQL? Scalability

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Why NoSQL? Flexibility

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Why NoSQL? Performance

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Why NoSQL? Availability

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JSON Data Modeling

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Models for Representing Data
Data Concern Relational Model JSON Document Model
Rich Structure
Relationships
Value Evolution
Structure
Evolution

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Properties of Real-World Data

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Modeling Data in RelationalWorld
Billing
ConnectionsPurchases
Contacts
Customer

20. CustomerID Name DOB
CBL2015 Jane Smith 1990-01-30
Table: Customer
{
"Name" : "Jane Smith",
"DOB" : "1990-01-30”
}
Customer DocumentKey: CBL2015

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CustomerID Name DOB
CBL2015 Jane Smith 1990-01-30
Table: Customer {
"Name" : "Jane Smith",
"DOB" : "1990-01-30",
"Purchases" : [
{
"item" : "laptop",
"amount" : 1499.99,
"date" : "2019-03",
}
]
}
Customer DocumentKey: CBL2015
CustomerID Item Amount Date
CBL2015 laptop 1499.99 2019-03
Table: Purchases

22. CustomerID Name DOB
CBL2015 Jane Smith 1990-01-30
Table: Customer {
"Name" : "Jane Smith",
"DOB" : "1990-01-30",
"Purchases" : [
{
"item" : "laptop",
"amount" : 1499.99,
"date" : "2019-03",
},
{
"item" : "phone",
"amount" : 99.99,
"date" : "2018-12"
}
]
}
Customer DocumentKey: CBL2015
CustomerID Item Amount Date
CBL2015 laptop 1499.99 2019-03
CBL2015 phone 99.99 2018-12
Table: Purchases

23. CustomerID ConnId Name
CBL2015 XYZ987 Joe Smith
CBL2015 SKR007 Sam Smith
Table: Connections {
"Name" : "Jane Smith",
"DOB" : "1990-01-30",
"Billing" : [
{
"type" : "visa",
"cardnum" : "5827-2842-...",
"expiry" : "2019-03"
}, ...
],
"Connections" : [
{
"ConnId" : "XYZ987",
"Name" : "Joe Smith"
},
{
"ConnId" : ”SKR007",
"Name" : ”Sam Smith"
}
}
Customer DocumentKey: CBL2015

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{
"Name" : "Jane Smith",
"DOB" : "1990-01-30",
"cardnum" : "5827-2842…",
"expiry" : "2019-03",
"cardType" : "visa",
"Connections" : [
{
"CustId" : "XYZ987",
"Name" : "Joe Smith"
},
{
"CustId" : "PQR823",
"Name" : "Dylan Smith"
}
{
"CustId" : "PQR823",
"Name" : "Dylan Smith"
}
],
"Purchases" : [
{ "id":12, item: "mac", "amt": 2823.52 }
{ "id":19, item: "ipad2", "amt": 623.52 }
]
}
DocumentKey: CBL2015
Custome
rID
Name DOB Cardnum Expiry CardType
CBL2015 Jane
Smith
1990-01-30 5827-
2842…
2019-03 visa
CustomerID ConnId Name
CBL2015 XYZ987 Joe Smith
CBL2015 SKR007 Sam Smith
CustomerID item amt
CBL2015 mac 2823.52
CBL2015 ipad2 623.52
CustomerID ConnId Name
CBL2015 XYZ987 Joe Smith
CBL2015 SKR007 Sam
Smith
Contacts
Customer
ConnectionsPurchases

25. {
"Name" : "Jane Smith",
"DOB" : "1990-01-30",
"Billing" : [
{
"type" : "visa",
"cardnum" : "5827-2842-2847-3909",
"expiry" : "2019-03"
},
{
"type" : "master",
"cardnum" : "6274-2842-2847-3909",
"expiry" : "2019-03"
}
],
"Connections" : [
{
"CustId" : "XYZ987",
"Name" : "Joe Smith"
},
{
"CustId" : "PQR823",
"Name" : "Dylan Smith"
}
{
"CustId" : "PQR823",
"Name" : "Dylan Smith"
}
],
"Purchases" : [
{ "id":12, item: "mac", "amt": 2823.52 }
{ "id":19, item: "ipad2", "amt": 623.52 }
]
}
DocumentKey: CBL2015
CustomerID Name DOB
CBL2015 Jane Smith 1990-01-30
Customer
ID
Type Cardnum Expiry
CBL2015 visa 5827… 2019-03
CBL2015 master 6274… 2018-12
CustomerID ConnId Name
CBL2015 XYZ987 Joe Smith
CBL2015 SKR007 Sam Smith
CustomerID item amt
CBL2015 mac 2823.52
CBL2015 ipad2 623.52
CustomerID ConnId Name
CBL2015 XYZ987 Joe Smith
CBL2015 SKR007 Sam
Smith
Contacts
Customer
Billing
ConnectionsPurchases

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Models for Representing Data
Data Concern Relational Model
JSON Document Model
(NoSQL)
Rich Structure
 Multiple flat tables
 Constant assembly / disassembly
 Documents
 No assembly required!
Relationships
 Represented
 Queried (SQL)
 Represented
 Queried? Not until now…
Value Evolution  Data can be updated  Data can be updated
Structure Evolution
 Uniform and rigid
 Manual change (disruptive)
 Flexible
 Dynamic change

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SQL for JSON

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Why SQL for JSON?

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Client Side Querying is Inadequate
Example: Find High-Value Customers with Orders > $10000
Query ‘customer’
objects from
database
For each ‘customer’
object
Find all the ‘order’
objects for the
‘customer’
Calculate the total
amount for each
‘order’
Sum up the grand
total amount for all
‘orders’
If grand total
amount > $10000,
Extract ‘customer’
data
Add ‘customer’ to
the high-value
customer list
Sort the high-value
customer list

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Goal of Query for JSON
Give developers and enterprises an expressive,
powerful, and complete language for querying,
transforming, and manipulating JSON data.

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route_5966 ← key
{
"type": "route", ← document type identifier
"airlineid": "airline_24", ← foreign key
"sourceairport": "MCO",
"destinationairport": "SEA",
"equipment": "737",
"schedule": [
{"day": 1, "utc": "13:25:00", "flight": "AA788"},
{"day": 4, "utc": "13:25:00", "flight": "AA419"},
{"day": 5, "utc": "13:25:00", "flight": "AA519"}
]
}

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airline_24 ← key
{
"active": "Y",
"callsign": "AMERICAN",
"country": "United States",
"iata": "AA",
"icao": "AAL",
"name": "American Airlines",
"type": "airline" ← document type identifier
}

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SELECT: JOIN

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SELECT: Aggregation

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SELECT: Aggregation
MIN
MAX
SUM
COUNT
AVG
ARRAY_AGG [ DISTINCT ]

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SELECT: UNION, UNION ALL, INTERSECT, EXCEPT

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USE KEYS

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UNNEST (aka JOIN)

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UNNEST (aka JOIN)

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UNNEST (aka JOIN)

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UNNEST (aka JOIN)

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Composability
JOIN, NEST, and UNNEST
can be chained in any combination

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SELECT Statement
SELECT customers.id,
customers.NAME.lastname,
customers.NAME.firstname
Sum(orderline.amount)
FROM `orders` UNNEST orders.lineitems AS orderline
JOIN `customers` ON KEYS orders.custid
WHERE customers.state = 'NY'
GROUP BY customers.id,
customers.NAME.lastname
HAVING sum(orderline.amount) > 10000
ORDER BY sum(orderline.amount) DESC
• Dotted sub-document reference
• Names are CASE-SENSITIVE
• UNNEST to flatten the arrays
JOINS with Document KEY of
customers

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ResultSet

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{
"Name" : "Jane Smith",
"DOB" : "1990-01-30",
"Billing" : [
{
"type" : "visa",
"cardnum" : "5827-2842-2847-3909",
"expiry" : "2019-03"
},
{
"type" : "master",
"cardnum" : "6274-2842-2847-3909",
"expiry" : "2019-03"
}
],
"Connections" : [
{
"CustId" : "XYZ987",
"Name" : "Joe Smith"
},
{
"CustId" : "PQR823",
"Name" : "Dylan Smith"
}
{
"CustId" : "PQR823",
"Name" : "Dylan Smith"
}
],
"Purchases" : [
{ "id":12, item: "mac", "amt": 2823.52 }
{ "id":19, item: "ipad2", "amt": 623.52 }
]
}
LoyaltyInfo ResultDocuments
Orders
CUSTOMER
CosmosDb
SQL

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N1QL (Couchbase)

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SQL (DocumentDb CosmosDb)
 https://www.documentdb.com/sql/demo

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SQL CosmosDb SQL
SQL standard JSON stuff

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Demo: N1QL

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Other JSON "Stuff"
©2014 Couchbase, Inc. 55
Ranging over collections
• WHERE ANY c IN children SATISFIES c.age > 10 END
• WHERE EVERY r IN ratings SATISFIES r > 3 END
Mapping with filtering • ARRAY c.name FOR c IN children WHEN c.age > 10 END
Deep traversal, SET, and
UNSET
• WHERE ANY node WITHIN request SATISFIES node.type = “xyz” END
• UPDATE doc UNSET c.field1 FOR c WITHIN doc END
DynamicConstruction
• SELECT { “a”: expr1, “b”: expr2 } AS obj1, name FROM … // Dynamic object
• SELECT [ a, b ] FROM … // Dynamic array
Nested traversal • SELECT x.y.z, a[0] FROM a.b.c …
IS [ NOT ] MISSING • WHERE name IS MISSING

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DataTypes in JSON
N1QL supports all JSON data types
 Numbers
 Strings
 Booleans
 Null
 Arrays
 Objects

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Data Modification Statements
INSERT INTO `orders` (KEY, VALUE)
VALUES ("mykey", {"field1":482, "field2":3, "field3":4});
UPDATE `order`
SET field4 = "ABC987"
WHERE field1 = 482 AND field2 = 3 AND field3 = 4
DELETE FROM `neworder`
WHERE fieldX = 291 AND
fieldY = 3482 AND
fieldZ = 2483
JSON literals can be used in any
expression

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Index Statements
 CREATE INDEX ON …
 DROP INDEX …
 EXPLAIN …
https://dzone.com/articles/index-first-and-query-faster

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Find High-Value Customers with Orders > $10000
Query customer
objects from
database
For each customer
object
Find all the order
objects for the
customer
Calculate the total
amount for each
order
Sum up the grand
total amount for all
orders
If grand total
amount > $10000,
Extract customer
data
Add customer to
the high-value
customer list
Sort the high-value
customer list
SELECT Customers.ID, Customers.Name, SUM(OrderLine.Amount)
FROM `Orders` UNNEST Orders.LineItems AS OrderLine
JOIN `Customers` ON KEYS Orders.CustID
GROUP BY Customers.ID, Customers.Name
HAVING SUM(OrderLine.Amount) > 10000
ORDER BY SUM(OrderLine.Amount) DESC

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Summary: SQL & SQL for JSON
Query Features SQL SQL for JSON
Statements
 SELECT, INSERT, UPDATE, DELETE,
MERGE
 SELECT, INSERT, UPDATE, DELETE,
MERGE
Query Operations
 Select, Join, Project, Subqueries
 Strict Schema
 StrictType checking
 Select, Join, Project, Subqueries
 Nest & Unnest
 Look Ma! NoType Mismatch Errors!
 JSON keys act as columns
Schema  Predetermined Columns
 Fully addressable JSON
 Flexible document structure
DataTypes
 SQL Data types
 Conversion Functions
 JSON Data types
 Conversion Functions
Query Processing
 INPUT: Sets ofTuples
 OUPUT: Set ofTuples
 INPUT: Sets of JSON
 OUTPUT: Set of JSON

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Summary: N1QL and CosmosDB SQL
Query Features N1QL CosmosDB SQL
SELECT Yes Yes
INSERT, UPDATE, DELETE, MERGE Yes No
Intra-document join Yes: UNNEST Yes: JOIN
Inter-document join Yes: JOIN, NEST No
Aggregation (GROUP BY, SUM,MAX,MIN) Yes No GROUP BY
Stored Procedures,Triggers, UDF No Kinda (JavaScript)

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SQL for JSON
SQL++
N1QL
http://tinyurl.com/UCSDsql

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Couchbase Analytics (CBAS) and SQL++

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Industry is Choosing SQL to Query JSON

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JSON in SQL Server

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Try SQL for JSON
http://tiny.cc/n1ql
http://tiny.cc/cosmosdb

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Podcast
https://blog.couchbase.com/tag/podcast/

65. Free event:
Couchbase Day Kansas City
August 15th
http://tiny.cc/cbkc

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Couchbase, everybody!

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Where do you find us?
• blog.couchbase.com
• @couchbasedev
• @mgroves
• http://tiny.cc/cbkc

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Frequently Asked Questions
1. How is Couchbase different than Mongo?
2. Is Couchbase the same thing as CouchDb?
3. How did you get to be both incredibly handsome and
tremendously intelligent?
4. What is the Couchbase licensing situation?
5. Is Couchbase a Managed Cloud Service?

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CouchDB and Couchbase
< Back
memcached

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MongoDB vs Couchbase
• Memory first architecture
• Master-master architecture
• Auto-sharding
• N1QL
• FullText Search
• Mobile & Sync
< Back

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Licensing
Couchbase Server Community
• Open source (Apache 2)
• Binary release is one release behind Enterprise
• Free to use in dev/test/qa/prod
• Forum support only
Couchbase Server Enterprise
• Mostly open source (Apache 2)
• Some features not available on Community (XDCRTLS, MDS, Rack Zone, etc)
• Free to use in dev/test/qa
• Need commercial license for prod
• Paid support provided
< Back

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Managed Cloud Service (DBaaS)?
No.
< Back