This document discusses storing product and order data as JSON in a database to support an agile development process. It describes creating tables with JSON columns to store this data, and using JSON functions like JSON_VALUE and JSON_TABLE to query and transform the JSON data. Examples are provided of indexing JSON columns for performance and updating product JSON to include unit costs by joining external data. The goal is to enable flexible and rapid evolution of the application through storing data in JSON.

1. 1
Agile Database Development
with JSON
Chris Saxon
Developer Advocate, @ChrisRSaxon & @SQLDaily
blogs.oracle.com/sql
youtube.com/c/TheMagicofSQL
asktom.oracle.com

2. Image by Semevent from Pixabay
We're creating a new online store
Selling boxes of brick models

3. Photo by Jon Tyson on Unsplash
We need to respond to customer feedback…

4. Photo by Johannes Plenio on Unsplash
…and evolve the application rapidly

5. Photo by Brannon Naito on Unsplash
working in short sprints and releasing often
So we need to be
Agile

6. { JSON }
To support this we'll store data as

7. 7
Agile Database Development
with JSON
Chris Saxon
Developer Advocate, @ChrisRSaxon & @SQLDaily
blogs.oracle.com/sql
youtube.com/c/TheMagicofSQL
asktom.oracle.com

8. The following is intended to outline our general product direction. It is intended for information purposes
only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code,
or functionality, and should not be relied upon in making purchasing decisions. The development,
release, timing, and pricing of any features or functionality described for Oracle’s products may change
and remains at the sole discretion of Oracle Corporation.
Statements in this presentation relating to Oracle’s future plans, expectations, beliefs, intentions and
prospects are “forward-looking statements” and are subject to material risks and uncertainties. A detailed
discussion of these factors and other risks that affect our business is contained in Oracle’s Securities and
Exchange Commission (SEC) filings, including our most recent reports on Form 10-K and Form 10-Q
under the heading “Risk Factors.” These filings are available on the SEC’s website or on Oracle’s website
at http://www.oracle.com/investor. All information in this presentation is current as of September 2019
and Oracle undertakes no duty to update any statement in light of new information or future events.
Safe Harbor

9. User Story #1
We must be able to store
product & order details
So we need to create the tables
and define CRUD operations on them

10. create table products (
product_id integer
not null
primary key,
product_json ##TODO##
not null,
check (
json_data is json
)
);
create table orders (
order_id integer
not null
primary key,
order_json ##TODO##
not null,
check (
json_data is json
)
);
The tables are just a
primary key, JSON column,
& is json constraint

11. create table products (
product_id integer
not null
primary key,
product_json ##TODO##
not null,
check (
json_data is json
)
);
create table orders (
order_id integer
not null
primary key,
order_json ##TODO##
not null,
check (
json_data is json
)
);
But which data type
to use for JSON?!

12. Which data type should you use for JSON?
"Small" documents varchar2
"Large" documents ???
<= 4,000 bytes / 32k

13. "Small" documents varchar2
"Large" documents blob
JSON data type
coming in 20c
Avoids character set conversions
Less storage than clob

14. create table products (
product_id integer
not null
primary key,
product_json blob
not null,
check (
json_data is json
)
);
create table orders (
order_id integer
not null
primary key,
order_json blob
not null,
check (
json_data is json
)
);

15. insert into products ( product_json )
values ( utl_raw.cast_to_raw ( '{
"productName": "..."
}' ) );
BLOBs need extra processing on insert

16. select product_json from products;
PRODUCT_JSON
7B202274686973223A20227468617422207D
and select to make them human readable

17. select json_serialize (
product_json
returning clob
pretty
) jdata
from products;
JDATA
{
"productName": "..."
}
Added in 19c
json_serialize
converts JSON data to
text; which you can
pretty print for
readability

18. select json_query (
product_json,
'$' returning clob
pretty
) jdata
from products;
JDATA
{
"productName": "..."
}
In earlier releases use
json_query
The clob return type
was added in 18c

19. User Story #2
Customers must be able to
search by price
So we need to query the products table for JSON
where the unitPrice is in the specified range

20. {
"productName": "GEEKWAGON",
"descripion": "Ut commodo in …",
"unitPrice": 35.97,
"bricks": [ {
"colour": "red", "shape": "cube",
"quantity": 13
}, {
"colour": "green", "shape": "cube",
"quantity": 17
}, …
]
}
We need to search for this
value in the documents

21. select * from products p
where p.product_json.unitPrice <= :max_price;
But remember it returns
varchar2
=> implicit conversion!
Use simple dot-notation to access the value

22. select * from products p
where json_value (
product_json,
'$.unitPrice' returning number
) <= :max_price;
json_value gives you more control
So this returns number
=> no implicit conversion! :)

23. select * from products p
where p.product_json.unitPrice.number()
<= :max_price;
From 19c you can state
the return type with
simple dot-notation

24. User Story #3
Customers must be able to view their
orders
Showing order details and a list of what they bought
So we need to join the order productIds to products

25. {
"customerId" : 2,
"orderDatetime" : "2019-01-01T03:25:43",
"products" : [ {
"productId" : 1,
"unitPrice" : 74.95
}, {
"productId" : 10,
"unitPrice" : 35.97
}, …
]
}
We need to extract these
from the product array

26. select o.order_json.products[*].productId
from orders o;
PRODUCTS
[2,8,5]
[3,9,6]
[1,10,7,4]
...
With simple dot-notation,
you can get an array of
the values…

27. select json_query (
order_json, '$.products[*].productId'
with array wrapper
)
from orders o;
PRODUCTS
[2,8,5]
[3,9,6]
[1,10,7,4]
...
But to join these to
products, we need to
convert them to rows…
…or with json_query

28. json_table
With json_table you can convert JSON…
…to relational rows-and-columns

29. with order_items as (
select order_id, t.*
from orders o, json_table (
order_json
columns (
customerId,
nested products[*] columns (
productId,
unitPrice
) )
) t
)
Simplified syntax 18c

30. with order_items as (
select order_id, t.*
from orders o, json_table (
order_json
columns (
customerId,
nested products[*] columns (
productId,
unitPrice
) )
) t
)
This tells the database to
return a row for each
element in the products
array…

31. select order_id,
p.product_json.productName product,
unitPrice
from order_items oi
join products p
on oi.productId = p.product_id
where customerId = :cust_var
order by oi.order_id desc, p.product_id
…So you can join output to the products table!

32. Minimum viable product complete!
Ship it!

33. Copyright © 2019 Oracle and/or its affiliates.
Soooo…
How many
orders today?
…people have lots of questions
As always, post release…
Ryan McGuire / Gratisography

34. User Story #4
Sales must be able to view
today's orders
We need to create a dashboard counting orders
So we need to search for orders placed today

35. {
"customerId" : 2,
"orderDatetime" : "2019-01-01T03:25:43",
"products" : [ {
"productId" : 1,
"unitPrice" : 74.95
}, {
"productId" : 10,
"unitPrice" : 35.97
}, …
]
}
We need to search
for this value in the
documents

36. select * from orders o
where o.order_json.orderDatetime >=
trunc ( sysdate );
ORA-01861: literal does
not match format string
Remember the
implicit conversions?
It fails for dates!
Use simple dot-notation to access the value

37. select * from orders o
where json_value (
order_json,
'$.orderDatetime' returning date
) >= trunc ( sysdate )
So you need to define the
return type; JSON dates
conform to ISO 8601 date

38. 2019-01-01
ISO 8601 date
Which is YYYY-MM-DD for dates
There is no time component in an ISO date!

39. 2019-01-01T03:25:43
ISO 8601 timestamp
Use ISO timestamps to include times
Note the "T" between the date and time!

40. select * from orders o
where json_value (
order_json,
'$.orderDatetime' returning date
) >= trunc ( sysdate )
But the query is very slow…

41. select * from orders o
where json_value (
order_json,
'$.orderDatetime' returning date
) >= trunc ( sysdate )
{ "customerId": 1, … }
{ "customerId": 2, … }
…

42. User Story #4b
… and make it fast!
currently the query does a full table scan
To speed it up we need to create an index!

43. create index orders_date_i
on orders ( order_json );
ORA-02327: cannot create index on
expression with datatype LOB
You can't index LOB data

44. create search index orders_json_i
on orders ( order_json )
for json
parameters ( 'sync (on commit)' );
Added in 12.2, a json search
index enables JSON queries
to use an index
JSON Search Indexes

45. select * from orders o
where json_value (
order_json,
'$.orderDatetime' returning date
) >= trunc ( sysdate )
{ "customerId": 1, … }
{ "customerId": 2, … }
…

46. -----------------------------------------------------
| Id | Operation | Name |
-----------------------------------------------------
| 0 | SELECT STATEMENT | |
|* 1 | TABLE ACCESS BY INDEX ROWID| ORDERS |
|* 2 | DOMAIN INDEX | ORDERS_JSON_I |
-----------------------------------------------------
With the search index in place,
the optimizer can use it

47. Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter(JSON_VALUE("ORDER_JSON" FORMAT JSON ,
'$.orderDatetime' RETURNING TIMESTAMP NULL
ON ERROR) >= TIMESTAMP' 2019-01-15 00:00:00')
2 - access("CTXSYS"."CONTAINS"("O"."ORDER_JSON",
'sdatap(TMS_orderDatetime >=
"2019-01-15T00:00:00+00:00" /orderDatetime)')>0)
Under the covers, this uses Oracle Text

48. create index order_date_i
on orders (
json_value (
order_json,
'$.orderDatetime'
returning date
error on error
null on empty
)
);
It's more efficient to
create a function-
based index,
matching the search
you'll do
This has some other
benefits…

49. create index order_date_i
on orders (
json_value (
order_json,
'$.orderDatetime'
returning date
error on error
null on empty
)
);
Data validation!
If the value is not
a JSON date;
inserts will raise
an exception

50. create index order_date_i
on orders (
json_value (
order_json,
'$.orderDatetime'
returning date
error on error
null on empty
)
);
From 12.2 you can
also raise an error
if the attribute is
not present

51. ------------------------------------------------------------
| Id | Operation | Name |
------------------------------------------------------------
| 0 | SELECT STATEMENT | |
| 1 | TABLE ACCESS BY INDEX ROWID BATCHED| ORDERS |
|* 2 | INDEX RANGE SCAN | ORDER_DATE_I |
------------------------------------------------------------
The function-based index is more
efficient, so the optimizer will choose
this over the search index

52. Search vs. Function-Based Indexes
JSON Search Index Function-based Index
Applicability Any JSON query Matching function
Performance Slower Faster
Use Ad-hoc queries Application queries

53. 0
5
10
15
20
25
With the dashboard in place, it's clear
sales are levelling off
We need a way to increase sales!

54. We need to offer
discounts!
…discount promotion codes
Marketing have a brilliant plan…
Ryan McGuire / Gratisography

55. User Story #5
Customers may be able to
enter a promotion code
This will give a discount
We need to store the code and discount value

56. {
…,
"promotion": {
"code": "20OFF",
"discountAmount": 20
}
}
The order JSON will include the an
promotion object…
…so there are no changes
needed in the database!

57. Nothing to do in the database!
relax!
So you can sit back and count the money!
Ryan McGuire / Gratisography

58. 0
20
40
60
80
100
120
Customers love the promotion
Sales are going through the roof!

59. Cake for everyone!
The promotion is a success!
Ryan McGuire / Gratisography

60. Where's the
$$$?!
…the $$$ tells a different story
But finance are unhappy…
Ryan McGuire / Gratisography

61. -250
-200
-150
-100
-50
0
50
100
150
Red bars = sales
Yellow line = profits
The discount is too big!
We're losing money!

62. Finance need to view order profitability
They need to understand where we're losing money

63. User Story #6
Store unit cost for each brick
We need to update the product JSON;
adding unitCost to every object in the bricks arrays

64. { …,
"bricks": [ {
"colour": "red",
"shape": "cube",
"quantity": 13
}, {
"colour": "green",
"shape": "cuboid",
"quantity": 17
}, …
]
}
Add unitCost

65. "Luckily" we have the costs in a spreadsheet!

66. "bricks": [ {
"colour": "red",
"shape": "cube",
"quantity": 13
}, {
"colour": "green",
"shape": "cuboid",
"quantity": 17
}, …
] join on
colour, shape
We need to combine the spreadsheet
data with the stored JSON

67. Step 1: transform JSON
to rows-and-columns
Step 3: convert
back to JSON
Step 2: join
the costs

68. Photo by Gus Ruballo on Unsplash
Buckle up!
This will be a bumpy ride!

69. select * from external ( (
colour varchar2(30),
shape varchar2(30),
unit_cost number
)
default directory tmp
location ( 'costs.csv' )
)
From 18c you can query files "on the
fly" with an inline external table

70. select product_id, j.*
from products, json_table (
product_json columns (
nested bricks[*] columns (
pos for ordinality,
colour path '$.colour',
shape path '$.shape',
brick format json path '$'
)
)
) j
Using JSON_table to
extract the bricks as rows

71. select product_id, j.*
from products, json_table (
product_json columns (
nested bricks[*] columns (
pos for ordinality,
colour path '$.colour',
shape path '$.shape',
brick format json path '$'
)
)
) j

72. select product_id, j.*
from products, json_table (
product_json columns (
nested bricks[*] columns (
pos for ordinality,
colour path '$.colour',
shape path '$.shape',
brick format json path '$'
)
)
) j

73. with costs as (
select * from external …
), bricks as (
select product_id, j.*
from products, json_table (
…
)
)
select …
from bricks join costs
on …
We've joined the data, but how do
we convert it back to JSON?

74. json_object
json_objectagg
json_array
json_arrayagg
(12.2)
JSON Generation Functions

75. select json_object (
'colour' value b.colour,
'shape' value b.shape,
'quantity' value b.quantity,
'unitCost' value c.cost
)
from bricks b
join costs c
on b.colour = c.colour
and b.shape = c.shape;
So you can create a brick
object with json_object…

76. select json_mergepatch (
brick,
'{ "unitCost": ' || c.cost || '}'
)
from bricks b
join costs c
on b.colour = c.colour
and b.shape = c.shape;
Add/replace this…
…to this document
… or use
json_mergepatch (19c)
to add it to the brick object

77. {
"colour": "red",
"shape": "cube",
"quantity": 13,
"unitCost": 0.59
}
{
"colour": "green",
"shape": "cuboid",
"quantity": 17,
"unitCost": 0.39
}
This returns a row
for each brick
To combine them
into an array for
each product, use
json_arrayagg

78. json_arrayagg (
json_mergepatch (
brick,
'{ "unitCost": ' || cost || '}'
) order by pos
)

79. [ {
"colour": "red",
"shape": "cube",
"quantity": 13,
"unitCost": 0.59
}, {
"colour": "green",
"shape": "cuboid",
"quantity": 17,
"unitCost": 0.39
}, …
]
Make the array into
an object with
json_object

80. json_object (
'bricks' value
json_arrayagg (
json_mergepatch (
brick,
'{ "unitCost": ' || cost || '}'
) order by pos
)
)

81. "bricks": [ {
"colour": "red",
"shape": "cube",
"quantity": 13,
"unitCost": 0.59
}, {
"colour": "green",
"shape": "cuboid",
"quantity": 17,
"unitCost": 0.39
}, …
]
And replace this
array in the product
JSON with
json_mergepatch

82. json_mergepatch (
product,
json_object (
'bricks' value
json_arrayagg (
json_mergepatch (
brick,
'{ "unitCost": ' || cost || '}'
) order by pos
)
)
)

83. {
"productName": "GEEKWAGON",
"descripion": "Ut commodo in …",
"unitPrice": 35.97,
"bricks": [ {
…, "unitCost": 0.59
}, {
…, "unitCost": 0.39
}, …
]
}
Finally!
We've added
unitCost to every
element in the array
We just need to
update the table…

84. update products
set product_json = (
with costs as (
select * from external …
), bricks as (
select …
)
select json_mergepatch …
)

85. …at least we can view order profitability now
That was hard work
Ryan McGuire / Gratisography

86. User Story #7
Create report
prices - discount – total cost
We've got the data;
but want an easier way to query it…

87. dbms_json.add_virtual_columns (
'orders', 'order_json'
);
JSON Data Guide
Added in 12.2, the JSON Data Guide
enables you to expose attributes as virtual
columns in the table. To do this, the column
must have a json search index

88. desc orders
Name Null? Type
ORDER_ID NOT NULL NUMBER(38)
ORDER_JSON NOT NULL BLOB
ORDER_JSON$customerId NUMBER
ORDER_JSON$orderDatetime VARCHAR2(32)
ORDER_JSON$code VARCHAR2(8)
ORDER_JSON$discountAmount NUMBER
Sadly it only exposes
scalar (non-array) values

89. dbms_json.create_view_on_path (
'product_bricks_vw', 'products',
'product_json', '$'
);
…using json_table
on this!
Create this view…
Luckily you can create
a view instead!

90. select product_id,
"PRODUCT_JSON$shape" shape,
"PRODUCT_JSON$colour" colour
from product_bricks_vw
order by product_id, shape, colour
You can now query the
view to see JSON as
rows-and-columns

91. PRODUCT_ID SHAPE COLOUR
1 cube green
1 cube red
1 cylinder blue
1 cylinder blue
1 cylinder green
1 cylinder green
… … …
The unique key
for a brick is
(colour, shape)
Some products have
duplicate entries
in the bricks array!
We're shipping too
many bricks!

92. User Story #8
FIX ALL THE DATAZ!
We need to remove all the duplicate entries
from the product brick arrays

93. { ..., "bricks" : [
{
"colour" : "red",
"shape" : "cylinder",
"quantity" : 20,
"unitCost" : 0.39
}, {
"colour" : "red",
"shape" : "cylinder",
"quantity" : 20,
"unitCost" : 0.39
}
{ ..., "bricks" : [
{
"colour" : "red",
"shape" : "cylinder",
"quantity" : 8,
"unitCost" : 0.39
}, {
"colour" : "blue",
"shape" : "cylinder",
"quantity" : 10,
"unitCost" : 0.98
}
Comparing the brick arrays
for two products shows
unitCost is duplicated

94. { ..., "bricks" : [
{
"colour" : "red",
"shape" : "cylinder",
"quantity" : 20,
"unitCost" : 0.39
}, {
"colour" : "red",
"shape" : "cylinder",
"quantity" : 20,
"unitCost" : 0.39
}
{ ..., "bricks" : [
{
"colour" : "red",
"shape" : "cylinder",
"quantity" : 8,
"unitCost" : 0.39
}, {
"colour" : "blue",
"shape" : "cylinder",
"quantity" : 10,
"unitCost" : 0.98
}
And the brick itself is
duplicated within an array

95. Wrong Data Model
PRODUCTS BRICKS
The JSON models the relationship between
products and bricks as 1:M
This is the wrong data model
the relationship is M:M

96. Fixed It!
PRODUCTS BRICKSPRODUCT_BRICKS
unique (
product_id,
brick_id
)
{ JSON } { JSON }{ JSON }
You need a junction table
between products and bricks
This avoids duplication &
enables constraints

97. You still need to model { JSON } data!

98. Copyright © 2019 Oracle and/or its affiliates.
"The more I work with existing NoSQL
deployments however, the more I believe that their
schemaless nature has become an excuse for
sloppiness and unwillingness to dwell
on a project’s data model beforehand"
- Florents Tselai
https://tselai.com/modern-data-practice-and-the-sql-tradition.html

99. select distinct "PRODUCT_JSON$shape" shape,
"PRODUCT_JSON$colour" colour,
"PRODUCT_JSON$unitCost" unit_cost
from product_bricks_vw
Moving from 1:M to M:M
Using the JSON Data Guide
view, you can find all the
unique brick types…

100. with vals as (
select distinct "PRODUCT_JSON$shape" shape,
"PRODUCT_JSON$colour" colour,
"PRODUCT_JSON$unitCost" unit_cost
from product_bricks_vw
)
select rownum brick_id,
v.*
from vals v;
…assign a unique ID to each
( colour, shape ) …

101. create table bricks as
with vals as (
select distinct "PRODUCT_JSON$shape" shape,
"PRODUCT_JSON$colour" colour,
"PRODUCT_JSON$unitCost" unit_cost
from product_bricks_vw
)
select rownum brick_id,
v.*
from vals v;
…and create a table
from the results!

102. create table bricks as
with vals as (
select distinct "PRODUCT_JSON$shape" "shape",
"PRODUCT_JSON$colour" "colour",
"PRODUCT_JSON$unitCost" "unitCost"
from product_bricks_vw
)
select rownum brick_id,
json_object ( v.* ) brick_json
from vals v;
19c simplification
(Storing the values as
JSON if you want)

103. create table product_bricks as
select distinct product_id, brick_id
from product_bricks_vw
join bricks
on ...
Create the Join Table

104. json_mergepatch (
product_json,
'{ "bricks": null }'
)
If you pass a null value for an
attribute to JSON_mergepatch,
it's removed from the source
Removing the bricks array from products

105. When should I store { JSON }?

106. Storing JSON can be the right choice for…
JSON responses
- 3rd party APIs
- IoT devices
Schema extensions
- flex fields
- sparse columns
1 2

107. Further Reading
How to Store, Query, and Create JSON
Documents in Oracle Database Blog Post
http://bit.ly/json-in-oracle-db
Presentation Live SQL Scripts
http://bit.ly/agile-json-livesql
Copyright © 2019 Oracle and/or its affiliates.

108. Copyright © 2019 Oracle and/or its affiliates.
VS
Some people suggest JSON and
relational are fundamentally different

109. Copyright © 2019 Oracle and/or its affiliates.
VS
This is not the case!
However you store data,
you still need to normalize it
to avoid duplication and errors

110. Copyright © 2019 Oracle and/or its affiliates.
How you store the data is a spectrum
from just rows-and-columns to wholly
JSON and everything in-between

111. Copyright © 2019 Oracle and/or its affiliates.
Oracle Database
supports it all!
However you store
your data

112. asktom.oracle.com
#MakeDataGreatAgain
Ryan McGuire / Gratisography