![Copyright © 2018, Oracle and/or its affiliates. All rights reserved. |
JSON Data Type
CREATE TABLE t1(json_col JSON);
INSERT INTO t1 VALUES (
'{ "people": [
{ "name":"John Smith", "address":"780 Mission St, San Francisco, CA 94103"},
{ "name":"Sally Brown", "address":"75 37th Ave S, St Cloud, MN 94103"},
{ "name":"John Johnson", "address":"1262 Roosevelt Trail, Raymond, ME 04071"}
] }'
);
2](https://image.slidesharecdn.com/jsontablelightning-180425225900/85/JSON_TABLE-The-best-of-both-worlds-2-320.jpg)
![Copyright © 2018, Oracle and/or its affiliates. All rights reserved. |
JSON_TABLE
SELECT people.*
FROM t1,
JSON_TABLE(json_col, '$.people[*]' COLUMNS (
name VARCHAR(40) PATH '$.name',
address VARCHAR(100) PATH '$.address')) people;
3
Convert JSON documents to relational tables
name address
John Smith 780 Mission St, San Francisco, CA 94103
Sally Brown 75 37th Ave S, St Cloud, MN 9410
John Johnson 1262 Roosevelt Trail, Raymond, ME 04071](https://image.slidesharecdn.com/jsontablelightning-180425225900/85/JSON_TABLE-The-best-of-both-worlds-3-320.jpg)
![Copyright © 2018, Oracle and/or its affiliates. All rights reserved. |
JSON_TABLE
SELECT people.*
FROM t1,
JSON_TABLE(json_col, '$.people[*]' COLUMNS (
name VARCHAR(40) PATH '$.name',
address VARCHAR(100) PATH '$.address')) people;
WHERE people.name LIKE 'John%';
4
Filter JSON data
name address
John Smith 780 Mission St, San Francisco, CA 94103
John Johnson 1262 Roosevelt Trail, Raymond, ME 04071](https://image.slidesharecdn.com/jsontablelightning-180425225900/85/JSON_TABLE-The-best-of-both-worlds-4-320.jpg)
![Copyright © 2018, Oracle and/or its affiliates. All rights reserved. |
Construct a New JSON Document
SELECT JSON_OBJECT("people",
JSON_ARRAYAGG(JSON_OBJECT("name", name, "address", address))) json_doc
FROM t1,
JSON_TABLE(json_col, '$.people[*]' COLUMNS (
name VARCHAR(40) PATH '$.name',
address VARCHAR(100) PATH '$.address')) people;
WHERE people.name LIKE 'John%';
5
json_doc
{"people": [{"name": "John Smith", "address": "780 Mission St, San Francisco,
CA 94103"}, {"name": "John Johnson", "address": "1262 Roosevelt Trail,
Raymond, ME 04071"}]}](https://image.slidesharecdn.com/jsontablelightning-180425225900/85/JSON_TABLE-The-best-of-both-worlds-5-320.jpg)
![Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | 6
JSON_TABLE
id father married child_id child age
1 John 1 1 Eric 12
1 John 1 2 Beth 10
2 Paul 0 1 Sarah 9
2 Paul 0 2 Noah 3
2 Paul 0 3 Peter 1
[
{ "father":"John", "mother":"Mary",
"marriage_date":"2003-12-05",
"children": [
{ "name":"Eric", "age":12 },
{ "name":"Beth", "age":10 } ] },
{ "father":"Paul", "mother":"Laura",
"children": [
{ "name":"Sarah", "age":9},
{ "name":"Noah", "age":3} ,
{ "name":"Peter", "age":1} ] }
]
Nested arrays](https://image.slidesharecdn.com/jsontablelightning-180425225900/85/JSON_TABLE-The-best-of-both-worlds-6-320.jpg)
![Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | 7
JSON_TABLE – Nested Arrays
id father married child_id child age
1 John 1 1 Eric 12
1 John 1 2 Beth 10
2 Paul 0 1 Sarah 9
2 Paul 0 2 Noah 3
2 Paul 0 3 Peter 1
JSON_TABLE (families, '$[*]' COLUMNS (
id FOR ORDINALITY,
father VARCHAR(30) PATH '$.father',
married INTEGER EXISTS PATH
'$.marriage_date',
NESTED PATH '$.children[*]' COLUMNS (
child_id FOR ORDINALITY,
child VARCHAR(30) PATH '$.name',
age INTEGER PATH '$.age') ) )](https://image.slidesharecdn.com/jsontablelightning-180425225900/85/JSON_TABLE-The-best-of-both-worlds-7-320.jpg)
![Copyright © 2018, Oracle and/or its affiliates. All rights reserved. |
JSON_TABLE
SELECT father, COUNT(*) "#children", AVG(age) "age average"
FROM t, JSON_TABLE (families, '$[*]' COLUMNS (
id FOR ORDINALITY,
father VARCHAR(30) PATH '$.father',
NESTED PATH '$.children[*]' COLUMNS (age INTEGER PATH '$.age' ) ) ) AS fam
GROUP BY id, father;
8
SQL aggregation on JSON data
father #children age average
John 2 11.0000
Paul 3 4.3333](https://image.slidesharecdn.com/jsontablelightning-180425225900/85/JSON_TABLE-The-best-of-both-worlds-8-320.jpg)
![Copyright © 2018, Oracle and/or its affiliates. All rights reserved. |
Put Computed Data Back Into JSON Document
SELECT JSON_ARRAYAGG(fam_obj) families
FROM (
SELECT JSON_MERGE_PATCH(family,
JSON_OBJECT("#children", COUNT(*), "avg_age" , AVG(age))) fam_obj
FROM t, JSON_TABLE (families, '$[*]' COLUMNS (
id FOR ORDINALITY,
family JSON PATH '$',
NESTED PATH '$.children[*]' COLUMNS (age INTEGER PATH '$.age' ) )
) fam
GROUP BY id, family) fams;
9](https://image.slidesharecdn.com/jsontablelightning-180425225900/85/JSON_TABLE-The-best-of-both-worlds-9-320.jpg)
![Copyright © 2018, Oracle and/or its affiliates. All rights reserved. |
Modify JSON Objects
SELECT
JSON_ARRAYAGG(JSON_MERGE_PATCH(JSON_OBJECT("id", id), family))
AS families
FROM t, JSON_TABLE (families, '$[*]' COLUMNS (
id FOR ORDINALITY,
family JSON PATH '$')) fam;
10
Put IDs in objects
families
[{"id": 1, "father": "John", "mother": "Mary", "children": [{"age": 12, "name":
"Eric"}, {"age": 10, "name": "Beth"}], "marriage_date": "2003-12-05"},
{"id": 2, "father": "Paul", "mother": "Laura", "children": [{"age": 9, "name":
"Sarah"}, {"age": 3, "name": "Noah"}, {"age": 1, "name": "Peter"}]}]](https://image.slidesharecdn.com/jsontablelightning-180425225900/85/JSON_TABLE-The-best-of-both-worlds-10-320.jpg)
JSON_TABLE -- The best of both worlds
- 1. Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | JSON_TABLE Øystein Grøvlen Optimizer Geek Oracle April 24, 2018 The Best of Both Worlds
- 2. Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | JSON Data Type CREATE TABLE t1(json_col JSON); INSERT INTO t1 VALUES ( '{ "people": [ { "name":"John Smith", "address":"780 Mission St, San Francisco, CA 94103"}, { "name":"Sally Brown", "address":"75 37th Ave S, St Cloud, MN 94103"}, { "name":"John Johnson", "address":"1262 Roosevelt Trail, Raymond, ME 04071"} ] }' ); 2
- 3. Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | JSON_TABLE SELECT people.* FROM t1, JSON_TABLE(json_col, '$.people[*]' COLUMNS ( name VARCHAR(40) PATH '$.name', address VARCHAR(100) PATH '$.address')) people; 3 Convert JSON documents to relational tables name address John Smith 780 Mission St, San Francisco, CA 94103 Sally Brown 75 37th Ave S, St Cloud, MN 9410 John Johnson 1262 Roosevelt Trail, Raymond, ME 04071
- 4. Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | JSON_TABLE SELECT people.* FROM t1, JSON_TABLE(json_col, '$.people[*]' COLUMNS ( name VARCHAR(40) PATH '$.name', address VARCHAR(100) PATH '$.address')) people; WHERE people.name LIKE 'John%'; 4 Filter JSON data name address John Smith 780 Mission St, San Francisco, CA 94103 John Johnson 1262 Roosevelt Trail, Raymond, ME 04071
- 5. Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Construct a New JSON Document SELECT JSON_OBJECT("people", JSON_ARRAYAGG(JSON_OBJECT("name", name, "address", address))) json_doc FROM t1, JSON_TABLE(json_col, '$.people[*]' COLUMNS ( name VARCHAR(40) PATH '$.name', address VARCHAR(100) PATH '$.address')) people; WHERE people.name LIKE 'John%'; 5 json_doc {"people": [{"name": "John Smith", "address": "780 Mission St, San Francisco, CA 94103"}, {"name": "John Johnson", "address": "1262 Roosevelt Trail, Raymond, ME 04071"}]}
- 6. Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | 6 JSON_TABLE id father married child_id child age 1 John 1 1 Eric 12 1 John 1 2 Beth 10 2 Paul 0 1 Sarah 9 2 Paul 0 2 Noah 3 2 Paul 0 3 Peter 1 [ { "father":"John", "mother":"Mary", "marriage_date":"2003-12-05", "children": [ { "name":"Eric", "age":12 }, { "name":"Beth", "age":10 } ] }, { "father":"Paul", "mother":"Laura", "children": [ { "name":"Sarah", "age":9}, { "name":"Noah", "age":3} , { "name":"Peter", "age":1} ] } ] Nested arrays
- 7. Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | 7 JSON_TABLE – Nested Arrays id father married child_id child age 1 John 1 1 Eric 12 1 John 1 2 Beth 10 2 Paul 0 1 Sarah 9 2 Paul 0 2 Noah 3 2 Paul 0 3 Peter 1 JSON_TABLE (families, '$[*]' COLUMNS ( id FOR ORDINALITY, father VARCHAR(30) PATH '$.father', married INTEGER EXISTS PATH '$.marriage_date', NESTED PATH '$.children[*]' COLUMNS ( child_id FOR ORDINALITY, child VARCHAR(30) PATH '$.name', age INTEGER PATH '$.age') ) )
- 8. Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | JSON_TABLE SELECT father, COUNT(*) "#children", AVG(age) "age average" FROM t, JSON_TABLE (families, '$[*]' COLUMNS ( id FOR ORDINALITY, father VARCHAR(30) PATH '$.father', NESTED PATH '$.children[*]' COLUMNS (age INTEGER PATH '$.age' ) ) ) AS fam GROUP BY id, father; 8 SQL aggregation on JSON data father #children age average John 2 11.0000 Paul 3 4.3333
- 9. Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Put Computed Data Back Into JSON Document SELECT JSON_ARRAYAGG(fam_obj) families FROM ( SELECT JSON_MERGE_PATCH(family, JSON_OBJECT("#children", COUNT(*), "avg_age" , AVG(age))) fam_obj FROM t, JSON_TABLE (families, '$[*]' COLUMNS ( id FOR ORDINALITY, family JSON PATH '$', NESTED PATH '$.children[*]' COLUMNS (age INTEGER PATH '$.age' ) ) ) fam GROUP BY id, family) fams; 9
- 10. Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Modify JSON Objects SELECT JSON_ARRAYAGG(JSON_MERGE_PATCH(JSON_OBJECT("id", id), family)) AS families FROM t, JSON_TABLE (families, '$[*]' COLUMNS ( id FOR ORDINALITY, family JSON PATH '$')) fam; 10 Put IDs in objects families [{"id": 1, "father": "John", "mother": "Mary", "children": [{"age": 12, "name": "Eric"}, {"age": 10, "name": "Beth"}], "marriage_date": "2003-12-05"}, {"id": 2, "father": "Paul", "mother": "Laura", "children": [{"age": 9, "name": "Sarah"}, {"age": 3, "name": "Noah"}, {"age": 1, "name": "Peter"}]}]
- 11. Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Want to Learn More About New and Upcoming SQL Features? • MySQL 8.0: What is New in Optimizer and Executor? – Manyi Lu – Tuesday 4:50PM-5:15PM; Room GS • Running JavaScript Stored-Programs Inside MySQL Server – Øystein Grøvlen, Vojin Jovanovic, Farhan Tauheed – Wednesday 11:00AM-11:50AM; Room GS 11 Presentations at Percona Live