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![Node.JS 26 // Connecting to MySQL Server and working with a Collection var mysqlx = require('@mysql/xdevapi'); var db; // Connect to server mysqlx .getSession({ user: 'user', password: 'password', host: 'localhost', port: '33060', }) .then(function (session) { db = session.getSchema('test'); // Create a new collection 'my_collection' return db.createCollection('my_collection'); }) .then(function (myColl) { // Insert documents return Promise .all([ myColl.add({ name: 'Sakila', age: 15 }).execute(), myColl.add({ name: 'Susanne', age: 24 }).execute(), myColl.add({ name: 'User', age: 39 }).execute() ]) .then(function () { // Find a document return myColl .find('name like :name && age < :age') .bind({ name: 'S%', age: 20 }) .limit(1) .execute(function (doc) { // Print document console.log(doc); }); }); }) .then(function(docs) { // Drop the collection return db.dropCollection('my_collection'); }) .catch(function(err) { // Handle error });](https://image.slidesharecdn.com/mysqldocumentstore-190308233157/75/MySQL-Document-Store-SCaLE-17x-Presentation-26-2048.jpg)
![Dump and load using MySQL Shell & Python This example is inspired by @datacharmer's work: https://www.slideshare.net/datacharmer/mysql-documentstore $ mongo quiet eval 'DBQuery.shellBatchSize=30000; db.restaurants.find().shellPrint()' | perl -pe 's/(?:ObjectId|ISODate)(("[^"]+"))/ $1/g' > all_recs.json 36](https://image.slidesharecdn.com/mysqldocumentstore-190308233157/75/MySQL-Document-Store-SCaLE-17x-Presentation-36-2048.jpg)
![41 More Examples! restaurants.find().fields([“name”,”cuisine”]).limit(2)](https://image.slidesharecdn.com/mysqldocumentstore-190308233157/75/MySQL-Document-Store-SCaLE-17x-Presentation-41-2048.jpg)
![42 Comparing Syntax: MongoDB vs MYSQL MongoDB: > db.restaurants.find({"cuisine": "French", "borough": { $not: /^Manhattan/} }, {"_id":0, "name": 1,"cuisine": 1, "borough": 1}).limit(2) MySQL: >restaurants.find(“cuisine=’French’ AND borough!=’Manhattan’”).fields([“name”,”cuisine”,”borough” ]).limit(2)](https://image.slidesharecdn.com/mysqldocumentstore-190308233157/75/MySQL-Document-Store-SCaLE-17x-Presentation-42-2048.jpg)
![Find the top 10 restaurants by grade for each cuisine 70 WITH cte1 AS (SELECT doc->>"$.name" AS name, doc->>"$.cuisine" AS cuisine, (SELECT AVG(score) FROM JSON_TABLE(doc, "$.grades[*]" COLUMNS (score INT PATH "$.score")) AS r) AS avg_score FROM restaurants) SELECT *, RANK() OVER (PARTITION BY cuisine ORDER BY avg_score DESC) AS `rank` FROM cte1 ORDER BY `rank`, avg_score DESC LIMIT 10; This query uses a Common Table Expression (CTE) and a Windowing Function to rank the average scores of each restaurant, by each cuisine assembled in a JSON_TABLE](https://image.slidesharecdn.com/mysqldocumentstore-190308233157/75/MySQL-Document-Store-SCaLE-17x-Presentation-70-2048.jpg)
MySQL Document Store -- SCaLE 17x Presentation
- 1. MySQL Document Store - ANoSQL JSON Document Database Dave Stokes @stoker david.stokes@oracle.com Elephantdolphin.blogger.com OpensourceDBA.wordpress.com
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- 3. Safe Harbor Agreement THEFOLLOWING 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, AND TIMING OF ANY FEATURES OR FUNCTIONALITY DESCRIBED FOR ORACLE'S PRODUCTS REMAINS AT THE SOLE DISCRETION OF ORACLE. 3
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- 7. Relational Databases ● DataIntegrity ○ Normalization ○ constraints (foreign keys, ...) ● Atomicity, Consistency, Isolation, Durability ○ ACID compliant ○ transactions ● SQL ○ powerful query language 7
- 8. Relational Databases ● Needto set up tables BEFORE use ● Relations, indexes, data normalization, query optimizations ● Hard to change on the fly ● Need a DBA or someone who has DBA skills 8
- 9. NoSQL or DocumentStore 9
- 10. NoSQL or DocumentStore ● Schemaless ○ No schema design, no normalization, no foreign keys, no data types, … ○ Very quick initial development ● Flexible data structure ○ Embedded arrays or objects ○ Valid solution when natural data can not be modelized optimally into a relational model ○ Objects persistence without the use of any ORM - *mapping object-oriented* 10
- 11. NoSQL or DocumentStore ● JSON ● close to frontend ● native in JS ● easy to learn 11
- 12. How DBAs seedata as opposed to how Developers see data { "GNP" : 249704, "Name" : "Belgium", "government" : { "GovernmentForm" : "Constitutional Monarchy, Federation", "HeadOfState" : "Philippe I" }, "_id" : "BEL", "IndepYear" : 1830, "demographics" : { "Population" : 10239000, "LifeExpectancy" : 77.8000030517578 }, "geography" : { "Region" : "Western Europe", "SurfaceArea" : 30518, "Continent" : "Europe" } } 12
- 13. What if therewas a way to provide both SQL and NoSQL on one stable platform that has proven stability on well know technology with a large Community and a diverse ecosystem ? With the MySQL Document Store it is now an option! 13
- 14. A Solution forall Developers: schemaless ★ rapid prototyping & simpler APIs ★ document model ★ transactions Operations: ★ performance management/visibility ★ robust replication, backup, restore ★ comprehensive tooling ecosystem ★ simpler application schema upgrades 14 Business Owner: ★ don't lose my data == ACID trx ★ capture all my data = extensible/schemaless ★ product on schedule/time to market = rapid development
- 15. Built on theMySQL JSON Data type and Proven MySQL Server Technology 15 ★ Provides a schema flexible JSON Document Store ★ No SQL required ★ No need to define all possible attributes, tables, etc. ★ Uses new X DevAPI ★ Can leverage generated column to extract JSON values into materialized columns that can be indexed for fast SQL searches.
- 16. Built on theMySQL JSON Data type and Proven MySQL Server Technology 16 ★ Document can be ~1GB ○ It's a column in a row of a table ★ Allows use of modern programming styles ○ No more embedded strings of SQL in your code ○ Easy to read ★ Also works with relational Tables ★ Proven MySQL Technology
- 17. ★ C++ ★ Java ★.Net ★ Node.js ★ JavaScript ★ Python ★ PHP ○ Working with other Communities to help them supporting it too 17 Connectors for
- 18. ★ Command Completion ★Python, JavaScripts & SQL modes ★ Admin functions ★ New Util object ★ A new high-level session concept that can scale from single MySQL Server to a multiple server environment 18 New MySQL Shell
- 19. ★ Non-blocking, asynchronouscalls follow common language patterns ★ Supports CRUD operations 19 New Model
- 20. 20 X Protocol builton Google Protobufs
- 21. 21 Architecture of bothOld and New Protocols
- 22. 22 How Your Applicationwill work with InnoDB Cluster
- 23. But what doesthis look like in PHP?? 23
- 24. JavaScript 24 // Connectingto MySQL Server and working with a Collection var mysqlx = require('mysqlx'); // Connect to server var mySession = mysqlx.getSession( { host: 'localhost', port: 33060, user: 'user', password: 'password'} ); var myDb = mySession.getSchema('test'); // Create a new collection 'my_collection' var myColl = myDb.createCollection('my_collection'); // Insert documents myColl.add({_id: '1', name: 'Sakila', age: 15}).execute(); myColl.add({_id: '2', name: 'Susanne', age: 24}).execute(); myColl.add({_id: '3', name: 'User', age: 39}).execute(); // Find a document var docs = myColl.find('name like :param1 AND age < :param2').limit(1). bind('param1','S%').bind('param2',20).execute(); // Print document print(docs.fetchOne()); // Drop the collection myDb.dropCollection('my_collection');
- 25. Python 25 # Connectingto MySQL Server and working with a Collection from mysqlsh import mysqlx # Connect to server mySession = mysqlx.get_session( { 'host': 'localhost', 'port': 33060, 'user': 'user', 'password': 'password'} ) myDb = mySession.get_schema('test') # Create a new collection 'my_collection' myColl = myDb.create_collection('my_collection') # Insert documents myColl.add({'_id': '1', 'name': 'Sakila', 'age': 15}).execute() myColl.add({'_id': '2', 'name': 'Susanne', 'age': 24}).execute() myColl.add({'_id': '3', 'name': 'User', 'age': 39}).execute() # Find a document docs = myColl.find('name like :param1 AND age < :param2') .limit(1) .bind('param1','S%') .bind('param2',20) .execute() # Print document doc = docs.fetch_one() print doc
- 26. Node.JS 26 // Connectingto MySQL Server and working with a Collection var mysqlx = require('@mysql/xdevapi'); var db; // Connect to server mysqlx .getSession({ user: 'user', password: 'password', host: 'localhost', port: '33060', }) .then(function (session) { db = session.getSchema('test'); // Create a new collection 'my_collection' return db.createCollection('my_collection'); }) .then(function (myColl) { // Insert documents return Promise .all([ myColl.add({ name: 'Sakila', age: 15 }).execute(), myColl.add({ name: 'Susanne', age: 24 }).execute(), myColl.add({ name: 'User', age: 39 }).execute() ]) .then(function () { // Find a document return myColl .find('name like :name && age < :age') .bind({ name: 'S%', age: 20 }) .limit(1) .execute(function (doc) { // Print document console.log(doc); }); }); }) .then(function(docs) { // Drop the collection return db.dropCollection('my_collection'); }) .catch(function(err) { // Handle error });
- 27. C++ 27 // Connectto server var mySession = MySQLX.GetSession("server=localhost;port=33060;user=user;password=password;"); var myDb = mySession.GetSchema("test"); // Create a new collection "my_collection" var myColl = myDb.CreateCollection("my_collection"); // Insert documents myColl.Add(new { name = "Sakila", age = 15}).Execute(); myColl.Add(new { name = "Susanne", age = 24}).Execute(); myColl.Add(new { name = "User", age = 39}).Execute(); // Find a document var docs = myColl.Find("name like :param1 AND age < :param2").Limit(1) .Bind("param1", "S%").Bind("param2", 20).Execute(); // Print document Console.WriteLine(docs.FetchOne()); // Drop the collection myDb.DropCollection("my_collection");
- 28. Java 28 // Connectto server Session mySession = new SessionFactory().getSession("mysqlx://localhost:33060/test?user=user&password=password"); Schema myDb = mySession.getSchema("test"); // Create a new collection 'my_collection' Collection myColl = myDb.createCollection("my_collection"); // Insert documents myColl.add("{"name":"Sakila", "age":15}").execute(); myColl.add("{"name":"Susanne", "age":24}").execute(); myColl.add("{"name":"User", "age":39}").execute(); // Find a document DocResult docs = myColl.find("name like :name AND age < :age") .bind("name", "S%").bind("age", 20).execute(); // Print document DbDoc doc = docs.fetchOne(); System.out.println(doc); // Drop the collection myDB.dropCollection("test", "my_collection");
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- 30. Starting using MySQLin few minutes 30
- 31. Quickly add adocument 31
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- 35. For this example,I will use the well known restaurants collection: We need to dump the data to a file and we will use the MySQL Shell with the Python interpreter to load the data. Migration from MongoDB to MySQL Document Store 35
- 36. Dump and loadusing MySQL Shell & Python This example is inspired by @datacharmer's work: https://www.slideshare.net/datacharmer/mysql-documentstore $ mongo quiet eval 'DBQuery.shellBatchSize=30000; db.restaurants.find().shellPrint()' | perl -pe 's/(?:ObjectId|ISODate)(("[^"]+"))/ $1/g' > all_recs.json 36
- 37. Or use newbulk loader in 8.0.13 37
- 38. BSON Support Now, itsupports the conversion of the following additional BSON types: ■ Date ■ Timestamp ■ NumberDecimal ■ NumberLong ■ NumberInt ■ Regular Expression ■ Binary 38 > util.importJson("/path_to_file/neighborhoods_mongo.json", {schema: "test", collection: "neighborhoods", convertBsonTypes: true});
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- 40. 40 Let’s query Too manyrecords to show here … let’s limit it! restaurants.find().limit(1)
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- 42. 42 Comparing Syntax: MongoDBvs MYSQL MongoDB: > db.restaurants.find({"cuisine": "French", "borough": { $not: /^Manhattan/} }, {"_id":0, "name": 1,"cuisine": 1, "borough": 1}).limit(2) MySQL: >restaurants.find(“cuisine=’French’ AND borough!=’Manhattan’”).fields([“name”,”cuisine”,”borough” ]).limit(2)
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- 48. 48 MySQL Document StoreObjects Summary
- 49. MySQL Document Storeis Fully ACID Compliant 49
- 50. MySQL Document Storeis Fully ACID Compliant 50
- 51. What about oldSQL? The Hidden Part of the Iceberg 51
- 52. ★ Native datatype (since 5.7.8) ★ JSON values are stored in MySQL tables using UTF8MB4 ★ Conversion from "native" SQL types to JSON values ★ JSON manipulation functions (JSON_EXTRACT, JSON_KEYS, JSON_SEARCH, JSON_TABLES, ...) ★ Generated/virtual columns ○ Indexing JSON data ○ Foreign Keys to JSON data ○ SQL Views to JSON data JSON datatype is behind the scene 52
- 53. How Does ItWork?? 53
- 54. What does acollection look like on the server ? 54
- 55. Every document hasa unique identifier called the document ID, which can be thought of as the equivalent of a table's primary key. The document ID value can be manually assigned when adding a document. If no value is assigned, a document ID is generated and assigned to the document automatically ! Use getDocumentId() or getDocumentIds() to get _ids(s) _id 55
- 56. Mapping to SQLExamples createCollection('mycollection') versus CREATE TABLE `test`.`mycoll` ( doc JSON, _id VARCHAR(32) GENERATED ALWAYS AS (doc->>'$._id') STORED PRIMARY KEY ) CHARSET utf8mb4; 56
- 57. Mapping to SQLExamples mycollection.add({‘test’: 1234}) versus INSERT INTO `test`.`mycoll` (doc) VALUES ( JSON_OBJECT( 'test',1234)); 57
- 58. More Mapping toSQL Examples mycollection.find("test > 100") Versus SELECT doc FROM `test`.`mycoll` WHERE (JSON_EXTRACT(doc,'$.test') >100); 58
- 59. 59 SQL and JSONExample
- 60. It's also possibleto create indexes without using SQL syntax 60
- 61. SQL and JSONExample (2): validation 61
- 62. SQL and JSONExample (3): explain 62
- 63. SQL and JSONExample (3): explain 63
- 64. SQL and JSONExample (4): add index 64
- 65. SQL and JSONExample (4): add index 65
- 66. SQL and JSONExample (5): arrays 66
- 67. NoSQL as SQL67 JSON_TABLE turns your un-structured JSON data into a temporary structured table!
- 68. NoSQL as SQL68 This temporary structured table can be treated like any other table -- LIMIT, WHERE, GROUP BY ...
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- 70. Find the top10 restaurants by grade for each cuisine 70 WITH cte1 AS (SELECT doc->>"$.name" AS name, doc->>"$.cuisine" AS cuisine, (SELECT AVG(score) FROM JSON_TABLE(doc, "$.grades[*]" COLUMNS (score INT PATH "$.score")) AS r) AS avg_score FROM restaurants) SELECT *, RANK() OVER (PARTITION BY cuisine ORDER BY avg_score DESC) AS `rank` FROM cte1 ORDER BY `rank`, avg_score DESC LIMIT 10; This query uses a Common Table Expression (CTE) and a Windowing Function to rank the average scores of each restaurant, by each cuisine assembled in a JSON_TABLE
- 71. Conclusion: What DoI Gain? 71
- 72. This is thebest of the two worlds in one product ! ● Data integrity ● ACID Compliant ● Transactions ● SQL ● Schemaless ● flexible data structure ● easy to start (CRUD) 72
- 73. Mutable Data!! Reduce Manyto many joins Replace ‘stub’ tables Change on the fly, aggregate new data 73
- 74. Non JSON DataTransforms to JSON 74
- 75. And JSON toRelational SELECT country_name, IndyYear FROM countryinfo, JSON_TABLE(doc,"$" COLUMNS (country_name CHAR(60) PATH "$.Name",IndyYear INT PATH "$.IndepYear")) AS stuff WHERE IndyYear > 1992; +----------------+----------+ | country_name | IndyYear | +----------------+----------+ | Czech Republic | 1993 | | Eritrea | 1993 | | Palau | 1994 | | Slovakia | 1993 | 75
- 76. New in MySQL8.0 1. True Data Dictionary 2. Default UTF8MB4 3. Windowing Functions, CTEs, Lateral Derived Joins 4. InnoDB SKIPPED LOCK and NOWAIT 5. Instant Add Column 6. Histograms 7. Resource Groups 8. Better optimizer with new temporary table engine 9. True Descending Indexes 10. 3D GIS 11. JSON Enhancements 76
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