Advance MySQL Docstore Features

MySQL Connectors :
Advanced DocStore Features
23rd August, 2019
Sankalita Chakraborty
Vishnu Priya

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | 2
Safe Harbour Statement
Copyright (c) 2019 Oracle and/or its affiliates. All rights reserved.
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, and
timing of any features or functionality described for
Oracle’s products remains at the sole discretion of
Oracle.
[1

3
DocStore Introduction
MySQL Document Store is essentially an alternative way using the MySQL Database.
It allows developers to work with SQL relational tables and schema-less JSON
collections
Two of the Key features of MySQL Document Store :
• Flexibility of a NoSQL Database
• Consistency of a RDBMS

4
Connector Products
 Connector Java
 Connector Python
 Connector NodeJS
 Connector Net
 Connector C++

5
Agenda
The following new features implemented in the Connectors as part of Docstore support:
 Connection Pooling
 Locking Read Operations
 Prepared Statement
 Create Index
We will be discussing each of these in details and will show a small demo in the end.

6Copyright (c) 2019 Oracle and/or its affiliates. All rights reserved.
Connection Pooling

7
Introduction
Connection pooling is a technique of creating and managing a pool
of connections that are ready for use, which greatly increase the
performance of your applications by reducing the connection
creation time.

8
Introduction Contd.
Creating physical connections to a remote database server is an
expensive operation, especially when SSL is used. It is possible that many
applications make use of many short lived database connections during
operation.
To speed up these operations a pooling option is made available so that
executing an open session operation will retrieve an existing and
currently unused network connection from a pool, reset it, and use it.
Closing a session would mark the underlying connection as unused and
return it to the pool.

9
Client Object
A Client object is created to use the Connection Pooling feature. Client
will contain the generic details of host, port etc, and few pool specific
information such as :
 MaxSize
 MaxIdleTime
 QueueTimeout.
A new session can be obtained from the pool by calling
client.getSession()

10
Client Object
Maxsize is the maximum number of connections that can be created as
part of the pool
When Maxsize connections have already been created, requesting a new
session from the Client will lead to a wait time for QueueTimeout.
If a connection in the pool stays unused for the MaxIdleTime, it will be
closed.

11
Client Object
std::string uri = "mysqlx://root@localhost:33370/test";
ClientSettings working_settings(uri,
R"( { "pooling": {
"enabled": true,
"maxSize": 5,
"queueTimeout": 9000,
"maxIdleTime": 60000}
})");
Client client(working_settings);
Session sess = client.getSession();

12
Client Object
When the Client is created , it is empty and there are no connections in
the pool.
Once getSession() is called for the first time a new connection is created.
This process can be repeated till the number of connections reach
MaxSize.
Once the session is closed, the connection is returned to the pool.
Now when a new session is requested, the connection from the pool is
given to the session.

13
Pool Setup
Database Server
Client
Connection Pool
Connection 1(Used)
Connection 2(Used)
Session 1
Session 2

14
Pool Setup
Database Server
Client
Connection Pool
Connection 1(Used)
Connection 2(Used)
Connection 3(Used)
Connection 4(Used)
Connection 5(Used)
Session 1
Session 2
Session 3
Session 4
Session 5

15
Pool Setup
Database Server
Client
Connection Pool
Connection 1(Unused)
Connection 2(Used)
Connection 3(Used)
Session 2
Session 3

16
Pool Setup
Database Server
Client
Connection Pool
Connection 1(Used)
Connection 2(Used)
Connection 3(Used)
Session 6
Session 2
Session 3

17
Performance Numbers
Sample Performance Results :
Time to create single session : 69.9482 ms
Time to create Client : 0.0737305 ms
Time to create Session from client first time : 69.2538 ms
Time to create Session from client second time on-wards : 0.247559 ms

18Copyright (c) 2019 Oracle and/or its affiliates. All rights reserved.
Locking read operations

19
Introduction
Document Store has introduced support for locking
read operations inside a transaction by two functions.
Transaction support is an important MySQL
differentiator compared to other NoSQL databases.
 Lock Exclusive
 Lock Shared
These functions can be called with Collection.find()
and Table.select().

20
Locking Scenario
Client A performs:
sessionA.startTransaction();
doc = collection.find("_id = 123").lockExclusive().execute().fetchOne();
doc["xxx"] = "foo";
doc["counter"] = doc["counter"] + 1;
collection.modify("_id = 123").replace(doc).execute();
Client B performs:
sessionB.startTransaction();
doc = collection.find("_id = 123").lockExclusive().execute().fetchOne();
# The document with _id = 123 is already locked by Client A, so Client B will block now.

21
Locking Scenario Contd.
Client A then commits:
sessionA.commit();
# The lock on _id = 123 is released, so Client B can now continue
Client B:
doc["xxx"] = "bla";
doc["yyy"] = "bar";
doc["counter"] = doc["counter"] + 1;
collection.modify("_id = 123").replace(doc).execute();
sessionB.commit();

22
Locking parameter
The LockContention parameter is available for LockExclusive and
LockShared. The valid options for LockContention parameter are
as follows:
 No Wait
A locking read that uses NOWAIT never waits to acquire a row
lock. The query executes immediately, failing with an error if a
requested row is locked.
 Skip Locked
A locking read that uses SKIP LOCKED never waits to acquire a
row lock. The query executes immediately, removing locked rows
from the result set.
 Default
Query waits if any row is already locked

23
Table1
Id:1 , Name:Jane
Id:2 , Name:Joanne
Id:3 , Name:Emily
Id:4 , Name:Charles
Id:5 , Name:Ernest
Id:6 , Name:Daphne
Id:7 , Name:Agatha
Query 1:
Table1.select(“id%2=0”).lockExclusive();
Skip Locked

24
Table1
Id:1 , Name:Jane
Id:2 , Name:Joanne
Id:3 , Name:Emily
Id:4 , Name:Charles
Id:5 , Name:Ernest
Id:6 , Name:Daphne
Id:7 , Name:Agatha
Query 1:
Query 2:
Table1.select(“id<5”).lockExclusive(mysql
x::LockContention::SKIP_LOCKED);
Skip Locked

25
Table1
Id:1 , Name:Jane
Id:2 , Name:Joanne
Id:3 , Name:Emily
Id:4 , Name:Charles
Id:5 , Name:Ernest
Id:6 , Name:Daphne
Id:7 , Name:Agatha
Query 1:
No Wait

26
Table1
Id:1 , Name:Jane
Id:2 , Name:Joanne
Id:3 , Name:Emily
Id:4 , Name:Charles
Id:5 , Name:Ernest
Id:6 , Name:Daphne
Id:7 , Name:Agatha
Query 1:
Query 3:
Table1.select(“id<5”).lockExclusive(mysql
x::LockContention::NOWAIT);
ERROR
No Wait

27
Prepared Statement

28
Introduction
Multiple executions of same SQL statement is one of
the expensive operations that can be optimized using
prepared statements.
Prepared statements are used
 To execute the same statement repeatedly for high efficiency.
 To allow clients to request the server to cache a parsed query and query
plan, allowing statements that are executed multiple times to execute
faster.
Implements support for preparing and executing
Read/Update/Delete requests.

29
 Two stages:
 Prepare: a statement template is sent to the MySQL server.
 Execute : client binds parameter values and sends them to the server

30
Session 1
Clien
t
Server
find = collection.find("_id = :id")
find.bind("id", 1).execute()
1st
call
SELECT doc FROM `test`.`EmpCollection`
WHERE (JSON_EXTRACT(doc,'$._id') =
1)

31
Session 1
Clien
t
1st
call
2nd
call
Server
1)
WHERE (JSON_EXTRACT(doc,'$._id') = ?)
Prepare & Cache
WHERE (JSON_EXTRACT(doc,'$._id') = 2)
Execute

32
Session 1
Clien
t
1st
call
2nd
call
3rd
call
Server
1)
WHERE (JSON_EXTRACT(doc,'$._id') = ?)
Prepare & Cache
Execute

33
 First call: Crud::Find
 Executing a statement first time (will not be prepared)
 Second call : Prepare::Prepare + Prepare::Execute
 Re-execution of the same statement (but with changed values) will
prepare and execute

34
Advantages:
 short-circuiting several steps of the execution pipeline
 client side parsing of expressions
 translation to SQL
 parsing of SQL
 query plan generation
c.find(expr) --> Parse/Serialize -->....--> Unserialize --> Translate -----> Parse --> Query Plan --> Execute ------> Read
| Application || Connector | | X Plugin | CRUD Translator || Parser | Optimizer | InnoDB |
|< NG Stack >|

35
Faster execution of the query
Example: Fetching documents from a collection which
matches certain name and age condition:
 Direct Execute: 1.24 ms
 Prepare+Execute PS: 1.9 ms
 Execute PS: 0.79 ms

36
Indexing Collections
 To make large collections of documents more efficient
to navigate you can create an index based on one or
more fields found in the documents in the collection.
 Collection indexes are ordinary MySQL indexes on
virtual columns that extract data from the documents
in the collection.
 Single Value Index
 Multi Value Index

37
EId EName Designation DeptId
1 Nikhil Manager 10
2 Sonali Developer 20
3 Anshita Test Engineer 10
4 Jayant Trainer 30
Create Index on DeptId
Employees table

38
Single Value Index
collection=schema.create_collection('Employees')
collection.add(
{"eid":1,"ename":"Nikhil","designation":”Manager”,”deptid”:10},
{"eid":2,"ename":"Sonali","designation":”Developer”,”deptid”:20},
{"eid":2,"ename":"Anshita","designation":”Test Engineer”,”deptid”:10},
{"eid":2,"ename":"Jayant","designation":”Trainer”,”deptid”:30},
).execute()
collection.create_index("deptIndex",
{"fields": [{"field": "$.deptid",
"type": "INT",
"required": True}],
"type": "INDEX"}).execute()

39
EId EName Designation Deptid Emails
1 Nikhil Manager 10 nikhil@company
.com,
nikhil@personal.
com
2 Sonali Developer 20 sonali@compan
y.com,
sonali@personal
.com
3 Anshita Test Engineer 10 Anshita@compa
ny.com,anshita
@personal.com
4 Jayant Trainer 30 jayant@compan
y.com,jayant@p
erconal.com
Create array Index on emails
Employees table

40
Array Index (8.0.17 and later)
collection=schema.create_collection('Employees')
collection.create_index("emails_idx",
{"fields": [{"field": "$.emails",
"type": "CHAR(128)",
"array": True}],
"type": "INDEX"}).execute()

41
Restrictions on MVI
 For each index, only one indexed field can be an array
 Supported data types
 INTEGER [SIGNED/UNSIGNED] √
 DECIMAL(m,n) √
 DATE, TIME and DATETIME √
 CHAR(n), BINARY(n) √
 TEXT X

Advance MySQL Docstore Features

More Related Content

What's hot

Similar to Advance MySQL Docstore Features

Recently uploaded

Advance MySQL Docstore Features