the scale db storage engine enabling high performance and scalability using materialized views and...

The ScaleDB Storage EngineEnabling high performance and scalability, using a Multi-Table Index, and a Shared-Disk Clustering Architecture

Moshe Shadmon [email protected]

mailto:[email protected]

Agenda

Overview

ScaleDB’s Clustering Architecture

o Shared-Disk vs. Shared-Nothing

o MySQL and a Shared-Disk Storage Engine

o ScaleDB Installation

o Demo

ScaleDB’s Indexing Technology

o Multi-Table Index

o Enabling Multi-Table Index in MySQL

o Demo

Summary

ScaleDB Status & Product Availability

Overview

Plug-in Storage Engine for MySQL Main Features:

o Shared-Disk Architectureo Innovative Multi-Table Indexingo Transactionalo Row-Level Lockingo ACID Compliant

o Atomicity: All tasks of a transaction performed or none of them are.

o Consistency: The database is in a consistent state before and after the transaction.

o Isolation: Data is not available in an intermediate state during a transaction

o Durability: When a transaction completes, the transaction’s data will persist

o Disk-Based Storage Engine

Shared-Disk vs. Shared-Nothing

Manageability

Adaptability

Availability/Fault-Tolerance

Scalability

Performance

Total Cost of Ownership (TCO)

Shared-Nothing:

DatabaseInstance 1

Table ATable B

Table C

DatabaseInstance 1

DatabaseInstance 2

DatabaseInstance 3

Table A

Table B

Table C

Vertical Partitioning

Shared Nothing: Partitioning Your Data…How

Predict usage patterns, application evolution, data growth patterns…all are moving targets

Avoid data skew: bottlenecks caused by frequently accessed data on just a few nodes

Avoid data shipping between nodes

Avoid delays from distributed 2-phase commit

Searches outside the partition column require participation by all nodes

Scaling becomes an exercise in fire fighting

Bob 20 10K

Shideh 18 35K

Ted 50 60K

Kevin 62 120K

Angela 55 140K

Mike 45 90K

Physical View

name age salary

Partitioned by Salary

Logical View

Shared-Nothing:Horizontal Partitioning

Ted 50 60K

Kevin 62 120K

Mike 46 90K

name age salary

Bob 20 10K

name age salaryShideh 18 35K

Angela 55 140K

name age salary

Horizontal Partitioning – Salary % 3

Selections with equality predicates referencing the partitioning attribute are directed to a single node:

o Retrieve Emp where salary = 60K

SELECT FROM Emp WHERE salary=60K

Equality predicates referencing a non-partitioning attribute and range predicates are directed to all nodes:

o Retrieve Emp where age = 20

o Retrieve Emp where salary < 20K

SELECT FROM Emp WHERE salary<20K

Shared-Nothing:Horizontal Partitioning Pitfalls

DB ClusterNode 1

DB ClusterNode 2

DB ClusterNode 3

Table A

Table B

Table C

Shared DiskSubsystemH

igh-

Sp

eed

Inte

rcon

nect

Shared-Disk:No Partitioning, Full Access to Data

DatabaseInstance 1

Table ATable B

Table C

Slave A

Slave B

Slave C

Scalability & Availability Shared Nothing

Scalability & Availability Shared Disk

No

de A

No

de B

No

de C

Data

MySQL Serverswith ScaleDBEngine

No

de D

No

de E

Grow by simply adding nodes to the clustero Servers can be added and removed dynamically

according to your needs

o No interruption to your application

High-Availability with dynamic failovero Existing nodes automatically take over

Significantly reduced maintenance costso Can be built on low-cost commodity hardwareo No data partitioning

o No need for slaves

Low Total Cost of Ownership (TCO)

Shared-Disk:Summarizing Shared-Disk Benefits

ScaleDB Engine Instance A

ClusterManager

Buffer Manager

Comm.Layer

Server Instance A

Shared-Disk:Making it work with MySQL

Node 1Node 1

ScaleDB Engine Instance B

Buffer Manager

ClusterManager

Comm.Layer

Node 2Node 2

Server Instance B

Shared Disk Sub-systemShared Disk Sub-system

Cluster InterconnectCluster Interconnect


ClusterManager

Buffer Manager

Comm.Layer

Node 1Node 1

Server Instance A


Buffer Manager

ClusterManager

Comm.Layer

Node 2Node 2

Server Instance B

Shared-Disk: Insert New Row




ClusterManager

Buffer Manager

Comm.Layer

Node 1Node 1

Server Instance A


Buffer Manager

ClusterManager

Comm.Layer

Node 2Node 2

Server Instance B

Shared-Disk: Select




ClusterManager

Buffer Manager

Comm.Layer

Node 1Node 1

Server Instance A


Buffer Manager

ClusterManager

Comm.Layer

Node 2Node 2

Server Instance B

Shared-Disk: Create Table



Table AMeta-DataTable A

Meta-Data

ScaleDB Installation Define cluster = true in ScaleDB Config file:

ScaleDB.cnf is at the same directory as my.cnf:

Cluster params:o cluster = true

o nodes_in_cluster = 2

o node_id = 1

o this_machine_port = 100

o next_machine_ip_address = 192.168.0.101

o next_machine_port = 100

o log_directory = /share/logs/

Demo - Sysbench ScaleDB cluster – one node – show throughput

ScaleDB cluster – 2nd node – show throughput

ScaleDB: Multi-Table Indexing

B-tree: Only indexes the data in tablesIndex

#1

#1 #2

Index #2

Index #3

Index #4

Index #5

#3 #4 #5

ScaleDB Index

#1

#2

#3

#4

#5

ScaleDB: Indexes the data and relationships

Advantages:• Faster• Smaller• Referential integrity

Example Scenario: Select information that is spread

across 3 tables: Colleges, Students and Enrollment

Relationships: Students are enrolled in courses within departments of colleges

SELECT c1.CollName, s.StudName, c2.CourseName , e.Grade

FROM College AS c1

JOIN Student AS s

JOIN Enrollment AS e

JOIN Course AS c2

ON ( c1.CollNo = s.CollNo AND

s.CollNo = e.CollNo AND

s.StudentNo = e.StudentNo AND

e.CollNo = c2.CollNo AND

e.DeptNo = c2.DeptNo AND

e.CourseNum = c2.CourseNum )

WHERE c1.CollNo = X

AND s.StudentNo = Y ;

Option #1: Conventional JoinsID College Students

234 Institute of Technology 1,334

167 High Tech Institute 5,742

85 Golden State College 2,119

298 Kaplan College 12,323

510 California College 1,926

ID Student Name SS# Phone

1220 Bruce Chizen 422-72-8495 (650) 234-2234

6778 Naomi Seligman 533-99-1234 (279) 331-2345

4435 Raymond Bingham

8872 Reed Hastings 412-44-5567 (312)676-8812

1129 Maria Klawe

1123 Bernard Vergnes

College ID Course Name Student Grade

510 C67 Mathematics 4435 87

167 C123 History 1 1129 70

167 C14 Photography 1 1120 88

Students Table

College Table

Enrollment Table

Search enrollment by College & Student

Get Student information

Get College information

Option #2: Materialized View

ID College Students ID Course Name ID Student Name

234 Institute of Technology 1,334 C134 Mathematics 1145 John Cheechoo …

234 Institute of Technology 1,334 C134 Mathematics 1837 Ryane Clowe …

234 Institute of Technology 1,334 C134 Mathematics 2256 Patrick Marleau …

234 Institute of Technology 1,334 C134 Mathematics 2277 Jamie McGinn …

234 Institute of Technology 1,334 C134 Mathematics 4113 Torrey Mitchell …

234 Institute of Technology 1,334 C134 Mathematics 1145 …

385 Golden State College 2,224 G85 World History 7783 Joe Pavelski …

385 Golden State College 2,224 G85 World History 2234 Jeremy Roenick …

385 Golden State College 2,224 G85 World History 1177 Devin Setoguchi …

385 Golden State College 2,224 G85 World History 4113 Torrey Mitchell …

. . .

Col_ID# Col_Name Col_Budget Col_DescriptionColleges

001 Agriculture $1,234,567 Nice place to visit

002 Arts $5,432,567 Sports not so good

003 Business $9,999,666 Cool logo

004 Education $3,234,567 Ugh Worcester

005 Engineering $8,238,568 Serious work

006 Law $7,237,767 Jumpy students

007 Liberal Arts $9,898,777 Pretty campus

008 Medicine $5,987,004 In Texas

Students

56-8033 008 Mike Hogan Caucasian

56-8045 008 Moshe Smith Caucasian

56-8044 008 Sally Shadmon Native American

56-8055 008 Billy Fleegle African American

56-8037 008 Saul Goode African American

56-8122 008 Tim Collins Polynesian

56-8233 008 Sam Gee Asian

56-8334 008 Rod Paulino Asian

Enrollment

008 4455 56-8037 B+

008 4455 56-8033 C

008 4455 56-8045 B+

008 4456 56-8044 A-

008 4456 56-8122 B-

008 4454 56-8233 C

008 4455 56-8334 F

008 4454 56-8055 D

Coll_ID# Coll_Name Coll_Budget Coll_Description Student_ID# College_ID# Student_Name Student_Desc College_ID# Dept_ID# Student_ID# Grade

Option #3: Multi-Table Index

CollegeCollege

StudentsStudents

EnrollmentEnrollment

DepartmentsDepartments

CoursesCourses

ScaleDB Multi-Table Index

EnrollmentEnrollment

Mapping Foreign Keys to Data Views

Create Students Table

o Foreign key – College

Students

Enrollment Create Enrollment Table

o Foreign key - Students

Course

Create Course Table

o Foreign Key – DepartmentDepartment

Create Department Table

o Foreign key – College

College Create College Table

The Parent-Child tables are Created in MySQL Such that MySQL is able to operate over the new tables

The data of the Parent-Child tables is assembled on the fly from the source tables

Mapping Foreign Keys to Data Views

Students

Enrollment

Course

DepartmentCollege

DepartmentCollege

College

StudentsCollege

Physical files:1. College2. Department3. Student4. Course5. Enrollment

ScaleDB

Meta-Data Tables:

1. College2. College-Dept3. College-Dept-Course4. College-Students5. College-Students-Enrollment6. Department7. Students8. Course9. Enrollment

Enabling the MySQL optimizer to use a Multi-Table IndexSELECT c1.CollName, s.StudName,

c2.CourseName , e.Grade

FROM College AS c1

JOIN Student AS s

JOIN Enrollment AS e

JOIN Course AS c2

ON ( c1.CollNo = s.CollNo AND

s.CollNo = e.CollNo AND

s.StudentNo = e.StudentNo AND

e.CollNo = c2.CollNo AND

e.DeptNo = c2.DeptNo AND

e.CourseNum = c2.CourseNum )

WHERE c1.CollNo = X

AND s.StudentNo = Y ;

CREATE TABLE sdb_view_college_course_student (

L1_CollNo INT NOT NULL,

L1_CollName CHAR(32) NOT NULL,

L1_CollBudget INT NOT NULL,

L1_CollDescription CHAR(60) NOT NULL,

… Table College Columns

L2_StudNo INT NOT NULL,

L2_StudName CHAR(48) NOT NULL,

… Table Student Columns

L3_CourseNum CHAR(9) NOT NULL,

L3_Grade CHAR(2) NOT NULL,

… Table Enrollment Columns

PRIMARY KEY ( L1_CollNo, L2_StudtNo, L3_CourseNum))

ENGINE = SCALEDB;

Select L1_CollName, L2_StudName, L3_CourseName, L3_Grade

FROM sdb_view_college_course_student WHERE l1_CollNo = X AND l2_StudentNo = Y ;

The Multi-Table Index

Multi-Table Index appears to MySQL as a data table

ScaleDB does not maintain data file associated with the Multi-Table Index

For a query using virtual table, ScaleDB assembles the rows on the fly using the Multi-Table Index

ScaleDB indexes are different than B-tree indexes

ScaleDB indexes provide the same functionality as B-tree, plus…

o They maintain referential integrity with minimal overhead

o They allow you to search for the data and relationships

o They are much smaller in size

Demo Query with join

Query with Multi-Table Index

2nd node virtual table

Benchmarking ScaleDB Index

0

10

20

30

40

50

60

Engine X Join ScaleDB MTI ScaleDB 2 Nodes

Queries/Sec

Summary

ScaleDB Cluster

o Multiple ScaleDB instances share the same physical data.

o Connecting to the cluster is similar to connecting to a single node.

o For the application, the cluster appears as a single node.

o Transparent application failover

o Transparent Scalability

ScaleDB Indexes

o Provide the B-tree functionality

o High performance

Map relationships

Maintain referential integrity

Smaller footprint

Independent of the key size

ScaleDB Status and Product Availability

Started Beta Process

o We are looking for beta companies

Product launch is scheduled for June timeframe

Please talk to us if you are developer interested in working with ScaleDB

[email protected]

mailto:[email protected]