ScaleDB MySQL storage Engine Enabling high performance and scalability, using a Multi-Table Index, and a Shared-Disk Clustering Architecture by Moshe Shadmon moshe@scaledb.com

1. The ScaleDB
Storage Engine
Enabling high performance and
scalability, using a Multi-Table Index,
and a Shared-Disk Clustering
Architecture
Moshe Shadmon moshe@scaledb.com

2. 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

3. Overview
 Plug-in Storage Engine for MySQL
 Main Features:
o Shared-Disk Architecture
o Innovative Multi-Table Indexing
o Transactional
o Row-Level Locking
o 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

4. Shared-Disk vs. Shared-Nothing
 Manageability
 Adaptability
 Availability/Fault-Tolerance
 Scalability
 Performance
 Total Cost of Ownership (TCO)

5. Shared-Nothing:
Database
Instance 1 Table A
Table B
Table C
Database
Instance 1
Database
Instance 2
Database
Instance 3
Table A
Table B
Table C
Vertical Partitioning

6. 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

7. Shared-Nothing:
Horizontal Partitioning
name age salary
Bob 20 10K
Shideh 18 35K
Ted 50 60K
Kevin 62 120K
Angela 55 140K
Mike 45 90K
name age salary
Physical View
Partitioned
by Salary
Logical View
name age salary
Ted 50 60K
Kevin 62 120K
Mike 46 90K
Bob 20 10K
name age salary
Shideh 18 35K
Angela 55 140K
Horizontal Partitioning – Salary % 3

8. Shared-Nothing:
Horizontal Partitioning Pitfalls
 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

9. Shared-Disk:
No Partitioning, Full Access to Data
DB Cluster
Node 1
DB Cluster
Node 2
DB Cluster
Node 3
Table A
Table B
Table C
Shared Disk
Subsystem
High-Speed Interconnect
Database
Instance 1 Table A
Table B
Table C

10. Slave A
Scalability & Availability
Slave B
Shared Nothing
Slave C

11. Scalability & Availability
Shared Disk
Node A
Node B
Node C
MySQL Servers
with ScaleDB
Engine
Data
Node D
Node E

12. Shared-Disk:
Summarizing Shared-Disk Benefits
 Grow by simply adding nodes to the cluster
o Servers can be added and removed dynamically
according to your needs
o No interruption to your application
 High-Availability with dynamic failover
o Existing nodes automatically take over
 Significantly reduced maintenance costs
o Can be built on low-cost commodity hardware
o No data partitioning
o No need for slaves
 Low Total Cost of Ownership (TCO)

13. Shared-Disk:
Making it work with MySQL
NNooddee 11
ScaleDB Engine
Instance A
Cluster
Manager
Buffer Manager
Comm.
Layer
Server
Instance A
NNooddee 22
Server
Instance B
ScaleDB Engine
Instance B
Cluster
Manager
Comm.
Layer
Buffer Manager
CClluusstteerr IInntteerrccoonnnneecctt
SShhaarreedd DDiisskk SSuubb--ssyysstteemm

14. Shared-Disk: Insert New Row
ScaleDB Engine
Instance A
Cluster
Manager
Buffer Manager
Comm.
Layer
NNooddee 11
Server
Instance A
NNooddee 22
Server
Instance B
ScaleDB Engine
Instance B
Cluster
Manager
Comm.
Layer
Buffer Manager
CClluusstteerr IInntteerrccoonnnneecctt
SShhaarreedd DDiisskk SSuubb--ssyysstteemm

15. ScaleDB Engine
Instance A
Cluster
Manager
Buffer Manager
Comm.
Layer
NNooddee 11
Server
Instance A
NNooddee 22
Server
Instance B
ScaleDB Engine
Instance B
Cluster
Manager
Comm.
Layer
Buffer Manager
Shared-Disk: Select
CClluusstteerr IInntteerrccoonnnneecctt
SShhaarreedd DDiisskk SSuubb--ssyysstteemm

16. Shared-Disk: Create Table
ScaleDB Engine
Instance A
Cluster
Manager
Buffer Manager
Comm.
Layer
NNooddee 11
Server
Instance A
NNooddee 22
Server
Instance B
ScaleDB Engine
Instance B
Cluster
Manager
Comm.
Layer
Buffer Manager
CClluusstteerr IInntteerrccoonnnneecctt
SShhaarreedd DDiisskk SSuubb--ssyysstteemm
Table A
Table A Meta-Data
Meta-Data

17. 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/

18. Demo - Sysbench
 ScaleDB cluster – one node – show throughput
 ScaleDB cluster – 2nd node – show throughput

19. ScaleDB: Multi-Table Indexing
B-tree: Only indexes the data in tables
Index
#1
#1 #2
Index
#2
Index
#3
Index
#4
Index
#5
#3 #4 #5
ScaleDB: Indexes the data and relationships
ScaleDB
Index
#1
#2
#3
#4
#5
Advantages:
• Faster
• Smaller
• Referential integrity

20. 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 ;

21. Option #1: Conventional Joins
College Table
ID 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
Students Table
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
Enrollment Table
College ID Course Name Student Grade
510 C67 Mathematics 4435 87
167 C123 History 1 1129 70
167 C14 Photography 1 1120 88
Get College information
Get Student information
Search enrollment by College & Student

22. 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 …

23. Option #3: Multi-Table Index
Colleges
Col_ID# Col_Name Col_Budget Col_Description
Coll_ID# Coll_Name Coll_Budget Coll_Description Student_ID# College_ID# Student_Name Student_Desc College_ID# Dept_ID# Student_ID# Grade
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
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CCoouursrseess
ScaleDB Multi-Table Index
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24. 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 – Department
Department
 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

25. Mapping Foreign Keys to Data Views
Students
Course
Enrollment
College Department
College Department
College
College Students
ScaleDB
Physical files:
1. College
2. Department
3. Student
4. Course
5. Enrollment
Meta-Data Tables:
1. College
2. College-Dept
3. College-Dept-Course
4. College-Students
5. College-Students-Enrollment
6. Department
7. Students
8. Course
9. Enrollment

26. Enabling the MySQL optimizer to
use a Multi-Table Index
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 ;
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 ;

27. 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

28. Demo
 Query with join
 Query with Multi-Table Index
 2nd node virtual table

29. Benchmarking ScaleDB Index
Queries/Sec
60
50
40
30
20
10
0
Engine X Join ScaleDB MTI ScaleDB 2 Nodes

30. 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

31. 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
moshe@scaledb.com