Data Partitioning Strategies in Parallel Database Systems
Partitioning the tables/databases is very important step in
parallelizing the database activities. By partitioning the data equally into
many different processors’ workload, we can achieve better performance (better
parallelism) of the whole system. We have the following types of fragmentation
(partitioning) techniques in Parallel database;
Horizontal Fragmentation – Partitioning the tables using the conditions
specified through WHERE clause of SQL
query to distribute bunch of tuples (records). For example, consider the
following schema;
STUDENT (Regno, SName, Address, Branch, Phone)
In this table, Branch column is used to store the academic branch in
which the student admitted in. suppose, there are various branches like ‘BTech
CIVIL’, ‘BTech MECH’, ‘BTech CSE’, and so on, then the following query would be
used to partition the table using horizontal partitioning.
SELECT * FROM student WHERE Branch = branch name;
In the result, there won’t be any changes in the schema of the table;
i.e the structure of the table is unaltered. Only, ‘BTech CIVIL’ students
fragmented into one partition, ‘BTech CSE’ students fragmented into one
partition and so on.
Vertical Fragmentation – Partitioning tables using the decomposition
rules to break and distribute the tables into multiple partitions vertically
(different schemas) is called Vertical fragmentation.
For example, if we would like to break STUDENT into different tables
like STUDENT(Regno, SName, Address, Branch) and STU_PHONE(Regno, Phone), i.e
into two different schema on columns is Vertical partitioning.
Among the above discussed fragmentation techniques, partitioning any
relation with respect to Parallel databases involves Horizontal Partitioning.
Horizontal data partition helps us to distribute the data into several
processors to execute queries on them simultaneously.
Partitioning Strategies:
There are various partitioning strategies proposed to manage the data
distribution into multiple processors evenly.
Let us assume that in our parallel database system we have n
processors P_{0}, P_{1}, P_{2}, …, P_{n1} and
n disks D_{0}, D_{1}, D_{2}, …, D_{n1} where
we partition our data. The value of n is chosen according to the degree of
parallelism required. The partitioning strategies are,
RoundRobin Partitioning
It is the
simplest form of partitioning strategy. Here, the data are distributed into
various disks in the fashion, first record into first disk, second record into
second disk, and so on. If the number of available disks n is 10, then first
record goes to D1 (1 mod 10 = 1), second record goes to D2 (2 mod 10 =2), and
so on and 10^{th} record goes to D0 (10 mod 10 = 0), 11^{th}
record goes to D1 (11 mod 10 = 1). This scheme distributes data evenly in all
the disks.
Hash Partitioning
This strategy
identifies one or more attributes as partitioning attributes (which is not
required in RoundRobin partitioning). We need a hash function which is
carefully chosen (careful to avoid data skewness) which takes the identified
partitioning attributes as input to hash function.
For example, consider the following table;
EMPLOYEE(ENo, EName, DeptNo, Salary, Age)
If we choose DeptNo
attribute as the partitioning attribute and if we have 10 disks to distribute
the data, then the following would be a hash function;
h(DeptNo) = DeptNo
mod 10
If we have 10 departments, then according to the hash function, all
the employees of department 1 will go into disk 1, department 2 to disk 2 and
so on.
As another example, if we choose the EName of the employees as partitioning attribute, then we could
have the following hash function;
h(EName) = (Sum of
ASCII value of every character in the name) mod n,
where n is the number of disks/partitions needed.
Range Partitioning
In Range Partitioning we identify one or more attributes as
partitioning attributes. Then we choose a range partition vector to partition
the table into n disks. The vector is the values present in the partitioning
attribute.
For example, for the EMPLOYEE relation given above, if the
partitioning attribute is Salary,
then the vector would be one as follows;
[5000, 15000, 30000],
where every value means the individual range of salaries. That is,
5000 represents the first range (0 – 5000), 15000 represents the range (5001 –
15000), 30000 represents the third range (15001 – 30000), and it includes the
final range which is (30001 – rest). Hence, the vector with 3 values represents
4 disks/partitions.
The above discussed partition strategies must be chosen carefully
according to the workload of your parallel database system. The workload may
involve many components like, which attribute is frequently used in any queries
as filtering condition, the number of records in a table, the size of the
database, approximate number of incoming queries in a day, etc.
Detailed Example:
Let us start with the following table Emp_table. Emp_table instance has 14 records and every record stores
information about the name of the employee; his/her work grade, and the
department name. Assume that we have 3 processors namely P_{0}, P_{1},
P_{2}, and 3 Disks associated with those 3 processors namely D_{0},
D_{1}, D_{2}.
Emp_table


ENAME

GRADE

DNAME

SMITH

1

RESEARCH

BLAKE

4

SALES

FORD

4

RESEARCH

KING

5

ACCOUNTING

SCOTT

4

RESEARCH

MILLER

2

ACCOUNTING

TURNER

3

SALES

WARD

2

SALES

MARTIN

2

SALES

ADAMS

1

RESEARCH

JONES

4

RESEARCH

JAMES

1

SALES

CLARK

4

ACCOUNTING

ALLEN

3

SALES

Table 1 – Emp_table
A. RoundRobin Partitioning:
In this strategy we partition records in a roundrobin manner using
the function i mod n, where i is the
record position in the table and n is the number of partitions/disks which is
in our case 3. On the application of partitioning technique, first record goes
into D1, second record goes into D2, third record goes into D0, fourth record
goes into D1, and so on. After distribution of records, we will get the
following partitions;
Emp_table_Partition0


ENAME

GRADE

DNAME

FORD

4

RESEARCH

MILLER

2

ACCOUNTING

MARTIN

2

SALES

JAMES

1

SALES

Table 2 – Records 3,
6, 9, 12 mod 3
Emp_table_Partition1


ENAME

GRADE

DNAME

SMITH

1

RESEARCH

KING

5

ACCOUNTING

TURNER

3

SALES

ADAMS

1

RESEARCH

CLARK

4

ACCOUNTING

Table 3 – Records 1,
4, 7, 10, 13 mod 3
Emp_table_Partition2


ENAME

GRADE

DNAME

BLAKE

4

SALES

SCOTT

4

RESEARCH

WARD

2

SALES

JONES

4

RESEARCH

ALLEN

3

SALES

Table 4 – Records 2,
5, 8, 11, 14 mod 3
B. Hash Partitioning:
Let us take GRADE attribute of
the Emp_table to explain Hash partitioning. Let us choose a hash function as
follows;
h(GRADE) = (GRADE mod n)
where GRADE is the value of GRADE
attribute of a record and n is number of partitions which is 3 in our case.
While applying the hash partitioning on GRADE, we will get the following
partitions of Emp_table. For example, the GRADE of ‘Smith’ is 1 and while
hashing the function shows partition 1 (i.e 1 mod 3 = 1). The GRADE of ‘Blake’
is 4, then (4 mod 3) directs to partition 1. The GRADE of ‘King’ is 5 which
directs to partition 2 (5 mod 3 = 2).
Emp_table_Partition0


ENAME

GRADE

DNAME

TURNER

3

SALES

ALLEN

3

SALES

Table 5 – GRADEs 3
mod 3
Emp_table_Partition1


ENAME

GRADE

DNAME

SMITH

1

RESEARCH

BLAKE

4

SALES

FORD

4

RESEARCH

SCOTT

4

RESEARCH

ADAMS

1

RESEARCH

JONES

4

RESEARCH

JAMES

1

SALES

CLARK

4

ACCOUNTING

Table 6 – GRADEs 1, 4
mod 3
Emp_table_Partition2


ENAME

GRADE

DNAME

KING

5

ACCOUNTING

MILLER

2

ACCOUNTING

WARD

2

SALES

MARTIN

2

SALES

Table 7 – GRADEs 2, 5
mod 3
C. Range Partitioning:
Let us consider GRADE of
Emp_table to partition under range partitioning. For applying range partition,
we need to first identify partitioning vector, [v0, v1, …, vn2]. Let us choose
the following vector as range partitioning vector for our case;
[2, 4]
According to the vector, the records having the GRADE value 2 and less will go into partition 0, greater than 2 and
less than or equal to 4 will go into partition 1, and all the other values
(greater than 4) will go into partition 2 as depicted in the following tables.
Emp_table_Partition0


ENAME

GRADE

DNAME

SMITH

1

RESEARCH

MILLER

2

ACCOUNTING

WARD

2

SALES

MARTIN

2

SALES

ADAMS

1

RESEARCH

JAMES

1

SALES

Table 8 – GRADE
values 1 and 2
Emp_table_Partition1


ENAME

GRADE

DNAME

BLAKE

4

SALES

FORD

4

RESEARCH

SCOTT

4

RESEARCH

TURNER

3

SALES

JONES

4

RESEARCH

CLARK

4

ACCOUNTING

ALLEN

3

SALES

Table 9 – GRADE
values 3 and 4
Emp_table_Partition2


ENAME

GRADE

DNAME

KING

5

ACCOUNTING

Table 10 – GRADE
value 5 and above