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Exercise 4 Conceptual and Logical Modelling

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ISIT312 Big Data Management
Exercise 4
Conceptual and Logical Modelling of Data Warehouse and more of Hive
In this exercise, you will learn how to use UMLet to create a conceptual and logical schema of a
data warehouse, how to create partitioned tables, how to create bucket tables, how to export and
import tables, and how to find a query processing plan in Hive.
Be careful when copying the Linux commands in this document to your working Terminal, because
it is error-prone. Maybe you should type those commands by yourself.
Prologue
Login to your system and start VirtualBox.
When ready start a virtual machine ISIT312-BigDataVM-07-SEP-2020.
(1) How to start UMLet ?
Find UMLet icon on desktop and use the icon to start UMLet 14.3.
(2) How to create a conceptual schema of a data warehouse ?
(2.1) Read he following specification of a sample data warehouse domain.
A multinational company consists of departments located in different countries. A department is
described by a name and mission statement. The employees work on the projects. A project is
described by a name and deadline. An employee is described by an employee number and full name.
Employees work on projects. When an employee completes a project then he/she is re-employed by a
company to work on another project. The company would like to record in a data warehouse
information about the total number of employees, length of each employment, total salary paid on
each employment per year, per month, per project, per department, per city and per country. For
example, it should be possible to find the projects and the total number of employees working on
each project, or the total number of employees employed in each in each month of a given year in
each department, or average salary in each month of each year and for each project etc.
(2.2) First, we identify a fact entity and the measures.
Consider the following fragment of a specification given above.
The company would like to record in a data warehouse information about the total number of
employees, length of each employment, total salary paid on each employment per year, per project,
per department, per city and per country.
The fragment contributes to a fact entity EMPLOYMENT described by the measures length and
salary.
To create a fact entity EMPLOYMENT drag a graphical component fact entity that looks like a
cube (Fact name and Measure1, Measure 2,…) from a panel with the graphical widgets to
the drawing area of UMLet. Next, change a name of fact from Fact name to EMPLOYMENT and
measures Measure1, Measure 2, … to length and salary (the measures are yellow
highlighted in a fragment of the specification above). To do so you have to leftclick at the fact entity
to make it blue and then modify its properties in a panel “Properties” in the right lower corner of
UMLet window.
(2.3) Next, we add the dimensions and attributes describing the entity types in dimensions.
The following fragment of specification indicates the existence of Time dimension that consists of
entity types MONTH and YEAR (see a yellow highlighted text in a fragment below).
The company would like to record in a data warehouse information about the total number of
employees, length of each employment, total salary paid on each employment per year, per month,
per project, per department, per city and per country.
First, we create Time dimension that consists of the entity types MONTH and YEAR. To do so drag
two entity type graphical components (Level name, attribute, …) from a panel with
graphical widgets to the drawing area of UMLet. Then, change the names of entity types to MONTH
and YEAR and connect the entities with one-to-many relationship graphical component,
Finally, use one-to-many relationship graphical component to connect MONTH entity to a
fact entity EMPLOYMENT.
Next, add an attribute name to an entity MONTH and attribute number to an entity YEAR. To do
so you have to leftclick at an entity to make it blue and then modify its properties in a panel
“Properties” in the right lower corner of UMLet window.
In the same way add three more dimensions: PROJECT, EMPLOYEE, and DEPARTMENT and
describe each entity type with the attributes listed in the specification (see below).
A project is described by a name and deadline.
An employee is described by an employee number and full name.
A department is described by a name and mission statement.
(2.4) Finally, we create the hierarchies over the dimensions.
In this step we create Location and Time hierarchies over the dimensions DEPARTMENT and
TIME(MONTH-YEAR). First add a hierarchy blob Criterion between entity type MONTH and
many-to-one relationship leading to an entity YEAR. Replace a text Criterion with a
text Time.
Next, create two more entity types CITY and COUNTRY and describe both of them with an
attribute name. Add one-to-many relationship between COUNTRY and CITY and oneto-many relationship between CITY and EMPLOYMENT.
Finally, to create Location hierarchy add a blob Criterion between entity DEPARTMENT and
many-to-one relationship leading to an entity CITY. Replace a text Criterion with a
text Location.
To save your design in a file employment.uxf use File->Save option from the main menu. To
create pdf file use File->Export as … option from the main menu. When creating pdf file
make sure that none of the graphical component in the main window of UMLet is blue highlighted !
(3) How to create a logical schema of a data warehouse ?
To create a logical schema (a collection of relational schemas) of a data warehouse we start from a
diagram of conceptual schema created in the previous step. First, we change in a panel located in
the right upper corner of UMLet window a collection of graphical widgets from “Conceptual
modeling” to “Logical modeling”.
Next, we add to each entity type, except fact entity EMPLOYMENT, a surrogate key. For example, we
add a surrogate key year_ID to entity YEAR, month_ID to entity MONTH, project_ID to
entity PROJECT, etc. We nominate all _ID attributes to be primary keys in the respective relational
tables. The names of relational tables remain the same as the names of the respective entity types.
Next, we migrate the primary keys from one side of one-to-many relationships to the relational
tables to many side of the relationships and we nominate the migrated _ID attribute as foreign
keys.
Next, we nominate a collection of all foreign keys in a table obtained from fact entity type
EMPLOYEMENT as a composite primary key in a relational table EMPLOYMENT.
Finally, we replace one-to-many relationships with arrows directed from the locations of
foreign keys to the locations of the respective primary keys. A logical schema can be saved and
exported in the same way as a conceptual schema.
(4) How to start Hadoop, Hive Server 2, beeline or SQL Developer ?
Open a new Terminal window and start Hadoop in the following way.
$HADOOP_HOME/sbin/.start-all.sh
When ready minimize the Terminal window used to start Hadoop. We shall refer to this window as
to “Hadoop window” and we shall use it later on.
When ready navigate to a folder where you plan to keep your HQL scripts from this lab (you may
have to create such folder now) and start Hive Server 2 in the following way.
To start Hive’s metastore service, open a Terminal window, type:
$HIVE_HOME/bin/hive –service metastore
A message shows that metastore is up and running:
SLF4J: Actual binding is of type
[org.apache.logging.slf4j.Log4jLoggerFactory]
To start hiveserver2, open another Terminal window and type:
$HIVE_HOME/bin/hiveserver2
The same message shows that hiveserver2 is up and running.
[IMPORTANT] Don’t use Zeppelin to run the above two commands, because the %sh interpreter
has a timeout threshold.
You can use Hive’s own interface to interact with Hive. Open another new Terminal window and
process the following command.
$HIVE_HOME/bin/beeline
Process the following cmmand in front of beeline> prompt.
!connect jdbc:hive2://localhost:10000
The statement connects you through JDBC interface to Hive 2 server running on a localhost and
listening to a port 10000.
Press Enter when prompted about user name. Press Enter when prompted about password. The
system should reply with a prompt
0: jdbc:hive2://localhost:10000>
To find what databases are available process the following command.
show databases;
At the moment only default database is available. We shall create new databases in the future.
To find what tables have been created so far process a statement
show tables;
If you plan to use SQL Developer then leftclick at SQL Developer Icon at the top of task bar. Then
rightclick at hive connection icon, pick “Connection” option from a menu, enter any user name
and password and leftclick at OK button.
We shall use a default database for Hive tables created in this laboratory exercise.
(5) How to create and load data into an internal table ?
Create the following internal table to store information about the items.
create table item(
code char(7),
name varchar(30),
brand varchar(30),
price decimal(8,2) )
row format delimited fields terminated by ‘,’
stored as textfile;
Next create a text file items.txt with sample data given below and save a file in a folder where
you plan to keep HQL scripts from this lab (you already started Hive Server 2 from this folder).
B000001,bolt,Golden Bolts,12.34
B000002,bolt,Platinum Parts,20.0
B000003,bolt,Unbreakable Spares,17.25
S000001,screw,Golden Bolts,45.00
S000002,screw,Platinum Parts,12.0
N000001,nut,Platinum Parts,21.99
When ready process the following HQL statement to load the contents of a file items.txt into
item table.
load data local inpath ‘items.txt’ into table item;
Verify the contents of a table item with a simple
select * from item;
(6) How to create a partitioned internal table ?
We would like to improve performance of processing a table item through partitioning We expect
that a lot of queries will retrieve only the items that have a given name, like for example a query
select min(price) from item where name =’bolt’;
Therefore, we shall create a new table pitem as a partitioned table. Process the following
statement to create a partitioned table pitem.
create table pitem(
code char(7),
brand varchar(30),
price decimal(8,2) )
partitioned by (name varchar(30))
row format delimited fields terminated by ‘,’
stored as textfile;
Note, that a column name has been removed from a list of columns and added as a partition key.
Next, we shall create the partitions for the values in a column name of item table. Process the
following alter table statements to create the partitions for bolt, screw, and nut.
alter table pitem add partition (name=’bolt’);
alter table pitem add partition (name=’screw’);
alter table pitem add partition (name=’nut’);
Next, process the following statement to check if all partitions have been created.
show partitions pitem;
Now, we shall copy data from a table item into a partitioned table pitem. Process the following
INSERT statements.
insert into table pitem partition (name=’bolt’) select code, brand,
price
from item
where name=’bolt’;
insert into table pitem partition (name=’screw’) select code,
brand, price
from item
where name=’screw’;
insert into table pitem partition (name=’nut’) select code, brand,
price
from item
where name=’nut’;
Finally, try
select * from pitem;
to check if all data has been copied.
(7) How to “explain” a query processing plan ?
Are there any differences in the ways how Hive processes an internal table and an internal
partitioned table ?
explain statement can be used to find the differences in processing of the following select
statements.
explain extended select * from item where name=’bolt’;
explain extended select * from pitem where name=’bolt’;
The first statement processed on a table item returns the following processing plan.
Explain
——————————————————————
——————————————————————
——————————————————————
———————————————————
STAGE DEPENDENCIES:
Stage-0 is a root stage

STAGE PLANS:
Stage: Stage-0
Fetch Operator
limit: -1
Processor Tree:
TableScan
alias: item
Statistics: Num rows: 1 Data size: 203 Basic stats:
COMPLETE Column stats: NONE
GatherStats: false
Filter Operator
isSamplingPred: false
predicate: (UDFToString(name) = ‘bolt’) (type: boolean)
Statistics: Num rows: 1 Data size: 203 Basic stats:
COMPLETE Column stats: NONE
Select Operator
expressions: code (type: char(7)), ‘bolt’ (type:
varchar(30)), brand (type: varchar(30)), price (type: decimal(8,2))
outputColumnNames: _col0, _col1, _col2, _col3
Statistics: Num rows: 1 Data size: 203 Basic stats:
COMPLETE Column stats: NONE
ListSink

22 rows selected.
The second statement processed on a partitioned table pitem returns a plan.
Explain
——————————————————————
——————————————————————
——————————————————————
———————————————————
STAGE DEPENDENCIES:
Stage-0 is a root stage

STAGE PLANS:
Stage: Stage-0
Fetch Operator
limit: -1
Partition Description:
Partition
input format: org.apache.hadoop.mapred.TextInputFormat
output format:
org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
partition values:
name bolt
properties:
COLUMN_STATS_ACCURATE {“BASIC_STATS”:”true”}
bucket_count -1
columns code,brand,price
columns.comments
columns.types char(7):varchar(30):decimal(8,2)
field.delim ,
file.inputformat
org.apache.hadoop.mapred.TextInputFormat
file.outputformat
org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
location
hdfs://localhost:8020/user/hive/warehouse/pitem/name=bolt
name default.pitem
numFiles 1
numRows 3
partition_columns name
partition_columns.types varchar(30)
rawDataSize 86
serialization.ddl struct pitem { char(7) code,
varchar(30) brand, decimal(8,2) price}
serialization.format ,
serialization.lib
org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
totalSize 89
transient_lastDdlTime 1534561429
serde:
org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe

input format:
org.apache.hadoop.mapred.TextInputFormat
output format:
org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
properties:
bucket_count -1
columns code,brand,price
columns.comments
columns.types char(7):varchar(30):decimal(8,2)
field.delim ,
file.inputformat
org.apache.hadoop.mapred.TextInputFormat
file.outputformat
org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
location
hdfs://localhost:8020/user/hive/warehouse/pitem
name default.pitem
partition_columns name
partition_columns.types varchar(30)
serialization.ddl struct pitem { char(7) code,
varchar(30) brand, decimal(8,2) price}
serialization.format ,
serialization.lib
org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
transient_lastDdlTime 1534561343
serde:
org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
name: default.pitem
name: default.pitem
Processor Tree:
TableScan
alias: pitem
Statistics: Num rows: 3 Data size: 86 Basic stats:
COMPLETE Column stats: NONE
GatherStats: false
Select Operator
expressions: code (type: char(7)), brand (type:
varchar(30)), price (type: decimal(8,2)), ‘bolt’ (type:
varchar(30))
outputColumnNames: _col0, _col1, _col2, _col3
Statistics: Num rows: 3 Data size: 86 Basic stats:
COMPLETE Column stats: NONE
ListSink

68 rows selected.
Comparison of Statistics: Num rows indicates a smaller amount of data processed in the
second case.
Statistics: Num rows: 1 Data size: 203 Basic stats: COMPLETE
Column stats: NONE
Statistics: Num rows: 3 Data size: 86 Basic stats: COMPLETE Column
stats: NON
(8) How partitions are implemented in HDFS ?
To find how partitions are implemented in HDFS move to “Hadoop window” and process the
following command.
$HADOOP_HOME/bin/hadoop fs -ls /user/hive/warehouse/pitem
The partitions of pitem tables are implemented as subfolders in
/user/hive/warehouse/pitem.
(8) How to create dynamically partitioned tables ?
The partitions of dynamically partitioned table are determined when data is loaded to the table.
Process the following statements to set the parameters that allow for dynamic partitioning.
set hive.exec.dynamic.partition.mode=nonstrict;
set hive.exec.max.dynamic.partitions=5;
Next, drop a table pitem and recreate it in the same way as at the beginning of step 6.
create table pitem(
code char(7),
brand varchar(30),
price decimal(8,2) )
partitioned by (name varchar(30))
row format delimited fields terminated by ‘,’
stored as textfile;
Next, process the following statement to dynamically the partitions and to copy the rows from a
table item into a table pitem.
insert overwrite table pitem partition(name)
select code, brand, price, name
from item;
Then process the statements to display the partitions and the contents of a table pitem.
show partitions pitem;
select * from pitem;
(9) How to create a bucket table ?
Another method to distribute a table in HDFS is to partition it into buckets. A bucket is equivalent to
a segment of file in HDFS. A column in a table can be selected to determine a distribution of rows
over buckets. The value of this column will be hashed into a number and a row will be stored in a
bucket with the same number. The rows with the same value in the selected column will be stored in
the same bucket. However, the same bucket may also contain the rows with other values from the
selected column. A concept of bucket is equivalent to a concept of clustered hash index on the
traditional relational database systems.
Process the following statement to create a table bitem distributed over two buckets.
create table bitem(
code char(7),
name varchar(30),
brand varchar(30),
price decimal(8,2) )
clustered by (name) into 2 buckets
row format delimited fields terminated by ‘,’
stored as textfile;
Process the following statement to copy the contents of a table item into a table bitem.
insert overwrite table bitem
select code, name, brand, price
from item;
Display a query processing plan for a bucket table.
explain extended select * from bitem where name=’bolt’;
Explain
——————————————————————
——————————————————————
——————————————————————
———————————————————
STAGE DEPENDENCIES:
Stage-0 is a root stage

STAGE PLANS:
Stage: Stage-0
Fetch Operator
limit: -1
Processor Tree:
TableScan
alias: bitem
Statistics: Num rows: 6 Data size: 199 Basic stats:
COMPLETE Column stats: NONE
GatherStats: false
Filter Operator
isSamplingPred: false
predicate: (UDFToString(name) = ‘bolt’) (type: boolean)
Statistics: Num rows: 3 Data size: 99 Basic stats:
COMPLETE Column stats: NONE
Select Operator
expressions: code (type: char(7)), ‘bolt’ (type:
varchar(30)), brand (type: varchar(30)), price (type: decimal(8,2))
outputColumnNames: _col0, _col1, _col2, _col3
Statistics: Num rows: 3 Data size: 99 Basic stats:
COMPLETE Column stats: NONE
ListSink
22 rows selected.
(10) How to export a table ?
To transfer Hive tables from one Hadoop installation to another one can use export and import
statements. To export an internal table item into a folder exp-item in HDFS process the
following statement.
export table item to ‘/tmp/exp-item’
To verify the storage structures created by export statement process the following commands in
“Hadoop window”.
$HADOOP_HOME/bin/hadoop fs -ls /tmp/exp*
$HADOOP_HOME/bin/hadoop fs -ls /tmp/exp-item/data
Just for fun you can also list the contents of metadata.
$HADOOP_HOME/bin/hadoop fs -cat /tmp/exp-item/_meta.
(11) How to import a table ?
Assume that we would like to import the contents of exported table item into a new table
imported_item. Process the following commands.
import table imported_item from ‘/tmp/exp-item’;
select * from imported_item;
End of Exercise 4

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