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Adbms final group project report

J
Jijo Johny

The document describes a database design project for an e-commerce system. It includes: 1) Logical and physical database designs showing the relationships between 11 tables. Table creation scripts are provided applying data integrity constraints. 2) Data generation and loading details where mock data was created and imported into the tables. 3) Seven SQL queries written to retrieve information from the tables such as most ordered inventory items, total shipments in a month, top selling items by price, top customers by purchase amount, orders with rejected payments, customers without orders, and users with many visits ordering high cost items. 4) Mention of a stored procedure being created to insert new inventory items with data validation checks.

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ISM6218.001F16 ADVANCED DATABSE MANAGEMENT SYSTEMS FINAL GROUP PROJECT “E-COMMERCE SYSTEMS” Group Members Himank Vats (U87964346) Jijo Johny (U12605351) Tess Thomas (U66417789) Vrishali Giri (U40856469)
CONTENTS Executive Summary Assumptions Part 1: Database Design Section 1.1: Data Integrity Section 1.2: Data Generation and Loading Section 1.3: Logical Database Design Section 1.3: Physical Database Design Part 2: Query Writing Section 2.1: SQL Queries Section 2.2: Database Programming Part 3: Performance Tuning Section 3.1: Indexing Section 3.2: Parallelism Part 4: Other Topics Section 4.1: DBA scripts Section 4.2: Database Security
Executive Summary The use of internet technology for performing business transaction has grown exponentially over the decade or so. Almost all companies have migrated their transactions to e-commerce and remaining ones are on their way to do so! While B2B ecommerce platforms are making billions of revenues, B2C has become successful in establishing good relation with huge consumer followers nonetheless. The rise of online shopping have turned shoppers into promiscuous deal-hunters armed with mobile technology that allows them to shop whenever they want, however they want with whomever they want.
The goal of this project is to build an E-Commerce system for online shoppers giving them flexibility to shop their favourite items without having to visit any shop. An e-commerce value chain represents a set of sequences which involves the interactions between online shoppers and e-commerce systems. We have focused on easy-to-understand user interface and query optimization in order to reduce the query processing time and give a user an hasslefree shopping experience. User role privileges are assigned in order to restrict the access between normal user and administrator. The system will generate up- to-date data based on number of items sold, availabe inventory and changes in prices. Below is the weights distribution according to the areas covered – Topic Area Description Points Database Design - Data Integrity - Data Generation and Loading - Logical Database Design - Physical Database Design 30 Query Writing - SQL Queries - Stored Procedure 25 Performance Tuning - Indexing - Parallelism 30 Other Topics - DBA scripts - Database security 15
Assumptions 1. INV_Item_Number from the Inventory Item table should be present in the Order Item but not as foreign key. Because if it is declared as foreign key then the referential integrity will be violated when INV_Item_Number from the Inventory Item table is deleted. 2. Join ORDER_ITEM and INVENTORY_ITEM by outer join rather than a natural join so that order item with deleted product will be included. 3. Credit card table has the 16-digit credit number as primary key this it is unique. 4. Order table provides detail on products and how many were ordered for a given invoice. 5. Residential and shipping address are stored for customers. 6. Customers can have multiple credit cards tied to their account. It can be different names and billing address. 7. Invoice contains the order details, shipping information and address of customer.
Part 1: Database Design Section 1.1 : Logical Database Design Below relational diagram depicts the interrelations between all the database tables. The se relationships are established by defining foreign key constraints.
Section 1.2 : Physical Database Design We have generated below relational diagram using SQL developer modeler function.
Section 1.3 : Data Integrity Below are the backend table creation queries which we have used. A. INVENTORY_ITEM CREATE TABLE "DB215"."INVENTORY_ITEM" ( "INV_ITEM" NUMBER(5,0) NOT NULL ENABLE, "INV_TITLE" VARCHAR2(6 BYTE) NOT NULL ENABLE, "INV_PRICE" NUMBER(5,0), "INV_TIMESTAMP" DATE, CONSTRAINT "INV_ITEM_PK" PRIMARY KEY ("INV_ITEM") );
B. ORDER_ITEM CREATE TABLE "DB215"."ORDER_ITEM" ( "ORDER_ITEM" NUMBER(5,0) NOT NULL ENABLE, "INV_ITEM" NUMBER(5,0) NOT NULL ENABLE, "ORDER_ID" NUMBER NOT NULL ENABLE, "ORDER_QUANTITY" NUMBER(5,0), "ORDER_AMOUNT" NUMBER(5,0), "ORDER_TIMESTAMP" DATE, "SHIPPING_NUMBER" NUMBER(5,0), CONSTRAINT "ORDER_ITEM_PK" PRIMARY KEY ("ORDER_ITEM") CONSTRAINT "INV_ITEM_FK" FOREIGN KEY ("INV_ITEM") REFERENCES "DB215"."INVENTORY_ITEM" ("INV_ITEM") ENABLE, CONSTRAINT "ORDER_ID_FK" FOREIGN KEY ("ORDER_ID") REFERENCES "DB215"."ORDERS" ("ORDER_ID") ENABLE, CONSTRAINT "SHIPPING_NUMBER_FK1" FOREIGN KEY ("SHIPPING_NUMBER") REFERENCES "DB215"."SHIPPING" ("SHIPPING_NUMBER") ENABLE );
C. INVOICE CREATE TABLE "DB215"."INVOICE" ( "INVOICE_NUMBER" NUMBER(5,0) NOT NULL ENABLE, "ORDER_ID" NUMBER NOT NULL ENABLE, "ADDRESS_NUMBER" NUMBER(5,0) NOT NULL ENABLE, "INVOICE_CREATION_DATE" DATE, "INVOICE_STATE" VARCHAR2(5 BYTE), "SHIPPING_NUMBER" NUMBER(5,0), CONSTRAINT "INVOICE_NUMBER_PK" PRIMARY KEY ("INVOICE_NUMBER") CONSTRAINT "ADDRESS_NUMBER_FK1" FOREIGN KEY ("ADDRESS_NUMBER") REFERENCES "DB215"."USER_INFO" ("ADDRESS_NUMBER") ENABLE, CONSTRAINT "ORDER_ID_FK2" FOREIGN KEY ("ORDER_ID") REFERENCES "DB215"."ORDERS" ("ORDER_ID") ENABLE, CONSTRAINT "SHIPPING_NUMBER_FK" FOREIGN KEY ("SHIPPING_NUMBER") REFERENCES "DB215"."SHIPPING" ("SHIPPING_NUMBER") ENABLE );
D. USER_SESSION CREATE TABLE "DB215"."USER_SESSION" ( "SESSION_ID" NUMBER(5,0) NOT NULL ENABLE, "SESSION_CLICKS" NUMBER(5,0), "SESSION_TIME" DATE, "SESSION_IPADDRESS" VARCHAR2(100 BYTE), "CART_ID" NUMBER(5,0) NOT NULL ENABLE, "ACC_NUMBER" NUMBER(5,0), CONSTRAINT "USER_SESSION_PK" PRIMARY KEY ("SESSION_ID") CONSTRAINT "CART_ID_FK" FOREIGN KEY ("CART_ID") REFERENCES "DB215"."SHOPPING_CART" ("CART_ID") ENABLE, CONSTRAINT "ACC_NUMBER_FK2" FOREIGN KEY ("ACC_NUMBER") REFERENCES "DB215"."USER_ACCOUNT" ("ACC_NUMBER") ENABLE );
E. USER_ACCOUNT CREATE TABLE "DB215"."USER_ACCOUNT" ( "ACC_NUMBER" NUMBER(5,0) NOT NULL ENABLE, "ACC_ID" VARCHAR2(50 BYTE) NOT NULL ENABLE, "ACC_PASSWORD" VARCHAR2(50 BYTE) NOT NULL ENABLE, "ACC_TIME" DATE, "ACC_VISITS" NUMBER(5,0), "ACC_N0_TRANS" NUMBER(5,0), "SESSION_ID" NUMBER(5,0) NOT NULL ENABLE, "USER_ROLE" VARCHAR2(15 BYTE), CONSTRAINT "USER_ACCOUNT_PK" PRIMARY KEY ("ACC_NUMBER") CONSTRAINT "SESSION_ID_FK" FOREIGN KEY ("SESSION_ID") REFERENCES "DB215"."USER_SESSION" ("SESSION_ID") ENABLE );
F. CREDIT_CARD CREATE TABLE "DB215"."CREDIT_CARD" ( "CC_NUMBER" NUMBER(16,0) NOT NULL ENABLE, "CC_HOLDER_NAME" VARCHAR2(50 BYTE) NOT NULL ENABLE, "CC_EXPIRY_DATE" DATE NOT NULL ENABLE, "ACC_NUMBER" NUMBER(6,0) NOT NULL ENABLE, "ADDRESS_NUMBER" NUMBER(6,0) NOT NULL ENABLE, CONSTRAINT "CREDIT_CARD_PK" PRIMARY KEY ("CC_NUMBER") CONSTRAINT "ACC_NUMBER_FK" FOREIGN KEY ("ACC_NUMBER") REFERENCES "DB215"."USER_ACCOUNT" ("ACC_NUMBER") ENABLE, CONSTRAINT "ADDRESS_NUMBER_FK" FOREIGN KEY ("ADDRESS_NUMBER") REFERENCES "DB215"."USER_INFO" ("ADDRESS_NUMBER") ENABLE );
G. SHOPPING_CART CREATE TABLE "DB215"."SHOPPING_CART" ( "CART_ID" NUMBER(5,0) NOT NULL ENABLE, "INV_ITEM" NUMBER(5,0) NOT NULL ENABLE, "ACTIVE" VARCHAR2(20 BYTE) NOT NULL ENABLE, "TIME_STAMP" DATE, "QTY" NUMBER(2,0) NOT NULL ENABLE, "SESSION_ID" NUMBER(5,0), CONSTRAINT "SHOPPING_CART_PK" PRIMARY KEY ("CART_ID") CONSTRAINT "INVENTORY_ITEM_FK1" FOREIGN KEY ("INV_ITEM") REFERENCES "DB215"."INVENTORY_ITEM" ("INV_ITEM") ENABLE, CONSTRAINT "SESSION_ID_FK1" FOREIGN KEY ("SESSION_ID") REFERENCES "DB215"."USER_SESSION" ("SESSION_ID") ENABLE );
H. ORDERS CREATE TABLE "DB215"."ORDERS" ( "ORDER_ID" NUMBER(5,0) NOT NULL ENABLE, "ORDER_DATE" DATE NOT NULL ENABLE, "TOTAL_AMT" NUMBER(5,0) NOT NULL ENABLE, "STATES" VARCHAR2(20 BYTE) NOT NULL ENABLE, "ACC_NUMBER" NUMBER(5,0), "SHIPPING_NUMBER" NUMBER(5,0), CONSTRAINT "ORDERS_PK" PRIMARY KEY ("ORDER_ID") CONSTRAINT "ACC_NUMBER_FK1" FOREIGN KEY ("ACC_NUMBER") REFERENCES "DB215"."USER_ACCOUNT" ("ACC_NUMBER") ENABLE, CONSTRAINT "SHIPPING_NUMBER_FK2" FOREIGN KEY ("SHIPPING_NUMBER") REFERENCES "DB215"."SHIPPING" ("SHIPPING_NUMBER") ENABLE );
I. PAYMENT CREATE TABLE "DB215"."PAYMENT" ( "PAYMENT_ID" NUMBER(5,0) NOT NULL ENABLE, "ORDER_ID" NUMBER(5,0) NOT NULL ENABLE, "CC_NUMBER" NUMBER(16,0) NOT NULL ENABLE, "AMOUNT" NUMBER(5,0), "STATES" VARCHAR2(20 BYTE), "TIME_STAMP" DATE, CONSTRAINT "PAYMENT_PK" PRIMARY KEY ("PAYMENT_ID") CONSTRAINT "PAYMENT_ORDERS_FK1" FOREIGN KEY ("ORDER_ID") REFERENCES "DB215"."ORDERS" ("ORDER_ID") ENABLE, CONSTRAINT "PAYMENT_CREDIT_CARD_FK2" FOREIGN KEY ("CC_NUMBER") REFERENCES "DB215"."CREDIT_CARD" ("CC_NUMBER") ENABLE );
J. USER_INFO CREATE TABLE "DB215"."USER_INFO" ( "ADDRESS_NUMBER" NUMBER(5,0) NOT NULL ENABLE, "FULLNAME" VARCHAR2(255 BYTE) NOT NULL ENABLE, "ADDRESS_LINE_1" VARCHAR2(255 BYTE) NOT NULL ENABLE, "ADDRESS_LINE_2" VARCHAR2(255 BYTE), "CITY" VARCHAR2(20 BYTE), "STATE" VARCHAR2(255 BYTE), "ZIP" NUMBER(5,0), "CC_NUMBER" NUMBER(16,0), CONSTRAINT "ADDRESS_NUMBER_PK" PRIMARY KEY ("ADDRESS_NUMBER") CONSTRAINT "CC_NUMBER_FK1" FOREIGN KEY ("CC_NUMBER") REFERENCES "DB215"."CREDIT_CARD" ("CC_NUMBER") ENABLE );
K. SHIPPING CREATE TABLE "DB215"."SHIPPING" ( "SHIPPING_NUMBER" NUMBER(5,0) NOT NULL ENABLE, "ORDER_ID" NUMBER(5,0) NOT NULL ENABLE, "INVOICE_NUMBER" NUMBER(5,0) NOT NULL ENABLE, "SHIPPING_METHOD" VARCHAR2(255 BYTE) NOT NULL ENABLE, "SHIPPING_CHARGE" NUMBER(5,0) NOT NULL ENABLE, "ZIP" NUMBER(5,0), "SHIPPING_DATE" DATE NOT NULL ENABLE, "ADDRESS_NUMBER" NUMBER(5,0), CONSTRAINT "SHIPPING_NUMBER_PK" PRIMARY KEY ("SHIPPING_NUMBER") CONSTRAINT "SHIPPING_ORDER_ID_FK" FOREIGN KEY ("ORDER_ID") REFERENCES "DB215"."ORDERS" ("ORDER_ID") ENABLE, CONSTRAINT "SHIP_INVOICE_NUMBER_FK" FOREIGN KEY ("INVOICE_NUMBER") REFERENCES "DB215"."INVOICE" ("INVOICE_NUMBER") ENABLE, CONSTRAINT "ADDRESS_NUMBER_FK2" FOREIGN KEY ("ADDRESS_NUMBER") REFERENCES "DB215"."USER_INFO" ("ADDRESS_NUMBER") ENABLE );
Section 1.4 : Data Generation and Loading A. Data Generation  The database design has 11 tables and the data is generate dfor all of them.  The user account table includes user_id, password, number of visits, total transaction amount.  Shipping information can be tracked form the shipping table.  Payment information can be tracked from the payment table.  The table user_session has 1000 rows rest all the tables 5000 rows each. The external data is created using: https://www.mockaroo.com http://www.yandataellan.com The user_session data has been created using the online generator as shown in the above snapshot. We can select the data type for each attribute value as specified in the data base design and download the csv format using the download button.
Here the session_id is created as random number ranging from 80000 to 87000, session_clicks as number ranging from 1 to 10, session time ranging from 11/10/2015 to 11/10/2016 and session_ip_address as ip address v4. Using this we can generate 1000 rows. Below is the screenshot for creating tables with 5000 rows each.
B. Data Loading All the required data is initially generated using the external data generator and is stored in the spreadsheets. Data is then imported to SQL developer using the import feature. Following are steps to import data to table: 1. Right click on the table and select “import data”. 2. Select the spread sheet containing data. 3. Select the format and spread sheet in the xls file (select headers). 4. Import method is insert. 5. Add all the columns required from file. 6. Map all the columns in spread sheet to columns in the database. 7. Verify parameters before import. 8. If the verification is successful, click finish to import the data. Below images shows the data import wizard and the way the data in imported into the INVENTORY_ITEM table.
Right click on the table and select “import data” Data Import Wizard
Data Preview in Data Import Wizard Choose Column in Data import Wizard
Column Definition in Data import Wizard Column Definition for Inventory_Item Table in Data import Wizard
Final Step in Data Import Wizard Data imported successful!
Data in the Inventory_Item Table
The number of record in each table is shown below: Table Number of Tuples INVENTORY_ITEM 5000 ORDER_ITEM 5000 INVOICE 5000 USER_SESSION 1000 USER_ACCOUNT 5000 CREDIT_CARD 5000 SHOPPING_CART 5000 ORDERS 5000 PAYMENT 5000 USER_INFO 5000 SHIPPING 5000
Part 2: Query Writing Section 2.1: SQL Queries Query 1: Inventory item which is most ordered. SELECT INVENTORY_ITEM.INV_TITLE AS NAME, SUM(SHOPPING_CART.QTY) AS TOTAL_QUANTITY FROM SHOPPING_CART INNER JOIN INVENTORY_ITEM ON SHOPPING_CART.INV_ITEM = INVENTORY_ITEM.INV_ITEM GROUP BY INVENTORY_ITEM.INV_TITLE ORDER BY SUM(SHOPPING_CART.QTY)DESC;
Query 2: Total shipments done in month of march. SELECT COUNT (SHIPPING_NUMBER) FROM SHIPPING WHERE SHIPPING_DATE BETWEEN '1/3/2016' AND '31/3/2016'; Query 3: Top selling items by Price. SELECT INVENTORY_ITEM.INV_TITLE AS NAME ,SUM(INVENTORY_ITEM.INV_PRICE) AS SALES, SUM(SHOPPING_CART.QTY) AS QUANTITY FROM SHOPPING_CART INNER JOIN INVENTORY_ITEM ON SHOPPING_CART.INV_ITEM = INVENTORY_ITEM.INV_ITEM GROUP BY INVENTORY_ITEM.INV_TITLE ORDER BY SUM(INVENTORY_ITEM.INV_PRICE)DESC;
Query 4: Top Customers by Purchase Amount SELECT UA.ACC_NUMBER,UA.ACC_ID, SUM(O.TOTAL_AMT) AS PURCHASES FROM ORDERS O INNER JOIN USER_ACCOUNT UA ON O.ACC_NUMBER=UA.ACC_NUMBER GROUP BY UA.ACC_NUMBER,UA.ACC_ID ORDER BY SUM(O.TOTAL_AMT)DESC;
Query 5: All the orders with rejected payment SELECT O.ORDER_ID, O.ORDER_DATE, O.TOTAL_AMT FROM ORDERS O JOIN PAYMENT P ON O.ORDER_ID=P.ORDER_ID WHERE P.STATES= 'FALSE' OR P.STATES='Failed' ORDER BY O.TOTAL_AMT;
Query 6: Customers who haven’t placed any orders so far SELECT UA.ACC_NUMBER, UA.ACC_ID FROM ORDERS O RIGHT OUTER JOIN USER_ACCOUNT UA ON O.ACC_Number=UA.ACC_NUMBER WHERE O.ACC_NUMBER IS NULL GROUP BY UA.ACC_NUMBER, UA.ACC_ID ORDER BY UA.ACC_NUMBER ;
Query 7: Users with more than 100 visits and have ordered high cost Items SELECT UA.ACC_NUMBER, UA.ACC_ID, II.INV_PRICE FROM USER_ACCOUNT UA JOIN USER_SESSION US ON UA.SESSION_ID = US.SESSION_ID JOIN SHOPPING_CART SC ON US.CART_ID = SC.CART_ID JOIN INVENTORY_ITEM II ON SC.INV_ITEM = II.INV_ITEM WHERE UA.ACC_VISITS > 100 ORDER BY II.INV_PRICE DESC;
Section 2.2: Database Programming / Stored Procedures Stored procedures in DBMS are group of SQL statements integrated with a programming block, which can be named and stored in backend. Stored procedures can be shared and invoked by their name from any program. Below stored procedure is created to insert new items in inventory. The procedure performs check on USER_ROLE value from table USER_ACCOUNT before inserting record into the table If the user is ‘Admin’, only then the insertion is allowed. Procedure will come out of the loop if the role is other than ‘Admin’. In other words, every time Administrator tries to insert new record in INVENTORY_ITEM table, procedure ‘UpdateInventory’ is invoked.
Part 3: Performance Tuning Section 3.1: Indexing Indexing is an essential part of performance tuning in database design. It helps improving the performance of query. We have used 2 types of indexing techniques while designing database - B-Tree and Bitmap Indexing. Before we begin explicitely creating indexes on tables, let us first take a look at current status of indexes in our database. The below query will extract the index structure from database schema. As we can see, currently we have only primary key index type which was assigned while creating tables. We will focus on tables those are densly populated and try to take a measurable stance to improve the query retrival time to some extent.
B-Tree Index Let us execute the below complex query without creating any explicit index on tables. As we can see, the query execution time is 0.062 seconds and the cost of query is 21.
We decided to create a B-Tree index on table ORDER_ITEM and on column ORDER_ID. Post index creation, we executed the same query again. We could see that this time the query took only 0.072 seconds to fetch data and query cost is reduced to 13.
B-trees are the most widely used index type and a ubiquitous structure in computer science. Let us now create Bitmap index on comparatively densed table and observe the outcome.
Bitmap Index Bitmap index structure uses bit-vector concept to indiacte which of the values occur in a row. Below query is executed without creating any index on tables used. As we can see, the query execution took 2.21 seconds with the cost of query as 31.
We have created bitmap index on CREDIT_CARD table as below. As we can see, the query execution time reduced to 0.21 secinds whereas query cost has reduced to 21.
Adbms final group project report
Section 3.2: Parallelism Parallel processing is used to speed up datbase operations. We can apply parallelism to a single query to attain high speed. Parallelism is a natural fit for relational database environment. Step 1: Run the query select acc_number,acc_password,acc_visits,session_id from user_account; Step 2: Alter the table to use parallelism. ALTER TABLE user_Account PARALLEL (DEGREE 4); Step 3: Run the query again and look at the new execution plan.
Adbms final group project report
Part 4: Other Topics Section 4.1: DBA scripts DBA scripts are an excellent ways to query data dictionary in order to better understand what’s happening inside the database engine. Below are few types of scripts an administrator can use in his routine checks. A. Script 1 The below query will give the active sessions of the database. This is useful to monitor t he database and to ensure the security of database. SELECT NVL(s.username, '(oracle)') AS username, s.osuser, s.sid, p.spid, s.serial#, s.lockwait, s.status, s.service_name, s.module, s.program,s.machine, TO_CHAR(s.logon_Time,'DD-MON-YYYY HH24:MI:SS') AS login_time FROM v$session s, v$process p WHERE s.paddr = p.addr ORDER BY login_time;
B. Script 2 The below script displays the free space per data file. This is useful for monitoring the datafiles when the storage is limited and this would help assimilate new datafiles. Select df.tablespace_name, df.file_name, df.bytes/1024/1024 total_size, nvl(fr.bytes/1024/1024,0) free_space, ((df.bytes-nvl(fr.bytes,0))/df.bytes)*100 pct_used from (select sum(bytes) bytes, file_id from dba_free_space group by file_id) fr, dba_data_files df where df.file_id = fr.file_id(+) order by 1, df.file_id;
C. Script 3 It shows the background proccess that are running currently in database instance. SELECT * FROM v$bgprocess WHERE PADDR <> '00' ORDER BY DESCRIPTION;
D. Script 4 This script displays the blocks and data size of users. SELECT OWNER, SUM(BLOCKS) TOTALBLOCKS, SUM(BYTES/(1024*1024)) TOTALMB FROM DBA_SEGMENTS GROUP BY OWNER ORDER BY totalblocks; E. Script 5 The below script displays the partitioning information. Partitioning helps in increasing the availability of mission critical databse. Critical tables or indexes are divided into partitions to reduce maintenance windows or recovery times or failures. SELECT p.table_name, p.partition_name,
p.tablespace_name, p.initial_extent, p.next_extent, p.num_rows, p.avg_row_len, p.pct_increase FROM dba_tab_partitions p ORDER BY p.table_name, p.partition_name; F. Script 6 This query displays the count fro the number of indexes for a particular user. SELECT COUNT(INDEX_NAME) AS "INDEXES COUNT", TABLE_OWNER FROM DBA_INDEXES GROUP BY TABLE_OWNER HAVING TABLE_OWNER LIKE '%DB215%';
G. Script 7 This script is to kill session. Before killing the session we have to identify the session that needs to be killed, if we kill the wrong session this can be very destructive. If a session belonging to a background process is killed this would cause an instance crash. To identify the session offending: SELECT s.inst_id, s.sid, s.serial#, p.spid, s.username, s.program FROM gv$session s JOIN gv$process p ON p.addr = s.paddr AND p.inst_id = s.inst_id WHERE s.type != 'BACKGROUND';
Adbms final group project report
Section 4.2: Database Security Data security will be applied to ensure that no unauthorized user can edit any critical data from the USER ACCOUNT, PAYMENT, INVENTORY ITEM, ORDER ITEM, ORDER DETAILS, SHIPPING, INVOICE, CREDIT CARD, SHOPPING CART, ADDRESS and USER SESSION tables. Roles will be assigned to all the players, security will be given to the users based on their roles. Sensitive and confidential data has to be encrypted. Encryption helps in preventing unauthorized access to both static as well as dynamic data even if the data is copied, backed up or stored offline. Data access is controlled rather than than database access. Data redaction strategies are another useful way of controlling access to data. It masks data at query time, so that the rules are aplied on the data being progressed. User authentication is a way to control database access in Oracle DBMS and other database systems. The three components are: users that must authenticate to access the database, system and object privileges to further control access and finally roles that can be used to organize groups of privileges. Privileges that are only necessary are given to other users. Without roles, granting privileges directly to individual and separate users would create a confusing and unwieldy network of access rights. A person with admin_role as ‘Administrator’ is only authorized to update all the information from all the tables.
Granting authentication for roles: Grant All on USER ACCOUNT for ‘Administrator’; Grant All on PAYMENT for ‘Administrator’; Grant All on INVENTORY ITEM for ‘Administrator’; Grant All on ORDER ITEM for ‘Administrator’; Grant All on ORDER DETAILS for ‘Administrator’; Grant All on SHIPPING for ‘Administrator’; Grant All on INVOICE for ‘Administrator’; Grant All on CREDIT CARD for ‘Administrator’; Grant All on SHOPPING CART for ‘Administrator’; Grant All on ADDRESS for ‘Administrator’; Grant All on USER SESSION for ‘Administrator’.

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Adbms final group project report

  • 1. ISM6218.001F16 ADVANCED DATABSE MANAGEMENT SYSTEMS FINAL GROUP PROJECT “E-COMMERCE SYSTEMS” Group Members Himank Vats (U87964346) Jijo Johny (U12605351) Tess Thomas (U66417789) Vrishali Giri (U40856469)
  • 2. CONTENTS Executive Summary Assumptions Part 1: Database Design Section 1.1: Data Integrity Section 1.2: Data Generation and Loading Section 1.3: Logical Database Design Section 1.3: Physical Database Design Part 2: Query Writing Section 2.1: SQL Queries Section 2.2: Database Programming Part 3: Performance Tuning Section 3.1: Indexing Section 3.2: Parallelism Part 4: Other Topics Section 4.1: DBA scripts Section 4.2: Database Security
  • 3. Executive Summary The use of internet technology for performing business transaction has grown exponentially over the decade or so. Almost all companies have migrated their transactions to e-commerce and remaining ones are on their way to do so! While B2B ecommerce platforms are making billions of revenues, B2C has become successful in establishing good relation with huge consumer followers nonetheless. The rise of online shopping have turned shoppers into promiscuous deal-hunters armed with mobile technology that allows them to shop whenever they want, however they want with whomever they want.
  • 4. The goal of this project is to build an E-Commerce system for online shoppers giving them flexibility to shop their favourite items without having to visit any shop. An e-commerce value chain represents a set of sequences which involves the interactions between online shoppers and e-commerce systems. We have focused on easy-to-understand user interface and query optimization in order to reduce the query processing time and give a user an hasslefree shopping experience. User role privileges are assigned in order to restrict the access between normal user and administrator. The system will generate up- to-date data based on number of items sold, availabe inventory and changes in prices. Below is the weights distribution according to the areas covered – Topic Area Description Points Database Design - Data Integrity - Data Generation and Loading - Logical Database Design - Physical Database Design 30 Query Writing - SQL Queries - Stored Procedure 25 Performance Tuning - Indexing - Parallelism 30 Other Topics - DBA scripts - Database security 15
  • 5. Assumptions 1. INV_Item_Number from the Inventory Item table should be present in the Order Item but not as foreign key. Because if it is declared as foreign key then the referential integrity will be violated when INV_Item_Number from the Inventory Item table is deleted. 2. Join ORDER_ITEM and INVENTORY_ITEM by outer join rather than a natural join so that order item with deleted product will be included. 3. Credit card table has the 16-digit credit number as primary key this it is unique. 4. Order table provides detail on products and how many were ordered for a given invoice. 5. Residential and shipping address are stored for customers. 6. Customers can have multiple credit cards tied to their account. It can be different names and billing address. 7. Invoice contains the order details, shipping information and address of customer.
  • 6. Part 1: Database Design Section 1.1 : Logical Database Design Below relational diagram depicts the interrelations between all the database tables. The se relationships are established by defining foreign key constraints.
  • 7. Section 1.2 : Physical Database Design We have generated below relational diagram using SQL developer modeler function.
  • 8. Section 1.3 : Data Integrity Below are the backend table creation queries which we have used. A. INVENTORY_ITEM CREATE TABLE "DB215"."INVENTORY_ITEM" ( "INV_ITEM" NUMBER(5,0) NOT NULL ENABLE, "INV_TITLE" VARCHAR2(6 BYTE) NOT NULL ENABLE, "INV_PRICE" NUMBER(5,0), "INV_TIMESTAMP" DATE, CONSTRAINT "INV_ITEM_PK" PRIMARY KEY ("INV_ITEM") );
  • 9. B. ORDER_ITEM CREATE TABLE "DB215"."ORDER_ITEM" ( "ORDER_ITEM" NUMBER(5,0) NOT NULL ENABLE, "INV_ITEM" NUMBER(5,0) NOT NULL ENABLE, "ORDER_ID" NUMBER NOT NULL ENABLE, "ORDER_QUANTITY" NUMBER(5,0), "ORDER_AMOUNT" NUMBER(5,0), "ORDER_TIMESTAMP" DATE, "SHIPPING_NUMBER" NUMBER(5,0), CONSTRAINT "ORDER_ITEM_PK" PRIMARY KEY ("ORDER_ITEM") CONSTRAINT "INV_ITEM_FK" FOREIGN KEY ("INV_ITEM") REFERENCES "DB215"."INVENTORY_ITEM" ("INV_ITEM") ENABLE, CONSTRAINT "ORDER_ID_FK" FOREIGN KEY ("ORDER_ID") REFERENCES "DB215"."ORDERS" ("ORDER_ID") ENABLE, CONSTRAINT "SHIPPING_NUMBER_FK1" FOREIGN KEY ("SHIPPING_NUMBER") REFERENCES "DB215"."SHIPPING" ("SHIPPING_NUMBER") ENABLE );
  • 10. C. INVOICE CREATE TABLE "DB215"."INVOICE" ( "INVOICE_NUMBER" NUMBER(5,0) NOT NULL ENABLE, "ORDER_ID" NUMBER NOT NULL ENABLE, "ADDRESS_NUMBER" NUMBER(5,0) NOT NULL ENABLE, "INVOICE_CREATION_DATE" DATE, "INVOICE_STATE" VARCHAR2(5 BYTE), "SHIPPING_NUMBER" NUMBER(5,0), CONSTRAINT "INVOICE_NUMBER_PK" PRIMARY KEY ("INVOICE_NUMBER") CONSTRAINT "ADDRESS_NUMBER_FK1" FOREIGN KEY ("ADDRESS_NUMBER") REFERENCES "DB215"."USER_INFO" ("ADDRESS_NUMBER") ENABLE, CONSTRAINT "ORDER_ID_FK2" FOREIGN KEY ("ORDER_ID") REFERENCES "DB215"."ORDERS" ("ORDER_ID") ENABLE, CONSTRAINT "SHIPPING_NUMBER_FK" FOREIGN KEY ("SHIPPING_NUMBER") REFERENCES "DB215"."SHIPPING" ("SHIPPING_NUMBER") ENABLE );
  • 11. D. USER_SESSION CREATE TABLE "DB215"."USER_SESSION" ( "SESSION_ID" NUMBER(5,0) NOT NULL ENABLE, "SESSION_CLICKS" NUMBER(5,0), "SESSION_TIME" DATE, "SESSION_IPADDRESS" VARCHAR2(100 BYTE), "CART_ID" NUMBER(5,0) NOT NULL ENABLE, "ACC_NUMBER" NUMBER(5,0), CONSTRAINT "USER_SESSION_PK" PRIMARY KEY ("SESSION_ID") CONSTRAINT "CART_ID_FK" FOREIGN KEY ("CART_ID") REFERENCES "DB215"."SHOPPING_CART" ("CART_ID") ENABLE, CONSTRAINT "ACC_NUMBER_FK2" FOREIGN KEY ("ACC_NUMBER") REFERENCES "DB215"."USER_ACCOUNT" ("ACC_NUMBER") ENABLE );
  • 12. E. USER_ACCOUNT CREATE TABLE "DB215"."USER_ACCOUNT" ( "ACC_NUMBER" NUMBER(5,0) NOT NULL ENABLE, "ACC_ID" VARCHAR2(50 BYTE) NOT NULL ENABLE, "ACC_PASSWORD" VARCHAR2(50 BYTE) NOT NULL ENABLE, "ACC_TIME" DATE, "ACC_VISITS" NUMBER(5,0), "ACC_N0_TRANS" NUMBER(5,0), "SESSION_ID" NUMBER(5,0) NOT NULL ENABLE, "USER_ROLE" VARCHAR2(15 BYTE), CONSTRAINT "USER_ACCOUNT_PK" PRIMARY KEY ("ACC_NUMBER") CONSTRAINT "SESSION_ID_FK" FOREIGN KEY ("SESSION_ID") REFERENCES "DB215"."USER_SESSION" ("SESSION_ID") ENABLE );
  • 13. F. CREDIT_CARD CREATE TABLE "DB215"."CREDIT_CARD" ( "CC_NUMBER" NUMBER(16,0) NOT NULL ENABLE, "CC_HOLDER_NAME" VARCHAR2(50 BYTE) NOT NULL ENABLE, "CC_EXPIRY_DATE" DATE NOT NULL ENABLE, "ACC_NUMBER" NUMBER(6,0) NOT NULL ENABLE, "ADDRESS_NUMBER" NUMBER(6,0) NOT NULL ENABLE, CONSTRAINT "CREDIT_CARD_PK" PRIMARY KEY ("CC_NUMBER") CONSTRAINT "ACC_NUMBER_FK" FOREIGN KEY ("ACC_NUMBER") REFERENCES "DB215"."USER_ACCOUNT" ("ACC_NUMBER") ENABLE, CONSTRAINT "ADDRESS_NUMBER_FK" FOREIGN KEY ("ADDRESS_NUMBER") REFERENCES "DB215"."USER_INFO" ("ADDRESS_NUMBER") ENABLE );
  • 14. G. SHOPPING_CART CREATE TABLE "DB215"."SHOPPING_CART" ( "CART_ID" NUMBER(5,0) NOT NULL ENABLE, "INV_ITEM" NUMBER(5,0) NOT NULL ENABLE, "ACTIVE" VARCHAR2(20 BYTE) NOT NULL ENABLE, "TIME_STAMP" DATE, "QTY" NUMBER(2,0) NOT NULL ENABLE, "SESSION_ID" NUMBER(5,0), CONSTRAINT "SHOPPING_CART_PK" PRIMARY KEY ("CART_ID") CONSTRAINT "INVENTORY_ITEM_FK1" FOREIGN KEY ("INV_ITEM") REFERENCES "DB215"."INVENTORY_ITEM" ("INV_ITEM") ENABLE, CONSTRAINT "SESSION_ID_FK1" FOREIGN KEY ("SESSION_ID") REFERENCES "DB215"."USER_SESSION" ("SESSION_ID") ENABLE );
  • 15. H. ORDERS CREATE TABLE "DB215"."ORDERS" ( "ORDER_ID" NUMBER(5,0) NOT NULL ENABLE, "ORDER_DATE" DATE NOT NULL ENABLE, "TOTAL_AMT" NUMBER(5,0) NOT NULL ENABLE, "STATES" VARCHAR2(20 BYTE) NOT NULL ENABLE, "ACC_NUMBER" NUMBER(5,0), "SHIPPING_NUMBER" NUMBER(5,0), CONSTRAINT "ORDERS_PK" PRIMARY KEY ("ORDER_ID") CONSTRAINT "ACC_NUMBER_FK1" FOREIGN KEY ("ACC_NUMBER") REFERENCES "DB215"."USER_ACCOUNT" ("ACC_NUMBER") ENABLE, CONSTRAINT "SHIPPING_NUMBER_FK2" FOREIGN KEY ("SHIPPING_NUMBER") REFERENCES "DB215"."SHIPPING" ("SHIPPING_NUMBER") ENABLE );
  • 16. I. PAYMENT CREATE TABLE "DB215"."PAYMENT" ( "PAYMENT_ID" NUMBER(5,0) NOT NULL ENABLE, "ORDER_ID" NUMBER(5,0) NOT NULL ENABLE, "CC_NUMBER" NUMBER(16,0) NOT NULL ENABLE, "AMOUNT" NUMBER(5,0), "STATES" VARCHAR2(20 BYTE), "TIME_STAMP" DATE, CONSTRAINT "PAYMENT_PK" PRIMARY KEY ("PAYMENT_ID") CONSTRAINT "PAYMENT_ORDERS_FK1" FOREIGN KEY ("ORDER_ID") REFERENCES "DB215"."ORDERS" ("ORDER_ID") ENABLE, CONSTRAINT "PAYMENT_CREDIT_CARD_FK2" FOREIGN KEY ("CC_NUMBER") REFERENCES "DB215"."CREDIT_CARD" ("CC_NUMBER") ENABLE );
  • 17. J. USER_INFO CREATE TABLE "DB215"."USER_INFO" ( "ADDRESS_NUMBER" NUMBER(5,0) NOT NULL ENABLE, "FULLNAME" VARCHAR2(255 BYTE) NOT NULL ENABLE, "ADDRESS_LINE_1" VARCHAR2(255 BYTE) NOT NULL ENABLE, "ADDRESS_LINE_2" VARCHAR2(255 BYTE), "CITY" VARCHAR2(20 BYTE), "STATE" VARCHAR2(255 BYTE), "ZIP" NUMBER(5,0), "CC_NUMBER" NUMBER(16,0), CONSTRAINT "ADDRESS_NUMBER_PK" PRIMARY KEY ("ADDRESS_NUMBER") CONSTRAINT "CC_NUMBER_FK1" FOREIGN KEY ("CC_NUMBER") REFERENCES "DB215"."CREDIT_CARD" ("CC_NUMBER") ENABLE );
  • 18. K. SHIPPING CREATE TABLE "DB215"."SHIPPING" ( "SHIPPING_NUMBER" NUMBER(5,0) NOT NULL ENABLE, "ORDER_ID" NUMBER(5,0) NOT NULL ENABLE, "INVOICE_NUMBER" NUMBER(5,0) NOT NULL ENABLE, "SHIPPING_METHOD" VARCHAR2(255 BYTE) NOT NULL ENABLE, "SHIPPING_CHARGE" NUMBER(5,0) NOT NULL ENABLE, "ZIP" NUMBER(5,0), "SHIPPING_DATE" DATE NOT NULL ENABLE, "ADDRESS_NUMBER" NUMBER(5,0), CONSTRAINT "SHIPPING_NUMBER_PK" PRIMARY KEY ("SHIPPING_NUMBER") CONSTRAINT "SHIPPING_ORDER_ID_FK" FOREIGN KEY ("ORDER_ID") REFERENCES "DB215"."ORDERS" ("ORDER_ID") ENABLE, CONSTRAINT "SHIP_INVOICE_NUMBER_FK" FOREIGN KEY ("INVOICE_NUMBER") REFERENCES "DB215"."INVOICE" ("INVOICE_NUMBER") ENABLE, CONSTRAINT "ADDRESS_NUMBER_FK2" FOREIGN KEY ("ADDRESS_NUMBER") REFERENCES "DB215"."USER_INFO" ("ADDRESS_NUMBER") ENABLE );
  • 19. Section 1.4 : Data Generation and Loading A. Data Generation  The database design has 11 tables and the data is generate dfor all of them.  The user account table includes user_id, password, number of visits, total transaction amount.  Shipping information can be tracked form the shipping table.  Payment information can be tracked from the payment table.  The table user_session has 1000 rows rest all the tables 5000 rows each. The external data is created using: https://www.mockaroo.com http://www.yandataellan.com The user_session data has been created using the online generator as shown in the above snapshot. We can select the data type for each attribute value as specified in the data base design and download the csv format using the download button.
  • 20. Here the session_id is created as random number ranging from 80000 to 87000, session_clicks as number ranging from 1 to 10, session time ranging from 11/10/2015 to 11/10/2016 and session_ip_address as ip address v4. Using this we can generate 1000 rows. Below is the screenshot for creating tables with 5000 rows each.
  • 21. B. Data Loading All the required data is initially generated using the external data generator and is stored in the spreadsheets. Data is then imported to SQL developer using the import feature. Following are steps to import data to table: 1. Right click on the table and select “import data”. 2. Select the spread sheet containing data. 3. Select the format and spread sheet in the xls file (select headers). 4. Import method is insert. 5. Add all the columns required from file. 6. Map all the columns in spread sheet to columns in the database. 7. Verify parameters before import. 8. If the verification is successful, click finish to import the data. Below images shows the data import wizard and the way the data in imported into the INVENTORY_ITEM table.
  • 22. Right click on the table and select “import data” Data Import Wizard
  • 23. Data Preview in Data Import Wizard Choose Column in Data import Wizard
  • 24. Column Definition in Data import Wizard Column Definition for Inventory_Item Table in Data import Wizard
  • 25. Final Step in Data Import Wizard Data imported successful!
  • 26. Data in the Inventory_Item Table
  • 27. The number of record in each table is shown below: Table Number of Tuples INVENTORY_ITEM 5000 ORDER_ITEM 5000 INVOICE 5000 USER_SESSION 1000 USER_ACCOUNT 5000 CREDIT_CARD 5000 SHOPPING_CART 5000 ORDERS 5000 PAYMENT 5000 USER_INFO 5000 SHIPPING 5000
  • 28. Part 2: Query Writing Section 2.1: SQL Queries Query 1: Inventory item which is most ordered. SELECT INVENTORY_ITEM.INV_TITLE AS NAME, SUM(SHOPPING_CART.QTY) AS TOTAL_QUANTITY FROM SHOPPING_CART INNER JOIN INVENTORY_ITEM ON SHOPPING_CART.INV_ITEM = INVENTORY_ITEM.INV_ITEM GROUP BY INVENTORY_ITEM.INV_TITLE ORDER BY SUM(SHOPPING_CART.QTY)DESC;
  • 29. Query 2: Total shipments done in month of march. SELECT COUNT (SHIPPING_NUMBER) FROM SHIPPING WHERE SHIPPING_DATE BETWEEN '1/3/2016' AND '31/3/2016'; Query 3: Top selling items by Price. SELECT INVENTORY_ITEM.INV_TITLE AS NAME ,SUM(INVENTORY_ITEM.INV_PRICE) AS SALES, SUM(SHOPPING_CART.QTY) AS QUANTITY FROM SHOPPING_CART INNER JOIN INVENTORY_ITEM ON SHOPPING_CART.INV_ITEM = INVENTORY_ITEM.INV_ITEM GROUP BY INVENTORY_ITEM.INV_TITLE ORDER BY SUM(INVENTORY_ITEM.INV_PRICE)DESC;
  • 30. Query 4: Top Customers by Purchase Amount SELECT UA.ACC_NUMBER,UA.ACC_ID, SUM(O.TOTAL_AMT) AS PURCHASES FROM ORDERS O INNER JOIN USER_ACCOUNT UA ON O.ACC_NUMBER=UA.ACC_NUMBER GROUP BY UA.ACC_NUMBER,UA.ACC_ID ORDER BY SUM(O.TOTAL_AMT)DESC;
  • 31. Query 5: All the orders with rejected payment SELECT O.ORDER_ID, O.ORDER_DATE, O.TOTAL_AMT FROM ORDERS O JOIN PAYMENT P ON O.ORDER_ID=P.ORDER_ID WHERE P.STATES= 'FALSE' OR P.STATES='Failed' ORDER BY O.TOTAL_AMT;
  • 32. Query 6: Customers who haven’t placed any orders so far SELECT UA.ACC_NUMBER, UA.ACC_ID FROM ORDERS O RIGHT OUTER JOIN USER_ACCOUNT UA ON O.ACC_Number=UA.ACC_NUMBER WHERE O.ACC_NUMBER IS NULL GROUP BY UA.ACC_NUMBER, UA.ACC_ID ORDER BY UA.ACC_NUMBER ;
  • 33. Query 7: Users with more than 100 visits and have ordered high cost Items SELECT UA.ACC_NUMBER, UA.ACC_ID, II.INV_PRICE FROM USER_ACCOUNT UA JOIN USER_SESSION US ON UA.SESSION_ID = US.SESSION_ID JOIN SHOPPING_CART SC ON US.CART_ID = SC.CART_ID JOIN INVENTORY_ITEM II ON SC.INV_ITEM = II.INV_ITEM WHERE UA.ACC_VISITS > 100 ORDER BY II.INV_PRICE DESC;
  • 34. Section 2.2: Database Programming / Stored Procedures Stored procedures in DBMS are group of SQL statements integrated with a programming block, which can be named and stored in backend. Stored procedures can be shared and invoked by their name from any program. Below stored procedure is created to insert new items in inventory. The procedure performs check on USER_ROLE value from table USER_ACCOUNT before inserting record into the table If the user is ‘Admin’, only then the insertion is allowed. Procedure will come out of the loop if the role is other than ‘Admin’. In other words, every time Administrator tries to insert new record in INVENTORY_ITEM table, procedure ‘UpdateInventory’ is invoked.
  • 35. Part 3: Performance Tuning Section 3.1: Indexing Indexing is an essential part of performance tuning in database design. It helps improving the performance of query. We have used 2 types of indexing techniques while designing database - B-Tree and Bitmap Indexing. Before we begin explicitely creating indexes on tables, let us first take a look at current status of indexes in our database. The below query will extract the index structure from database schema. As we can see, currently we have only primary key index type which was assigned while creating tables. We will focus on tables those are densly populated and try to take a measurable stance to improve the query retrival time to some extent.
  • 36. B-Tree Index Let us execute the below complex query without creating any explicit index on tables. As we can see, the query execution time is 0.062 seconds and the cost of query is 21.
  • 37. We decided to create a B-Tree index on table ORDER_ITEM and on column ORDER_ID. Post index creation, we executed the same query again. We could see that this time the query took only 0.072 seconds to fetch data and query cost is reduced to 13.
  • 38. B-trees are the most widely used index type and a ubiquitous structure in computer science. Let us now create Bitmap index on comparatively densed table and observe the outcome.
  • 39. Bitmap Index Bitmap index structure uses bit-vector concept to indiacte which of the values occur in a row. Below query is executed without creating any index on tables used. As we can see, the query execution took 2.21 seconds with the cost of query as 31.
  • 40. We have created bitmap index on CREDIT_CARD table as below. As we can see, the query execution time reduced to 0.21 secinds whereas query cost has reduced to 21.
  • 42. Section 3.2: Parallelism Parallel processing is used to speed up datbase operations. We can apply parallelism to a single query to attain high speed. Parallelism is a natural fit for relational database environment. Step 1: Run the query select acc_number,acc_password,acc_visits,session_id from user_account; Step 2: Alter the table to use parallelism. ALTER TABLE user_Account PARALLEL (DEGREE 4); Step 3: Run the query again and look at the new execution plan.
  • 44. Part 4: Other Topics Section 4.1: DBA scripts DBA scripts are an excellent ways to query data dictionary in order to better understand what’s happening inside the database engine. Below are few types of scripts an administrator can use in his routine checks. A. Script 1 The below query will give the active sessions of the database. This is useful to monitor t he database and to ensure the security of database. SELECT NVL(s.username, '(oracle)') AS username, s.osuser, s.sid, p.spid, s.serial#, s.lockwait, s.status, s.service_name, s.module, s.program,s.machine, TO_CHAR(s.logon_Time,'DD-MON-YYYY HH24:MI:SS') AS login_time FROM v$session s, v$process p WHERE s.paddr = p.addr ORDER BY login_time;
  • 45. B. Script 2 The below script displays the free space per data file. This is useful for monitoring the datafiles when the storage is limited and this would help assimilate new datafiles. Select df.tablespace_name, df.file_name, df.bytes/1024/1024 total_size, nvl(fr.bytes/1024/1024,0) free_space, ((df.bytes-nvl(fr.bytes,0))/df.bytes)*100 pct_used from (select sum(bytes) bytes, file_id from dba_free_space group by file_id) fr, dba_data_files df where df.file_id = fr.file_id(+) order by 1, df.file_id;
  • 46. C. Script 3 It shows the background proccess that are running currently in database instance. SELECT * FROM v$bgprocess WHERE PADDR <> '00' ORDER BY DESCRIPTION;
  • 47. D. Script 4 This script displays the blocks and data size of users. SELECT OWNER, SUM(BLOCKS) TOTALBLOCKS, SUM(BYTES/(1024*1024)) TOTALMB FROM DBA_SEGMENTS GROUP BY OWNER ORDER BY totalblocks; E. Script 5 The below script displays the partitioning information. Partitioning helps in increasing the availability of mission critical databse. Critical tables or indexes are divided into partitions to reduce maintenance windows or recovery times or failures. SELECT p.table_name, p.partition_name,
  • 48. p.tablespace_name, p.initial_extent, p.next_extent, p.num_rows, p.avg_row_len, p.pct_increase FROM dba_tab_partitions p ORDER BY p.table_name, p.partition_name; F. Script 6 This query displays the count fro the number of indexes for a particular user. SELECT COUNT(INDEX_NAME) AS "INDEXES COUNT", TABLE_OWNER FROM DBA_INDEXES GROUP BY TABLE_OWNER HAVING TABLE_OWNER LIKE '%DB215%';
  • 49. G. Script 7 This script is to kill session. Before killing the session we have to identify the session that needs to be killed, if we kill the wrong session this can be very destructive. If a session belonging to a background process is killed this would cause an instance crash. To identify the session offending: SELECT s.inst_id, s.sid, s.serial#, p.spid, s.username, s.program FROM gv$session s JOIN gv$process p ON p.addr = s.paddr AND p.inst_id = s.inst_id WHERE s.type != 'BACKGROUND';
  • 51. Section 4.2: Database Security Data security will be applied to ensure that no unauthorized user can edit any critical data from the USER ACCOUNT, PAYMENT, INVENTORY ITEM, ORDER ITEM, ORDER DETAILS, SHIPPING, INVOICE, CREDIT CARD, SHOPPING CART, ADDRESS and USER SESSION tables. Roles will be assigned to all the players, security will be given to the users based on their roles. Sensitive and confidential data has to be encrypted. Encryption helps in preventing unauthorized access to both static as well as dynamic data even if the data is copied, backed up or stored offline. Data access is controlled rather than than database access. Data redaction strategies are another useful way of controlling access to data. It masks data at query time, so that the rules are aplied on the data being progressed. User authentication is a way to control database access in Oracle DBMS and other database systems. The three components are: users that must authenticate to access the database, system and object privileges to further control access and finally roles that can be used to organize groups of privileges. Privileges that are only necessary are given to other users. Without roles, granting privileges directly to individual and separate users would create a confusing and unwieldy network of access rights. A person with admin_role as ‘Administrator’ is only authorized to update all the information from all the tables.
  • 52. Granting authentication for roles: Grant All on USER ACCOUNT for ‘Administrator’; Grant All on PAYMENT for ‘Administrator’; Grant All on INVENTORY ITEM for ‘Administrator’; Grant All on ORDER ITEM for ‘Administrator’; Grant All on ORDER DETAILS for ‘Administrator’; Grant All on SHIPPING for ‘Administrator’; Grant All on INVOICE for ‘Administrator’; Grant All on CREDIT CARD for ‘Administrator’; Grant All on SHOPPING CART for ‘Administrator’; Grant All on ADDRESS for ‘Administrator’; Grant All on USER SESSION for ‘Administrator’.