Combining the NoSQL(Cosmos DB) & traditional Azure RDBMS in Azure (Time stone solo from Python verse)

Hi Guys!

Today, our main objective is to extend our last post & blending two different kinds of data using Python.

Please refer the earlier post if you didn’t go through it – “Building Azure cosmos application.“.

What is the Objective?

In this post, our objective is to combine traditional RDBMS from the cloud with Azure’s NO SQL, which is, in this case, is Cosmos DB. And, try to forecast some kind of blended information, which can be aggregated further.

Examining Source Data.

No SQL Data from Cosmos:

Let’s check one more time the No SQL data created in our last post.

CosmosData

Total, we’ve created 6 records in our last post.

As you can see in red marked areas. From item, one can check the total number of records created. You can also filter out specific record using the Edit Filter blue color button highlighted with blue box & you need to provide the “WHERE CLAUSE” inside it.

Azure SQL DB:

Let’s create some data in Azure SQL DB.

But, before that, you need to create SQL DB in the Azure cloud. Here is the official Microsoft link to create DB in Azure. You can refer to it here.

I won’t discuss the detailed steps of creating DB here.

From Azure portal, it looks like –

Azure SQL DB Main Screen

Let’s see how the data looks like in Azure DB. For our case, we’ll be using the hrMaster DB.

Let’s create the table & some sample data aligned as per our cosmos data.

Azure SQL DB

We will join both the data based on subscriberId & then extract our required columns in our final output.

CombinedData

Good. Now, we’re ready for python scripts.

Python Scripts:

In this installment, we’ll be reusing the following python scripts, which is already discussed in my earlier post –

  • clsL.py
  • clsColMgmt.py
  • clsCosmosDBDet.py

So, I’m not going to discuss these scripts.

Before we discuss our scripts, let’s look out the directory structures –

Win_Vs_MAC

Here is the detailed directory structure between the Windows & MAC O/S.

1. clsConfig.py (This script will create the split csv files or final merge file after the corresponding process. However, this can be used as usual verbose debug logging as well. Hence, the name comes into the picture.)

##############################################
#### Written By: SATYAKI DE               ####
#### Written On: 25-May-2019              ####
#### Updated On: 02-Jun-2019              ####
####                                      ####
#### Objective: This script is a config   ####
#### file, contains all the keys for      ####
#### azure cosmos db. Application will    ####
#### process these information & perform  ####
#### various CRUD operation on Cosmos DB. ####
##############################################

import os
import platform as pl

class clsConfig(object):
    Curr_Path = os.path.dirname(os.path.realpath(__file__))
    db_name = 'rnd-de01-usw2-vfa-cdb'
    db_link = 'dbs/' + db_name
    CONTAINER1 = "RealtimeEmail"
    CONTAINER2 = "RealtimeTwitterFeedback"
    CONTAINER3 = "RealtimeHR"

    os_det = pl.system()
    if os_det == "Windows":
        sep = '\\'
    else:
        sep = '/'

    config = {
        'SERVER': 'xxxx-xxx.database.windows.net',
        'DATABASE_1': 'SalesForceMaster',
        'DATABASE_2': 'hrMaster',
        'DATABASE_3': 'statMaster',
        'USERNAME': 'admin_poc_dev',
        'PASSWORD': 'xxxxx',
        'DRIVER': '{ODBC Driver 17 for SQL Server}',
        'ENV': 'pocdev-saty',
        'ENCRYPT_FLAG': "yes",
        'TRUST_FLAG': "no",
        'TIMEOUT_LIMIT': "30",
        'PROCSTAT': "'Y'",
        'APP_ID': 1,
        'EMAIL_SRC_JSON_FILE': Curr_Path + sep + 'src_file' + sep + 'srcEmail.json',
        'TWITTER_SRC_JSON_FILE': Curr_Path + sep + 'src_file' + sep + 'srcTwitter.json',
        'HR_SRC_JSON_FILE': Curr_Path + sep + 'src_file' + sep + 'srcHR.json',
        'COSMOSDB_ENDPOINT': 'https://rnd-de01-usw2-vfa-cdb.documents.azure.com:443/',
        'CONFIG_TABLE': 'ETL_CONFIG_TAB',
        'COSMOS_PRIMARYKEY': "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXIsI00AxKXXXXXgg==",
        'ARCH_DIR': Curr_Path + sep + 'arch' + sep,
        'COSMOSDB': db_name,
        'COSMOS_CONTAINER1': CONTAINER1,
        'COSMOS_CONTAINER2': CONTAINER2,
        'COSMOS_CONTAINER3': CONTAINER3,
        'CONFIG_ORIG': 'Config_orig.csv',
        'ENCRYPT_CSV': 'Encrypt_Config.csv',
        'DECRYPT_CSV': 'Decrypt_Config.csv',
        'PROFILE_PATH': Curr_Path + sep + 'profile' + sep,
        'LOG_PATH': Curr_Path + sep + 'log' + sep,
        'REPORT_PATH': Curr_Path + sep + 'report',
        'APP_DESC_1': 'Feedback Communication',
        'DEBUG_IND': 'N',
        'INIT_PATH': Curr_Path,
        'SQL_QRY_1': "SELECT c.subscriberId, c.sender, c.orderNo, c.orderDate, c.items.orderQty  FROM RealtimeEmail c",
        'SQL_QRY_2': "SELECT c.twitterId, c.Twit, c.DateCreated, c.Country FROM RealtimeTwitterFeedback c WHERE c.twitterId=@CrVal",
        'DB_QRY': "SELECT * FROM c",
        'AZURE_SQL_1': "SELECT DISTINCT subscriberId, state, country, annualIncome, customerType FROM dbo.onboardCustomer",
        'COLLECTION_QRY': "SELECT * FROM r",
        'database_link': db_link,
        'collection_link_1': db_link + '/colls/' + CONTAINER1,
        'collection_link_2': db_link + '/colls/' + CONTAINER2,
        'collection_link_3': db_link + '/colls/' + CONTAINER3,
        'options': {
            'offerThroughput': 1000,
            'enableCrossPartitionQuery': True,
            'maxItemCount': 2
        }
    }

Here, we’ve added a couple of more entries compared to the last time, which points the detailed configuration for Azure SQL DB.

‘SERVER’: ‘xxxx-xxx.database.windows.net’,
‘DATABASE_1’: ‘SalesForceMaster’,
‘DATABASE_2’: ‘hrMaster’,
‘DATABASE_3’: ‘statMaster’,
‘USERNAME’: ‘admin_poc_dev’,
‘PASSWORD’: ‘xxxxx’,
‘DRIVER’: ‘{ODBC Driver 17 for SQL Server}’,
‘ENV’: ‘pocdev-saty’,
‘ENCRYPT_FLAG’: “yes”,
‘TRUST_FLAG’: “no”,
‘TIMEOUT_LIMIT’: “30”,
‘PROCSTAT’: “‘Y'”, 

Here, you need to supply your DB credentials accordingly.

2. clsDBLookup.py (This script will look into the Azure SQL DB & fetch data from the traditional RDBMS of Azure environment.)

#####################################################
#### Written By: SATYAKI DE                      ####
#### Written On: 25-May-2019                     ####
####                                             ####
#### Objective: This script will check &         ####
#### test the connection with the Azure          ####
#### SQL DB & it will fetch all the records      ####
#### name resied under the same DB of a table.   ####
#####################################################

import pyodbc as py
import pandas as p
from clsConfig import clsConfig as cdc

class clsDBLookup(object):
    def __init__(self, lkpTableName = ''):
        self.server = cdc.config['SERVER']
        self.database = cdc.config['DATABASE_1']
        self.database1 = cdc.config['DATABASE_2']
        self.database2 = cdc.config['DATABASE_3']
        self.username = cdc.config['USERNAME']
        self.password = cdc.config['PASSWORD']
        self.driver = cdc.config['DRIVER']
        self.env = cdc.config['ENV']
        self.encrypt_flg = cdc.config['ENCRYPT_FLAG']
        self.trust_flg = cdc.config['TRUST_FLAG']
        self.timeout_limit = cdc.config['TIMEOUT_LIMIT']
        self.lkpTableName = cdc.config['CONFIG_TABLE']
        self.ProcStat = cdc.config['PROCSTAT']
        self.AppId = cdc.config['APP_ID']

    def LookUpData(self):
        try:
            # Assigning all the required values
            server = self.server
            database = self.database1
            username = self.username
            password = self.password
            driver = self.driver
            env = self.env
            encrypt_flg = self.encrypt_flg
            trust_flg = self.trust_flg
            timout_limit = self.timeout_limit
            lkpTableName = self.lkpTableName
            ProcStat = self.ProcStat
            AppId = self.AppId

            # Creating secure connection
            str_conn = 'Driver=' + driver + ';Server=tcp:' + server + ',1433;' \
                       'Database=' + database + ';Uid=' + username + '@' + env + ';' \
                       'Pwd=' + password + ';Encrypt=' + encrypt_flg + ';' \
                       'TrustServerCertificate=' + trust_flg + ';Connection Timeout=' + timout_limit + ';'

            db_con_azure = py.connect(str_conn)

            query = " SELECT [ruleId] as ruleId, [ruleName] as ruleName, [ruleSQL] as ruleSQL, " \
                    " [ruleFlag] as ruleFlag, [appId] as appId, [DBType] as DBType, " \
                    " [DBName] as DBName FROM [dbo][" + lkpTableName + "] WHERE ruleFLag = " + ProcStat + " " \
                    " and appId = " + AppId + " ORDER BY ruleId "

            df = p.read_sql(query, db_con_azure)

            # Closing the connection
            db_con_azure.close()

            return df
        except Exception as e:
            x = str(e)
            print(x)
            df = p.DataFrame()

            return df

    def azure_sqldb_read(self, sql):
        try:
            # Assigning all the required values
            server = self.server
            database = self.database1
            username = self.username
            password = self.password
            driver = self.driver
            env = self.env
            encrypt_flg = self.encrypt_flg
            trust_flg = self.trust_flg
            timout_limit = self.timeout_limit
            lkpTableName = self.lkpTableName
            ProcStat = self.ProcStat
            AppId = self.AppId

            # Creating secure connection
            str_conn = 'Driver=' + driver + ';Server=tcp:' + server + ',1433;' \
                       'Database=' + database + ';Uid=' + username + '@' + env + ';' \
                       'Pwd=' + password + ';Encrypt=' + encrypt_flg + ';' \
                       'TrustServerCertificate=' + trust_flg + ';Connection Timeout=' + timout_limit + ';'

            # print("Connection Details:: ", str_conn)
            db_con_azure = py.connect(str_conn)

            query = sql

            df = p.read_sql(query, db_con_azure)

            # Closing the connection
            db_con_azure.close()

            return df
        except Exception as e:
            x = str(e)
            print(x)
            df = p.DataFrame()

            return df

Major lines to discuss –

azure_sqldb_read(self, sql):

Getting the source SQL supplied from the configuration script.

db_con_azure = py.connect(str_conn)

query = sql

df = p.read_sql(query, db_con_azure)

After creating a successful connection, our application will read the SQL & fetch the data & store that into a pandas dataframe and return the output to the primary calling function.

3. callCosmosAPI.py (This is the main script, which will call all the methods to blend the data. Hence, the name comes into the picture.)

##############################################
#### Written By: SATYAKI DE               ####
#### Written On: 25-May-2019              ####
#### Modified On 02-Jun-2019              ####
####                                      ####
#### Objective: Main calling scripts.     ####
##############################################

import clsColMgmt as cm
import clsCosmosDBDet as cmdb
from clsConfig import clsConfig as cf
import pandas as p
import clsLog as cl
import logging
import datetime
import json
import clsDBLookup as dbcon

# Disbling Warning
def warn(*args, **kwargs):
    pass

import warnings
warnings.warn = warn

def getDate(row):
    try:
        d1 = row['orderDate']
        d1_str = str(d1)
        d1_dt_part, sec = d1_str.split('.')
        dt_part1 = d1_dt_part.replace('T', ' ')

        return dt_part1
    except Exception as e:
        x = str(e)
        print(x)
        dt_part1 = ''

        return dt_part1

# Lookup functions from
# Azure cloud SQL DB

var = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")

def main():
    try:
        df_ret = p.DataFrame()
        df_ret_2 = p.DataFrame()
        df_ret_2_Mod = p.DataFrame()

        debug_ind = 'Y'

        # Initiating Log Class
        l = cl.clsLog()

        general_log_path = str(cf.config['LOG_PATH'])

        # Enabling Logging Info
        logging.basicConfig(filename=general_log_path + 'consolidated.log', level=logging.INFO)

        # Moving previous day log files to archive directory
        arch_dir = cf.config['ARCH_DIR']
        log_dir = cf.config['LOG_PATH']

        print("Archive Directory:: ", arch_dir)
        print("Log Directory::", log_dir)

        print("*" * 157)
        print("Testing COSMOS DB Connection!")
        print("*" * 157)

        # Checking Cosmos DB Azure
        y = cmdb.clsCosmosDBDet()
        ret_val = y.test_db_con()

        if ret_val == 0:
            print()
            print("Cosmos DB Connection Successful!")
            print("*" * 157)
        else:
            print()
            print("Cosmos DB Connection Failure!")
            print("*" * 157)
            raise Exception

        print("*" * 157)

        # Accessing from Azure SQL DB
        x1 = dbcon.clsDBLookup()
        act_df = x1.azure_sqldb_read(cf.config['AZURE_SQL_1'])

        print("Azure SQL DB::")
        print(act_df)
        print()

        print("-" * 157)

        # Calling the function 1
        print("RealtimeEmail::")

        # Fetching First collection data to dataframe
        print("Fethcing Comos Collection Data!")

        sql_qry_1 = cf.config['SQL_QRY_1']
        msg = "Documents generatd based on unique key"
        collection_flg = 1

        x = cm.clsColMgmt()
        df_ret = x.fetch_data(sql_qry_1, msg, collection_flg)

        l.logr('1.EmailFeedback_' + var + '.csv', debug_ind, df_ret, 'log')
        print('RealtimeEmail Data::')
        print(df_ret)
        print()

        # Checking execution status
        ret_val = int(df_ret.shape[0])

        if ret_val == 0:
            print("Cosmos DB Hans't returned any rows. Please check your queries!")
            print("*" * 157)
        else:
            print("Successfully fetched!")
            print("*" * 157)

        # Calling the 2nd Collection
        print("RealtimeTwitterFeedback::")

        # Fetching First collection data to dataframe
        print("Fethcing Cosmos Collection Data!")

        # Query using parameters
        sql_qry_2 = cf.config['SQL_QRY_2']
        msg_2 = "Documents generated based on RealtimeTwitterFeedback feed!"
        collection_flg = 2

        val = 'crazyGo'
        param_det = [{"name": "@CrVal", "value": val}]
        add_param = 2

        x1 = cm.clsColMgmt()
        df_ret_2 = x1.fetch_data(sql_qry_2, msg_2, collection_flg, add_param, param_det)

        l.logr('2.TwitterFeedback_' + var + '.csv', debug_ind, df_ret, 'log')
        print('Realtime Twitter Data:: ')
        print(df_ret_2)
        print()

        # Checking execution status
        ret_val_2 = int(df_ret_2.shape[0])

        if ret_val_2 == 0:
            print("Cosmos DB hasn't returned any rows. Please check your queries!")
            print("*" * 157)
        else:
            print("Successfuly row feteched!")
            print("*" * 157)

        # Merging NoSQL Data (Cosmos DB) with Relational DB (Azure SQL DB)
        df_Fin_temp = p.merge(df_ret, act_df, on='subscriberId', how='inner')

        df_fin = df_Fin_temp[['orderDate', 'orderNo', 'sender', 'state', 'country', 'customerType']]

        print("Initial Combined Data (From Cosmos & Azure SQL DB) :: ")
        print(df_fin)

        l.logr('3.InitCombine_' + var + '.csv', debug_ind, df_fin, 'log')

        # Transforming the orderDate as per standard format
        df_fin['orderDateM'] = df_fin.apply(lambda row: getDate(row), axis=1)

        # Dropping the old column & renaming the new column to old column
        df_fin.drop(columns=['orderDate'], inplace=True)
        df_fin.rename(columns={'orderDateM': 'orderDate'}, inplace=True)

        print("*" * 157)
        print()
        print("Final Combined & Transformed result:: ")
        print(df_fin)

        l.logr('4.Final_Combine_' + var + '.csv', debug_ind, df_fin, 'log')
        print("*" * 157)

    except ValueError:
        print("No relevant data to proceed!")

    except Exception as e:
        print("Top level Error: args:{0}, message{1}".format(e.args, e.message))

if __name__ == "__main__":
    main()

The key lines from this script –

def getDate(row):
    try:
        d1 = row['orderDate']
        d1_str = str(d1)
        d1_dt_part, sec = d1_str.split('.')
        dt_part1 = d1_dt_part.replace('T', ' ')

        return dt_part1
    except Exception as e:
        x = str(e)
        print(x)
        dt_part1 = ''

        return dt_part1

This function converts NoSQL date data type more familiar format.

NoSQL Date:
NoSQL_Date
Transformed Date:
Transformed Date
# Accessing from Azure SQL DB
x1 = dbcon.clsDBLookup()
act_df = x1.azure_sqldb_read(cf.config['AZURE_SQL_1'])

print("Azure SQL DB::")
print(act_df)
print()

Above lines are calling the Azure SQL DB method to retrieve the RDBMS data into our dataframe.

# Merging NoSQL Data (Cosmos DB) with Relational DB (Azure SQL DB)
df_Fin_temp = p.merge(df_ret, act_df, on='subscriberId', how='inner')

df_fin = df_Fin_temp[['orderDate', 'orderNo', 'sender', 'state', 'country', 'customerType']]

In these above lines, we’re joining the data retrieved from two different kinds of the database to prepare our initial combined dataframe. Also, we’ve picked only the desired column, which will be useful for us.

# Transforming the orderDate as per standard format
df_fin['orderDateM'] = df_fin.apply(lambda row: getDate(row), axis=1)

# Dropping the old column & renaming the new column to old column
df_fin.drop(columns=['orderDate'], inplace=True)
df_fin.rename(columns={'orderDateM': 'orderDate'}, inplace=True)

In the above lines, we’re transforming our date field, as shown above in one of our previous images by calling the getDate method.

Let’s see the directory structure of our program –

Win_Vs_MAC

Let’s see how it looks when it runs –

Windows:

Win_Run_1
Win_Run_2

MAC:

MAC_Run_1
MAC_Run_2

So, finally, we’ve successfully blended the data & make more meaningful data projection.

Following python packages are required to run this application –

pip install azure

pip install azure-cosmos

pip install pandas

pip install requests

pip install pyodbc

This application tested on Python3.7.1 & Python3.7.2 as well. As per Microsoft, their official supported version is Python3.5.

I hope you’ll like this effort.

Wait for the next installment. Till then, Happy Avenging. 😀

[Note: All the sample data are available/prepared in the public domain for research & study.]

How to store data from XML to Tables

In the previous post we have discussed about generating an XML file using Oracle SQL XML functions. Today we will do that in reverse manner. That means we will load the data generated by that query in the database tables.

At the end of this post (Which is a continue of the previous post) – you will be successfully generate an XML file from Oracle Tables & also able to load the data from XML on that same structured tables. So, that will complete the full life cycle of XML in oracle(Obviously concentrate on some basics).

Lets see –

Our main ingredients for this class – is the XML file named – emp_oracle.xml

And, it looks like –




200
<
First>Whalen</First>
4400
1987-09-17


201
<
First>Billy</First>
4500
1985-06-10


202
<
First>Bireswar</First>
9000
1978-06-10


We need to create one Oracle Directories to map with the Operating System directories in the following manner ->

sys@ORCL>
sys@ORCL>select * from v$version;

BANNER
----------------------------------------------------------------------------
Oracle Database 11g Enterprise Edition Release 11.1.0.6.0 - Production
PL/SQL Release 11.1.0.6.0 - Production
CORE 11.1.0.6.0 Production
TNS for 32-bit Windows: Version 11.1.0.6.0 - Production
NLSRTL Version 11.1.0.6.0 - Production

Elapsed: 00:00:00.00
sys@ORCL>
sys@ORCL>
sys@ORCL>CREATE OR REPLACE DIRECTORY SATY_DIR AS 'D:\XML_Output'
2 /

Directory created.

Elapsed: 00:00:00.23
sys@ORCL>
sys@ORCL>GRANT READ, WRITE ON DIRECTORY SATY_DIR TO SCOTT, HR;

Grant succeeded.

Elapsed: 00:00:00.08
sys@ORCL>

Once you have created the directory successfully and give the proper privileges to the users like Scott or Hr – you have completed one important component of today’s test. Still we are far to go. Now the second part is –

scott@ORCL>
scott@ORCL>select * from v$version;

BANNER
--------------------------------------------------------------------------------
Oracle Database 11g Enterprise Edition Release 11.1.0.6.0 - Production
PL/SQL Release 11.1.0.6.0 - Production
CORE 11.1.0.6.0 Production
TNS for 32-bit Windows: Version 11.1.0.6.0 - Production
NLSRTL Version 11.1.0.6.0 - Production

Elapsed: 00:00:00.00
scott@ORCL>
scott@ORCL>
scott@ORCL>CREATE TABLE t
2 (
3 serialNo NUMBER(10),
4 fileName VARCHAR2(100),
5 xml XMLTYPE,
6 constraints pk_serialNo primary key(serialNo)
7 );

Table created.

Elapsed: 00:00:04.13
scott@ORCL>
scott@ORCL>
scott@ORCL>CREATE SEQUENCE x_seq
2 START WITH 1
3 INCREMENT BY 1;

Sequence created.

Elapsed: 00:00:00.31
scott@ORCL>
scott@ORCL>CREATE OR REPLACE PROCEDURE load_xml(
2 p_dir IN VARCHAR2,
3 p_filename IN VARCHAR2
4 )
5 IS
6 l_bfile BFILE := BFILENAME(p_dir, p_filename);
7 l_clob CLOB;
8 BEGIN
9 DBMS_LOB.createtemporary (l_clob, TRUE);
10
11 DBMS_LOB.fileopen(l_bfile, DBMS_LOB.file_readonly);
12 DBMS_LOB.loadfromfile(l_clob, l_bfile, DBMS_LOB.getlength(l_bfile));
13 DBMS_LOB.fileclose(l_bfile);
14
15 INSERT INTO t(
16 serialNo,
17 fileName,
18 xml
19 )
20 VALUES (
21 x_seq.NEXTVAL,
22 p_filename,
23 XMLTYPE.createXML(l_clob)
24 );
25
26 COMMIT;
27
28 DBMS_LOB.freetemporary(l_clob);
29 END;
30 /

Procedure created.

Elapsed: 00:00:00.88
scott@ORCL>
scott@ORCL>EXEC load_xml(p_dir => 'SATY_DIR', p_filename => 'emp_oracle.xml');

PL/SQL procedure successfully completed.

Elapsed: 00:00:00.16
scott@ORCL>
scott@ORCL>set long 5000
scott@ORCL>
scott@ORCL>set pagesize 0
scott@ORCL>
scott@ORCL>select xml from t;



200
Whalen
4400
1987-09-17


201
Billy
4500
1985-06-10


202
Bireswar
9000
1978-06-10




Elapsed: 00:00:00.10
scott@ORCL>

Ok. So, we’ve initially load the data into the temp table t. But, we need to load the data from this temp table t to our target table revive_xml which will look like –

scott@ORCL>create table revive_xml
2 (
3 rev_emp_id number(4),
4 rev_f_name varchar2(40),
5 rev_salary number(10,2),
6 rev_jn_dt date,
7 constraints pk_rev_emp_id primary key(rev_emp_id)
8 );

Table created.

Elapsed: 00:00:00.40
scott@ORCL>

Ok. So, we have done another important part of our job. Let’s concentrate on our final mission –

scott@ORCL>insert into revive_xml(
2 rev_emp_id,
3 rev_f_name,
4 rev_salary,
5 rev_jn_dt
6 )
7 select cast(t1.EmployeeId as number(4)) EmployeeId,
8 t2.FirstName,
9 cast(t3.Salary as number(10,2)) Salary,
10 to_date(t4.JoiningDt,'YYYY-MM-DD') JoiningDt
11 from (
12 select rownum rn1,
13 extractValue(value(EmployeeId),'/Emp/Employee_ID') EmployeeId
14 from t,
15 table(xmlsequence(extract(xml, '/EmployeeList/Emp'))) EmployeeId
16 ) t1,
17 (
18 select rownum rn2,
19 extractValue(value(FirstName),'/Emp/First') FirstName
20 from t,
21 table(xmlsequence(extract(xml, '/EmployeeList/Emp'))) FirstName
22 ) t2,
23 (
24 select rownum rn3,
25 extractValue(value(Salary),'/Emp/Sal') Salary
26 from t,
27 table(xmlsequence(extract(xml, '/EmployeeList/Emp'))) Salary
28 ) t3,
29 (
30 select rownum rn4,
31 extractValue(value(HireDate),'/Emp/HireDate') JoiningDt
32 from t,
33 table(xmlsequence(extract(xml, '/EmployeeList/Emp'))) HireDate
34 ) t4
35 where t1.rn1 = t2.rn2
36 and t1.rn1 = t3.rn3
37 and t1.rn1 = t4.rn4;

3 rows created.

Elapsed: 00:00:00.16
scott@ORCL>
scott@ORCL>commit;

Commit complete.

Elapsed: 00:00:00.22
scott@ORCL>
scott@ORCL>
scott@ORCL>select * from revive_xml;

REV_EMP_ID REV_F_NAME REV_SALARY REV_JN_DT
---------- ---------------------------------------- ---------- ---------
200 Whalen 4400 17-SEP-87
201 Billy 4500 10-JUN-85
202 Bireswar 9000 10-JUN-78

scott@ORCL>

So, you have done it finally.

You can do it another way but that is limited to single record parsing –

scott@ORCL>with t
2 as (
3 select xmlType('
4
5
6 200
7 Whalen
8 4400
9 1987-09-17
10
11 ') xml from dual
12 )
13 SELECT rownum rn,
14 a.EmployeeId,
15 a.FirstName,
16 a.Salary,
17 a.JoiningDt
18 FROM t,
19 XMLTABLE('/EmployeeList'
20 PASSING t.xml
21 COLUMNS
22 EmployeeId varchar2(10) PATH '/EmployeeList/Emp/Employee_ID',
23 FirstName varchar2(20) PATH '/EmployeeList/Emp/First',
24 Salary number(10) PATH '/EmployeeList/Emp/Sal',
25 JoiningDt date PATH '/EmployeeList/Emp/HireDate'
26 ) a;

RN EMPLOYEEID FIRSTNAME SALARY JOININGDT
---------- ---------- -------------------- ---------- ---------
1 200 Whalen 4400 17-SEP-87

scott@ORCL>
scott@ORCL>

Hope this will solve your purpose.

Also you can refer to the following XML In Oracle link.

Regards.