Real-time Data Replication from Amazon RDS to Oracle Autonomous Database using OCI GoldenGate

March 23, 2022March 23, 2022 ~ easyoradba ~ Leave a comment

Article first appeared here

Introduction

Goldengate Microservices 21c is the latest version of the microservices architecture which makes creating data mesh and data fabric across different public clouds as easy as a few clicks. Goldengate is available on OCI as a fully managed service with auto-scaling. It does not.require installation of Goldengate software at either the source or Target db instances. Goldengate uses a capture and apply mechanism for replication using trail files. Both the extract (capture) and replicat (apply) processes run on the Goldengate replication instance which acts as a hub.

Let us go ahead and create a data pipeline for replicating Data in real-time using Oracle Cloud Infrastructure (OCI) Goldengate 21c from Amazon RDS Oracle Instance to an Oracle Autonomous database in OCI. Below are some of the common use cases for this solution :

Use Cases

Cross-cloud replication of Oracle Database from AWS RDS to OCI
Migration of Oracle Database with Zero Downtime from AWS RDS to OCI
Creating Multi-Cloud Microservices Application with Oracle database as the persistent data store
Creating a Multi-cloud Data Mesh for Oracle Database

Architecture

Source : Amazon RDS Oracle 19c EE

Target : OCI Autonomous Transaction Processing 19c

Replication Hub : OCI Goldengate 21c Microservices

Network : Site-to Site IPsec VPN or Fastconnect (Direct Connect on AWS)

The solution is broadly divided into four phases :

Setup of RDS Instance and Preparing Source for Goldengate replication
Setup of OCI Autonomous Database and Preparing Target for Goldengate Replication
Deployment of OCI Goldengate and Creation of Deployment and Register Source and Target Databases
Create Extract (Capture) and Replicate (Apply) process on OCI Goldengate

Phase 1 — AWS Setup : RDS Source and Enable Goldengate Capture

The first part of the setup requires us to provision a VPC, Subnet Group and Oracle 19c RDS Instance on AWS. Please ensure all the requistie Network constructs like security groups are in place for connectivity from OCI Goldengate to RDS. In a production scenario it would be betetr to have the RDS instance without a public endpoint and have a Fastconnect setup from AWS to OCI

Create a VPC and RDS Subnet Group

2. Create RDS Oracle Instance 19.1 EE with super user as ‘admin’

3. Create a new DB Parameter Group for 19.1 EE with parameter ENABLE_GOLDENGATE_REPLICATION set to TRUE

4. Change the parameter group of the RDS instance and reboot the RDS Oracle instance once the parameter group has been applied. Double-check to confirm the parameter ENABLE_GOLDENGATE_REPLICATION is set to True and the correct parameter group is applied to the RDS isntance

5. Set the log retention period on the source DB with ‘admin’ user

exec rdsadmin.rdsadmin_util.set_configuration('archivelog retention hours',24);commit;

6. Create a new user account to be used for Goldengateon the RDS instance with ‘admin’ user

CREATE TABLESPACE administrator;

CREATE USER oggadm1 IDENTIFIED BY “*********” DEFAULT TABLESPACE ADMINISTRATOR TEMPORARY TABLESPACE TEMP;

commit;

7. Grant account privileges on the source RDS instance with ‘admin’ user

GRANT CREATE SESSION, ALTER SESSION TO oggadm1;

GRANT RESOURCE TO oggadm1;

GRANT SELECT ANY DICTIONARY TO oggadm1;

GRANT FLASHBACK ANY TABLE TO oggadm1;

GRANT SELECT ANY TABLE TO oggadm1;

GRANT SELECT_CATALOG_ROLE TO admin WITH ADMIN OPTION;

exec rdsadmin.rdsadmin_util.grant_sys_object (‘DBA_CLUSTERS’, ‘OGGADM1’);

exec rdsadmin.rdsadmin_util.grant_sys_object (‘DBA_CLUSTERS’, ‘ADMIN’);

GRANT EXECUTE ON DBMS_FLASHBACK TO oggadm1;

GRANT SELECT ON SYS.V_$DATABASE TO oggadm1;

GRANT ALTER ANY TABLE TO oggadm1;

grant unlimited tablespace TO oggadm1;

grant EXECUTE_CATALOG_ROLE to admin WITH ADMIN OPTION;

commit;

8. Finally, grant the privileges needed by a user account to be a GoldenGate administrator. The package that you use to perform the grant, dbms_goldengate_auth or rdsadmin_dbms_goldengate_auth, depends on the Oracle DB engine version.

— With admin user on RDS Oracle instance for Oracle Database version lower than 12.2 —

exec dbms_goldengate_auth.grant_admin_privilege (grantee=>’OGGADM1′,privilege_type=>’capture’,grant_select_privileges=>true, do_grants=>TRUE);

exec dbms_goldengate_auth.grant_admin_privilege(‘OGGADM1′,container=>’all’);

exec dbms_goldengate_auth.grant_admin_privilege(‘OGGADM1’);

commit;

— For Oracle DB versions that are later than or equal to Oracle Database 12c Release 2 (12.2), which requires patch level 12.2.0.1.ru-2019–04.rur-2019–04.r1 or later, run the following PL/SQL program.

exec rdsadmin.rdsadmin_dbms_goldengate_auth.grant_admin_privilege (grantee=>’OGGADM1′, privilege_type=>’capture’,grant_select_privileges=>true, do_grants=>TRUE);

commit;

To revoke privileges, use the procedure revoke_admin_privilege in the same package.

9. TNS entry for AWS RDS Instance

OGGTARGET=(DESCRIPTION=(ENABLE=BROKEN)(ADDRESS_LIST=(ADDRESS=(PROTOCOL=TCP)(HOST=orcl.*****.ap-southeast-2.rds.amazonaws.com)(PORT=1521)))(CONNECT_DATA=(SID=ORCL)))– To be added to Registered Database in OCI –(DESCRIPTION=(ENABLE=BROKEN)(ADDRESS_LIST=(ADDRESS=(PROTOCOL=TCP)(HOST=orcl.****.ap-southeast-2.rds.amazonaws.com)(PORT=1521)))(CONNECT_DATA=(SID=ORCL)))

Alias (to be used later in OCI GG configuration) : ORCLAWS

10. Create Test Table in RDS Oracle Instance

CREATE TABLE oggadm1.test (id number,name varchar2(100));

insert into oggadm1.test values (1,’Shadab’);

insert into oggadm1.test values (2,’Mohammad’);

commit;

11. Enable supplemental logging on with Admin user

Ref :https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Appendix.Oracle.CommonDBATasks.Log.html#Appendix.Oracle.CommonDBATasks.SettingForceLogging

— Enable Force logging —

EXEC rdsadmin.rdsadmin_util.force_logging(p_enable => true);

— Enable Supplemental logging —

begin rdsadmin.rdsadmin_util.alter_supplemental_logging(p_action => ‘ADD’);

end;

— Enable Force logging —

EXEC rdsadmin.rdsadmin_util.force_logging(p_enable => true);

— Enable Supplemental logging —

begin rdsadmin.rdsadmin_util.alter_supplemental_logging(p_action => ‘ADD’);

end;

— Enable Force logging —

EXEC rdsadmin.rdsadmin_util.force_logging(p_enable => true);

— Enable Supplemental logging —

begin rdsadmin.rdsadmin_util.alter_supplemental_logging(p_action => ‘ADD’);

end;

Phase 2 — OCI Setup : Autonomous Database

We will provision the VCN, Autonomous Database on OCI and enable the goldengate replication user

Create VCN

2. Create Autonomous Transaction Processing Database with Network Options and mTLS not required

3. Unlock ggadmin user in the ATP

                           alter user ggadmin identified by ****** account unlock;

4. Create Table ‘test’ in admin schema and do initial load (Normally this has to be done using data pump but it is beyond the scope of this article)

CREATE TABLE test (id number,name varchar2(100));

insert into test values (1,’Shadab’);

insert into test values (2,’Mohammad’);

commit;

select * from test;

Phase 3 — OCI Setup : Goldengate

Go to OCI Console Go to Oracle Database > Goldengate > Deployments > Create Deployment

2. Go to Oracle Database > Goldengate > Registered Databases

a. Add the ATP database created above with the ggadmin user

b. Add the RDS instance database using oggadm1 user

3. Test the connectivity to both databases , it should in console as Active

4. Go the launch URL for the Goldengate deployment username and password as per step 1.

                         eg : https://e*******q.deployment.goldengate.ap-sydney-1.oci.oraclecloud.com/

Phase 4 — Create , Extract (Capture) and Replicat (Apply) and Start the Replication

1. Create an Integrated Extract from Administration Service, click on the plus symbol next to the extract section

Go to Main Page > Configuration > Login to AWS RDS instance

a. Create Checkpoint table oggadm1.ckpt

b. Add Tran Data for Schema oggadm1

EXTRACT AWSEXT

USERIDALIAS ORCLAWS DOMAIN OracleGoldenGate

EXTTRAIL AW

TABLE OGGADM1.*;

2. Create Non-integrated replicat for ADB on trail file ‘aw’. click on the plus symbol next to the Replicat section

Go to Main Page > Configuration > Login to ATP instance

a. Create Checkpoint table admin.ckpt

b. Add Tran Data for Schema admin

c. Add heartbeat table

REPLICAT adbrep

USERIDALIAS FundsInsight DOMAIN OracleGoldenGate

MAP OGGADM1.TEST, TARGET ADMIN.TEST;

The status should be green on the OCI Goldengate Administration Dashboard

3. Insert transaction at RDS source

                            insert into oggadm1.test values(3,'Utuhengal');commit;

4. Check at ADB Target

                            select * from test;

Conclusion:

We have created cross-cloud replication from an Oracle Database sitting inside AWS to an Oracle Autonomous Database running on OCI. The idea was to demonstrate the capability and ease of Goldengate Microservices to run a a replication hub on OCI and let you create real-time change data capture across two different public clouds. Every component used in this architecture is a fully managed service without the need of managing any servers or installing any agents on either source or target as they are fully managed cloud services without access to under-lying host.

References:

Setup of Goldengate for RDS : https://jinyuwang.weebly.com/cloud-service/how-to-capture-data-from-oracle-database-on-aws-rds-with-oracle-goldengate
Goldengate Setup for RDS Source :https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Appendix.OracleGoldenGate.html#Appendix.OracleGoldenGate.rds-source-ec2-hub
RDS Common Tasks :https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Appendix.Oracle.CommonDBATasks.Log.html
OCI Goldengate Database Registration : https://docs.oracle.com/en/cloud/paas/goldengate-service/using/database-registrations.html#GUID-899B90FF-DF9A-481D-A531-BB9D25005EB9
Apex Livelab for OCI Goldengate Microservices 21c :https://apexapps.oracle.com/pls/apex/dbpm/r/livelabs/workshop-attendee-2?p210_workshop_id=797&p210_type=3&session=113817274271778
OCI Goldengate Blog : https://blogs.oracle.com/dataintegration/post/new-oci-goldengate-service-is-first-of-any-major-cloud-provider-to-deliver-operational-and-analytic-integration-into-a-single-data-fabric
Getting Started with Goldengate : https://docs.oracle.com/goldengate/c1230/gg-winux/GGCON/getting-started-oracle-goldengate.htm#GGCON-GUID-61088509-F951-4737-AE06-29DAEAD01C0C

Backup and Restore PostgreSQL with Few Easy Shell Scripts

November 30, 2021November 30, 2021 ~ easyoradba ~ Leave a comment

PostgreSQL is the most popular Open source database and there is a lot of information available when it comes to backing up and restoring PgSQL I have used these scripts to backup production databases and restored them to new Postgres Servers. So here it goes

Backup PostgreSQL Database – Backup_Pgsql.sh

#!/bin/bash
hostname=`hostname`
# Dump DBs
  date=`date +"%Y%m%d_%H%M%N"`
  backupdir='/home/opc'
  dbname='demo'
  filename="$backupdir/${hostname}_${dbname}_${date}"
 pg_dump -U postgres --encoding utf8 -F c -f $filename.dump $dbname

Restore PostgreSQL Database – Restore_Pgsql.sh

#!/bin/bash
# Restore DB
filename='/home/opc/pgimportmaster-demo-20211129_1013.dump'
  pg_restore -U postgres -d demo -c < ./$1
exit 0

Usage for Restore

$ ./Restore_Pgsql.sh pgimportmaster-demo-20211129_1013.dump

Federated Query from Redshift to Aurora PostgreSQL

December 27, 2019 ~ easyoradba ~ Leave a comment

Create Public Accessible Redshift Cluster and Aurora PostgreSQL/ RDS PostgreSQL cluster. The RDS PostgreSQL or Aurora PostgreSQL must be in the same VPC as your Amazon Redshift cluster. If the instance is publicly accessible, configure its security group’s inbound rule to: Type: PostgreSQL, Protocol: TCP, Port Range: 5432, Source: 0.0.0.0/0. Otherwise, if the instance is not publicly accessible, you don’t need to configure an inbound rule.

Go to AWS Console > Secrets Manager > Create Secret Managers for RDS Database and Select your PostgreSQL database
Create IAM policy with ARN of above Secrets manager

{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "AccessSecret",
"Effect": "Allow",
"Action": [
"secretsmanager:GetResourcePolicy",
"secretsmanager:GetSecretValue",
"secretsmanager:DescribeSecret",
"secretsmanager:ListSecretVersionIds"
],
"Resource": "arn:aws:secretsmanager:us-east-1:111111111111:secret:federated-query-L9EBau"
},
{
"Sid": "VisualEditor1",
"Effect": "Allow",
"Action": [
"secretsmanager:GetRandomPassword",
"secretsmanager:ListSecrets"
],
"Resource": "*"
}
]
}

Create IAM Redshift customizable role and attach the Above Policy to it.
Attach the Role to your Redshift Cluster
Create External Schema in Redshift

CREATE EXTERNAL SCHEMA IF NOT EXISTS myRedshiftSchema
FROM POSTGRES
DATABASE 'testdb' SCHEMA 'aurora_schema'
URI 'federated-cluster-instance-1.c2txxxxupg1.us-east-1.rds.amazonaws.com' PORT 5432
OPTIONS 'application_name=psql'
IAM_ROLE 'arn:aws:iam::1111111111111:role/federated-query-role'
SECRET_ARN 'arn:aws:secretsmanager:us-east-1:11111111111111:secret:federated-query-L9EBau';

In RDS Aurora PostgreSQL create the schema which will hold the objects for federated query access

testdb=> create schema aurora_schema;
CREATE SCHEMA

testdb=> create table aurora_schema.federatedtable (id int8, name varchar(50), log_txn_date timestamp);
CREATE TABLE

testdb=> insert into aurora_schema.federatedtable values(1,'shadab','2019-12-26 00:00:00');
INSERT 0 1

testdb=> select * from aurora_schema.federatedtable;
id | name | log_txn_date
----+--------+---------------------
1 | shadab | 2019-12-26 00:00:00
(1 row)

testdb=# select * from myredshiftschema.federatedtable;
id | name | log_txn_date
----+--------+---------------------
1 | shadab | 2019-12-26 00:00:00
(1 row)

-- Trying out CTAS from Redshift to PostgreSQL --
testdb=# create table test as select * from myredshiftschema.federatedtable;
SELECT

testdb=# select * from test;
id | name | log_txn_date
----+--------+---------------------
1 | shadab | 2019-12-26 00:00:00

testdb=# select pg_last_query_id();

(1 row)

select * from svl_s3query_summary where query='1251';

-[ RECORD 1 ]-----------+---------------------------
userid | 100
query | 1251
xid | 7338
pid | 11655
segment | 0
step | 0
starttime | 2019-12-27 06:17:30.800652
endtime | 2019-12-27 06:17:30.947853
elapsed | 147201
aborted | 0
external_table_name | PG Subquery
file_format | Text
.
.
.

References:

[1]
Federated Query in Amazon Redshift (Preview) – https://docs.aws.amazon.com/redshift/latest/dg/federated-overview.html
[2]
CREATE EXTERNAL SCHEMA – https://docs.aws.amazon.com/redshift/latest/dg/federated-external-schema.html
[3]
Create a Secret and an IAM Role for Federated Query – https://docs.aws.amazon.com/redshift/latest/dg/federated-create-secret-iam-role.html

Python Script to Create a Data Pipeline Loading Data From RDS Aurora MySQL To Redshift

April 27, 2019April 27, 2019 ~ easyoradba ~ 2 Comments

In this tutorial we will create a Python script which will build a data pipeline to load data from Aurora MySQL RDS to an S3 bucket and copy that data to a Redshift cluster.

One of the assumptions is you have basic understanding of AWS, RDS, MySQL, S3, Python and Redshift. Even if you don’t it’s alright I will explain briefly about each of them to the non-cloud DBA’s

AWS- Amazon Web Services. It is the cloud infrastructure platform from Amazon which can be used to build and host anything from a static website to a globally scalable service like Netflix

RDS – Relational Database Service or RDS or short is Amazons managed relational database service for databases like it’s own Aurora, MySQL, Postgres, Oracle and SQL Server

S3- Simple Storage Service is AWS’s distributed storage which can scale almost infinitely. Data in S3 is stored in Buckets. Think of buckets as Directories but DNS name compliant and cloud hosted

Python – A programming language which is now the defacto standard for data science and engineering

Redshift- AWS’s Petabyte scale Data warehouse which is binary compatible to PostgreSQL but uses a columnar storage engine

The source in this tutorial is a RDS Aurora MySQL database and target is a Redshift cluster. The data is staged in an S3 bucket. With Aurora MySQL you can unload data directly to a S3 bucket but in my script I will offload the table to a local filesystem and then copy it to the S3 bucket. This will give you flexibility in-case you are not using Aurora but a standard MySQL or Maria DB

Environment:

Python 3.7.2 with pip
Ec2 instance with the Python 3.7 installed along with all the Python packages
Source DB- RDS Aurora MySQL 5.6 compatible
Destination DB – Redshift Cluster
Database : Dev , Table : employee in both databases which will be used for the data transfer
S3 bucket for staging the data
AWS Python SDK Boto3

Make sure both the RDS Aurora MySQL and Redshift cluster has security groups which have have IP of the Ec2 instance for inbound connections (Host and Port)

Create the table ’employee’ in both the Aurora and Redshift Clusters

Aurora MySQL 5.6

CREATE TABLE `employee` (
  `id` int(11) NOT NULL,
  `first_name` varchar(45) DEFAULT NULL,
  `last_name` varchar(45) DEFAULT NULL,
  `phone_number` varchar(45) DEFAULT NULL,
  `address` varchar(200) DEFAULT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=latin1;

Redshift

DROP TABLE IF EXISTS employee CASCADE;

CREATE TABLE employee
(
   id            bigint         NOT NULL,
   first_name    varchar(45),
   last_name     varchar(45),
   phone_number  bigint,
   address       varchar(200)
);

ALTER TABLE employee
   ADD CONSTRAINT employee_pkey
   PRIMARY KEY (id);

COMMIT;

2. Install Python 3.7.2 and install all the packages needed by the script

sudo /usr/local/bin/python3.7 -m pip install boto3
sudo /usr/local/bin/python3.7 -m pip install psycopg2-binary
sudo /usr/local/bin/python3.7 -m pip install pymysql
sudo /usr/local/bin/python3.7 -m pip install json
sudo /usr/local/bin/python3.7 -m pip install pymongo

3. Insert sample data into the source RDS Aurora DB

$ mysql -u awsuser -h shadmha-cls-aurora.ap-southeast-2.rds.amazonaws.com -p dev

INSERT INTO `employee` VALUES (1,'shadab','mohammad','04447910733','Randwick'),(2,'kris','joy','07761288888','Liverpool'),(3,'trish','harris','07766166166','Freshwater'),(4,'john','doe','08282828282','Newtown'),(5,'mary','jane','02535533737','St. Leonards'),(6,'sam','rockwell','06625255252','Manchester');

SELECT * FROM employee;

4. Download and Configure AWS command line interface

The AWS Python SDK boto3 requires AWS CLI for the credentials to connect to your AWS account. Also for uploading the file to S3 we need boto3 functions. Install AWS CLI on Linux and configure it.

$ aws configure
AWS Access Key ID [****************YGDA]:
AWS Secret Access Key [****************hgma]:
Default region name [ap-southeast-2]:
Default output format [json]:

5. Python Script to execute the Data Pipeline (datapipeline.py)

import boto3
import psycopg2
import pymysql
import csv
import time
import sys
import os
import datetime
from datetime import date
datetime_object = datetime.datetime.now()
print ("###### Data Pipeline from Aurora MySQL to S3 to Redshift ######")
print ("")
print ("Start TimeStamp")
print ("---------------")
print(datetime_object)
print ("")


# Connect to MySQL Aurora and Download Table as CSV File
db_opts = {
    'user': 'awsuser',
    'password': '******',
    'host': 'shadmha-cls-aurora.ap-southeast-2.rds.amazonaws.com',
    'database': 'dev'
}

db = pymysql.connect(**db_opts)
cur = db.cursor()

sql = 'SELECT * from employee'
csv_file_path = '/home/centos/my_csv_file.csv'

try:
    cur.execute(sql)
    rows = cur.fetchall()
finally:
    db.close()

# Continue only if there are rows returned.
if rows:
    # New empty list called 'result'. This will be written to a file.
    result = list()

    # The row name is the first entry for each entity in the description tuple.
    column_names = list()
    for i in cur.description:
        column_names.append(i[0])

    result.append(column_names)
    for row in rows:
        result.append(row)

    # Write result to file.
    with open(csv_file_path, 'w', newline='') as csvfile:
        csvwriter = csv.writer(csvfile, delimiter='|', quotechar='"', quoting=csv.QUOTE_MINIMAL)
        for row in result:
            csvwriter.writerow(row)
else:
    sys.exit("No rows found for query: {}".format(sql))


# Upload Generated CSV File to S3 Bucket
s3 = boto3.resource('s3')
bucket = s3.Bucket('mybucket-shadmha')
s3.Object('mybucket-shadmha', 'my_csv_file.csv').put(Body=open('/home/centos/my_csv_file.csv', 'rb'))


#Obtaining the connection to RedShift
con=psycopg2.connect(dbname= 'dev', host='redshift-cluster-1.ap-southeast-2.redshift.amazonaws.com',
port= '5439', user= 'awsuser', password= '*********')

#Copy Command as Variable
copy_command="copy employee from 's3://mybucket-shadmha/my_csv_file.csv' credentials 'aws_iam_role=arn:aws:iam::775888:role/REDSHIFT' delimiter '|' region 'ap-southeast-2' ignoreheader 1 removequotes ;"

#Opening a cursor and run copy query
cur = con.cursor()
cur.execute("truncate table employee;")
cur.execute(copy_command)
con.commit()

#Close the cursor and the connection
cur.close()
con.close()

# Remove the S3 bucket file and also the local file
DelLocalFile = 'aws s3 rm s3://mybucket-shadmha/my_csv_file.csv --quiet'
DelS3File = 'rm /home/centos/my_csv_file.csv'
os.system(DelLocalFile)
os.system(DelS3File)


datetime_object_2 = datetime.datetime.now()
print ("End TimeStamp")
print ("-------------")
print (datetime_object_2)
print ("")

6. Run the Script or Schedule in Crontab as a Job

$ python3.7 datapipeline.py

Crontab to execute Job daily at 10:30 am

30 10 * * * /usr/local/bin/python3.7 /home/centos/datapipeline.py &>> /tmp/datapipeline.log

7. Check the table in destination Redshift Cluster and all the records should be visible their

SELECT * FROM employee;

This tutorial was done using a small table and very minimum data. But with S3’s distributed nature and massive scale and Redshift as a Data warehouse you can build data pipelines for very large datasets. Redhsift being an OLAP database and Aurora OLTP, many real-life scenarios requires offloading data from your OLTP apps to data warehouses or data marts to perform Analytics on it.

AWS also has an excellent managed solution called Data Pipelines which can automate the movement and transform of Data. But many a times for developing customized solutions Python is the best tool for the job.

Enjoy this script and please let me know in your comments or on Twitter (@easyoradba) if you have any issues or what else would you like me to post for data engineering.

Python Script to Copy-Unload Data to Redshift from S3

April 23, 2019April 23, 2019 ~ easyoradba ~ Leave a comment

import psycopg2
import time
import sys
import datetime
from datetime import date
datetime_object = datetime.datetime.now()
print ("Start TimeStamp")
print ("---------------")
print(datetime_object)
print("")

#Progress Bar Function
def progressbar(it, prefix="", size=60, file=sys.stdout):
    count = len(it)
    def show(j):
        x = int(size*j/count)
        file.write("%s[%s%s] %i/%i\r" % (prefix, "#"*x, "."*(size-x), j, count))
        file.flush()
    show(0)
    for i, item in enumerate(it):
        yield item
        show(i+1)
    file.write("\n")
    file.flush()

#Obtaining the connection to RedShift
con=psycopg2.connect(dbname= 'dev', host='redshift.amazonaws.com',
port= '5439', user= 'awsuser', password= '*****')

#Copy Command as Variable
copy_command="copy users from 's3://redshift-test-bucket/allusers_pipe.txt' credentials 'aws_iam_role=arn:aws:iam::775088:role/REDSHIFTROLE' delimiter '|' region 'ap-southeast-2';"

#Unload Command as Variable
unload_command="unload ('select * from users') to 's3://redshift-test-bucket/users_"+str(datetime.datetime.now())+".csv' credentials 'aws_iam_role=arn:aws:iam::7755088:role/REDSHIFTROLE' delimiter '|' region 'ap-southeast-2';"

#Opening a cursor and run copy query
cur = con.cursor()
cur.execute("truncate table users;")
cur.execute(copy_command)
con.commit()

#Display Progress Bar and Put a sleep condition in seconds to make the program wait
for i in progressbar(range(100), "Copying Data into Redshift: ", 10):
    time.sleep(0.1) # any calculation you need

print("")

#Display Progress Bar and Put a sleep condition in seconds to make the program wait
for i in progressbar(range(600), "Unloading Data from Redshift to S3: ", 60):
    time.sleep(0.1) # any calculation you need

print("")

#Opening a cursor and run unload query
cur.execute(unload_command)

#Close the cursor and the connection
cur.close()
con.close()

datetime_object_2 = datetime.datetime.now()
print ("End TimeStamp")
print ("-------------")
print(datetime_object_2)
print("")

Move Oracle Database 12c from On-Premise to AWS RDS Oracle Instance using SQL Developer

March 17, 2019 ~ easyoradba ~ Leave a comment

Amazon Web Services has been gaining popularity in the last few years since cloud computing has been in the spotlight. Slowly the Traditional Enterprises are making the journey to the cloud. Oracle is considered one of the most mission critical application in the Enterprise. Moving Oracle Database to cloud can bring its own benefits both from an operational and financial perspective.

In this exercise we will move an on-premise Oracle DB schema to an AWS RDS Instance running Oracle 12cR1

Pre-requisites :

1. You already have a source Oracle database installed

2. You know how to provision an AWS RDS Oracle Instance

3. You have access to both instances

4. You have basic understanding of AWS S3 and AWS console

5. You have the latest version of SQL Developer installed on your machine

Source DB:

Oracle 12cR1 (12.1.0.2) running on CentOS 7.1

Destination DB:

Oracle 12cR1 running on AWS RDS Instance

High Level Steps to Migrate:

1. Create the destination Oracle 12CR1 instance on AWS. It is one of the easiest things to provision an Oracle DB on AWS RDS

2. Connect to Both Source(on-Prem) and Destination(AWS) Database from SQL Developer

3. Go to Tools > Database Copy and Select Source and Destination Databases

I prefer to do Tablespace Copy since most of the Apps i work reside in a single tablespace. But this depends on your choice. You can either chose Objects, Schemas or even entire Tablespaces to be copied across.

IMPORTANT : Make sure you have created the source schema in destination database before proceeding to next step else you will get an error “User does not exist”

In Destination AWS RDS run below commands

SQL> create user <source-schema-name> identified by <password123>;

SQL> grant dba to <source-schema-name>;