Tuesday, November 14, 2023

PostgreSQL Table Partition on AWS RDS

If we grow bigger in the business , seamlessly our customer and transaction data also increases . In the meantime performance needs to consider as well 

So in this case of bigger tables indexes will not help us to achieve good performance on peak times . Alternatively we have partitioning to split the tables data into multiple pieces on all the relational database environments 

Like wise we are going to do range partition on sample table in PostgreSQL Database , In PostgreSQL three types of partition methods are available , 

Below are the important concern in the PostgreSQL Partition ,

  • Possible to attach regular table into partitioned one 
  • Not Possible to transform regular table to partitioned one   

So based on the above formula , we have tried to transform regular table into partitioned one for your reference 

Any one can use this example and perform partitioning in AWS PostgreSQL RDS easily 

Click GitHub Link for Code : AWS-PostgreSQL-RDS-Table-Partition

Step 1 : Create base datablogspaycheck table and insert some sample records 


DROP SEQUENCE IF EXISTS public.paycheck_id_seq;

CREATE SEQUENCE public.paycheck_id_seq





    CACHE 1;

create table datablogspaycheck


    payment_id int NOT NULL DEFAULT nextval('public.paycheck_id_seq'::regclass), 

    created timestamptz NOT NULL,

    updated  timestamptz NOT NULL DEFAULT now(),

    amount float,

    status varchar DEFAULT 'new'


CREATE INDEX idx_paycheck ON datablogspaycheck (created);

INSERT INTO datablogspaycheck (created) VALUES (

generate_series(timestamp '2023-01-01'

               , now()

               , interval  '5 minutes') ); 

Step 2 : Rename base table with new name

ALTER TABLE datablogspaycheck RENAME TO datablogspaycheck_basetable;

Step 3 : Create Partitioned table 

create table datablogspaycheck


    payment_id int NOT NULL DEFAULT nextval('public.paycheck_id_seq'::regclass), 

    created timestamptz NOT NULL,

    updated  timestamptz NOT NULL DEFAULT now(),

    amount float,

    status varchar DEFAULT 'new'


Step 4 : Create Separate Partition for each create date 

CREATE TABLE datablogspaycheck_202303 PARTITION OF datablogspaycheck

    FOR VALUES FROM ('2023-01-01') TO ('2023-03-01');


CREATE TABLE datablogspaycheck_20230304 PARTITION OF datablogspaycheck

    FOR VALUES FROM ('2023-03-01') TO ('2023-04-01');


CREATE TABLE datablogspaycheck_202304 PARTITION OF datablogspaycheck

    FOR VALUES FROM ('2023-04-01') TO ('2023-05-01');


CREATE TABLE datablogspaycheck_202311 PARTITION OF datablogspaycheck

    FOR VALUES FROM ('2023-05-01') TO ('2023-11-01');


CREATE TABLE datablogspaycheck_2024 PARTITION OF datablogspaycheck

    FOR VALUES FROM ('2023-11-01') TO ('2024-01-01');

Step 5 : Migrate the all records

insert into datablogspaycheck (payment_id,created,updated,amount,status) select payment_id,created,updated,amount,status from datablogspaycheck_basetable;

Step 6 : Validate each partition 

select * from datablogspaycheck_202303 order by 2 desc

select * from datablogspaycheck_20230304 order by 2 desc

select * from datablogspaycheck_202311 order by 2 desc

Its done , Easily migrated normal table data into partitioned table 

Thanks for Reading !!!

Sunday, June 25, 2023

Deep dive into Babelfish Compass

        Wow !!! If suppose on the migration projects we need to more stuffs and things to convert when coming to procedures , functions and other database objects 

But AWS is providing good things to migrate with easy steps , Ha Ha ... Don't overthink still you need to do 40% code migration works 

In this part Babelfish Compass is giving various options to support migration the codes from SQL Server to PostgreSQL with bebelfish feature enabled PaaS servers 

Below are easy steps on the Script Conversations

  • Install a 64-bit Java Runtime Environment (JRE) version 8 or higher

1.Download Compass Tool in Below

Needs to download .zip file to work with Babelfish Compatibility 

2.Unzip and Place the files in separate folder 

3.Be Ready with you SQL Database Generated Scripts file and Copy it in Same Folder 

Database name has been highlighted in below , 

4.Next , we can start running report with Babelfish Compass 

C:\Users\Admin\Downloads\BabelfishCompass_v.2023-03-a\BabelfishCompass>BabelfishCompass.bat reportfinal datablogsdbprod.sql

5.Finally Reports are generated in Documents Directory 

6.We can review the reports in any format , for me its easy with in HTML browser 

Just double click the HTML document , So like below we will get supported and unsupported features details in depth . 

We can directly go and debug the code . Also bebelfish compass is having plenty of methods to rewrite the code , we will check it in next blog 

Happy Coding !!!

Wednesday, June 21, 2023

Oracle RDS Audit log enable

Oracle Audit Log : 

Oracle Audit Log refers to the feature in Oracle Database that records and stores information about various database activities and events. It provides a mechanism to track and monitor user activities, system events, and changes made to the database.

  1. User Logins: Recording user login attempts and authentication information.
  2. Database Activities: Logging SQL statements executed by users, including select, insert, update, and delete operations.
  3. Privilege Usage: Monitoring the usage of privileges, such as granting or revoking permissions.
  4. Schema Changes: Tracking modifications to database objects, such as creating or altering tables, views, or indexes.
  5. System Events: Recording system-level events, such as startup and shutdown of the database.
  6. Security Violations: Detecting unauthorized access attempts or suspicious activities.
  7. Administrative Operations: Logging administrative tasks performed by database administrators, such as user management or database configuration changes.

The Oracle Audit Log provides an essential tool for security, compliance, and troubleshooting purposes.

Types of Auditing in Amazon RDS for Oracle : 

  1. Standard Auditing 
  2. Unified Auditing 
  3. Fine-grained Auditing

We are going to see , how do we enable Standard auditing in Oracle RDS 

How to enable Audit Log in Oracle RDS?

Make sure you have enabled custom parameter group for Oracle RDS 

  • Modify below values for Audit_Trail Parameter 

            Audit_Trail - DB, EXTENDED

  • Next ,Just needs to modify below DDL or DML statements to capture the logs from the server 





            AUDIT READ ON DIRECTORY datapump_dir;

Its all done , we have enabled required logs to capture for security purpose 

How to we monitor Audit Logs ? 

We can just run the below command get the captured audit logs in Oracle RDS ,

SELECT * FROM DBA_AUDIT_TRAIL order by 1 desc 

Its just for normal scenario , explained the process . Still we can separate Audit Table space and many further things are available in Oracle . Let see on another blogs 

Happy Auditing !!!

Friday, February 24, 2023

How to Automate MongoDB Database Backups in Linux

We have setup of One Primary with Multiple Secondary

Even if we configured highly available setup and backups , native backup are so special to take it and keep it somewhere in the cloud 

using the below script we can easily schedule backup in Linux environments

export PATH=/bin:/usr/bin:/usr/local/bin #Decalre Today Date TODAY=`date +"%d%b%Y"` #Declare Variables Required to pass for mongo dump command DB_BACKUP_PATH='/mnt/mongobackup' MONGO_HOST='localhost' MONGO_PORT='27017' MONGO_USER='xxxxxxxxxxx' MONGO_PASSWD='xxxxxxxxxxxxx' DATABASE_NAMES='ALL' #Remove Old Backup Files find ${DB_BACKUP_PATH} -name "*.zip" -type f -mtime +3 -delete find ${DB_BACKUP_PATH} -type d -mtime +3 -exec rm -rf {} \; #Create Directory for Backup mkdir -p ${DB_BACKUP_PATH}/${TODAY} cd ${DB_BACKUP_PATH}/${TODAY}/ if [ ${DATABASE_NAMES} = "ALL" ]; then echo "You have choose to backup all database" mongodump --uri="mongodb://${MONGO_USER}:${MONGO_PASSWD}@${MONGO_HOST}:${MONGO_PORT}" else echo "Running backup for selected databases" for DB_NAME in ${DATABASE_NAMES} do mongodump --uri="mongodb://${MONGO_USER}:${MONGO_PASSWD}@${MONGO_HOST}:${MONGO_PORT}/${DB_NAME}" done fi #Compress The Backup cd ${DB_BACKUP_PATH}/${TODAY} zip -r ${DB_BACKUP_PATH}_${TODAY}.zip ${DB_BACKUP_PATH}/${TODAY} cd ${DB_BACKUP_PATH}/${TODAY} #Copy the Compressed file into Azure Container using Shared Access Token azcopy cp ${DB_BACKUP_PATH}_${TODAY}.zip "" --recursive=true #Send Mail with Backup Logs if [ $? -ne 0 ] then echo "Mongo Native backup Failed in $(hostname) $(date). Please contact administrator." | mail -r -s "Mongo Native backup Failed $(hostname)" < /mongodata/cronscripts/mongo_backup_log.log else echo "Mongo Native backup completed in $(hostname)." | mail -r -s "Mongo Native backup completed in $(hostname)" < /mongodata/cronscripts/mongo_backup_log.log fi

Friday, February 17, 2023

How to tune long running Queries on Azure Synapse DW (Dedicated Pool) - Part 2

Its took little long time to derive our approach tunning on azure data factory with Azure Synapse why because we need to run the system atleast two months validate our approach is smooth 

Yes its all running good as expected performance on the ETL loads and Processes 

Here are the major things we need to take care on Azure Synapse Dedicated Pool ,

  1. Dedicated SQL Pool Scaling 
  2. Dedicated SQL Pool Workload Management

Dedicated SQL Pool Scaling : 

We have decide below metrices to optimize the Azure Synapse 

  1. Data Load and Processing timelines
  2. Critical Process running timelines 
  3. Minimal Processing timelines 
  4. Reporting and Visualtions Timelines 
Based on above requirements , we can easily scalable the Dedicated SQL Pool based on the load 

Consider we need to load the data and process with in 2 hours , we can upscale the Dedicated SQL Pool to as like we want 

Key Part to increase the DW is concurrency , Instance Capacity and allocated resource for each queries 

Before start our critical process , we can automate upscale process with ADF Pipelines itself . So many blogs available to configure that 

Best method configure the authentication method with service principle ,

Dedicated SQL Pool Workload Management : 

We have decide below metrices to prepare workload management 

  1. Short Queries
  2. Critical Queries 
  3. High Intensive Queries 
  4. Reporting Queries

Based on the classifications , we have to split workload group for above queries 

Step 1 :

We need to create login and user for workload management in Dedicated SQL Pool 

--CREATE LOGIN [Analyticsprocess] WITH PASSWORD='xxxxxxxxx'

--CREATE USER [Analyticsprocess] FOR LOGIN [Analyticsprocess]

--GRANT CONTROL ON DATABASE::[sql-datablogs-dw] TO 


Step 2 :

Consider you have upscaled instance into DW400c below are the resources allocation for the DW400c instance concurrency requirements 

In the workload group --> New workload group --> Click ELT

Consider analytics process user is used for high intensive queries we have to allocate as much as minimum resource for workload group 

Click Classifiers --> Add Classifiers --> Name it as ELT --> and specify Member should be the login and Label is important to mention 

Once click Add , we will get below concurrency range based on DW400c

By Default , its having system level workload group to handle the queries but its not effective we have to force our workload group 

Step 3 :

This is very important to utilize the workload group properly . We need to specify Label on the heavy processing queries so that it will utilized properly  

CREATE TABLE rpt_datablogs_finalreport.smgreport WITH (HEAP,DISTRIBUTION = REPLICATE) AS select * into rpt_datablogs_finalreport.smgreport_vw from rpt_datablogs_finalreport.vw_smgreport OPTION (LABEL='highintensiveprocess')


So these two things given much improvement on performance as well as reduced operational cost below 60% 

Still few more things to learn to optimize it on Azure Data Factory lets see on that next blog 

Happy Troubleshooting !!!