20 tips and tricks with the Autonomous Database

VP AIOps for the Autonomous Database
Sandesh Rao
LAOUC - Aug 2020
Top 20 FAQs on the Autonomous Database
@sandeshr
https://www.linkedin.com/in/raosandesh/
https://www.slideshare.net/SandeshRao4

1. Get started with Free Tier
2. Get started with OML
3. Using OCI Resource Mgr
4. Modify endpoints for ADB
5. Wallet rotation
6. Partitions with external tables
7. Track instance creation -
workflow
8. Performance monitoring
9. Alarms on resource metrics
10. Upgrade to 19c
11. Performance hub
12. SQL monitor report
13. ASH analytics in Performance
hub
14. APEX in Autonomous Database
15. OML Notebooks
16. Auto scaling
17. Download AWR reports
18. Load data using DBMS_CLOUD
19. Access Tenancy Information
20. Autonomous Dataguard
Agenda

1: How to get started with the
Autonomous Database Free Tier

Always Free services enable developers and students to learn, build and get
hands-on experience with Oracle Cloud for unlimited time
Anyone can try for an unlimited time the full functionality of:
• Oracle Autonomous Database
• Oracle Cloud Infrastructure including:
- Compute VMs
- Block and Object Storage
- Load Balancer
Free tier

Free tier – Tech spec
2 Autonomous Databases (Autonomous Data Warehouse or Autonomous
Transaction Processing), each with 1 OCPU and 20 GB storage
2 Compute VMs, each with 1/8 OCPU and 1 GB memory
2 Block Volumes, 100 GB total, with up to 5 free backups
10 GB Object Storage, 10 GB Archive Storage, and 50,000/month API requests
1 Load Balancer, 10 Mbps bandwidth
10 TB/month Outbound Data Transfer
500 million ingestion Datapoints and 1 billion Datapoints for Monitoring
Service
1 million Notification delivery options per month and 1000 emails per month

Creating a new Always Free ADB

Creating a new Always Free ADB
Simple toggle to
enable “Always
Free”

• As of December 5, 2019, the Machine Learning (formerly known as Advanced Analytics), Spatial and
Graph features of Oracle Database may be used for development and deployment purposes with all
on-prem editions and Oracle Cloud Database Services.
• See the Oracle Database Licensing Information Manual (pdf) for more details.
• Oracle’s multi-model converged architecture by supporting multiple data types, data models (e.g.
spatial, graph, JSON, XML) and algorithms (e.g. machine learning, graph and statistical functions)
and workload types (e.g. operational and analytical) within a single database.
• Processing and analyzing all types of spatial data in business applications, GIS and operational
systems
• Using graph analysis to discover relationships in social networks, detect fraud, and make
informed recommendations
• Building and deploying machine learning models for predictive analytics
OML , Spatial and Graph is free with the Oracle Database

How to get started with
Oracle Machine Learning

Algorithms automatically sift through large amounts of data to discover
hidden patterns, new insights and make predictions
Categories in Machine Learning
Identify most important factor (Attribute Importance)
Predict customer behavior (Classification)
Find profiles of targeted people or items (Classification
Predict or estimate a value (Regression)
Segment a population (Clustering)
Find fraudulent or “rare events” (Anomaly Detection)
Determine co-occurring items in a “basket” (Associations)X1
X2
A1A2A3A4 A5A6 A7
SupervisedLearningUnsupervisedLearning
Copyright © 2020 Oracle and/or its affiliates.

Database Developer to Data Scientist Journey

STEP 1: Creating OML Users
•Go back to the Cloud Console
and open the Instances
screen. Find your database,
click the action menu and
select Service Console.
Creating OML Users

Log in to the service with your admin password.
Creating OML Users

Go to the Administration tab and click Manage Oracle ML Users to go to the OML user management page - this
page will allow you to manage OML users.
Creating OML Users

Click Create button to create a new OML user. Note that this will also create a new database user with
the same name.
This newly created user will be able to use the OML notebook application.
Note that you can also enter an email address to send an email confirmation to your user (for this lab
you can use your own personal email address) when creating the user.
Creating OML Users

Enter the required information for this user, name the user as omluser1. If you supplied a
valid email address, a welcome email should arrive within a few minutes to your Inbox.
Click the Create button, in the top-right corner of the page, to create the user.
Creating OML Users

Here is the email which each user receives
welcoming them to the OML application.
It includes a direct link to the OML application
for that user which they can bookmark.
Creating OML Users

After you click Create you will see that user listed in the Users section.
Creating OML Users

Use your new user account omluser1:
Exploring the OML Home Page

Once you have successfully signed in to OML the application home page will be displayed.
Overview of OML Home Page
The grey menu bar at the top of the screen provides links to the main OML menus for the application (left corner)
and the workspace/project and user maintenance on the right-hand side.
Exploring the OML Home Page

Simple SQL Syntax—Statistical Comparisons (t-tests)
Compare AVE Purchase Amounts Men vs. Women Grouped_By INCOME_LEVEL
Statistical Functions
SELECT SUBSTR(cust_income_level, 1, 22) income_level,
AVG(DECODE(cust_gender, 'M', amount_sold, null)) sold_to_men,
AVG(DECODE(cust_gender, 'F', amount_sold, null)) sold_to_women,
STATS_T_TEST_INDEPU(cust_gender, amount_sold, 'STATISTIC', 'F') t_observed,
STATS_T_TEST_INDEPU(cust_gender, amount_sold) two_sided_p_value
FROM customers c, sales s
WHERE c.cust_id = s.cust_id
GROUP BY ROLLUP(cust_income_level)
ORDER BY income_level, sold_to_men, sold_to_women, t_observed;
STATS_T_TEST_INDEPU (SQL) Example;
P_Values < 05 show statistically
significantly differences in the amounts
purchased by men vs. women

Simple SQL Syntax—Attribute Importance - ML Model Build (PL/SQL)
OAA Model Build and Real-time SQL Apply Prediction
BEGIN
DBMS_DATA_MINING.CREATE_MODEL(
model_name => 'BUY_INSURANCE_AI',
mining_function => DBMS_DATA_MINING.ATTRIBUTE_IMPORTANCE,
data_table_name => 'CUST_INSUR_LTV',
case_id_column_name => 'cust_id',
target_column_name => 'BUY_INSURANCE',
settings_table_name => 'Att_Import_Mode_Settings');
END;
/
SELECT attribute_name, rank , attribute_value
FROM BUY_INSURANCE_AI
ORDER BY rank, attribute_name;
Model Results (SQL query)
ATTRIBUTE_NAME RANK ATTRIBUTE_VALUE
BANK_FUNDS 1 0.2161
MONEY_MONTLY_OVERDRAWN 2 0.1489
N_TRANS_ATM 3 0.1463
N_TRANS_TELLER 4 0.1156
T_AMOUNT_AUTOM_PAYMENTS 5 0.1095
A1A2A3A4 A5A6 A7

Coming Soon! | AutoML – new with OML4Py
Auto Feature Selection
– Reduce # of features by
identifying most
predictive
– Improve performance
and accuracy
Increase data scientist productivity – reduce overall compute time
Auto Algorithm
Selection
Much faster than
exhaustive search
Auto Feature
Selection
De-noise data and
reduce # of features
AutoTune
Significant accuracy
improvement
Auto Algorithm Selection
– Identify in-database
algorithm that achieves
highest model quality
– Find best algorithm faster
than with exhaustive search
Auto Tune Hyperparameters
– Significantly improve
model accuracy
– Avoid manual or exhaustive
search techniques
Enables non-expert users to leverage Machine Learning
Data
Table ML Model

Coming Soon! | OML AutoML User Interface
Automate production and deployment of ML models
• Enhance Data Scientist productivity
and user-experience
• Enable non-expert users to leverage ML
• Unify model deployment and monitoring
• Support model management
Features
• Minimal user input: data, target
• Model leaderboard
• Model deployment via REST
• Model monitoring
• Cognitive features for image and text
“Code-free” user interface supporting automated end-to-end machine
learning

Oracle Machine Learning
Key Features:
• Collaborative UI for data
scientist and analysts
• Packaged with Autonomous Databases
• Quick start Example notebooks
• Easy access to shared notebooks,
templates, permissions, scheduler, etc.
• OML4SQL
• OML4Py coming soon
• Supports deployment of OML models
Machine Learning Notebooks included in Autonomous Databases

3 - Using OCI Resource Manager
to Deploy Data Warehouse
Reference Architecture Patterns

4 - Modify Network
Settings for ADB Instances

5: Wallet rotation for
ADW or ATP

Per-database with Instance Wallet selected:
• All existing database specific instance wallets will be void.
• Post rotation need to download new wallet to connect to database.
• NOTE - Regional wallets with all database certification keys continue to work
Regional level with Regional Wallet selected:
• Both regional and database specific instance wallets are voided.
• Post rotation need to download new regional or instance wallets to connect to
any database in region
• All user sessions are terminated for databases whose wallet is rotated.
• User session termination begins after wallet rotation completes, however this
process does not happen immediately.
New Option To Rotate Wallets For ADB
1
2

6: Partitions with
external tables in Cloud

All data outside the database
• Files in Object Store buckets
Exposes the power of Oracle partitioning to
external data
• Partition pruning
• Partition maintenance
Enables order-of-magnitudes faster query
performance and enhanced data maintenance
Partitioned External Tables
…2016,04,01 2016,04,02
2016,04,0
3
File-02 in
Object Store
Bucket
File-03 in
Object Store
Bucket
File-01 in
Object Store
Bucket

Note only use of DBMS_CLOUD syntax is supported
Partitioned External Tables
BEGIN DBMS_CLOUD.CREATE_EXTERNAL_PART_TABLE(
table_name =>'PET1’,
credential_name =>'DEF_CRED_NAME’,
format => json_object('delimiter' value ‘,’,
'recorddelimiter' value 'newline’,
'characterset' value 'us7ascii’),
column_list => 'col1 number, col2 number, col3 number’
partitioning_clause => 'partition by range (col1) (
partition p1 values less than (1000) location (
‘https://swiftobjectstorage.us-ashburn-1 ... /file_01.txt') ,
'https://swiftobjectstorage.us-ashburn-1 ... /file_02.txt'') ,
'https://swiftobjectstorage.us-ashburn-1 ... /file_03.txt'') )
)
END;
/

Single table contains both internal (RDBMS) and
external partitions
• Full functional support, such as partial indexing,
partial read only, constraints, etc.
Partition maintenance for information lifecycle
management
• Currently limited support
• Enhancements in progress
Hybrid Partitioned Tables
…2016,04,01 2016,04,02
2016,04,
03
File-02 in
Object Store
Bucket
File-01 in
Object Store
Bucket
DB Partition

Data in any object store can be accessed
• Oracle Object Store, AWS S3 or Azure
Explicit authentication or pre-authenticated URIs
(Admittedly not a specific Partitioning feature,
but cool nevertheless)
Access data in Object Stores
Any Object
Storage

Note only use of DBMS_CLOUD syntax is supported
Hybrid Partitioned Tables
BEGIN DBMS_CLOUD.CREATE_HYBRID_PART_TABLE(
table_name =>'HPT1’,
credential_name =>'OBJ_STORE_CRED’,
format => json_object('delimiter' value ',', ‘
recorddelimiter' value 'newline', ‘
characterset' value 'us7ascii’),
column_list => 'col1 number, col2 number, col3 number’
partitioning_clause => 'partition by range (col1)
(partition p1 values less than (1000) external location (
'https://swiftobjectstorage.us-ashburn-1 .../file_01.txt') ,
partition p2 values less than (2000) external location (
‘https://swiftobjectstorage.us-ashburn-1 .../file_02.txt') ,
partition p3 values less than (3000) ) )
END;

7: How to track activity
with work requests

8: Performance monitoring
with Autonomous Database

• Monitor health, capacity, performance of ADB instances
• Uses metrics, alarms, and notifications
• Metrics accessible via OCI console or using APIs
Monitor Performance with ADB Metrics

1. CPU Utilization
2. Memory Utilization
3. Sessions
4. Failed Connections
5. Execute Count
6. Queued Statements
7. Running Statements
8. Failed Logons
9. Current Logons
10. Transaction Count
11. User Calls
12. Parse Count (Total)
Available Service Metrics

Autonomous Database Details page
provides top 6 view of library of
service metrics.
Viewing Top 6 Metrics on
ADB Console

Complete library of
only metrics
available via
the OCI Console
Service Metrics
page or by using
the Monitoring API
Viewing Full Library Database Metrics

Step 1 – Select the required compartment
Setting Up Monitoring Page

Step 2 – Select the metric namespace for autonomous database
Setting Up Monitoring Page

9: Setting alarms on
metrics like CPU Utilization

92 Confidential – © 2019 Oracle Internal/Restricted/Highly Restricted
Monitoring Query Language (MQL)
• Components in an MQL Expression
• An MQL expression includes the following components:
• metric , interval , dimensions as one or more name-value pairs (optional)
• grouping function (optional)
• statistic
• comparison operation (optional). Useful for defining alarms.
Examples
The number of hosts with CPU utilization greater than 80 percent:
(CpuUtilization[1m].max() > 80).grouping().sum()
The number of availability domains with a success rate lower than 0.99 :
(SuccessRate[1m].groupBy(availabilityDomain).mean() < 0.99).grouping().sum()

• Oracle Database 19c is now available on Autonomous Database for all regions.
• In select regions, customers can choose between Oracle Database 18c and Oracle Database
19c for new databases
• The default version for all new databases is 19c and most new Autonomous Database
features will only be available for Oracle Database 19c
• For most applications, there should not be any changes required
• However, it is recommended you should test your applications with Database 19c
• You can create a clone of your current database to Oracle Database 19c
• Test your application using this clone.
Upgrade an Autonomous Database Instance to Oracle Database 19c

• Oracle Database 18c is scheduled to be available in Autonomous Database until September,
2020
• You can choose to upgrade your database at any time during this period.
• After that time, all existing databases will be automatically upgraded to Oracle Database
19c.
• Autonomous Database offers a single button that provides a one-way, in-place upgrade
Upgrade an Autonomous Database Instance to Oracle Database 19c

101 Confidential – Oracle Internal
Time Range selector is displayed on the top of the Performance Hub page
Use the Select Duration field to set the time duration
Default, Last 60 mins is selected
• Specify to view Last 8 hours, Last 24 hours, Last week
• Specify a custom time range
Time Range field shows active sessions in chart form.
• Active sessions chart displays avg number of active sessions broken down by CPU, User
I/O, and Wait.
Time Range field and time slider

A time zone control has been added
to Performance Hub.
This will allow users to view data
using an alternate time zone to UTC.
They will have the choice of viewing
data using either UTC (default), the
time zone of their browser client, or
the time zone of the database.
Timezone Selector

SQL only monitored if running for at least five seconds or run in parallel
Displays monitored SQL statement executions by dimensions including Last Active Time, CPU Time,
and Database Time
Displays currently running SQL statements and SQL statements that completed, failed, or were
terminated.
Information includes Status, Duration, and SQL ID
• Status column has the following icons:
- A spinning icon indicates that the SQL statement is executing.
- A green check indicates SQL statement completed
- A red cross icon indicates that the SQL statement did not complete
- A clock icon indicates that the SQL statement is queued
SQL Monitoring

13: ASH Analytics in
Performance Hub

Shows Active Session History (ASH) analytics charts to explore ASH data
Drill down into database performance across multiple dimensions such as Consumer
Group, Wait Class, SQL ID, and User Name
Select an Average Active Sessions dimension and view the top activity for that dimension for
the selected time period.
For information on ASH, see Active Session History (ASH) in Oracle Database Concepts.
Active Session History (ASH) Analytics

Performance Hub has been expanded to include a new tab, "Workload". This can be used to monitor the
workload in the database and helps the user identify performance spikes and bottlenecks.
Workload Metrics on Performance Hub
There are 4 regions:
Each region contains
a group of charts that
indicate the
characteristics of the
workload and the
distribution of the
resources, as shown

14: Oracle APEX in
Autonomous Database

Free tier – Includes free developer tools
APEX SQL Developer
Web
ML Notebooks REST Interfaces
Cloud Developer Images
Including OCI Software
Development Kits (SDKs),
and database connectors
Terraform
for automation

144
Database-centric web application development framework
Oracle APEX
Develop desktop and
mobile web apps
Visualize and
maintain
database data
Leverage SQL Skills
and database
capabilities

Oracle
APEX
Oracle
Autonomous
Database
145 Copyright © 2019, Oracle and/or its affiliates. All rights reserved.
Included with New Always Free Service

Oracle Autonomous Database
Analytics = Autonomous Data Warehouse (ADW)
Fully Managed
Includes Oracle APEX at no additional cost
Autonomous
Transaction
Processing
Autonomous
Data Warehouse
OLTP = Autonomous Transaction Processing (ATP)

147
Home Page
Comprehensive Development IDE, Web Application Development, and SQL Database Development

148
SQL Workshop
Browser based maintenance of database objects and data
Designed to meet application developers’ needs, especially in hosted environments

149
SQL Workshop – Quick SQL
Rapidly design and prototype data models using a markdown-like shorthand syntax
that expands to standards-based Oracle SQL.

15: Oracle Machine
Learning Notebooks

The following
screen should
appear:
Running an SQL Statement

The white panel below the main title (SQL Query Scratchpad – this name is automatically generated) is an area
known as “paragraph”. Within a scratchpad you can have multiple paragraphs.
Each paragraph can contain one SQL statement or a SQL script.

In the SQL paragraph area copy and paste this code snippet. Your screen should now look like this:

Press the icon shown in the red box to execute the SQL statement….

…and display the
results in a
tabular format:

Using the report menu bar you can change the table to a graph and/or export the result set to a CSV or TSV file.
When you change the report type to one of the graphs, then a Settings link will appear to the right of
the menu which allows you to control the layout of columns within the graph.
Click on the bar graph icon to change the output to a bar graph (see below)

Click on the Settings link to unfold the
settings panel for the graph.
To add a column to one of
the Keys, Groups of Values panel
s just drag and drop the column
name into the required panel.
To remove a column from the
Keys, Groups of Values panel just
click on the x next to the column
name displayed in the relevant
panel.

Changing the layout of the graph
With the graph settings panel
visible:
• Remove all columns from the
both
the Keys and Values panels.
• Drag and drop MONTH into
the Keys panel
• Drag and drop REVENUE into
the Values panel
• Drag and
drop AVG_12M_REVENUE int
o the Values panel

Tidying up the report
•Click on the Settings link to hide the layout controls.
•Click on the Hide editor button which is to the right of the "Run this
paragraph" button.

Now only the output is visible.

Saving the Scratchpad as a New Notebook
The SQL Scratchpad in the previous section is simply a default type notebook with a system
generated name. But we can change the name of the scratchpad we have just created SQL
Query Scratchpad.
•Click on the Back link in the top left corner of the Scratchpad window to return to the OML
home page.

Notice that in the Recent Activities panel there is a potted history of what has happened to
your SQL scratchpad “notebook”.

Click on Go to Notebooks in the Quick Actions panel

Confidential
ADB autonomously and continuously monitors overall system performance
ADB scales CPU-IO resources based on overall workload requirements
• Scaling up autonomously expands CPU-IO resources by up to 3x
Enabled when provisioning new ADB instance or using Scale Up/Down on the Oracle Cloud
Infrastructure Console.
What is “Auto Scaling”

Confidential
ADB is completely responsive to actual usage patterns
Reduces cost of having too many OCPUs just to manage peak traffic load
Auto scaling manages unexpected spikes in workload and ensures consistent performance
BUT…Enabling auto scaling does not change the concurrency and parallelism settings for the
predefined services
Key Benefits of Auto Scaling

1 2 3 4 5 6 7
Auto scaling up as workload increases – ETL, adhoc analytics, data mining
As Workload Increases More OCPUs Automatically Added…

1 2 3 4 5 6 7
Auto scaling downwards as workload decreases
As Workload Decreases Number of OCPUs Reduces ...

Confidential
Ensure sufficient resources for query
workloads
• Month-end reporting can leverage
additional resources
• Run more sophisticated processing
(machine learning models) without
impacting other users
• Support increase in concurrent usage:
Monday morning sales reports for
weekend trading
• Support more dynamic range of users
(more adhoc queries, more machine
learning models, more data discovery…)
Ensure sufficient resources for ETL
workloads
• Time-dependent data loads can
automatically get access to more
resources
• Unexpected data load operations can
run without impacting other users
• Integrates nicely with CPU-I/O Shares
feature
Uses Cases for Data Warehousing

Confidential
Ensure sufficient resources for mixed
workloads
• Month-end transaction reporting can
leverage additional resources as required
• Run more sophisticated app processing
(using machine learning models) without
impacting application users
Better manage peak application workloads
• Time-dependent application loads can
automatically get access to more
resources
• Unexpected peak in operations can run
without impacting users
Integrates nicely with CPU-I/O Shares
feature
Uses Cases for Transaction Processing

Creating a new Autonomous Instance
Enabling Auto Scaling – One-Click!

17 - Download AWR Reports
From Performance Hub

1. Open the navigation menu. Under Database, click Autonomous
Transaction Processing or Autonomous Data Warehouse.
2. Choose your Compartment.
3. In the list of Autonomous Databases, click the display name of
the database you want to analyze using Performance Hub
reports.
4. Click Performance Hub.
1) Navigate to Performance Hub in Oracle Cloud Infrastructure Console

2) Adjust the Time Range selector

2. Click the Reports drop-down to display a list of available reports.
2. Click AWR to display the Generate AWR Report dialog.
3. You can choose to generate a report either from two
snapshots closest to the current time and date or from a
custom time range of your choice.
- If you choose the default time range, then select
Use the two snapshots closest to date and click Download.
- If you choose to generate a report from a custom time range,
then select Custom and select start and end times for your
range. Click Download.
2. Oracle Database generates a report named AWRReport_date_range.html that downloads to
the default download folder for your browser. View the report after the download completes.
3) Download the AWR Report

18 - Load data using
DBMS_CLOUD

• For data loading from files in the Cloud
• Store your object storage credentials
• Use the procedure DBMS_CLOUD.COPY_DATA to
load data
• The source file in this example is channels.txt
Load data using DBMS_CLOUD
File-02 in
Object Store
Bucket
File-03 in
Object Store
Bucket
File-01 in
Object Store
Bucket
SET DEFINE OFF BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'DEF_CRED_NAME',
username => 'adwc_user@example.com',
password => 'password' ); END; /

CREATE TABLE CHANNELS (channel_id CHAR(1), channel_desc VARCHAR2(20),
channel_class VARCHAR2(20) );
BEGIN DBMS_CLOUD.COPY_DATA( table_name =>'CHANNELS', credential_name =>'DEF_CRED_NAME’,
file_uri_list =>
'https://objectstorage.us-phoenix-1.oraclecloud.com/n/namespace-string/b/
bucketname/o/channels.txt', format => json_object('delimiter' value ',') );
END;
BEGIN DBMS_CLOUD.COPY_DATA( table_name =>'CHANNELS', credential_name =>'DEF_CRED_NAME’,
file_uri_list =>'https://objectstorage.us-phoenix-1.oraclecloud.com/n/namespace-string/b/
bucketname/o/exp01.dmp,
https://objectstorage.us-phoenix-1.oraclecloud.com/n/namespace-string/b/
bucketname/o/exp02.dmp', format => json_object('type' value 'datapump') );
END;

BEGIN DBMS_CLOUD.COPY_COLLECTION( collection_name => 'fruit', credential_name =>
'DEF_CRED_NAME', file_uri_list => 'https://objectstorage.us-ashburn-1.oraclecloud.com/n/
namespace-string/b/fruit_bucket/o/myCollection.json’,
format => JSON_OBJECT('recorddelimiter' value '''n''') );
END;
BEGIN DBMS_CLOUD.COPY_COLLECTION( collection_name => 'fruit2', credential_name =>
'DEF_CRED_NAME', file_uri_list => 'https://objectstorage.us-ashburn-1.oraclecloud.com/n/
namespace-string/b/json/o/fruit_array.json’,
format => '{"recorddelimiter" : "0x''01''", "unpackarrays" : TRUE}' );
END;

SELECT table_name, owner_name, type, status, start_time, update_time,
logfile_table, badfile_table FROM user_load_operations WHERE
type = 'COPY’;
TABLE_NAME OWNER_NAME TYPE STATUS START_TIME UPDATE_TIME LOGFILE_TABLE BADFILE_TABLE
------------------------------------------------------------------------------------
FRUIT ADMIN COPY COMPLETED 2020-04-23 22:27:37 2020-04-23 22:27:38 "" ""
FRUIT ADMIN COPY FAILED 2020-04-23 22:28:36 2020-04-23 22:28:37 COPY$2_LOG COPY$2_BAD
SELECT credential_name, username, comments FROM all_credentials;
CREDENTIAL_NAME USERNAME COMMENTS
---------------------------–----------------------------- --------------------
ADB_TOKEN user_name@example.com {"comments":"Created via
DBMS_CLOUD.create_credential"}
DEF_CRED_NAME user_name@example.com {"comments":"Created via
DBMS_CLOUD.create_credential"}

19 - ADB now supports SQL
access to tenancy details

183
When you file a service request for Autonomous Database, you need to provide the tenancy details for
your instance. Tenancy details for the instance are available on the Oracle Cloud Infrastructure console.
However, if you are connected to the database, you can now obtain these details by querying
the CLOUD_IDENTITY column of the V$PDBS view. For example:
...will generate something similar to the following:
How to get the tenancy details for your instance
SELECT cloud_identity FROM v$pdbs;
{"DATABASE_NAME" : "DBxxxxxxxxxxxx",
"REGION" : "us-phoenix-1",
"TENANT_OCID" :
"OCID1.TENANCY.REGION1..ID1",
"DATABASE_OCID" :
"OCID1.AUTONOMOUSDATABASE.OC1.SEA.ID2
",
"COMPARTMENT_OCID" :
"ocid1.tenancy.region1..ID3"}

20 - Autonomous
Dataguard for ADB

Thank You
Any Questions ?
Sandesh Rao
VP AIOps for the Autonomous Database
@sandeshr
https://www.linkedin.com/in/raosandesh/
https://www.slideshare.net/SandeshRao4

20 tips and tricks with the Autonomous Database

20 tips and tricks with the Autonomous Database

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