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Extract OCI Resource Status Using APIs

In this blog, I want to show you a way to extract every resource in a compartment along with their status using a Python script and OCI APIs.

There are several pre-reqs that you need to fulfil in order to be able to connect using APIs to OCI. Follow the same pre-reqs as in my previous blog about Metric Extensions.

Step 1 – Prerequisites



Once you have created an OCI user, setup API Keys and setup your Python host; then, go ahead and create a Python script similar to the one below.

#!/usr/bin/python3

# This is a sample python script that searches resources in a compartment
# Run this script on the client that you want to monitor.
# Command: python script_name.py

import oci,subprocess,os,datetime,json
from pytz import timezone

# using default configuration file (~/.oci/config)
from oci.config import from_file
config = from_file()

# initialize service client with default config file

search_client = oci.resource_search.ResourceSearchClient(config)
query = f"query all resources where compartmentId = '<compartment OCID>' && lifeCycleState != 'AVAILABLE' && lifeCycleState != 'ACTIVE' && lifeCycleState != 'Assigned' && lifeCycleState != 'Running' && lifeCycleState != 'Succeeded' && lifeCycleState != 'Deleted'"
search_response = search_client.search_resources(
    search_details=oci.resource_search.models.StructuredSearchDetails(
        type="Structured",
        query=query,
    ),
    limit=1000,
)
print(f"Compartment has {len(search_response.data.items)} resources")

json_format = json.loads(str(search_response.data.items))

# Iterate through the JSON array
for item in json_format:
    print(item["display_name"], item["lifecycle_state"])

As you can see from the script. We are using OCI Search in order to get all resources in a compartment (<compartment OCID>) and also we all filtering the search to only show resources that are either down, terminated or failed.

Once you get the list of resources in your compartment you can create a text/csv file and share the list with all the interested parties.

Another option is the ability to combine this with the Monitoring Service and create a Metric Extension (ME). The metric extension will hold the list of resources that have that specific lifecycle_state. Once the date is contained in the ME you can send notifications or create a dashboard showing these resources.

Thanks,
Alfredo

Send OCI Alerts Notifications To 3rd Party Systems (Object Storage)

Hi there!

In this blog, I want to show how you can send an OCI Alert Notification to a 3rd party system. The way I describe this tutorial is by sending the notification to an OCI Storage bucket so you can ingest it using the tool of preference.

First of all I would describe the services used:

  • OCI Monitoring Service
  • OCI Streams Service
  • OCI Connector Hub
  • OCI Storage Service

Let’s go an create a Storage bucket in order to hold these notifications.
Create a Storage bucket

Once you have the Storage bucket ready, go ahead and create a Stream pool and a Stream.
Create a Stream Pool

Don’t forget to create the Stream.

Now is time to create an alarm and send the notification to the Stream we just created.
Create an Alarm

During the alarm creation, be sure to select the Stream as a destination.

At this point every single time the alarm generates a notification, it will be send to the Streaming service. Now we need to send this notification from the Streaming service to our Storage bucket. We need to create a Connector Hub for that.
Create a Connector Hub

The Connector Hub configuration will look like below.

After this, the notification that was stored in the Streaming service will be moved to your Storage bucket.

Keep in mind that you need to set several policies for this to work.
Connector Hub policies

Another important consideration is the fact that the Streaming service encrypts the data at rest. So when you open one of those files inside your Storage bucket, you will find them encrypted. You could decrypt them using base64 functions.

Thanks,
Alfredo

Monitor MySQL HeatWave with OCI Database Management

In this post I want to show you how you can monitor your MySQL Heatwave databases with OCI Database Management Cloud Service. Is worth to mention that this Cloud Service provides this functionality free of cost and is automatically enabled for below configurations:

  • Standalone
  • High Availability

Let’s start by creating a new Heatwave instance.

You will notice that Database Management is automatically enabled.

The Database Management service will then publish several metrics related to your Heatwave instance.

You can of course use the alarms and notification services to setup alerts on important metrics.

Last but not least navigate to the Performance Hub page to find out real time performance information for the queries inside your Heatwave instance.

If you want to learn more about this service follow the link below.



Thanks,
Alfredo

Integrate EM Metrics With Third-Party Tools Using REST APIs

I want to share how to use Enterprise Manager’s (EM) REST APIs to query metric data and integrate with 3rd party tools.

First of all, always follow the documentation. Below is the link to the EM’s REST API functionality.



Using the EM’s REST API functionality we can query the EM repository using a web service. Let’s follow below steps.

a) Create a new EM user who is going to have the privileges to query the EM’s repository. Navigate to Setup -> Security -> Administrators.

Administrators navigation

b) Create a new Repository User.

Administrator creation

c) I’m going to create a new Administrator named ABC.

Administrator creation

d) Create a new Named Credential. Navigate to Setup -> Security -> Named Credentials.

Named Credential creation

e) Click on Create.

Named Credential creation

f) I’m going to create NC_ABC Named Credential.

Named Credential creation

g) Enable the repository REST APIs in EM. For that execute below emctl command.

emctl set property -name oracle.sysman.db.restfulapi.executesql.repository.query.enable -value true -sysman_pwd "sysman"

Now is time to test our REST APIs.

I’m going to test them out using 2 different mechanisms. For the first one, let’s use CURL.

$ curl -k -u ABC -H "Content-Type: application/json" -d '{"sqlStatement": "SELECT * FROM sysman.MGMT$TARGET_METRIC_SETTINGS", "credential": {"DBCredsMonitoring":"NC_ABC"}, "maxRowLimit": 10, "maxColumnLimit": 2}' https://<EM_Host>:<EM Port>/em/websvcs/restful/emws/oracle.sysman.db/executesql/repository/query/v1

Here are the results based on my demo environment:

REST API results from CURL

The second test is by using Ansible. If you are not familiar on how to configure Ansible in order to work with EM, please follow the Oracle Live Labs that we created about this.



I’m going to create a new YAML file (test.yml) in order to test the REST API.

---
- name: Using a REST API
  become: false
  hosts: emserver
  gather_facts: false
  tasks:

    - name: Query Metric Settings
      uri:
        url: https://emcc.marketplace.com:7803/em/websvcs/restful/emws/oracle.sysman.db/executesql/repository/query/v1
        method: POST
        return_content: yes
        force_basic_auth: yes
        validate_certs: no
        body_format: json
        url_username: ABC
        url_password: *******
        headers:
           Content-Type: application/json
        body:
          {
            "sqlStatement": "SELECT * FROM sysman.MGMT$TARGET_METRIC_SETTINGS", "maxRowLimit": 2, "maxColumnLimit": 29
          }
      register: results

    - name: Print returned json dictionary
      debug:
        var: results.json

Let’s execute this using Ansible.

ansible-playbook /home/oracle/ansible/yml/test.yml -u oracle --private-key=~/.ssh/dtkey.ssh

Here the results:

REST API results from Ansible

With this you can get the desired results from the EM’s repository using this REST API.

Thanks,
Alfredo

Extend Your Oracle Monitoring With Configuration Extensions

As soon as you enable the Database Lifecycle Management Pack; Oracle Enterprise Manager will automatically collect hundreds of configuration items of your database estate. However if you need to customize this collection, Configuration Extensions provide a way to identify files and other configuration data that Cloud Control does not already collect.

In this blog, I’ll show you how I created a Configuration Extension (CE) in order to collect DB Services data.

CE’s are found under the Enterprise -> Configuration menus.

While in the CE dashboard, you can create, edit, delete and deploy CE’s using the available menu.

Let’s click the “Create” button.

Using the CE create wizard type a name for the CE, a target type and a Sample Target. As soon as you select “Database Instance” for the Target Type a new option will enable. This option is to select between “Files & Commands” or “SQL”.

In this example I’m going to choose “SQL” and then type the required SQL required to retrieve the data I need from the DB. Let’s type “select name as service_name from dba_services”. Also type “service_name” in the alias section and choose the “Database Query Parser”.

The entries should look similar to the screenshot above.

The data extracted from the DB in the CE is in XML format. We need to add parser rules in order to match and identify nodes in the parsed tree. Click on the number of “Rules” in the table. In this case click on the number “0”.

On the Parser Rule page click “Add” and type this condition and expression:

  • Condition: /root/row
  • Expression: SERVICE_NAME/text()

This will allow the parser to use the text inside the SERVICE_NAME node as the name of the row. Click the return button.

Now we can click on the “Preview” button from the menu.

It will show you the preview output. Similar like this:

Click “OK” and then click “Save”.

Select your newly created CE and click on “Manage Deployments” button. Select the targets where you want to deploy the CEs and click “Apply”.

It will look similar to the screen below:

Navigate to the configuration of your target and verify the CE is correctly pulling the desired configuration data.

Now we have our CE correctly gathering the desired configuration data of our DB.

You can find the documentation for Configuration Extensions below.



Hope this helps to provide some clarity on how to create Configuration Extensions in Oracle Enterprise Manager.

Thanks,
Alfredo

How To Discover ExaCC/ExaCS in Enterprise Manager – Detailed

In this post I want to share the required steps to discover ExaCC or ExaCS in Oracle Enterprise Manager (EM). I’ll provide as much detail as possible.

  1. Make sure your EM is at least on 13.5 RU 16. If not, please apply RU 16 to EM. Follow this link if you want to know how to patch EM. HERE!
  2. You need to designate a “monitoring” agent for the discovery. Is recommended that this agent sits outside your Exadata rack and has access to the OCI REST APIs. More information HERE!
  3. Make sure this agent has both the Database and the Exadata plugins deployed and is patched to the same RU level as the OMS. HERE!
  4. The agent must be able to reach the OCI REST APIs. There are 3 ways to achieve this.
    a) You can use a Proxy
    b) You can use the OCI Management Gateway
    c) You can have direct network connection

    Below is a list of APIs that you will need access to:
    Either grant access to (*.oci.oraclecloud.com) or individual URLS:
    https://query.<oci_region>.oci.oraclecloud.com
    https://identity.<oci_region>.oci.oraclecloud.com
    https://database.<oci_region>.oci.oraclecloud.com
    https://wss.exacc.<oci_region>.oci.oraclecloud.com
    https://management-agent.<oci_region>.oci.oraclecloud.com
    https://certificatesmanagement.<oci_region>.oci.oraclecloud.com
    https://certificates.<oci_region>.oci.oraclecloud.com
    https://telemetry-ingestion.<oci_region>.oci.oraclecloud.com
    https://auth.<oci_region>.oci.oraclecloud.com
    https://objectstorage.<oci_region>.oci.oraclecloud.com

    You may also test that connectivity by executing:
    $ curl -v https://query.<oci_region>.oci.oraclecloud.com
  5. Create or use an OCI account that has access to read your Exadata racks. These are the policies I have used:
    allow group <domain/group name> to read database-family in compartment <compartment name>
    You can find more information about the required policies HERE!
  6. Setup API Keys for authentication. Please follow instructions in the MOS note below:
    EM13.5: Manage OCI Connectivity Named Credentials Test Failed with Invalid Private Key. (Doc ID 2792126.1)
  7. Create an EM Named Credential using the API Keys created on the previous step. More details HERE!
  8. You also need the proper Storage Server credentials. If you don specify them during the discovery process, Storage Server targets will not be discovered. Instructions on how to retrieve this credential can be found HERE!
    If you can’t retrieve these credentials, please open a Service Request with support.
  9. Discover your Exadata Infrastructure following the discovery wizard. More information HERE!

After finishing the discovery, you should have your Exadata Cloud target in EM. Please wait between 15 to 20 minutes for the target information to be populated.

If your network setup does not allow direct connectivity to the OCI REST APIs then you will have to use an internal Proxy or use the OCI Management Gateway. More information about the OCI Management Gateway can be found HERE!

There’s also a very good post from Simone about setting up the Management Gateway for the EM agents.
https://blogs.oracle.com/observability/post/setup-oem-agents-cloud-mgmt-gw

Update:
You will also have to modify 2 parameters. One at the OMS level and one at the agent level. Please follow below MOS notes:

OEM 13c : SSH Key Credential Test On a Host Target Fails With “Remote operation timed out.” (Doc ID 2415262.1)
EM 13.2: Agent Patching From EM Console Fails With Error “concurrent job tasks limit (50) has been reached” (Doc ID 2476803.1)

Thanks,
Alfredo

Extract OCI Monitoring Metrics Using REST APIs

In my previous post I showed you how you can create custom metrics in order to monitor a Standby database. Those steps detailed how to ingest (post) metrics to the OCI Monitoring service.



In this post I’ll show you how you can list metric definitions and how you can extract metric data from the Monitoring service. This metric extraction is really useful when you need to integrate the Monitoring service with 3rd party tools.

Step 1 – Prerequisites

Please follow all the prerequisites executed in the previous blog.

Step 2 – List Metrics

In this step we are going to create the Python script that list our custom metric.

Copy the code below and paste it into a file name list_metric.py

#!/usr/bin/python3

# This is an automatically generated code sample.
# To make this code sample work in your Oracle Cloud tenancy,
# please replace the values for any parameters whose current values do not fit
# your use case (such as resource IDs, strings containing EXAMPLE or unique_id, and
# boolean, number, and enum parameters with values not fitting your use case).

import oci

# Create a default config using DEFAULT profile in default location
# Refer to
# https://docs.cloud.oracle.com/en-us/iaas/Content/API/Concepts/sdkconfig.htm#SDK_and_CLI_Configuration_File
# for more info
config = oci.config.from_file()


# Initialize service client with default config file
monitoring_client = oci.monitoring.MonitoringClient(config)


# Send the request to service, some parameters are not required, see API
# doc for more info
list_metrics_response = monitoring_client.list_metrics(
    compartment_id="<YOUR COMPARTMENT ID>",
    list_metrics_details=oci.monitoring.models.ListMetricsDetails(
        name="<YOUR METRIC NAME>",
        namespace="<YOUR CUSTOM NAMESPACE>"))


# Get the data from response
print(list_metrics_response.data)

Amend the inputs needed depending on your METRIC and OCI configuration:

  • <YOUR METRIC NAME>
  • <YOUR CUSTOM NAMESPACE>
  • <YOUR COMPARTMENT ID>

Let’s execute our script:

$ ./list_metric.py
[{
  "compartment_id": "<YOUR COMPARTMENT ID>",
  "dimensions": {
    "server_id": "<YOUR SERVER ID>"
  },
  "name": "<YOUR METRIC NAME>",
  "namespace": "<YOUR CUSTOM NAMESPACE>",
  "resource_group": null
}]

Step 3 – Query Metric Data

In this step we are going to create a Python script that query metric data.

Copy the code below and paste it into a file name query_metric.py

#!/usr/bin/python3

# This is an automatically generated code sample.
# To make this code sample work in your Oracle Cloud tenancy,
# please replace the values for any parameters whose current values do not fit
# your use case (such as resource IDs, strings containing EXAMPLE or unique_id, and
# boolean, number, and enum parameters with values not fitting your use case).

import oci
from datetime import datetime

# Create a default config using DEFAULT profile in default location
# Refer to
# https://docs.cloud.oracle.com/en-us/iaas/Content/API/Concepts/sdkconfig.htm#SDK_and_CLI_Configuration_File
# for more info
config = oci.config.from_file()


# Initialize service client with default config file
monitoring_client = oci.monitoring.MonitoringClient(config)


# Send the request to service, some parameters are not required, see API
# doc for more info
summarize_metrics_data_response = monitoring_client.summarize_metrics_data(
    compartment_id="<YOUR COMPARTMENT ID>",
    summarize_metrics_data_details=oci.monitoring.models.SummarizeMetricsDataDetails(
        namespace="<YOUR CUSTOM NAMESPACE>",
        query="<YOUR METRIC NAME>[1m].mean()",
        start_time=datetime.strptime(
            "2023-10-11T14:38:22.574Z",
            "%Y-%m-%dT%H:%M:%S.%fZ"),
        end_time=datetime.strptime(
            "2023-10-11T17:27:08.471Z",
            "%Y-%m-%dT%H:%M:%S.%fZ"),
        resolution="5m"),
    compartment_id_in_subtree=False)

# Get the data from response
# Get the data from response
string = ' '.join([str(item) for item in summarize_metrics_data_response.data])
json_object = json.loads(string)
print(json_object["aggregated_datapoints"])

Amend the inputs needed depending on your METRIC and OCI configuration:

  • <YOUR CUSTOM NAMESPACE>
  • <YOUR COMPARTMENT ID>
  • <YOUR METRIC NAME>

Let’s execute our script:

$ ./query_metric.py
[{'timestamp': '2023-10-11T14:41:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T14:46:00+00:00', 'value': 0.016666667}, {'timestamp': '2023-10-11T14:51:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T14:56:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T15:01:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T15:06:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T15:11:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T15:16:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T15:21:00+00:00', 'value': 0.016666667}, {'timestamp': '2023-10-11T15:26:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T15:31:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T15:36:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T15:41:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T15:46:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T15:51:00+00:00', 'value': 0.016666667}, {'timestamp': '2023-10-11T15:56:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T16:01:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T16:06:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T16:11:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T16:16:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T16:21:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T16:26:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T16:31:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T16:36:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T16:41:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T16:46:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T16:51:00+00:00', 'value': 0.033333333}, {'timestamp': '2023-10-11T16:56:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T17:01:00+00:00', 'value': 0.016666667}, {'timestamp': '2023-10-11T17:06:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T17:11:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T17:16:00+00:00', 'value': 0.0}, {'timestamp': '2023-10-11T17:21:00+00:00', 'value': 0.016666667}]

Now that we have extracted our metric data set, we can spool it off to a local file or ingest it into a 3rd party tool.

I also want to highlight another blog from Kay Singh about Monitoring metric extraction.



Hope this blog shows you how to extract Monitoring metric data from OCI.

Thanks,
Alfredo

Monitor Oracle Standby Databases In OCI With Custom Metrics

Monitoring Oracle Standby Database (Data Guard) has been always a tricky task. Just by the nature of them (the fact that the instance is not open in read-write mode) is hard to gather information about them. Even using specialized tools like Oracle Enterprise Manager requires SYSDBA credentials in order to effectively monitor them. But what about when running them on OCI?

Oracle OCI Monitoring service allow us to monitor cloud resources using metrics and alarms.

Oracle OCI Monitoring Service Architecture

In this post I want to show you how you can create a custom metric to monitor the “Apply Lag” on your Oracle Standby database, so you can create an alarm if it crosses a threshold.

I’m going to follow most of the steps detailed by Liu-Wei on this post “https://qiita.com/liu-wei/items/5e8e04f1e58cc6406ca9” .

Step 1 – Prerequisites

Add an API Key

First of all. You will have to designate an OCI user that has the proper permissions to access the Monitoring Service metrics and post them using custom metrics. This could be your account or a service account. Once you have designated this user, then login to the OCI console and choose the region where the Standby DB resides. Then click on the Profile icon and click on the account name.

Once there, scroll down and click on API Keys from the left menu.

Then click on the Add API Keys button.

Then generate the API Keys, save them nd store them in a secure place and click Add.

This will allow the script to login to the Monitoring Service in order to post custom metric data.

Create an OCI configuration file

For this exercise we will use the API Keys we just generated and we will create a config file in the host where the Standby DB is running using the oracle account.

I used the location /home/oracle/.oci in order to store the OCI config file and the private key. You may use another location depending on your internal standards.

Using the Configuration File Preview copy the contents and save them in the configuration file we are creating in the DB host.

This Preview already has the correct setting for the user, fingerprint, tenancy and region. However, you should amend the key_file setting. This setting is the path where your private key file is stored.

For this exercise it will be:

key_file=/home/oracle/.oci/mykey_private.pem

At the end of this, you should have 2 files. The config file and the private key file in the DB host.

[oracle]$ ls
config mykey_private.pem

Setup Python on DB Host

For this exercise we are going to use Python in order to consume the required REST APIs to post the metric data to the Monitoring Service.

Verify the Python installation on the DB Host using the oracle account. Python3 was already installed on this host.

[oracle]$ which python3
/bin/python3

However we need to install the oci module in Python. Before we install the oci module we need to upgrade pip in Python.

For this, logout from the oracle account and use the opc account. Execute the command below:

[opc]$ sudo pip3 install --upgrade pip

Login again with the oracle account and execute:

[oracle]$ pip3 install -U oci

This should install the oci module correctly.

Step 2 – Create the Python script

In this step we are going to create the Python script that connects to the Standby DB, gathers the Apply Lag and posts the data to the Monitoring service.

Copy the code below and paste it into a file name post_lag_value.py

#!/usr/bin/python3

# This is a sample python script that post a custom metric(lag_value) to oci monitoring.
# Run this script on the client that you want to monitor.
# Command: python post_lag_value.py

import oci,subprocess,os,datetime
from pytz import timezone

# using default configuration file (~/.oci/config)
from oci.config import from_file
config = from_file()

# initialize service client with default config file
monitoring_client = oci.monitoring.MonitoringClient(config,service_endpoint="https://telemetry-ingestion.us-ashburn-1.oraclecloud.com")

os.environ['ORACLE_HOME'] = "<YOUR ORACLE HOME>"
os.environ['ORACLE_SID'] = "<YOUR SID>"

def run_sqlplus(sqlplus_script):

    """
    Run a sql command or group of commands against
    a database using sqlplus.
    """

    p = subprocess.Popen(['<YOUR ORACLE HOME>/sqlplus','-s','/nolog'],stdin=subprocess.PIPE,
        stdout=subprocess.PIPE,stderr=subprocess.PIPE)
    (stdout,stderr) = p.communicate(sqlplus_script.encode('utf-8'))
    stdout_lines = stdout.decode('utf-8').split("\n")

    return stdout_lines

sqlplus_script="""
connect / as sysdba
set heading off
SELECT extract(day from p.val) *1440 + extract(hour from p.val)*60 +
extract(minute from p.val) + extract(second from p.val)/60 lag_minutes
from (SELECT name,to_dsinterval(value) val from v$dataguard_stats where name ='apply lag') p;
exit
"""

sqlplus_output = run_sqlplus(sqlplus_script)

for line in sqlplus_output:
     if line.strip():
         lag_value=float(line)

print(lag_value)

times_stamp = datetime.datetime.now(timezone('UTC'))

# post custom metric to oci monitoring
# replace "compartment_ocid string with your compartmet ocid
post_metric_data_response = monitoring_client.post_metric_data(
    post_metric_data_details=oci.monitoring.models.PostMetricDataDetails(
        metric_data=[
            oci.monitoring.models.MetricDataDetails(
                namespace="<YOUR CUSTOM NAMESPACE>",
                compartment_id="<YOUR COMPARTMENT ID>",
                name="<YOUR METRIC NAME>",
                dimensions={'server_id': '<YOUR SERVER ID>'},
                datapoints=[
                    oci.monitoring.models.Datapoint(
                        timestamp=datetime.datetime.strftime(
                            times_stamp,"%Y-%m-%dT%H:%M:%S.%fZ"),
                        value=lag_value)]
                )]
    )
)

# Get the data from response
print(post_metric_data_response.data)

Amend the inputs needed depending on your DB and OCI configuration:

  • <YOUR ORACLE HOME>
  • <YOUR SID>
  • <YOUR CUSTOM NAMESPACE>
  • <YOUR COMPARTMENT ID>
  • <YOUR METRIC NAME>
  • <YOUR SERVER ID>

One important thing to mention is the ingestion service endpoint. I’m using Ashburn as my region, therefore my ingestion endpoint is “https://telemetry-ingestion.us-ashburn-1.oraclecloud.com”. Yours should be different depending on your region.

https://docs.oracle.com/en-us/iaas/api/#/en/monitoring/20180401/

Next, let’s make the post_lag_value.py file executable.

[oracle]$ chmod +x post_lag_value.py

Let’s try our Python script.

./post_lag_value.py 
/home/oracle/.local/lib/python3.6/site-packages/oci/_vendor/httpsig_cffi/sign.py:10: CryptographyDeprecationWarning: Python 3.6 is no longer supported by the Python core team. Therefore, support for it is deprecated in cryptography. The next release of cryptography (40.0) will be the last to support Python 3.6.
  from cryptography.hazmat.backends import default_backend  # noqa: F401
0.0
{
  "failed_metrics": [],
  "failed_metrics_count": 0
}

As you can see from the output of the file, the current lag is “0.0” minutes and the failed_metrics_count is also “0”. This means that we successfully posted this data to the Monitoring service.

Let’s now find out if our custom metric is visible from the OCI console.

Using the hamburger menu navigate to “Observability & Management” and under the Monitoring Service click on Metrics Explorer.

Inside Metrics Explorer choose the correct Compartment, Namespace and metric. Remember that you provided them in the Python script. Verify you can see data in the graph.

The script is now posting Apply Lag data to the monitoring service.

Step 3 – Schedule

Now we need to schedule the execution of our Python script every “x” minutes. For this I’m using a Cron job. Follow the instructions in the MOS note to enable Cron. How To Use Crontab In OCI DBCS? (Doc ID 2639985.1)

My Cron looks as follows:

[opc]$ sudo cat /etc/crontab
SHELL=/bin/bash
PATH=/sbin:/bin:/usr/sbin:/usr/bin
MAILTO=root

# For details see man 4 crontabs

# Example of job definition:
# .---------------- minute (0 - 59)
# |  .------------- hour (0 - 23)
# |  |  .---------- day of month (1 - 31)
# |  |  |  .------- month (1 - 12) OR jan,feb,mar,apr ...
# |  |  |  |  .---- day of week (0 - 6) (Sunday=0 or 7) OR sun,mon,tue,wed,thu,fri,sat
# |  |  |  |  |
# *  *  *  *  * user-name  command to be executed


# System should configure AIDE for Periodic Execution 
05 4 * * * root /usr/sbin/aide --check

*/5 * * * * oracle /home/oracle/post_lag_value.py >> post_lag.log 2>&1

I schedule this Cron job every 5 minutes. You may adjust it to your desired frequency.

Step 4 – Create an Alarm

Go to the Monitoring service and create an Alarm using the Alarm Definitions option.

After this, we will have a notification when the Apply Lag is more than 60 minutes in our Standby DB.

This concludes this small exercise of monitoring the Apply Lag for a Standby Oracle Database using the OCI Monitoring service.

Hope this helps,
Alfredo

Maximize Your Exadata Cloud at Customer Efficiency Using Oracle Enterprise Manager 13.5

Oracle Enterprise Manager (EM) it’s been the go-to tool to monitor and manage on-premises Exadata deployments for quite some time. By doing a quick search in this blog I found presentations dated back from 2019 about choosing EM 12c or 13c to monitor and manage Exadata. Time has gone quick and now it’s time to discuss a new generation of Cloud services.

During the recent years Exadata appliances have evolved into Exadata Cloud Service (ExaCS) and Exadata Cloud @ Customer (ExaCC). Customers have the same monitoring requirements though. It is more than necessary to have proper observability mechanisms in place for these cloud services.

This post is meant to guide you through the process of discovering your Exadata Cloud @ Customer services into Oracle Enterprise Manager 13.5

First things first. You can find all the required information in the EM 13.5 documentation.



One of the questions that I get from customers is, what are the features in EM for your ExaCC deployment? Here are some of them:

  • Automatically identify and organize related targets
  • Visualize the database and related targets associated with ExaCC
  • Monitor performance for ExaCC components like Storage Grids, ExaCC DomU, GI and database targets on a single pane of glass

For more information about these features, refer to the Features section in the Oracle documentation.

Let’s get now into the discovery process. There are some pre-reqs that you will have to meet before attempting the discovery process. Please read carefully Part II of the Oracle documentation. Here are some highlights:

  • Create Named Credentials for OCI
    In this step you will generate the required OCI API Signing Keys in order to create an EM Named Credential. Follow the instructions in this section but also look at below MOS note:
    EM13.5: Manage OCI Connectivity Named Credentials Test Failed with Invalid Private Key. (Doc ID 2792126.1)
  • Discover Exadata C@C
    On this step we will discover ExaCC in EM. Staring with EM 13.5 RU14 you can discover it using the UI (console). Before that the only available option was to use CLI. I strongly recommend you apply RU14 before attempting the discovery process.
    Follow the steps on the documentation in order to discover your ExaCC service.

During the discovery process you will have to designate or install an EM agent that will be responsible of monitoring your ExaCC service. This agent must have access to Oracle Cloud Infrastructure (OCI).

Open the Discovery Wizard in EM

On the “Add Targets Manually” page choose the guided process.

Select “Oracle Exadata Infrastructure” and click the “Add” button.

On the “Exadata Cloud Discovery” wizard select the required inputs:

  • Management Agent
  • Backup Agent (optional)
  • Agent’s Named Credential
  • OCI Cloud Home Region
  • OCI Named Credential (created on the pre-reqs section)
  • OCI Subscribed Region (where your tenancy resides)

After this, click on the “Next” button.

On the “Discovery” section of the wizard, EM will discover all the ExaCC related targets. On this page, set the “Storage Server Credentials” in order to monitor the Storage Servers. Click “Next”

On the next page a summary will be displayed. Verify all the targets and then click “Submit”.

At this point you will have your ExaCC service, discovered in EM. You can see all your ExaCC services by navigating to “Targets” -> “Exadata”.

The “Exadata” dashboard shows all your Exadata appliances and services. Click on the recently discovered ExaCC service.

The “Home Page” shows all ExaCC components along with resource utilization.

Use the “Overview Tab” in order to navigate on all the available monitoring sections. Below screenshot provides an insight into “CPU” utilization by selecting the “Resource” section.

Follow the Oracle documentation in order to understand all the available options and sections in the ExaCC Home Page.

At this point your ExaCC is discovered and ready to be managed and monitored.

Next steps are to setup your monitoring metrics, thresholds and rules in order to receive notifications. Also look at the available OAS reports available for your ExaCC.

We will cover the integration of this EM monitoring and management of ExaCC with OCI Observability & Management services on future posts. These O&M services like Operations Insights help you to forecast utilization, provide advanced capacity planning insights by using Machine Learning algorithms.

Update!!!: Royce Fu just created a wonderful post about EM and O&M integration for ExaCC. Please find the link below.



Thanks,
Alfredo

How To Monitor Oracle DBs On Docker Containers With EM 13c

This post continues the thread of DevOps and automation for the Oracle Database. During the last couple of months I’ve seen more interest in deploying Oracle Databases on Docker containers. Even more now that Oracle released full support of RAC Databases running on Docker.



After you deploy your first Oracle Database on Docker, several questions come into mind; like:

  • How do I monitor the status of the Oracle Database?
  • How do I manage the performance of the Oracle Database and the SQL’s being executed on it?
  • How do I alert on issues on a timely manner?

Well, if you have the same questions or concerns… you are not alone!

On this post, I’m trying to explain the process I followed in order to deploy an Oracle Enterprise Manager agent on a Docker container and how I do monitor and manage the Oracle Database running on it.

First things first. Just a quick recap of my environment.

  • Oracle Linux 7 host running Docker 19.03
  • Create a MACVLAN network on Docker. My network adapter is ens3 on the host running Docker.
sudo docker network create -d macvlan --subnet=192.168.56.0/24 --gateway=192.168.56.1 -o parent=ens3 orcl_nw
  • Create a Docker volume type “nfs” using an NFS share named /NFSSHARE
sudo docker volume create --driver local --opt type=nfs --opt o=addr=<nfs server>,rw,bg,hard,tcp,vers=3,timeo=600,rsize=32768,wsize=32768,actimeo=0 --opt device=:/NFSSHARE agentstorage
  • Pull the 19.3 Docker image from container-registry.oracle.com
sudo docker pull container-registry.oracle.com/database/enterprise:19.3.0.0
  • Create the Docker container using the recently pulled image and mounting the volume “agentstorage” into “/u01” inside the container
sudo docker create -t -i \
--name ol7-orcl --hostname ol7-orcl --user oracle --ip 192.168.56.101 \
-e ORACLE_SID=ORCL \
-e ORACLE_PDB=PDB1 \
-v agentstorage:/u01 \
container-registry.oracle.com/database/enterprise:19.3.0.0
  • Disconnect the bridged Docker network from the container
sudo docker network disconnect bridge ol7-orcl
  • Connect the container to the MACVLAN network
sudo docker network connect orcl_nw --ip 192.168.56.101 ol7-orcl
  • Start our Docker container
sudo docker start ol7-orcl
  • Review the startup process and the creation of the database
sudo docker logs -f ol7-orcl

At this point we have our Docker container and the ORCL database up and running. But now I need a way to connect my ORCL database to the external world. Because I’m running a MACVLAN, I can create another Docker container in the same network and install Oracle Enterprise Manager (EM) in order to monitor it. Or I can also install Oracle EM on the host running Docker and setup a network link and a route to my Docker container. Let’s do the second option.

sudo ip link add mydocker-net link ens3 type macvlan mode bridge
sudo ip addr add 192.168.56.1/32 dev mydocker-net
sudo ip link set mydocker-net up
sudo ip route add 192.168.56.0/24 dev mydocker-net

I now have connectivity between my host and the Docker container.

$ ping 192.168.56.101
PING 192.168.56.101 (192.168.56.101) 56(84) bytes of data.
64 bytes from 192.168.56.101: icmp_seq=1 ttl=64 time=0.056 ms
64 bytes from 192.168.56.101: icmp_seq=2 ttl=64 time=0.070 ms
64 bytes from 192.168.56.101: icmp_seq=3 ttl=64 time=0.062 ms
64 bytes from 192.168.56.101: icmp_seq=4 ttl=64 time=0.073 ms
64 bytes from 192.168.56.101: icmp_seq=5 ttl=64 time=0.070 ms

sh-4.2$ ping 192.168.56.1
PING 192.168.56.1 (192.168.56.1) 56(84) bytes of data.
64 bytes from 192.168.56.1: icmp_seq=1 ttl=64 time=0.064 ms
64 bytes from 192.168.56.1: icmp_seq=2 ttl=64 time=0.079 ms
64 bytes from 192.168.56.1: icmp_seq=3 ttl=64 time=0.043 ms
64 bytes from 192.168.56.1: icmp_seq=4 ttl=64 time=0.082 ms
64 bytes from 192.168.56.1: icmp_seq=5 ttl=64 time=0.077 ms

The next step is to have name resolution setup between both hosts. For the Docker container it was easy to setup the full host name into the external DNS service. For the host resolution inside the Docker container you can use static entries in the /etc/hosts file or use the Docker embedded DNS server. For my exercise, I setup an entry in my /etc/hosts file providing the host name and the IP address of the host where EM is running.

The rest of the process is very straight forward. I use the silent install option for the Oracle EM agent. Below link has the steps to download the agent binaries for your specific platform and version.



Once you have the ZIP file of the agent binaries. Then just copy it inside the NFS share and connect to the Docker container.

sudo docker exec -it --user oracle ol7-orcl /bin/sh

After you login to the container just follow the steps in the provided agent install guide. Update the response file with the required information and run agentDeploy.sh. Make sure that the Agent Home resides in the Docker volume created. For this exercise will be inside /u01 directory.

After the agent deploy succeeds disconnect from the Docker session and re-connect as root.

sudo docker exec -it --user root ol7-orcl /bin/sh

Execute root.sh inside the Agent Home directory.

Let’s now verify that our EM has a new host target named ol7-orcl and is up and running.

Docker Container ol7-orcl on Oracle Enterprise Manager

The next step is to discover the ORCL database in EM. You can change the DBSNMP password by logging to the Docker container, then use SQLPlus to change the password and unlock the account.

alter user dbsnmp identified by <password> account unlock;

After the database discovery, verify the database target in the EM console.

ORCL database home page

Now that you have both the host target and the database target you can set metrics, thresholds and notifications on them.

Another important option is that if you have Diagnostics and Tuning packs, you can also monitor performance, get AWR/ASH reports and use EM to graphically analyze performance issues.

ORCL performance information

Hope this post helps understand the options you have in order to setup the required monitoring for the Oracle databases running on Docker containers.

Thanks,
Alfredo