MySQL HeatWave Report – December 2022 update

January 16, 2023

Tags: MySQL HeatWave

The following is just a summary of new features in MySQL HeatWave.

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MySQL HeatWave Report – December 2022 update by Olivier DASINI

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MySQL HeatWave Report – December 2022 update by Olivier DASINI

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Iris Data Set with MySQL HeatWave Machine Learning & Zeppelin

July 18, 2022

Tags: Cloud, ML, tuto

MySQL – The world’s most popular open source database – is also probably the most popular database in the Cloud.
MySQL HeatWave database service is THE MySQL PaaS, a fully managed cloud service in Oracle Cloud Infrastructure (OCI).

The only MySQL service 100% developed, managed and supported by the MySQL Team.

In short:

MySQL HeatWave is a massively parallel, high performance, in-memory query accelerator that accelerates MySQL performance by orders of magnitude for analytics workloads, mixed workloads, and Machine Learning.

For your analytics needs you can explore and visualize your data using a data visualization tool like Tableau, Qlik, Oracle Analytics Cloud, Apache Superset (see: Explore & visualize your MySQL HeatWave data with Superset)…

For your Machine Learning needs you can use a notebook like Jupyter, Apache Zeppelin (see: Interactively explore & visualize your MySQL HeatWave data with Apache Zeppelin),…

In this article I will show you how easy it is to use the Machine Learning capabilities of MySQL HeatWave. We will use the famous Iris dataset and we’re going to build, train, deploy and explain a machine learning model within MySQL HeatWave using Apache Zeppelin.

Prerequisites

To be able to reproduce this tutorial, you must have a MySQL HeatWave instance running (details here and also RTFM).
The data used in this article are available on my GitHub account here.

You need a MySQL client to load the data into MySQL HeatWave. I’m using MySQL Shell.
Furthermore if you want to use Zeppelin (although optional because we’re going to run mainly sql queries) , it must be properly setup.
Please read: Interactively explore & visualize your MySQL HeatWave data with Apache Zeppelin
You can also download the Zeppelin notebook here.

Finally, you might need to install some packages.
ex: (pip install pandas numpy unlzw3 sklearn seaborn matplotlib jupyter grpcio protobuf)

MySQL HeatWave ML

MySQL HeatWave ML makes it easy to use machine learning, whether you are a novice user or an experienced ML practitioner.

You provide the data, and HeatWave ML analyzes the characteristics of the data and creates an optimized machine learning model that you can use to generate predictions and explanations.

HeatWave ML supports supervised machine learning. That is, it creates a machine learning model by analyzing a labeled dataset to learn patterns that enable it to predict labels based on the features of the dataset. HeatWave ML supports both classification and regression models.
More information here.

**In-database Machine Learning with MySQL HeatWave**

All HeatWave ML operations are initiated by running CALL or SELECT statements, which can be easily integrated into your applications.

HeatWave ML routines include:

ML_TRAIN: Trains a machine learning model for a given training dataset.
ML_PREDICT_ROW: Makes predictions for one or more rows of data.
ML_PREDICT_TABLE: Makes predictions for a table of data.
ML_EXPLAIN_ROW: Explains predictions for one or more rows of data.
ML_EXPLAIN_TABLE: Explains predictions for a table of data.
ML_SCORE: Computes the quality of a model.
ML_MODEL_LOAD: Loads a machine learning model for predictions and explanations.
ML_MODEL_UNLOAD: Unloads a machine learning model.

Load the data

In a real life scenario, your data are already inside your MySQL instance. Although you might need to prepare them upfront, because the relevant data set must be inside a single table.

I’m using MySQL Shell (installed on the server that host Zeppelin) to load the data into the iris_ML schema. The dump file – MySQL_HeatWave-iris_ML.sql – was previously uploaded to the server into /home/ubuntu.

mysqlsh --version

mysqlsh myUser:MyP4s$W0rd@10.0.1.2 --sql -f /home/ubuntu/MySQL_HeatWave-iris_ML.sql

mysqlsh --version

mysqlsh myUser:MyP4s$W0rd@10.0.1.2 --sql -f /home/ubuntu/MySQL_HeatWave-iris_ML.sql

The dump contains 4 tables:

iris : the reference table ie source of truth, production data
iris_test : contains the test dataset
iris_train : contains the training dataset
iris_validate : contains the validation dataset

Check HeatWave ML status

HeatWave ML is enable by default as soon as your MySQL HeatWave Cluster is active.

The rapid_ml_status variable provides the status of HeatWave ML:

SHOW GLOBAL STATUS LIKE 'rapid_ml_status';

1	SHOW GLOBAL STATUS LIKE 'rapid_ml_status';

HeatWave ML is up and running \o/

Data exploration

The first step of any project based on data is to look at the data.
Summarize and visualize the data will help you to have a better understanding of the project.

It may surprise you but SQL provides some commands and functions for data exploration, although much less extensive than R or Python 🙂 :

-- Tables structure 
DESCRIBE iris_ML.iris;  -- Reference table (production table)
DESCRIBE iris_ML.iris_train; -- Table that contains the training dataset
DESCRIBE iris_ML.iris_test; -- Test dataset table
DESCRIBE iris_ML.iris_validate; -- Validation dataset table

-- Tables structure

DESCRIBE iris_ML.iris; -- Reference table (production table)

DESCRIBE iris_ML.iris_train; -- Table that contains the training dataset

DESCRIBE iris_ML.iris_test; -- Test dataset table

DESCRIBE iris_ML.iris_validate; -- Validation dataset table

-- Number of rows
SELECT count(*) FROM iris_ML.iris;
SELECT count(*) FROM iris_ML.iris_train;
SELECT count(*) FROM iris_ML.iris_test;
SELECT count(*) FROM iris_ML.iris_validate;

-- Number of rows

SELECT count(*) FROM iris_ML.iris;

SELECT count(*) FROM iris_ML.iris_train;

SELECT count(*) FROM iris_ML.iris_test;

SELECT count(*) FROM iris_ML.iris_validate;

iris simulate the live production table. It contains the original data (150 rows) and actually it will not be used (as is) by HeatWave ML.
To keep things simple as possible in this article, iris as the same structure than iris_train. But in a real life scenario it will most likely not the case.
This production table may have additional columns like for example: a primary key, timestamp, etc… any useful business related information that are not relevant for building our model.

iris_train contains the training dataset a subset of iris table (120 rows), used to train the machine learning model.
This table is typically a subset of the reference table(s).

iris_test contains the test dataset a subset of iris table (30 rows), different than iris_train. Its structure is almost similar to iris_train but without the target column (class) .

iris_validate contains the validation dataset (30 rows). Same data than iris_test but same structure than iris_train in other words this table has the target column (class).

General requirements for HeatWave ML here.

-- Data sample
SELECT * FROM iris_ML.iris LIMIT 10;


-- Class distribution
SELECT class, count(*) FROM iris_ML.iris GROUP BY class;


-- Summary Statistics
SELECT MIN(sepal_length), MAX(sepal_length), ROUND(AVG(sepal_length), 2), ROUND(STD(sepal_length), 2) FROM iris_ML.iris;
SELECT MIN(sepal_width), MAX(sepal_width), ROUND(AVG(sepal_width), 2), ROUND(STD(sepal_width), 2) FROM iris_ML.iris;
SELECT MIN(petal_length), MAX(petal_length), ROUND(AVG(petal_length), 2), ROUND(STD(petal_length), 2) FROM iris_ML.iris;
SELECT MIN(petal_width), MAX(petal_width), ROUND(AVG(petal_width), 2), ROUND(STD(petal_width), 2) FROM iris_ML.iris;

-- Data sample

SELECT * FROM iris_ML.iris LIMIT 10;

-- Class distribution

SELECT class, count(*) FROM iris_ML.iris GROUP BY class;

-- Summary Statistics

SELECT MIN(sepal_length), MAX(sepal_length), ROUND(AVG(sepal_length), 2), ROUND(STD(sepal_length), 2) FROM iris_ML.iris;

SELECT MIN(sepal_width), MAX(sepal_width), ROUND(AVG(sepal_width), 2), ROUND(STD(sepal_width), 2) FROM iris_ML.iris;

SELECT MIN(petal_length), MAX(petal_length), ROUND(AVG(petal_length), 2), ROUND(STD(petal_length), 2) FROM iris_ML.iris;

SELECT MIN(petal_width), MAX(petal_width), ROUND(AVG(petal_width), 2), ROUND(STD(petal_width), 2) FROM iris_ML.iris;

Class distribution is well balanced:

Iris-virginica: 50 rows
Iris-setosa: 50 rows
Iris-versicolor: 50 rows

Data visualization

Visualize your data is probably the more convenient way to explore and understand them.

Below a little Python script to generate some graphs.

Beforehand, I had to edit the Zeppelin python interpreter – zeppelin.python – and replace python by python3:

Update zeppelin.python: replace python by python3 — *zeppelin.python*: python3:

The interpreter is: %python.ipython
The script is going to connect to MySQL HeatWave, thus you must update the database information (host, database, user, password)

%python.ipython

## Edit python interpreter on Zeppelin - zeppelin.python: python3  instead of python
## Update host, database, user and password (from you're MySQL HeatWave)



import mysql.connector as connection
import pandas as pd
import seaborn as sns
sns.set_palette('husl')
%matplotlib inline
import matplotlib.pyplot as plt


try:
    mydb = connection.connect(host="10.0.1.2", database='iris_ML', user="<user>", passwd="<password>", port=3306)
    query = "SELECT * FROM iris;"
    data = pd.read_sql(query,mydb)
    mydb.close() #close the connection
except Exception as e:
    my_conn.close()
    print(str(e))

data.head()

data.info()

data.describe()

data['class'].value_counts()

g = sns.pairplot(data, hue='class', markers='*')

plt.show()

%python.ipython

## Edit python interpreter on Zeppelin - zeppelin.python: python3 instead of python

## Update host, database, user and password (from you're MySQL HeatWave)

import mysql.connector as connection

import pandas as pd

import seaborn as sns

sns.set_palette('husl')

%matplotlib inline

import matplotlib.pyplot as plt

try:

mydb = connection.connect(host="10.0.1.2", database='iris_ML', user="<user>", passwd="<password>", port=3306)

query = "SELECT * FROM iris;"

data = pd.read_sql(query,mydb)

mydb.close() #close the connection

except Exception as e:

my_conn.close()

print(str(e))

data.head()

data.info()

data.describe()

data['class'].value_counts()

g = sns.pairplot(data, hue='class', markers='*')

plt.show()

HeatWave Machine Learning (ML) inclut tout ce dont les utilisateurs ont besoin pour créer, former, déployer et expliquer des modèles d’apprentissage automatique dans MySQL HeatWave, sans coût supplémentaire.

Dans ce webinaire vous apprendrez

The information above come from the Iris table (reference table). It could interesting to compare these data with the training dataset (iris_train) and the test dataset in order to evaluate the quality of these samples.

Replace query = “SELECT * FROM iris;” by query = “SELECT * FROM iris_train;” and then query = “SELECT * FROM iris_test;”

Training a Model

Now we have a better understanding of the data, let’s moving forward and train the model. We’re having a classification problem.

The ML_TRAIN routine, when run on a training dataset, produces a trained machine learning (ML) model.

# Train the model using ML_TRAIN

CALL sys.ML_TRAIN('iris_ML.iris_train', 'class', JSON_OBJECT('task', 'classification'), @iris_model);

# Train the model using ML_TRAIN

CALL sys.ML_TRAIN('iris_ML.iris_train', 'class', JSON_OBJECT('task', 'classification'), @iris_model);

You can show the current model, selecting the session variable @iris_model:

SELECT @iris_model;

1	SELECT @iris_model;

The lifetime duration of a session variable is… the session lifetime duration. So when the session is closed the session variable content is lost.

ML_TRAIN stores the machine learning model – @iris_model – in the MODEL_CATALOG table:

# Model information

SELECT model_id, model_handle, model_owner, target_column_name, train_table_name, model_type, task, model_object_size  FROM ML_SCHEMA_admin.MODEL_CATALOG;

# Model information

SELECT model_id, model_handle, model_owner, target_column_name, train_table_name, model_type, task, model_object_size FROM ML_SCHEMA_admin.MODEL_CATALOG;

You can take the last created model using the following query:

SELECT model_handle FROM ML_SCHEMA_admin.MODEL_CATALOG ORDER BY model_id DESC LIMIT 1 INTO @iris_model;

1	SELECT model_handle FROM ML_SCHEMA_admin.MODEL_CATALOG ORDER BY model_id DESC LIMIT 1 INTO @iris_model;

When the model is created (using ML_TRAIN) you should load it into HeatWave ML (ML_MODEL_LOAD):

CALL sys.ML_MODEL_LOAD(@iris_model, NULL);

1	CALL sys.ML_MODEL_LOAD(@iris_model, NULL);

Row Prediction

HeatWave ML allows you to make prediction for individual rows or the entire table.

Row(s) predictions are generated by running ML_PREDICT_ROW.
Data are specified in JSON format.

# Predict 1 Row

SET @row_input = JSON_OBJECT( 
   "sepal_length", 7.3, 
   "sepal_width", 2.9, 
   "petal_length", 6.3, 
   "petal_width", 1.8
);  


SELECT sys.ML_PREDICT_ROW(@row_input, @iris_model);

# Predict 1 Row

SET @row_input = JSON_OBJECT(

"sepal_length", 7.3,

"sepal_width", 2.9,

"petal_length", 6.3,

"petal_width", 1.8

);

SELECT sys.ML_PREDICT_ROW(@row_input, @iris_model);

Row Explanation

Being able to understand and explain a prediction is important in order to trust your model and to be able to explain the results. It might also be required by your local regulation.

ML_EXPLAIN_ROW generates explanations for one or more rows of data. Explanations help you understand which features have the most influence on a prediction.
Feature importance is presented as a value ranging from -1 to 1.

A positive value indicates that a feature contributed toward the prediction.
A negative value indicates that the feature contributed toward a different prediction

# Prediction explanation for 1 Row

SET @row_input = JSON_OBJECT( 
   "sepal_length", 7.3, 
   "sepal_width", 2.9, 
   "petal_length", 6.3, 
   "petal_width", 1.8
);    


SELECT sys.ML_EXPLAIN_ROW(@row_input, @iris_model);

# Prediction explanation for 1 Row

SET @row_input = JSON_OBJECT(

"sepal_length", 7.3,

"sepal_width", 2.9,

"petal_length", 6.3,

"petal_width", 1.8

);

SELECT sys.ML_EXPLAIN_ROW(@row_input, @iris_model);

Table Prediction

ML_PREDICT_TABLE generates predictions for an entire table and saves the results to an output table.

# Generate predictions for a table 

-- CALL sys.ML_MODEL_LOAD(@iris_model, NULL); -- Uncomment if the model is not yet loaded

DROP TABLE IF EXISTS iris_ML.iris_predictions; -- Useful if a table prediction was already generated

CALL sys.ML_PREDICT_TABLE('iris_ML.iris_test', @iris_model, 'iris_ML.iris_predictions');

# Generate predictions for a table

-- CALL sys.ML_MODEL_LOAD(@iris_model, NULL); -- Uncomment if the model is not yet loaded

DROP TABLE IF EXISTS iris_ML.iris_predictions; -- Useful if a table prediction was already generated

CALL sys.ML_PREDICT_TABLE('iris_ML.iris_test', @iris_model, 'iris_ML.iris_predictions');

A new table called iris_predictions is created. You can display Its first 5 rows with:

SELECT * FROM iris_ML.iris_predictions LIMIT 5;

1	SELECT * FROM iris_ML.iris_predictions LIMIT 5;

Table Explanation

ML_EXPLAIN_TABLE explains predictions for an entire table and saves results to an output table.

# Prediction explanation for a table 

-- CALL sys.ML_MODEL_LOAD(@iris_model, NULL); -- Uncomment if the model is not yet loaded

DROP TABLE IF EXISTS iris_ML.iris_explanations;  -- Usueful if a table explanation was already done

CALL sys.ML_EXPLAIN_TABLE('iris_ML.iris_test', @iris_model, 'iris_ML.iris_explanations');

# Prediction explanation for a table

-- CALL sys.ML_MODEL_LOAD(@iris_model, NULL); -- Uncomment if the model is not yet loaded

DROP TABLE IF EXISTS iris_ML.iris_explanations; -- Usueful if a table explanation was already done

CALL sys.ML_EXPLAIN_TABLE('iris_ML.iris_test', @iris_model, 'iris_ML.iris_explanations');

A new table called iris_explanations is created. You can display Its first 5 rows with:

SELECT * FROM iris_ML.iris_explanations LIMIT 5;

1	SELECT * FROM iris_ML.iris_explanations LIMIT 5;

Scores

Scoring the model allows to assess the model’s reliability.

Models with a low score can be expected to perform poorly, producing predictions and explanations that cannot be relied upon. A low score typically indicates that the provided feature columns are not a good predictor of the target values.

HeatWave ML supports a variety of scoring metrics to help you understand how your model performs across a series of benchmarks.
Details here.

ML_SCORE returns a computed metric indicating the quality of the model.

-- This example uses the accuracy: Computes the fraction of labels a model predicts correctly

CALL sys.ML_SCORE('iris_ML.iris_validate', 'class', @iris_model, 'accuracy', @accuracy_score);

SELECT @accuracy_score;

-- This example uses the accuracy: Computes the fraction of labels a model predicts correctly

CALL sys.ML_SCORE('iris_ML.iris_validate', 'class', @iris_model, 'accuracy', @accuracy_score);

SELECT @accuracy_score;

Other metrics are also available. See here.

Here we go!
We’ve seen the machine learning life cycle.

I told you that Machine Learning with MySQL HeatWave was easy 🙂

You only need to use a limited set of SQL routines:

ML_TRAIN: Trains a machine learning model for a given training dataset.
ML_PREDICT_ROW: Makes predictions for one or more rows of data.
ML_PREDICT_TABLE: Makes predictions for a table of data.
ML_EXPLAIN_ROW: Explains predictions for one or more rows of data.
ML_EXPLAIN_TABLE: Explains predictions for a table of data.
ML_SCORE: Computes the quality of a model.

During the last 20 years MySQL has democratized the usage of transactional databases.
Now with MySQL HeatWave we are in the process of democratizing Analytics and Machine Learning.

With MySQL HeatWave,
valorizing your data has never been so easy!

Resources

Follow me on twitter

Watch my videos on my YouTube channel and subscribe.

My Slideshare account.

My Speaker Deck account.

Thanks for using MySQL!

Filed under:
MySQL, Tuto by Olivier DASINI

Interactively explore & visualize your MySQL HeatWave data with Apache Zeppelin

July 5, 2022

Tags: Cloud, tuto

MySQL – The world’s most popular open source database – is a very good and safe choice as a transactional database, on-premise and in the cloud.

In the article – Explore & visualize your MySQL HeatWave data with Superset – I introduced MySQL HeatWave – a fully managed database service, that combines transactions, analytics, and machine learning services into one MySQL Database, delivering real-time, secure analytics without the complexity, latency, and cost of ETL duplication.
MySQL HeatWave is a native MySQL solution thus current MySQL applications work without changes.

Enabling a HeatWave Cluster also provides access to HeatWave Machine Learning (ML), which is a fully managed, highly scalable, cost-efficient, machine learning solution for data stored in MySQL.
HeatWave ML provides a simple SQL interface for training and using predictive machine learning models, which can be used by novice and experienced ML practitioners alike.
Machine learning expertise, specialized tools, and algorithms are not required.
With HeatWave ML, you can train a model with a single call to an SQL routine.

Below some numbers comparing MySQL HeatWave ML vs AWS Redshift ML:

MySQL HeatWave ML is 25x faster than Redshift ML
25x faster on average without compromising accuracy — **MySQL HeatWave ML is 25x faster than Redshift ML**

MySQL HeatWave ML is 1% of cost of Redshift ML
1% of cost and no additional cost for MySQL HeatWave customers — **MySQL HeatWave ML is 1% of cost of Redshift ML**

**Better performance with HeatWave ML with larger cluster size**

For more details & raw numbers read: Performance comparison of HeatWave ML with Redshift ML.

MySQL HeatWave is currently available on Oracle Cloud Infrastructure (OCI). Availability on Amazon Web Services (AWS) and Microsoft Azure is coming (Oracle’s “HeatWave” is going multicloud: How hot will things get for MySQL?).

MySQL HeatWave ML can also takes models tuned in MySQL and use them to answer queries from popular machine learning notebook services for code and data, such as Jupyter and Apache Zeppelin.

Apache Zeppelin is an open source web-based notebook that allows data ingestion, data discovery, data analytics, data visualization and data collaboration.
Apache Zeppelin supports many interpreters such as MySQL, Python, JDBC, Shell, Spark, …
More: https://zeppelin.apache.org/

In this article I will show you how to properly configure Apache Zeppelin in order to take advantage of the Analytics and Machine Learning capabilities of MySQL HeatWave the MySQL in-memory query accelerator.

MySQL HeatWave

I’m using OCI and I will not go into all the installation details (RTFM). Below the main steps.

Using your favorite Internet browser, open your OCI console, select a compartment and go to Databases / MySQL / DB Systems.

Create a DB Systems and select HeatWave.

The rest is pretty straightforward!

Please note the Show Advanced Options link at the bottom of the page.

It’s Data Import tab is useful if you want to create an instance with data.

More information in this article: Discovering MySQL Database Service – Episode 5 – Create a MySQL DB system from a MySQL Shell dump

Now we must add the HeatWave cluster.

On the left, under Resources click HeatWave.

Add the HeatWave Cluster…

Set the Node Count

I would recommend to estimate the node count, click Estimate Node Count then Generate Estimate:

Copy the Load Command.
It will be used to load the chosen data into the HeatWave cluster.

Apply the node count estimate:

Finally create the cluster by pushing the button: Add HeatWave Cluster

When the cluster is running, the very last step is to offload the data to the cluster by running the stored procedure (i.e. the “Load Command”) copied previously using your favorite MySQL client.

e.g. Offload to the HeatWave cluster all the tables inside the airportdb schema:
CALL sys.heatwave_load(JSON_ARRAY(‘airportdb’), NULL);

Now you have a MySQL HeatWave cluster up and running with some data loaded into the cluster;
Cheers!

Apache Zeppelin

I assume that you already know what is Apache Zeppelin. If not RTFM.

Please note this is a test installation, probably not suitable for a production environment.

Requirements

Apache Zeppelin officially supports and is tested, among other, on Ubuntu 20.04, so I’ve installed Zeppelin on a VM Ubuntu 20.04 from Oracle Cloud Infrastructure (OCI).

In addition to the OS, you also need the good version of OpenJDK or Oracle JDK.

$ lsb_release -a
No LSB modules are available.
Distributor ID:	Ubuntu
Description:	Ubuntu 20.04.4 LTS
Release:	20.04
Codename:	focal

$ lsb_release -a

No LSB modules are available.

Distributor ID: Ubuntu

Description: Ubuntu 20.04.4 LTS

Release: 20.04

Codename: focal

$ java -version
openjdk version "17.0.3" 2022-04-19
OpenJDK Runtime Environment (build 17.0.3+7-Ubuntu-0ubuntu0.20.04.1)
OpenJDK 64-Bit Server VM (build 17.0.3+7-Ubuntu-0ubuntu0.20.04.1, mixed mode, sharing)

$ java -version

openjdk version "17.0.3" 2022-04-19

OpenJDK Runtime Environment (build 17.0.3+7-Ubuntu-0ubuntu0.20.04.1)

OpenJDK 64-Bit Server VM (build 17.0.3+7-Ubuntu-0ubuntu0.20.04.1, mixed mode, sharing)

I’m good to go!

Binary

I downloaded the binary package with all interpreters. Available here.

wget https://dlcdn.apache.org/zeppelin/zeppelin-0.10.1/zeppelin-0.10.1-bin-all.tgz

1	wget https://dlcdn.apache.org/zeppelin/zeppelin-0.10.1/zeppelin-0.10.1-bin-all.tgz

Unpack it in a directory of your choice and you’re ready to go!

tar xvf zeppelin-0.10.1-bin-all.tgz
mv zeppelin-0.10.1-bin-all/ zeppelin
cd zeppelin

tar xvf zeppelin-0.10.1-bin-all.tgz

mv zeppelin-0.10.1-bin-all/ zeppelin

cd zeppelin

Firewalls

Depending on your architecture, you may have to deal with some firewalls.

In OCI I must update my Ingress Rules for the Default Security List (OCI virtual firewall) for the protocol TCP and the port you are going to use, 8080 in this article:

On the Ubuntu side I also had to update IPtable.

Starting Apache Zeppelin

By default Zeppelin is listening at 127.0.0.1:8080, so you can’t access it when it is deployed on another remote machine.
To access a remote Zeppelin, you need to change zeppelin.server.addr to 0.0.0.0 in conf/zeppelin-site.xml.

~/zeppelin$ cp conf/zeppelin-site.xml.template conf/zeppelin-site.xml

~/zeppelin$ vim conf/zeppelin-site.xml
...

~/zeppelin$ cp conf/zeppelin-site.xml.template conf/zeppelin-site.xml

~/zeppelin$ vim conf/zeppelin-site.xml

...

When the update is done:

~/zeppelin$ grep -m2  -a4  "<property>" conf/zeppelin-site.xml
-->

<configuration>

<property>
  <name>zeppelin.server.addr</name>
  <value>0.0.0.0</value>
  <description>Server binding address</description>
</property>

<property>
  <name>zeppelin.server.port</name>
  <value>8080</value>
  <description>Server port.</description>
</property>

~/zeppelin$ grep -m2 -a4 "<property>" conf/zeppelin-site.xml

-->

<name>zeppelin.server.addr</name>

<description>Server binding address</description>

</property>

<name>zeppelin.server.port</name>

<description>Server port.</description>

</property>

Address is 0.0.0.0 and Port is 8080.

Now I can start Zeppelin:

~/zeppelin$ bin/zeppelin-daemon.sh start

1	~/zeppelin$ bin/zeppelin-daemon.sh start

~/zeppelin$ bin/zeppelin-daemon.sh status
Zeppelin is running                                        [  OK  ]

1 2	~/zeppelin$ bin/zeppelin-daemon.sh status Zeppelin is running [ OK ]

Yippee!

MySQL Connector J

The Zeppelin SQL interpreter needs the JDBC MySQL Connector, available at https://dev.mysql.com/downloads/connector/j/

Obviously, MySQL customers can use the commercial version available on MOS.

$ wget https://dev.mysql.com/get/Downloads/Connector-J/mysql-connector-java_8.0.29-1ubuntu20.04_all.deb

$ sudo dpkg -i mysql-connector-java_8.0.29-1ubuntu20.04_all.deb

$ wget https://dev.mysql.com/get/Downloads/Connector-J/mysql-connector-java_8.0.29-1ubuntu20.04_all.deb

$ sudo dpkg -i mysql-connector-java_8.0.29-1ubuntu20.04_all.deb

The JDBC MySQL Connector is now installed!

Setup the Zeppelin interpreter

Create a new interpreter:

Enter a name for the Interpreter Name box and select jdbc as Interpreter group:

Interpreter Name: MySQL HeatWave
Interpreter group: jdbc

JDBC interpreter lets you create a JDBC connection to any data sources seamlessly. Inserts, Updates, and Upserts are applied immediately after running each statement.

Properties

default.url : jdbc:mysql://<MySQL HeatWave IP Address>:3306/
default.user : <MySQL User>
default.password: <MySQL Password>
default.driver : com.mysql.jdbc.Driver

Dependencies

Dependencies artifact : mysql:mysql-connector-java:8.0.29

Don’t forget to save the configuration 🙂

To sum up, the interpreter configuration looks like:

MySQL HeatWave usage

I can now create a notebook – Check MySQL HeatWave (download the notebooks) – in order to check if HeatWave is enable:

The Default Interpreter must be MySQL_HeatWave:

For the check, let’s run these 2 queries:

SHOW VARIABLES LIKE 'rapid_bootstrap';

SHOW STATUS WHERE Variable_name IN ('rapid_cluster_status', 'rapid_ml_status', 'rapid_cluster_ready_number', 'rapid_load_progress');

SHOW VARIABLES LIKE 'rapid_bootstrap';

SHOW STATUS WHERE Variable_name IN ('rapid_cluster_status', 'rapid_ml_status', 'rapid_cluster_ready_number', 'rapid_load_progress');

The HeatWave cluster is up and ML is enable.
All good!

Analytics

Out of curiosity, let’s try some analytic queries.

I already loaded the airportdb sample database (see here) into the MySQL DB system.

Let’s create a new note – Airportdb Analytics Queries (download the notebooks):

airportdb must now be loaded into the HeatWave cluster in order to take advantage of the tremendous performance:

CALL sys.heatwave_load(JSON_ARRAY('airportdb'), NULL)

1	CALL sys.heatwave_load(JSON_ARRAY('airportdb'), NULL)

And here are the queries:

-- Number of Tickets > $500.00, Grouped By Price
SELECT booking.price, count(*) 
FROM airportdb.booking 
WHERE booking.price > 500 
GROUP BY booking.price 
ORDER BY booking.price 
LIMIT 10;


-- Average Age of Passengers By Country, Per Airline
SELECT airline.airlinename, AVG(datediff(departure,birthdate)/365.25) as avg_age, count(*) as nb_people
FROM airportdb.booking, airportdb.flight, airportdb.airline, airportdb.passengerdetails
WHERE booking.flight_id=flight.flight_id AND
    airline.airline_id=flight.airline_id AND
    booking.passenger_id=passengerdetails.passenger_id AND
    country IN ("SWITZERLAND", "FRANCE", "ITALY")
GROUP BY airline.airlinename
ORDER BY airline.airlinename, avg_age
LIMIT 10;

-- Number of Tickets > $500.00, Grouped By Price

SELECT booking.price, count(*)

FROM airportdb.booking

WHERE booking.price > 500

GROUP BY booking.price

ORDER BY booking.price

LIMIT 10;

-- Average Age of Passengers By Country, Per Airline

SELECT airline.airlinename, AVG(datediff(departure,birthdate)/365.25) as avg_age, count(*) as nb_people

FROM airportdb.booking, airportdb.flight, airportdb.airline, airportdb.passengerdetails

WHERE booking.flight_id=flight.flight_id AND

airline.airline_id=flight.airline_id AND

booking.passenger_id=passengerdetails.passenger_id AND

country IN ("SWITZERLAND", "FRANCE", "ITALY")

GROUP BY airline.airlinename

ORDER BY airline.airlinename, avg_age

LIMIT 10;

Great results, respectively less than 0 second and 1 second.

Without HeatWave the query execution times would be respectively 10 and 17 seconds:

Machine Learning

What about the Machine Learning possibilities of MySQL HeatWave ML?

Well this article is already long enough, so it will be the topic of the next article 🙂

Stay tune!

Resources

Follow me on twitter

Watch my videos on my YouTube channel and subscribe.

My Slideshare account.

My Speaker Deck account.

Thanks for using MySQL!

Filed under:
Machine Learning, MySQL, Tuto by Olivier DASINI

Explore & visualize your MySQL HeatWave data with Superset

June 9, 2022

Tags: Analytics, Cloud, performance, tuto

MySQL – The world’s most popular open source database – is probably the best choice for a transactional database especially when considering the costs / performance ratio, and that on-premise or in the cloud.

But what about business intelligence (BI) needs? Analytics workloads?

We’ve got you covered now with the versatile MySQL HeatWave – a fully managed database service, that combines transactions, analytics, and machine learning services into one MySQL Database, delivering real-time, secure analytics without the complexity, latency, and cost of ETL duplication.
MySQL HeatWave is a native MySQL solution thus current MySQL applications work without changes.

MySQL HeatWave is also fast, super fast, easy to use and provides an incredible price/performance ratio…

Details & more numbers: Performance comparison of MySQL HeatWave with Snowflake, Amazon Redshift, Amazon Aurora, and Amazon RDS for MySQL.

Indeed, having a fast and easy to use database is valuable. But analytics also imply that you need to explore and visualize your data.
There are plenty of tools available on the market, one of the them is Apache Superset, an open source modern data exploration and visualization platform.

In this article I will show you how to properly configure Apache Superset in order to take advantage of a high performance, in-memory query accelerator: MySQL HeatWave.

MySQL HeatWave

I’m on OCI and I will not go into all the installation details (RTFM). Below the main steps.

Using your favorite Internet browser, open your OCI console, select a compartment and go to Databases / MySQL / DB Systems.

Create a DB Systems and select HeatWave.

The rest is pretty straightforward!

Please note the Show Advanced Options link at the bottom of the page.

It’s Data Import tab is useful if you want to create an instance with data.

More information in this article: Discovering MySQL Database Service – Episode 5 – Create a MySQL DB system from a MySQL Shell dump

Now we must add the HeatWave cluster.

On the left, under Resources click HeatWave.

Add the HeatWave Cluster…

Set the Node Count

I would recommend to estimate the node count, click Estimate Node Count then Generate Estimate:

Copy the Load Command.
It will be used to load the chosen data into the HeatWave cluster.

Apply the node count estimate:

Finally create the cluster by pushing the button: Add HeatWave Cluster

When the cluster is running, the very last step is to offload the data to the cluster by running the stored procedure (i.e. the “Load Command”) copied previously using your favorite MySQL client.

e.g. Offload to the HeatWave cluster all the tables inside the airportdb schema:
CALL sys.heatwave_load(JSON_ARRAY(‘airportdb’), NULL);

Apache Superset

I assume that you know what is Apache Superset. If not RTFM.
I also assume that it is already installed. You’ll find the necessary information in their documentation: Installing Superset from Scratch or Installing Superset Locally Using Docker Compose.

it is worth noting that I had some issues during the installation process following the documentation.

For the record, below what I’ve done using a VM Ubuntu 20.04 from Oracle Cloud Infrastructure (OCI).
Please note this is a test installation, not suitable for a production environment.

Install the required dependencies (Ubuntu or Debian):

$ sudo apt update && sudo apt -y install build-essential libssl-dev libffi-dev python3-dev python3-pip libsasl2-dev libldap2-dev default-libmysqlclient-dev libprotobuf17 python3-protobuf

1	$ sudo apt update && sudo apt -y install build-essential libssl-dev libffi-dev python3-dev python3-pip libsasl2-dev libldap2-dev default-libmysqlclient-dev libprotobuf17 python3-protobuf

Upgrade pip (and prevent some issues)

$ pip3 install --upgrade pip

$ pip3 install zipp==3.8.0

$ pip3 install Flask-WTF==0.14.3

$ pip3 install --upgrade pip

$ pip3 install zipp==3.8.0

$ pip3 install Flask-WTF==0.14.3

Install Superset

$ pip3 install apache-superset

1	$ pip3 install apache-superset

Binaries are in $HOME/.local/bin
I decided to put the following information into my ~/.bashrc (however not sure it is a good practice, but good enough for testing)

export PATH="$HOME/.local/bin:$PATH"
export FLASK_APP=superset

1 2	export PATH="$HOME/.local/bin:$PATH" export FLASK_APP=superset

then

$ source ~/.bashrc

1	$ source ~/.bashrc

Initialize the database:

$ superset db upgrade

$ superset fab create-admin


# If you want to load the embeded examples
# superset load_examples

# Create default roles and permissions
$ superset init

$ superset db upgrade

$ superset fab create-admin

# If you want to load the embeded examples

# superset load_examples

# Create default roles and permissions

$ superset init

Please not that you will probably have to update your firewall.

If like me you are on OCI you must update the security list (OCI virtual firewall) for the protocol TCP and the port you are going to use, 8088 in this article

On Ubuntu you may have to update IPtable.

Start Superset

Superset is listening on port 8088

$ superset run -h 0.0.0.0 -p 8088 --with-threads --reload

1	$ superset run -h 0.0.0.0 -p 8088 --with-threads --reload

If everything worked, you should be able to navigate to hostname:port in your browser and login using the username and password you created.

If you have everything on your laptop, you can remove -h 0.0.0.0 and then only local connections will be allowed (i.e. navigate to localhost:8088).

Install MySQL Connector Python 8.0

Unlike what is written here, I would recommend to use MySQL 8.0, to download and use the official MySQL Python connector available on the MySQL website.

Go to https://dev.mysql.com/downloads/connector/python/ for the latest version.

$ wget https://dev.mysql.com/get/Downloads/Connector-Python/mysql-connector-python-py3_8.0.29-1ubuntu20.04_amd64.deb

$ sudo dpkg -i mysql-connector-python-py3_8.0.29-1ubuntu20.04_amd64.deb

$ wget https://dev.mysql.com/get/Downloads/Connector-Python/mysql-connector-python-py3_8.0.29-1ubuntu20.04_amd64.deb

$ sudo dpkg -i mysql-connector-python-py3_8.0.29-1ubuntu20.04_amd64.deb

Obviously, MySQL customers can use the commercial version available on MOS.

Configure Superset for MySQL HeatWave

Using the Apache Superset UI you can add a new backend database.

Go to Data / Databases

Then click + DATABASE

Add your MySQL HeatWave database.
There are 2 important parts:

1/ SQLALCHEMY URI :

mysql+mysqlconnector://<MySQL user>:<MySQL password>@<MySQL host>

You need the MySQL user, password and IP.

2/ ENGINE PARAMETERS

In order to take advantage of the power of MySQL HeatWave, autocommit must be enabled.

If autocommit is disabled, queries are not offloaded and execution is performed on the MySQL DB System. In other words, the queries will be much slower. Details here.

Go to Advanced / Other / ENGINE PARAMETERS

{“isolation_level”:”AUTOCOMMIT”}

You all set 🙂

Thanks to MySQL HeatWave, you can now add your datasets and build awesome and very fast dashboards, explore and visualize your data at the speed of light.

Resources

Follow me on twitter

Watch my videos on my YouTube channel and subscribe.

My Slideshare account.

My Speaker Deck account.

Thanks for using MySQL!

Filed under:
MySQL, Tuto by Olivier DASINI

Data Migration from MariaDB to MySQL

March 9, 2022

Tags: MariaDB, MDS, Migration, MySQL Shell

Yet another customer wanted to migrate from MariaDB to MySQL. So I decided to write down the overall data migration process.

Indeed each migration are a unique story so I won’t provide you a “How to migrate”…
The idea is to highlight the general path and help you to avoid common pitfalls and thus help you save some precious time.

Context

Source database
- MariaDB 10.3
- Around 8GB of data
- Only InnoDB tables
- Many user accounts (important information)
Target databases
- MySQL Database Service 8.0.28
- MySQL 8.0.28

In fact, the (real) target was MySQL Database Service the MySQL PaaS on Oracle Cloud Infrastructure, but I will also show the workflow for on-premise MySQL.

Please find below a reenactment of the events…

MySQL Shell

The first step is to install the MySQL DBA BFF: MySQL Shell.
MySQL Shell is an advanced client and code editor for MySQL. You can manage your data using SQL, JavaScript and Python.

In addition to data manipulation (btw MySQL 8.0 also provides a NoSQL CRUD API to handle JSON documents), there are also a lot of things you can do with MySQL Shell like create a MySQL cluster (InnoDB Cluster, InnoDB ReplicaSet, InnoDB ClusterSet), verify whether your MySQL server instances is ready for upgrade, dump and restore, etc… :

Believe me if you try it, you will love it 🙂

It is way better than the old mysql text client and some of these features work with MariaDB (at least the ones needed for the migration).

Easy to install.
e.g.

[opc@mariadb10-3 ~]$ 
sudo yum install -y mysql-shell

1 2	[opc@mariadb10-3 ~]$ sudo yum install -y mysql-shell

The source dataset

Let’s have a look on the dataset.

Unfortunately MariaDB does not have the sys schema…
We cannot use this very elegant query:

-- MySQL 5.7 / 8.0+

SELECT sys.format_bytes(sum(data_length)) DATA,
       sys.format_bytes(sum(index_length)) INDEXES,
       sys.format_bytes(sum(data_length + index_length)) 'TOTAL SIZE'
FROM information_schema.TABLES ORDER  BY data_length + index_length
;

-- MySQL 5.7 / 8.0+

SELECT sys.format_bytes(sum(data_length)) DATA,

sys.format_bytes(sum(index_length)) INDEXES,

sys.format_bytes(sum(data_length + index_length)) 'TOTAL SIZE'

FROM information_schema.TABLES ORDER BY data_length + index_length

;

So back in the time and let’s use the information_schema instead and the following ugly query:

-- MySQL (old version) / MariaDB --

SQL>
SELECT 
    CONCAT( SUM( ROUND( DATA_LENGTH / (1024 / 1024) ,2)), 'MB' ) AS Data,
    CONCAT( SUM( ROUND( INDEX_LENGTH / (1024 / 1024) ,2)), 'MB' ) AS Indexes,
    CONCAT( SUM( ROUND( (DATA_LENGTH + INDEX_LENGTH) / (1024 * 1024) ,2)), 'MB' ) AS 'Total Size' 
FROM information_schema.TABLES ORDER BY DATA_LENGTH + INDEX_LENGTH
;

+-----------------+-----------------+------------+
| Data            | Indexes         | Total Size |
+-----------------+-----------------+------------+
| 3044154215.00MB | 5688978432.00MB | 8328.61MB  |
+-----------------+-----------------+------------+

-- MySQL (old version) / MariaDB --

SQL>

SELECT

CONCAT( SUM( ROUND( DATA_LENGTH / (1024 / 1024) ,2)), 'MB' ) AS Data,

CONCAT( SUM( ROUND( INDEX_LENGTH / (1024 / 1024) ,2)), 'MB' ) AS Indexes,

CONCAT( SUM( ROUND( (DATA_LENGTH + INDEX_LENGTH) / (1024 * 1024) ,2)), 'MB' ) AS 'Total Size'

FROM information_schema.TABLES ORDER BY DATA_LENGTH + INDEX_LENGTH

;

+-----------------+-----------------+------------+

| Data | Indexes | Total Size |

+-----------------+-----------------+------------+

| 3044154215.00MB | 5688978432.00MB | 8328.61MB |

+-----------------+-----------------+------------+

Approximately 8GB of data.

What about the storage engines?

Again because of lack of sys schema we cannot use this pretty query:

-- MySQL 5.7 / 8.0+

SELECT count(*) as '# TABLES', sys.format_bytes(sum(data_length)) DATA, 
       sys.format_bytes(sum(index_length)) INDEXES,   
       sys.format_bytes(sum(data_length + index_length)) 'TOTAL SIZE', 
       engine `ENGINE` FROM information_schema.TABLES 
WHERE TABLE_SCHEMA NOT IN ('sys','mysql', 'information_schema', 'performance_schema', 'mysql_innodb_cluster_metadata')
GROUP BY engine
ORDER BY engine;

-- MySQL 5.7 / 8.0+

SELECT count(*) as '# TABLES', sys.format_bytes(sum(data_length)) DATA,

sys.format_bytes(sum(index_length)) INDEXES,

sys.format_bytes(sum(data_length + index_length)) 'TOTAL SIZE',

engine `ENGINE` FROM information_schema.TABLES

WHERE TABLE_SCHEMA NOT IN ('sys','mysql', 'information_schema', 'performance_schema', 'mysql_innodb_cluster_metadata')

GROUP BY engine

ORDER BY engine;

So let’s back in the time and use instead:

-- MySQL (old version) / MariaDB --

SQL>
SELECT 
   count(*) as '# TABLES',
   CONCAT( SUM( ROUND( DATA_LENGTH / (1024 / 1024) ,2)), 'MB' ) AS Data,
   CONCAT( SUM( ROUND( INDEX_LENGTH / (1024 / 1024) ,2)), 'MB' ) AS Indexes,
   CONCAT( SUM( ROUND( (DATA_LENGTH + INDEX_LENGTH) / (1024 * 1024) ,2)), 'MB' ) AS 'Total Size',
   engine `ENGINE` FROM information_schema.TABLES 
WHERE TABLE_SCHEMA NOT IN ('sys','mysql', 'information_schema', 'performance_schema', 'mysql_innodb_cluster_metadata')
GROUP BY engine
ORDER BY engine
;

+----------+-----------------+-----------------+------------+--------+
| # TABLES | Data            | Indexes         | Total Size | ENGINE |
+----------+-----------------+-----------------+------------+--------+
|        2 | NULL            | NULL            | NULL       | NULL   |
|       26 | 3043442688.00MB | 5688705024.00MB | 8327.66MB  | InnoDB |
+----------+-----------------+-----------------+------------+--------+

-- MySQL (old version) / MariaDB --

SQL>

SELECT

count(*) as '# TABLES',

CONCAT( SUM( ROUND( DATA_LENGTH / (1024 / 1024) ,2)), 'MB' ) AS Data,

CONCAT( SUM( ROUND( INDEX_LENGTH / (1024 / 1024) ,2)), 'MB' ) AS Indexes,

CONCAT( SUM( ROUND( (DATA_LENGTH + INDEX_LENGTH) / (1024 * 1024) ,2)), 'MB' ) AS 'Total Size',

engine `ENGINE` FROM information_schema.TABLES

WHERE TABLE_SCHEMA NOT IN ('sys','mysql', 'information_schema', 'performance_schema', 'mysql_innodb_cluster_metadata')

GROUP BY engine

ORDER BY engine

;

+----------+-----------------+-----------------+------------+--------+

+----------+-----------------+-----------------+------------+--------+

| 26 | 3043442688.00MB | 5688705024.00MB | 8327.66MB | InnoDB |

+----------+-----------------+-----------------+------------+--------+

All tables are in InnoDB \o/
That’s how DBA’s life should be 🙂

Migrate the data

The source is MariaDB 10.3 and the target is MySQL 8.0, on-premise or using MySQL Database Service.

With such amount of data ~10GB a dump is not an issue especially if you use MySQL Shell utilities (please forget about the aging mysqldump – it deserves a nice retirement).

Migrate to MySQL on-premise

Because I need the whole instance, I’m going to use MySQL Shell dumpInstance utility.

An important option of dumpInstance is compatibility. It can

enforce the InnoDB storage engine (force_innodb) ie change CREATE TABLE statements to use the InnoDB storage engine for any tables that do not already use it
ignore any missing primary keys (ignore_missing_pks),
automatically add primary keys in invisible columns (create_invisible_pks)
etc…

Here, I will only use force_innodb. (if your memory is better than mine you remember that all the tables are already in InnoDB, so not really needed in this case, but I must admit that I’m paranoid :-O).
Joke aside, it is in my opinion a good habit to use this option.

If you are worried about primary keys below a query that would help you to check your schema:

-- Find tables without PK

SELECT tables.table_schema , tables.table_name , tables.engine  
FROM information_schema.tables  LEFT JOIN (     
   SELECT table_schema , table_name
   FROM information_schema.statistics
   GROUP BY table_schema, table_name, index_name 
   HAVING SUM( 
     case when non_unique = 0 and nullable != 'YES' then 1 else 0 end ) = count(*) ) puks   
   ON tables.table_schema = puks.table_schema 
  AND tables.table_name = puks.table_name   
  WHERE puks.table_name IS null 
  AND tables.table_type = 'BASE TABLE' 
  AND tables.TABLE_SCHEMA NOT IN ('mysql', 'performance_schema', 'information_schema', 'sys');

-- Find tables without PK

SELECT tables.table_schema , tables.table_name , tables.engine

FROM information_schema.tables LEFT JOIN (

SELECT table_schema , table_name

FROM information_schema.statistics

GROUP BY table_schema, table_name, index_name

HAVING SUM(

case when non_unique = 0 and nullable != 'YES' then 1 else 0 end ) = count(*) ) puks

ON tables.table_schema = puks.table_schema

AND tables.table_name = puks.table_name

WHERE puks.table_name IS null

AND tables.table_type = 'BASE TABLE'

AND tables.TABLE_SCHEMA NOT IN ('mysql', 'performance_schema', 'information_schema', 'sys');

The MySQL Shell dump instance command for dumping data from MariaDB to MySQL 8.0 on-premise looks like:

JS>
util.dumpInstance("/bkp/dumpMariadb1", {threads: 8, compatibility: ["force_innodb"]})

1 2	JS> util.dumpInstance("/bkp/dumpMariadb1", {threads: 8, compatibility: ["force_innodb"]})

The dump will be stored in the /bkp/dumpMariadb1 directory
threads: 8 -> 8 parallel threads to dump chunks of data from the MariaDB instance
compatibility: force_innodb -> update to InnoDB for any tables that do not already use it

Depending on your context you may have to tune this command.

Move the dump on the MySQL 8.0 host and then restore the dump.

In MySQL 8.0 there is a special ninja technique 🙂 to load a dump at the speed of light… All right, I may be exaggerating a bit, but at least at the speed of the sound by disabling crash recovery aka disabling redo logging.

DISCLAIMER: This feature is intended only for loading data into a new MySQL instance. Do not disable redo logging on a production system. It is permitted to shutdown and restart the server while redo logging is disabled, but an unexpected server stoppage while redo logging is disabled can cause data loss and instance corruption.

The commands to restore the dump on MySQL 8.0 on-premise look like:

JS>
\sql SET GLOBAL local_infile=1;
\sql ALTER INSTANCE DISABLE INNODB REDO_LOG;

util.loadDump("/restore/dumpMariadb1", {threads: 8, ignoreVersion: true , skipBinlog: true, loadUsers: false})

\sql ALTER INSTANCE ENABLE INNODB REDO_LOG;
\sql SET GLOBAL  local_infile=0;

JS>

\sql SET GLOBAL local_infile=1;

\sql ALTER INSTANCE DISABLE INNODB REDO_LOG;

util.loadDump("/restore/dumpMariadb1", {threads: 8, ignoreVersion: true , skipBinlog: true, loadUsers: false})

\sql ALTER INSTANCE ENABLE INNODB REDO_LOG;

\sql SET GLOBAL local_infile=0;

local_infile=1 -> allows local data loading
ALTER INSTANCE DISABLE INNODB REDO_LOG -> disable redo logging to speed up data loading
The dump has been moved to the MySQL instance at /restore/dumpMariadb1
threads: 8 -> 8 parallel threads to upload chunks of data to the MySQL instance
ignoreVersion: true -> allow the import of the MariaDB dump into MySQL (MariaDB is taken for a MySQL 5.5 version)
skipBinlog: true -> skips binary logging for the sessions used during the restore
loadUsers: false -> do not import users and their roles and grants. This one is very important because MariaDB users are not compatibles. Please note that the default is false (paranoid mode enable)

Migrate to MySQL Database Service

As a prerequisites I’m assuming that you have the basic Oracle Cloud Infrastructure and MySQL Database Service knowledge.
If it is not yet the case, please read my Discovering MySQL Database Service series.

To migrate your data into MySQL Database Service (MDS), MySQL Shell is again your best friend to export the data.
You can either store the data locally or very easily in an OCI object storage bucket.

If the data are located in a bucket you can then use the MDS data Import option to automatically create a new MDS instance populated with your data.
If you choose the “local” storage, the import will be manual only.

The main thing to keep in mind during the import process is that you will most likely have a user account issue (MariaDB users are not compatibles), so my recommendation is to take a MySQL Shell dumps with all the business data (i.e. mysql, information_schema, performance_schema, sys are not include) without the user accounts.

Because I need the whole instance, in both case I’m using MySQL Shell dumpInstance utility.

Backup the database into a bucket

I’m going to store my dump inside an Object Storage bucket so I must create the bucket before the dump. This is very easy by using the OCI interface.

If you are not familiar with OCI Object Storage you can follow this tutorial: Discovering MySQL Database Service – Episode 4 – Dump your MySQL data into an Object Storage bucket.

In order to put these data into the OCI bucket, we must make some configuration for allowing MySQL Shell to easily connect to the OCI object storage service.

To this end, we will create an OCI CLI configuration file.

The CLI is a small-footprint tool that you can use on its own or with the Console to complete Oracle Cloud Infrastructure tasks. The CLI provides the same core functionality as the Console, plus additional commands. Some of these, such as the ability to run scripts, extend Console functionality.

To be clear, we will not use the CLI but its configuration file. The configuration file name and default location (on Linux) is /home/opc/.oci/config.

It should have the following information:

user: OCID of the user calling the API.
fingerprint: Fingerprint for the public key that was added to this user.
key_file: Full path and filename of the private key.
tenancy: OCID of your tenancy.
region: An Oracle Cloud Infrastructure region.

For more details please click here.

As an example, mine looks like:

[DEFAULT]
user=ocid1.user.oc1..xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
fingerprint=xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx
key_file=/home/opc/.oci/oci_api_key.pem
tenancy=ocid1.tenancy.oc1..xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
compartment=ocid1.compartment.oc1..xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
region=eu-frankfurt-1

[DEFAULT]

user=ocid1.user.oc1..xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

fingerprint=xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx

key_file=/home/opc/.oci/oci_api_key.pem

tenancy=ocid1.tenancy.oc1..xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

compartment=ocid1.compartment.oc1..xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

region=eu-frankfurt-1

You may be wondering how to get this information? Fair question!
Don’t panic I already explained everything in this tutorial: Discovering MySQL Database Service – Episode 4 – Dump your MySQL data into an Object Storage bucket

My Bucket is named: migrationMariadbNoUser

The MySQL Shell dump instance command for migrating data from MariaDB to MDS and stored data into a bucket looks like:

JS>
util.dumpInstance("", {users: false, osBucketName: "migrationMariadbNoUser", osNamespace: "abcdefghijkl", ocimds: true,  threads: 8, compatibility: ["force_innodb", "strip_restricted_grants", "strip_definers", "strip_tablespaces"]})

1 2	JS> util.dumpInstance("", {users: false, osBucketName: "migrationMariadbNoUser", osNamespace: "abcdefghijkl", ocimds: true, threads: 8, compatibility: ["force_innodb", "strip_restricted_grants", "strip_definers", "strip_tablespaces"]})

users: false -> exclude users and their roles and grants in the dump
osBucketName: “migrationMariadbNoUser” -> name of the Object Storage bucket to which the dump is to be written
osNamespace: “abcdefghijkl” -> namespace where the Object Storage bucket named by osBucketName is located
ocimds: true -> enables checks and modifications for compatibility with MDS
threads: 8 -> 8 parallel threads to dump chunks of data from the MariaDB instance
compatibility: ->
- force_innodb -> change CREATE TABLE statements to use the InnoDB
- strip_restricted_grants -> remove specific privileges that are restricted by MDS
- strip_definers -> remove the DEFINER clause from views, routines, events, and triggers
- strip_tablespaces -> all tables are created in their default tablespace

Complete documentation is available here.

The command above is storing the dump, compatible with MDS, into the bucket migrationMariadbNoUser.

Backup the database locally

If your dataset is not big and/or you are too lazy to spend a little time in configuration, the alternative is to dump your data locally.
The main drawback is that you will have to import the data into MDS manually – but this should not be a big deal if your dataset is not huge.

If you want to store the dump locally the command looks like:

JS>
util.dumpInstance("/bkp/dumpMariadbNoUser", {users: false, threads: 8, compatibility: ["force_innodb", "strip_restricted_grants", "strip_definers", "strip_tablespaces", "create_invisible_pks"]})

1 2	JS> util.dumpInstance("/bkp/dumpMariadbNoUser", {users: false, threads: 8, compatibility: ["force_innodb", "strip_restricted_grants", "strip_definers", "strip_tablespaces", "create_invisible_pks"]})

The command is close to the one for the on-prem.
Please note – users: false – option. You can also dump the user and then run the import without loading the users (default behavior).

In fact, the most import difference compare to an on-prem data migration are the compatibility options. Because MDS is a PaaS you have less flexibility than the on-prem MySQL so more compatibility needs.

Restore into MySQL Database Service from a bucket

You have 2 solutions, the automated one, easiest but less flexible or the manual.

The first one is the MDS Data Import feature using the OCI interface.

MySQL Data Service - Data Import — MySQL Data Service – Data Import

You’ll find the “How To” in: Discovering MySQL Database Service – Episode 5 – Create a MySQL DB system from a MySQL Shell dump

Restore into MySQL Database Service from a local dump

In this case we cannot use the crash recovery trick nor the Data Import feature.
Anyway the MySQL Shell dumpInstance function will still do the job.

The command looks like:

JS>
util.loadDump("/restore/dumpMariadbNoUser", {threads: 8, ignoreVersion: true})

1 2	JS> util.loadDump("/restore/dumpMariadbNoUser", {threads: 8, ignoreVersion: true})

threads: 8 -> 8 parallel threads to upload chunks of data to the MySQL instance
ignoreVersion: true -> allow the import of the MariaDB dump into MySQL (MariaDB is taken for a MySQL 5.5 version)

loadUsers: false is not necessary here because the dump was made with users: false.

And don’t forget to re-create your users 🙂
You have plenty of authentication plugins to create secure user accounts.
We also provide Multifactor Authentication.

Btw if security is one of your concern (and it should be) below some MySQL password management features:

MySQL Security Serie – Password Management

Database migration is not only about data migration, so you still have some work to do. But now you should have a better understanding of the overall data migration process.

Follow me on twitter

Watch my videos on my YouTube channel and subscribe.

My Slideshare account.

My Speaker Deck account.

Thanks for using MySQL!

Filed under:
Cloud, Migration, MySQL by Olivier DASINI

Discovering MySQL Database Service – Episode 10 – Connect to MySQL Database Service Using OCI Cloud Shell

October 5, 2021

Tags: Cloud, MDS, OCI

MySQL, the world’s most popular open source database, is available as a managed cloud service in Oracle Cloud Infrastructure (OCI) under the name of MySQL Database Service (MDS).

MySQL Database Service is a fully managed cloud service, 100% Developed, Managed and Supported by the MySQL Team.

This is the tenth episode of “Discovering MySQL Database Service“, a series of tutorials where I will show you, step by step, how to use MySQL Database Service and some other Oracle Cloud Infrastructure services.

Please also note that you can run this tutorial and thus try MySQL Database Service & the other Oracle Cloud Infrastructure services for free by starting your 30-day trial.

Planning episodes

Episode 10 – Connect to MySQL Database Service Using OCI Cloud Shell

In the previous episode we’ve seen how to connect to our MySQL Database Service instance with MySQL Workbench using a SSH port forwarding from the OCI Bastion service.

And in episode 8 – Discovering MySQL Database Service – Episode 8 – Connect to MySQL Database Service Using MySQL Shell – we’ve seen how to connect to our MySQL Database Service instance with MySQL Shell using here again a SSH port forwarding from the OCI Bastion service.

Two more steps to our Discovering MySQL Database Service journey.

In this episode, we’ll learn how to connect to our MySQL Database Service instance using the Oracle Cloud Infrastructure Cloud Shell.
It provides a pre-authenticated Oracle Cloud Infrastructure CLI and preinstalled developer tools for easily managing Oracle Cloud resources.

Cloud Shell

Oracle Cloud Infrastructure Cloud Shell gives you access to an always available Linux shell directly in the Oracle Cloud Infrastructure Console.

You can use the shell to interact with resources like MySQL Database Service, Oracle Container Engine for Kubernetes cluster, Oracle Autonomous Database, …

Cloud Shell provides:

An ephemeral machine to use as a host for a Linux shell, pre-configured with the latest version of the OCI Command Line Interface (CLI) and a number of useful tools
5GB of storage for your home directory
A persistent frame of the Console which stays active as you navigate to different pages of the console

For more information, please see the Cloud Shell documentation

Create a SSH port forwarding session

In episode 7 – Discovering MySQL Database Service – Episode 7 – Use a Bastion SSH port forwarding session – we created a Bastion, a SSH port forwarding (SSH tunneling) session and generated SSH key pair.

We are now going to use these in order to connect to MDS with Cloud Shell. However if you remember well the CIDR block allowlist is set to your location (IP of your office, of your home if you WFH, …), but not the Cloud Shell IP.

So many possibilities, we can setup this bastion only for Cloud Shell, we can add another bastion, dedicated for the Cloud Shell, we can update the current bastion. I’ll go for this last option.

So the plan is to connect to Cloud Shell and get the IP address.

Open the OCI console and click on the Cloud Shell icon, on the top right corner:

It will open the Cloud Shell in the bottom of the browser (the first time it can takes some time).

To get the IP address run the following command:

$ curl ifconfig.me

1	$ curl ifconfig.me

We can now update (or setup) the Bastion.

Go to the OCI console, in the menu, go to: Identity & Security / Bastion, Check that you are in the right compartment then click on the Bastion we previously created – BastionMDS in this example – or create a new one if you prefer/can.

Click on Edit

Update the CIDR Block Allowlist with the IP that you got when you executed curl ifconfig.me.
Don’t forget the /32 (CIDR block)

Then saves the changes.

We can now create a session and then get the SSH port forwarding command generated by OCI.

Click on Create Session

And like we have seen in Discovering MySQL Database Service – Episode 7 – Use a Bastion SSH port forwarding session, create the session with the relevant parameters:

the Session Type: SSH port forwarding session
a session name: a meaningful one 🙂
how to connect to the target host : we’ll use IP Address.
the MySQL Database Service instance IP Address: the IP address that we got in Discovering MySQL Database Service – Episode 5 – Create a MySQL DB system from a MySQL Shell dump
the MySQL Database Service instance port: one of the port that we got in Discovering MySQL Database Service – Episode 5 – Create a MySQL DB system from a MySQL Shell dump. I choose 3306 but you could have chosen 33060.

Then you can either generate (a new) SSH key pair or choose the public key we’ve created in Discovering MySQL Database Service – Episode 7 – Use a Bastion SSH port forwarding session.

The private SSH key must be download into the Cloud Shell and be protected (chmod 600)

Drag and drop your private SSH key to the Cloud Shell and execute chmod 600 on the file

$ chmod 600 myPrivateKey.key

1	$ chmod 600 myPrivateKey.key

For the last steps we must now get the SSH port forwarding command generated by OCI and copy/paste it to the Cloud Shell.

To do so, when the session is active, go to the right and click on the action menu (3 vertical dots on the right).
A contextual menu is showing up then click on copy SSH command.

Paste this command in your Cloud Shell prompt.

The SSH command looks like :

ssh -i <privateKey> -N -L <localPort>:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com

1	ssh -i <privateKey> -N -L <localPort>:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com

Replace <privateKey> with the path to the private key we’ve just downloaded.

Replace <localPort> with a free port on the Cloud Shell – 3306 (and 33060) should be free.

Example:

$ ssh -i ./ssh-key-discovering_MDS.key -N -L 3306:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com &

1	$ ssh -i ./ssh-key-discovering_MDS.key -N -L 3306:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com &

Before run the command add an ampersand (&) at the end.

FYI, the error message “bind: Cannot assign requested address” is not a problem, this is just because the Cloud Shell tries to bind on ipv6 too.
If you want to avoid it, just add -4 between ssh and -i like this: ssh -4 -i

Connect to MDS using MySQL Shell in Cloud Shell

As you can see in the picture above, MySQL Shell is part of Cloud Shell.

So we will use MySQL Shell to connect to our MySQL instance through the SSH tunnel using MySQL

I recommend you to read Discovering MySQL Database Service – Episode 8 – Connect to MySQL Database Service Using MySQL Shell for more details.

Basically you’ll need the following information for the connection:

MySQL client is here MySQL Shell : mysqlsh
MySQL Database Service user you’ve created : admin
IP to connect to MDS (through the SSH tunnel) : localhost
Port to connect to MDS (through the SSH tunnel) : 3306
Your MDS password

We are now connected to our MySQL Database Service instance \o/
The server version is here 8.0.26-cloud MySQL Enterprise

And as expected we can see and query the data we download during Discovering MySQL Database Service – Episode 5 – Create a MySQL DB system from a MySQL Shell dump.

Well, that’s all for today!
In this episode, we have seen how to securely connect to a MySQL Database Service instance using the Oracle Cloud Infrastructure Cloud Shell through a SSH tunnel (port forwarding session) using the OCI Bastion service.

Next episode is:

MySQL Database Service Features Overview

Resources

Planning episodes

Follow me on twitter

Watch my videos on my YouTube channel and subscribe.

My Slideshare account.

My Speaker Deck account.

Thanks for using MySQL!

Filed under:
Cloud, Howto, MDS, MySQL, Tuto by Olivier DASINI

Discovering MySQL Database Service – Episode 9 – Connect to MySQL Database Service Using MySQL Workbench

September 28, 2021

Tags: Cloud, MDS, OCI

MySQL Database Service is a fully managed cloud service, 100% Developed, Managed and Supported by the MySQL Team.

This is the ninth episode of “Discovering MySQL Database Service“, a series of tutorials where I will show you, step by step, how to use MySQL Database Service and some other Oracle Cloud Infrastructure services.

Please also note that you can run this tutorial and thus try MySQL Database Service & the other Oracle Cloud Infrastructure services for free by starting your 30-day trial.

Planning episodes

Episode 9 – Connect to MySQL Database Service Using MySQL Workbench

In the previous episode we’ve seen how to connect to our MySQL Database Service instance with MySQL Shell using a SSH port forwarding from the OCI Bastion service.
One more step to our Discovering MySQL Database Service journey.

In this episode, we’ll learn how to connect to our MySQL Database Service instance using MySQL Workbench – a unified visual tool for database architects, developers, and DBAs that provides data modeling, SQL development, and comprehensive administration tools for server configuration, user administration, backup, and much more.

MySQL Workbench

MySQL Workbench is a GUI client for MySQL. It’s an unified visual tool for database architects, developers, and DBAs.

MySQL Workbench provides data modeling, SQL development, and comprehensive administration tools for server configuration, user administration, backup, and much more.

It is available on Windows, GNU Linux and Mac OS X.
You can download the community version here.

Create a SSH port forwarding session

In episode 7 – Discovering MySQL Database Service – Episode 7 – Use a Bastion SSH port forwarding session – we created a Bastion, a SSH port forwarding (SSH tunneling) session and generated SSH key pair.

We are now going to use these in order to connect to MDS with MySQL Workbench.

If your session is not valid anymore, you must create a new one (see Create a session paragraph from Discovering MySQL Database Service – Episode 7 – Use a Bastion SSH port forwarding session).

Please note that you need a SSH client on your computer. If you are using a GNU Linux, MacOS, Windows 10 system you are good to go.

If not… all is not lost 🙂
Please read episode 10 : Connect to MySQL Database Service Using OCI Cloud Shell.

In the console menu, go to Identity & Security / Bastion, check that you are in the right compartment then click on the Bastion we previously created (BastionMDS in this example).

You are now seeing the session we already created (if not create a new one).

We now must get the SSH port forwarding command generated by OCI.

To do so, go to the right and click on the action menu (3 vertical dots on the right). A contextual menu is showing up then click on copy SSH command.

Paste this command in your shell prompt.

The SSH command looks like :

ssh -i <privateKey> -N -L <localPort>:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com

1	ssh -i <privateKey> -N -L <localPort>:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com

Replace <privateKey> with the path to the private key we downloaded in the previous episode – Discovering MySQL Database Service – Episode 7 – Use a Bastion SSH port forwarding session

Replace <localPort> with a free port on the local machine from which you want to connect to the bastion (ie your computer/laptop).

Example:

ssh -i ./ssh-key-discovering_MDS.key -N -L 3333:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com

1	ssh -i ./ssh-key-discovering_MDS.key -N -L 3333:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com

Then run the command…

If you hit the following error: WARNING: UNPROTECTED PRIVATE KEY FILE!

Don’t worry 🙂
Like stated in the error message, the private key must be protected. A chmod 600 (or equivalent regarding your OS) will deliver you.

$ chmod 600 ssh-key-discovering_MDS.key

1	$ chmod 600 ssh-key-discovering_MDS.key

Then run the command again:

ssh -i ./ssh-key-discovering_MDS.key -N -L 3333:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com

1	ssh -i ./ssh-key-discovering_MDS.key -N -L 3333:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com

No error anymore and no prompt.
But do not close the window if not it will close the SSH tunnel (thus you will need to run the command again).

Alternatively, you can add an ampersand (&) at the end of the command.

ssh -i ./ssh-key-discovering_MDS.key -N -L 3333:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com  &

1	ssh -i ./ssh-key-discovering_MDS.key -N -L 3333:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com &

Connect to MDS using MySQL Workbench

Now it is the time to connect to our MySQL instance through the SSH tunnel using MySQL Workbench.

Open MySQL Workbench.
Go to Database / Manage Connections…

The Manage Server Connections appears, then click the New button (at the bottom left).

We can now setup our MDS connection in the form.

The main information must be entered in the Connection tab:

Connection Name : MDS1 (sorry I’m very bad at naming)
Connection Method : Standard (TCP/IP)
Hostname or IP to connect to MDS (through the SSH tunnel) : 127.0.0.1 (or localhost)
Port to connect to MDS (through the SSH tunnel) : 3333
MDS Username : admin
MDS Password : (come on! I don’t know your password)

Test the connection:

Can you see “Successfully made the MySQL connection“. ?

If the answer is no, please check that you SSH tunnel is still enable, the host Bastion could have closed the connection.
Also check your MDS credentials and host and port.

You can close the form. Your connection is now available in MySQL Workbench.

You can find it in the connection main page or go to Database / Connect to database…

And in Stored Connection drop list select your MDS connection

Then click OK.

We are now connected to our MySQL Database Service instance \o/
The server version is here 8.0.26-cloud MySQL Enterprise.

And as expected we can see and query the data we download during Discovering MySQL Database Service – Episode 5 – Create a MySQL DB system from a MySQL Shell dump.

Well, that’s all for today!
In this episode, we have seen how to securely connect to a MySQL Database Service instance using MySQL Workbench through a SSH tunnel (port forwarding session) using the OCI Bastion service.

Next episode is:

Connect to MySQL Database Service Using OCI Cloud Shell

Resources

Planning episodes

Follow me on twitter

Watch my videos on my YouTube channel and subscribe.

My Slideshare account.

My Speaker Deck account.

Thanks for using MySQL!

Filed under:
Cloud, Howto, MDS, MySQL, Tuto by Olivier DASINI

Discovering MySQL Database Service – Episode 8 – Connect to MySQL Database Service Using MySQL Shell

September 21, 2021

Tags: Cloud, MDS, OCI

MySQL Database Service is a fully managed cloud service, 100% Developed, Managed and Supported by the MySQL Team.

This is the eight episode of “Discovering MySQL Database Service“, a series of tutorials where I will show you, step by step, how to use MySQL Database Service and some other Oracle Cloud Infrastructure services.

Please also note that you can run this tutorial and thus try MySQL Database Service & the other Oracle Cloud Infrastructure services for free by starting your 30-day trial.

Planning episodes

Episode 8 – Connect to MySQL Database Service Using MySQL Shell

In the previous episode we’ve seen how to use an OCI Bastion session in order to provide a restricted and time-limited access to administer our MySQL instance.
One more step to our Discovering MySQL Database Service journey.

In this episode, we’ll learn how to connect to our MySQL Database Service instance using the fantastic MySQL Shell – an interactive Javascript, Python, or SQL interface supporting development and administration for the MySQL Server.

MySQL Shell

MySQL Shell is an advanced client & code editor for MySQL. In addition to the provided SQL functionality, it provides scripting capabilities for JavaScript and Python.

MySQL Shell includes utilities for working with MySQL, among others:

Instance & schema dump utilities support the export of all schemas or a selected schema from an on-premise MySQL instance into an OCI Object Storage bucket or a set of local files
Dump loading utility supports the import into a MySQL DB System or a MySQL Server instance of schemas or tables dumped using MySQL Shell’s Dump Utility

The complete list is available in the documentation: MySQL Shell utilities.

You’ll also find practical resources on my blog.

Create a SSH port forwarding session

In the previous episode – Discovering MySQL Database Service – Episode 7 – Use a Bastion SSH port forwarding session – we created a Bastion, a SSH port forwarding (SSH tunneling) session and generated SSH key pair.

We are now going to use these in order to connect to MDS with MySQL Shell.

If your session is not valid anymore, you must create a new one (see Create a session paragraph from Discovering MySQL Database Service – Episode 7 – Use a Bastion SSH port forwarding session).

Please note that you need a SSH client on your computer. If you are using a GNU Linux, MacOS, Windows 10 system you are good to go.

If not… all is not lost 🙂
Please read episode 10 : Connect to MySQL Database Service Using OCI Cloud Shell.

In the console menu, go to Identity & Security / Bastion, check that you are in the right compartment then click on the Bastion we previously created (BastionMDS in this example).

You are now seeing the session we already created (if not create a new one).

We now must get the SSH port forwarding command generated by OCI.

To do so, go to the right and click on the action menu (3 vertical dots on the right). A contextual menu is showing up then click on copy SSH command.

Paste this command in your shell prompt.

The SSH command looks like :

ssh -i <privateKey> -N -L <localPort>:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com

1	ssh -i <privateKey> -N -L <localPort>:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com

Replace <privateKey> with the path to the private key we downloaded in the previous episode – Discovering MySQL Database Service – Episode 7 – Use a Bastion SSH port forwarding session

Replace <localPort> with a free port on the local machine from which you want to connect to the bastion (ie your computer/laptop).

Example:

ssh -i ./ssh-key-discovering_MDS.key -N -L 3333:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com

1	ssh -i ./ssh-key-discovering_MDS.key -N -L 3333:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com

Then run the command…

If you hit the following error: WARNING: UNPROTECTED PRIVATE KEY FILE!

Don’t worry 🙂
Like stated in the error message, the private key must be protected. A chmod 600 (or equivalent, it depends on your OS) will fix that issue.

$ chmod 600 ssh-key-discovering_MDS.key

1	$ chmod 600 ssh-key-discovering_MDS.key

Then run the command again:

ssh -i ./ssh-key-discovering_MDS.key -N -L 3333:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com

1 2	ssh -i ./ssh-key-discovering_MDS.key -N -L 3333:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com

No error anymore and no prompt.
But do not close the window because it will close the SSH tunnel (thus you will need to run the command again).

Alternatively, you can add an ampersand (&) at the end of the command.

ssh -i ./ssh-key-discovering_MDS.key -N -L 3333:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com  &

1	ssh -i ./ssh-key-discovering_MDS.key -N -L 3333:10.0.1.231:3306 -p 22 ocid1.bastionsession.oc1.eu-frankfurt-1.amaaaaaacicuulyams3rrxazerty1uiopqsd2fghjklm3wxcvbr4m7wpu52a@host.bastion.eu-frankfurt-1.oci.oraclecloud.com &

Connect to MDS using MySQL Shell

Now it is the time to connect to our MySQL instance through the SSH tunnel using MySQL Shell.

The information needed are:

MySQL client is here MySQL Shell : mysqlsh
MySQL Database Service user you’ve created : admin
IP to connect to MDS (through the SSH tunnel) : localhost
Port to connect to MDS (through the SSH tunnel) : 3333
Your MDS password

$ mysqlsh admin@localhost:3333

1	$ mysqlsh admin@localhost:3333

We are now connected to our MySQL Database Service instance \o/
The server version is here 8.0.26-cloud MySQL Enterprise

By default, MySQL Shell is in JavaScript mode. We can easily switch to SQL mode with the command \sql. And run any valid SQL command

Another way to do that would be to initiate the connection in SQL mode instead of JS by providing the – – sql option when calling mysqlsh:

$ mysqlsh admin@localhost:3333 --sql
...
MySQL  localhost:3333 ssl  SQL > SHOW SCHEMAS;
+--------------------+
| Database           |
+--------------------+
| information_schema |
| mysql              |
| performance_schema |
| sakila             |
| sys                |
+--------------------+
5 rows in set (0.0138 sec)

MySQL  localhost:3333 ssl  SQL > SHOW TABLES IN sakila;
+----------------------------+
| Tables_in_sakila           |
+----------------------------+
| actor                      |
| actor_info                 |
...

$ mysqlsh admin@localhost:3333 --sql

...

MySQL localhost:3333 ssl SQL > SHOW SCHEMAS;

+--------------------+

| Database |

+--------------------+

| information_schema |

| mysql |

| performance_schema |

| sakila |

| sys |

+--------------------+

5 rows in set (0.0138 sec)

MySQL localhost:3333 ssl SQL > SHOW TABLES IN sakila;

+----------------------------+

| Tables_in_sakila |

+----------------------------+

| actor |

| actor_info |

...

And as expected we can see and query the data we download during Discovering MySQL Database Service – Episode 5 – Create a MySQL DB system from a MySQL Shell dump.

Well, that’s all for today!
In this episode, we have seen how to securely connect to a MySQL Database Service instance using MySQL Shell through a SSH tunnel (port forwarding session) using the OCI Bastion service.

Next episode is:

Connect to MySQL Database Service Using MySQL Workbench

Resources

Planning episodes

Follow me on twitter

Watch my videos on my YouTube channel and subscribe.

My Slideshare account.

My Speaker Deck account.

Thanks for using MySQL!

Filed under:
Cloud, Howto, MDS, MySQL, Tuto by Olivier DASINI

Discovering MySQL Database Service – Episode 7 – Use a Bastion SSH port forwarding session

September 14, 2021

Tags: Cloud, MDS, OCI

MySQL Database Service is a fully managed cloud service, 100% Developed, Managed and Supported by the MySQL Team.

This is the seventh episode of “Discovering MySQL Database Service“, a series of tutorials where I will show you, step by step, how to use MySQL Database Service and some other Oracle Cloud Infrastructure services.

Please also note that you can run this tutorial and thus try MySQL Database Service & the other Oracle Cloud Infrastructure services for free by starting your 30-day trial.

Planning episodes

Episode 7 – Use a Bastion SSH port forwarding session

In the previous episode we’ve seen how to allow traffic from the VCN to the MySQL Database Service instance on ports 3306 & 33060.
One more step to our Discovering MySQL Database Service journey.

In this episode, we’ll discover and use the OCI Bastion service to provide a restricted and time-limited access to administer our MySQL instance.

Bastion

Oracle Cloud Infrastructure Bastion provides restricted and time-limited access to target resources that don’t have public endpoints.

Bastions let authorized users connect from specific IP addresses to target resources using Secure Shell (SSH) sessions. When connected, users can interact with the target resource by using any software or protocol supported by SSH.

So, Bastions are logical entities that provide secured, public access to target resources in the cloud that you cannot otherwise reach from the internet.
Bastions reside in a public subnet and establish the network infrastructure needed to connect a user to a target resource in a private subnet.

Another important concept is Bastion sessions.
Bastion sessions let authorized users in possession of the private key in an SSH key pair connect to a target resource for a predetermined amount of time.
You provide the public key in the SSH key pair at the time you create the session, and then supply the private key when you connect.
In addition to presenting the private key, an authorized user must also attempt the SSH connection to the target resource from an IP address within the range allowed by the bastion’s client CIDR block allowlist.

There are 2 kind of session types, managed ssh session and ssh port forwarding session. In this article, we’re going to use only the ssh port forwarding session.
Port forwarding (also known as SSH tunneling) creates a secure connection between a specific port on the client machine and a specific port on the target resource.
Using this connection you can relay other protocols.

Note
Your OCI user must be part of a group that has the right privileges in order to use all Bastion features.
At least the following rules:
Allow group SecurityAdmins to manage bastion-family in tenancy
Allow group SecurityAdmins to manage virtual-network-family in tenancy
Allow group SecurityAdmins to read instance-family in tenancy
Allow group SecurityAdmins to read instance-agent-plugins in tenancy
Allow group SecurityAdmins to inspect work-requests in tenancy
Please see: Bastion IAM Policies and Required IAM policy to managing Bastions

Create a bastion

Go to the OCI console, in the menu, go to: Identity & Security / Bastion

Check that you are in the right compartment and click Create Bastion

You should provide:

a bastion name – BastionMDS
the target VCN, in the right compartment – Demo_VCN
the target subnet, the private one – Private Subnet-Demo_VCN
the CIDR block allowlist ie the IP from where you want to provide the secure access (e.g. 11.0.0.0/24, <your IP>/32, …). You can have multiple CIDR blocks.
You can also choose the maximum session ttl (max 3 hours, and you can redefine it per session later) if you click on Show Advanced Options
Then click on Create Bastion

Create a session

Now it is the time to create a Bastion SSH port forwarding session.

Click on your brand new Bastion (or go to the OCI console and in the menu, go to: Identity & Security / Bastion and click on your Bastion).

Click on Create Session, a new form will show up.

You should provide:

the Session Type: SSH port forwarding session
a session name: a meaningful one 🙂
how to connect to the target host : we’ll use IP Address.
the MySQL Database Service instance IP Address: the IP address that we got in Discovering MySQL Database Service – Episode 5 – Create a MySQL DB system from a MySQL Shell dump
the MySQL Database Service instance port: one of the port that we got in Discovering MySQL Database Service – Episode 5 – Create a MySQL DB system from a MySQL Shell dump. I choose 3306 but you could have chosen 33060.

Like we mentioned earlier, Bastion sessions let authorized users in possession of the private key in an SSH key pair connect to a target resource. You provide the public key in the SSH key pair at the time you create the session, and then supply the private key when you connect.

If you do not already have a SSH key pair (or want to use another one) select Generate SSH Key pair.
Download the private key so that you can connect to the instance using SSH.

Be careful because,

It will not be shown again!

You can also download the public key if needed.
Please note that you will need to modify the private key file permissions.

Example:

ex: chmod 600 ssh-key-YYYY-MM-DD.key

1	ex: chmod 600 ssh-key-YYYY-MM-DD.key

If you want to setup the session time-to-live click on Show Advanced Options

Then click Create Session:

Our Bastion session is now created and active.

Well, that’s all for today!
In this episode, we have seen how to use an OCI Bastion session in order to provide a restricted and time-limited access to administer our MySQL instance.

Next episode is:

Connect to MySQL Database Service Using MySQL Shell

Resources

Planning episodes

Follow me on twitter

Watch my videos on my YouTube channel and subscribe.

My Slideshare account.

My Speaker Deck account.

Thanks for using MySQL!

Filed under:
Cloud, Howto, MDS, MySQL, Tuto by Olivier DASINI

Discovering MySQL Database Service – Episode 6 – Update the Private Subnet Security List

September 7, 2021

Tags: Cloud, MDS, OCI

MySQL Database Service is a fully managed cloud service, 100% Developed, Managed and Supported by the MySQL Team.

This is the sixth episode of “Discovering MySQL Database Service“, a series of tutorials where I will show you, step by step, how to use MySQL Database Service and some other Oracle Cloud Infrastructure services.

Please also note that you can run this tutorial and thus try MySQL Database Service & the other Oracle Cloud Infrastructure services for free by starting your 30-day trial.

Planning episodes

Episode 6 – Update the Private Subnet Security List

In the previous episode we’ve seen how to create a MySQL DB system from a MySQL Shell dump stored into an Oracle Cloud Infrastructure object storage bucket. One more step to our Discovering MySQL Database Service journey.

In this episode, we’ll introduce the concept of Security Lists a virtual firewall to control traffic at the network packet level.

Security Lists

A security list acts as a virtual firewall for an instance, with ingress and egress rules that specify the types of traffic allowed in and out.
Each security list is enforced at the VNIC level. However, you configure your security lists at the subnet level, which means that all VNICs in a given subnet are subject to the same set of security lists.
The security lists apply to a given VNIC whether it’s communicating with another instance in the VCN or a host outside the VCN.

The default security list comes with an initial set of stateful rules, which should in most cases be changed to only allow inbound traffic from authorized subnets relevant to the region that homes that VCN or subnet.

Note
Your OCI user must be part of a group that has the right privileges in order to work with Security Lists.
At least the following rules:
Allow group SecListAdmins to manage security-lists in tenancy
Allow group SecListAdmins to manage vcns in tenancy
Please see: Working with Security Lists

Update the Security List

Go to the OCI console, in the menu, go to: Networking / Virtual Cloud Networks
And select (click on) your VCN ( Demo_VCN in this example), and please check that you are in the right compartment.

Select (click on) the private subnet (Private Subnet-Demo_VCN in this example):

Select the Security List for the Private Subnet (Security List for Private Subnet-Demo_VCN in this example):

Click on Add Ingress Rules:

Now we are adding our ingress rules in order to be able to connect to the MDS instance from the VCN on ports 3306 (MySQL classic protocol) and 33060 (MySQL X protocol).

The information to provide are (please adapt to your context):
+ Source type: CIDR
+ Source CIDR: 10.0.0.0/16
+ IP protocol: TCP
+ Source port range: All
+ Destination port range: 3306,33060

Then click on Add Ingress Rules.

You should end up with something like:

Well, that’s all for today!
In this episode, we have seen how to allow traffic from the VCN to the MySQL Database Service instance on ports 3306 & 33060.
We are now able to connect to our MySQL instance…

Next episode is:

Use a Bastion SSH port forwarding session

Resources

Planning episodes

Follow me on twitter

Watch my videos on my YouTube channel and subscribe.

My Slideshare account.

My Speaker Deck account.

Thanks for using MySQL!

Filed under:
Cloud, Howto, MDS, MySQL, Tuto by Olivier DASINI

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Slides

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Prerequisites

MySQL HeatWave ML

Load the data

Check HeatWave ML status

Data exploration

Data visualization

Training a Model

Row Prediction

Row Explanation

Table Prediction

Table Explanation

Scores

Resources

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MySQL HeatWave

Apache Zeppelin

Requirements

Binary

Firewalls

Starting Apache Zeppelin

MySQL Connector J

Setup the Zeppelin interpreter

MySQL HeatWave usage

Analytics

Machine Learning

Resources

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MySQL HeatWave

Apache Superset

Start Superset

Install MySQL Connector Python 8.0

Configure Superset for MySQL HeatWave

1/ SQLALCHEMY URI :

2/ ENGINE PARAMETERS

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Context

MySQL Shell

The source dataset

Migrate the data

Migrate to MySQL on-premise

Migrate to MySQL Database Service

Backup the database into a bucket

Backup the database locally

Restore into MySQL Database Service from a bucket

Restore into MySQL Database Service from a local dump

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Planning episodes

Episode 10 – Connect to MySQL Database Service Using OCI Cloud Shell

Cloud Shell

Create a SSH port forwarding session

Connect to MDS using MySQL Shell in Cloud Shell

Resources

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Planning episodes

Episode 9 – Connect to MySQL Database Service Using MySQL Workbench

MySQL Workbench

Create a SSH port forwarding session

Connect to MDS using MySQL Workbench

Resources

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Planning episodes

Episode 8 – Connect to MySQL Database Service Using MySQL Shell

MySQL Shell

Create a SSH port forwarding session

Connect to MDS using MySQL Shell

Resources