Using Snowflake, Debezium and Kafka with Lenses

In this blog post will use a Debezium Connector to capture changes from a MySQL database, send them to Kafka and then move the events into Snowflake.

We will configure, deploy and monitor this flow using a single DataOps tool.

What is Lenses

Lenses is lightweight and delivers DataOps for your data infrastructure on Apache Kafka and Kubernetes. It provides APIs and a

for streaming data discovery and exploration. It can also monitor your infrastructure, data integration and micro-services, and help you troubleshoot your real-time data.

We will be using low-code methods to deploy data integration using Kafka Connect and will manage them via GitOps.

What is Debezium

Debezium offers an open source collection of Kafka Connectors that can turn data stores into event streams. That means that you can easily move to event-based architectures and build logic that responds to row-level changes in a data store.

You can read more about the Debezium MySQL connector here.

What is CDC

CDC means Change Data Capture and is a design pattern that monitors and captures any changes in a data store by reading the bin-logs, avoiding the performance impact that a typical JDBC connector applies.

In our example, we will capture all the changes on a MySQL database using CDC and pass the change events to Kafka.

What is Snowflake

Snowflake is a Cloud data warehouse that can store both structured and semi-structured data.

Pre-requisites

You are going to need to have Lenses for this tutorial.

You can point Lenses to your Kafka and Kubernetes (if you have one) clusters. To make things easier for this guide, we are going to use the Lenses+Kafka all-in-one free “Lenses Community Edition” docker compose file.

Additionally, you will need to have a valid account to Snowflake.

Use our free, open-source Debezium Connector to communicate with your MySQL database.

The scenario that will be implemented

The scenario that is going to be implemented has the following steps:

Setup a MySQL database with sample data.
Import data from the MySQL to a Lenses with Kafka using Debezium.
Export the data into Snowflake.

Setting up MySQL

You do not need to do something special in the MySQL database that you are using. What is important is that MySQL is running and that you know the TCP port, username and password required to connect. For the purposes of this tutorial, MySQL will run on a Docker image. You can get the MySQL Docker image as follows:

docker pull debezium/example-mysql:0.10

Notice that this Docker image contains sample data on a database named

inventory

, which is the one that is going to be monitored.

After that you can execute that Docker image as follows:

```
docker run -it --name mysql -p 3306:3306 -e MYSQL_ROOT_PASSWORD=debezium \ 
-e MYSQL_USER=mysqluser -e MYSQL_PASSWORD=dbz-pass debezium/example-
mysql:0.10
```

The username that we will be using is

root

and the password is debezium – both will be used when setting up the Debezium Connector. Also notice that the name of the image as defined using --name mysql is mysql.

If you do not want to keep the Debezium Docker image on your computer, you can bypass the

docker pull

step and execute the docker run command with the --rm parameter, which tells Docker to remove the container when it stops.

If you want to connect to that Docker image using the

sqlclient

utility, you can execute the following command, which runs the mysql utility inside a Docker image named mysql:5.7:

```
docker run -it --rm --name mysqlterm --link mysql --rm mysql:5.7 \ 
sh -c 'exec mysql -h"$MYSQL_PORT_3306_TCP_ADDR" -
P"$MYSQL_PORT_3306_TCP_PORT" \ 
-uroot -p"$MYSQL_ENV_MYSQL_ROOT_PASSWORD"'
```

From there you can inspect the contents of the

inventory

MySQL database and make changes to the contents of its tables.

Setting Up Lenses

Setting up Lenses Community Edition is as simple as executing the command:

```
curl -L https://lenses.io/preview -o docker-compose.yml && ACCEPT_EULA=true docker compose up -d --wait && echo "Lenses.io is running on http://localhost:9991"
```

IMPORTANT: When you registered for the “Lenses Community Edition”, you would have received the above docker command with a valid free license (instead of the “xxxxxxxxx” in the id parameter). Check your emails for your unique command.

You need the

--link mysql:mysql

parameter so that the Lenses image can find the Docker image with MySQL, which is going to be called mysql. Notice that a MySQL Docker image named mysql should already be running for the above docker run command to work.

You might need to wait a little for the Lenses Docker image to load all necessary components. After that you will be able to connect to Lenses from your favorite web browser on port 9991 – the username and the password are both

admin

After connecting, you will see the image that follows on your web browser.

Setting Up Snowflake

You will need a Snowflake account in order to be able to use it. The easiest way is to visit https://trial.snowflake.com and create a trial account.

For the purposes of this tutorial the Snowflake username is

mihalis

. Notice that the password will not be needed for creating the connection between Lenses and Snowflake. However, you will still need your Snowflake password for connecting to the Snowflake UI.

The name of the Snowflake server is

re91701.eu-west-1.snowflakecomputing.com

. Yours will be different, That means if you try to follow this tutorial, you will have to replace re91701.eu-west-1.snowflakecomputing.com with your Snowflake server address.

Installing Snowflake Kafka Connector

We will need to install the Snowflake Kafka Connector to a Lenses Community Edition. We will be using version 0.4.0 of the Snowflake Kafka Connector. Please visit this page for the full list of available Snowflake Kafka Connectors.

You will need to connect to the Docker image of the Lenses with the following command:

docker exec -it lenses-dev bash

Notice that the name of the Lenses being used is

lenses-dev

and was specified using the --name=lenses-dev parameter.

The previous step is required because you will need to install the Snowflake Connector inside the running Lenses.

You will need to download the Snowflake Kafka Connector with the command:

```
wget https://repo1.maven.org/maven2/com/snowflake/snowflake-kafka-
connector/0.4.0/snowflake-kafka-connector-0.4.0.jar
```

Put the downloaded file in the right directory with:

mv snowflake-kafka-connector-0.4.0.jar /run/connect/connectors/third-party/

Congratulations, you have just installed the Snowflake Kafka Connector jar file by putting it in the

/run/connect/connectors/third-party/

directory of Lenses.

However, you will need to edit

/run/lenses/lenses.conf

and add the following text at the end of the Lenses configuration file in order for Lenses to understand the Snowflake Connector:

```
lenses {
  connectors.info = [
      {
           class.name = "com.snowflake.kafka.connector.SnowflakeSinkConnector"
           name = "Snowflake Kafka Connector"
           sink = true
           description = "A description for the connector"
           author = "The connector author"
      }
  ]
}
```

Execute the following commands, which are needed for the Snowflake Connector to work:

```
mkdir -p /.cache/snowflake
chmod -R 777 /.cache/snowflake
```

The last commands that you need to execute is the following:

```
supervisorctl restart connect-distributed
supervisorctl restart lenses
```

The former command tells Lenses to search for new Connectors whereas the latter command restarts Lenses in order to reread the new configuration from

/run/lenses/lenses.conf

Using the Snowflake Connector for Kafka

In order to make sure that the Snowflake Kafka Connector is property installed you will need to visit the Connectors link inside Lenses and press the + New Connector button.

The following image proves that the Snowflake Connector is recognized by Lenses Community Edition and therefore is properly installed.

You can find more information about the Snowflake Kafka Connector here.

Getting data from MySQL to Lenses and Kafka

As stated before, we are going to use the Debezium Connector to get row-level changes from MySQL to Kafka.

Using Debezium to put data into Lenses Community Edition

We will now create a new Connector using the Debezium MySQL Connector available in Lenses. The Debezium configuration will be the following:

```
name=MySQL-CDC
connector.class=io.debezium.connector.mysql.MySqlConnector
tasks.max=1
database.hostname=mysql
database.port=3306
database.user=root
database.password=debezium
database.server.name=dbserver1
database.whitelist=inventory
database.history.kafka.bootstrap.servers=localhost:9092
database.history.kafka.topic=schema-changes.inventory
value.converter=org.apache.kafka.connect.json.JsonConverter
key.converter=org.apache.kafka.connect.json.JsonConverter
```

The name of the MySQL database that we are going to watch is

inventory

and exists in the debezium/example-mysql:0.10 Docker image.

Now, connect to the MySQL database using the command we talked about earlier:

```
docker run -it --rm --name mysqlterm --link mysql --rm mysql:5.7 \ 
sh -c 'exec mysql -h"$MYSQL_PORT_3306_TCP_ADDR" -
P"$MYSQL_PORT_3306_TCP_PORT" \ 
-uroot -p"$MYSQL_ENV_MYSQL_ROOT_PASSWORD"'
```

Then, create a new table named

profits

to the inventory database as follows:

```
use inventory;
CREATE TABLE `profits` ( `product_id` int(11) NOT NULL, `amount` int(11) NOT 
NULL, PRIMARY KEY (`product_id`)) ENGINE=InnoDB DEFAULT CHARSET=latin1;
```

Verify data published to Kafka

Within Lenses, go to the Topics >> schema-changes.inventory

There will be a new record to the

schema-changes.inventory

Kafka topic, which in JSON format will look similar to the following:

```
{
    "value": {
        "source": {
            "server": "dbserver1"
        },
        "position": {
            "ts_sec": 1569607738,
            "file": "mysql-bin.000003",
            "pos": 450,
            "server_id": 223344
        },
        "databaseName": "inventory",
        "ddl": "CREATE TABLE `profits` ( `product_id` int(11) NOT NULL, `amount` int(11) NOT NULL, PRIMARY KEY (`product_id`)) ENGINE=InnoDB DEFAULT CHARSET=latin1"
    },
    "metadata": {
        "offset": 16,
        "partition": 0,
        "timestamp": 1569607738666,
        "__keysize": 0,
        "__valsize": 366
    }
}
```

This proves that everything works well with Debezium and Lenses.

Stream Process events before sending to Snowflake

So, imagine that you want to transfer data from

dbserver1.inventory.products

to Snowflake.

In this case, you will need to rename that topic into something simpler because it looks like Snowflake hates dot characters in topic names. The easiest way to do that is using a SQL processor (SQL Processor >> New Processor) that will continuously process events and transform them via an SQL statement.

For the purposes of this tutorial, the code for the SQL processor will be the following:

```
set autocreate=true;
INSERT INTO mytopic SELECT * FROM dbserver1.inventory.products
```

The new name for

dbserver1.inventory.products

will be mytopic – however the dbserver1.inventory.products still exists. The following figure shows that in a graphical way.

Connecting Lenses to Snowflake

After the installation of the Snowflake Kafka Connector, the process of connecting Lenses and Snowflake is straightforward.

Create a new Sink using the Snowflake Connector in Connectors >> New Connector >> Snowflake. The configuration for the Snowflake Sink in Lenses will be the following:

```
connector.class=com.snowflake.kafka.connector.SnowflakeSinkConnector
snowflake.topic2table.map=mytopic:external
tasks.max=1
topics=mytopic
snowflake.url.name=https://re91701.eu-west-1.snowflakecomputing.com
snowflake.database.name=mydb
snowflake.schema.name=public
value.converter.schema.registry.url=http://localhost:8081
buffer.count.records=10000
snowflake.user.name=mihalis
snowflake.private.key=REALLY_LOOOOOOOONG_STRING_READ_HOW_TO_FIND_IT
name=snowflake
snowflake.user.role=sysadmin
value.converter=com.snowflake.kafka.connector.records.SnowflakeJsonConverter
key.converter=com.snowflake.kafka.connector.records.SnowflakeJsonConverter
buffer.size.bytes=5242880
```

In order to get the correct value for

snowflake.private.key

you will need to visit this and follow the instructions.

Visiting Snowflake will verify that the data from Lenses is sent to Snowflake successfully. Data is read from

mytopic

in Lenses and written to external in Snowflake. The Snowflake database and schema used are mydb and public, respectively.

Conclusions

Lenses provides a DataOps layer to configure, manage, debug and deploy flows. You can manage these flows via GitOps and leverage Lenses to bake in monitoring, alerting and data compliance. Check out our other blogs for more info.

Links & Next Steps

Get familar with everything you can do with Lenses Community Edition in this tour: