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315 changes: 315 additions & 0 deletions yaml/README_SqlServer_To_Iceberg_Yaml.md
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SQL Server to Iceberg (YAML) template
---
The SQL Server to Iceberg template is a batch pipeline executes the user provided
SQL query to read data from SQL Server table and outputs the records to Iceberg
table.



:bulb: This is a generated documentation based
on [Metadata Annotations](https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/contributor-docs/code-contributions.md#metadata-annotations)
. Do not change this file directly.

## Parameters

### Required parameters

* **jdbcUrl**: The JDBC connection URL. For example, `jdbc:sqlserver://localhost:12345;databaseName=your-db`.
* **table**: A fully-qualified table identifier, e.g., my_dataset.my_table. For example, `my_dataset.my_table`.
* **catalogName**: The name of the Iceberg catalog that contains the table. For example, `my_hadoop_catalog`.
* **catalogProperties**: A map of properties for setting up the Iceberg catalog. For example, `{"type": "hadoop", "warehouse": "gs://your-bucket/warehouse"}`.

### Optional parameters

* **username**: The database username. For example, `my_user`.
* **password**: The database password. For example, `my_secret_password`.
* **driverClassName**: The fully-qualified class name of the JDBC driver to use. For example, `com.microsoft.sqlserver.jdbc.SQLServerDriver`. Defaults to: com.microsoft.sqlserver.jdbc.SQLServerDriver.
* **driverJars**: A comma-separated list of GCS paths to the JDBC driver JAR files. For example, `gs://your-bucket/mssql-jdbc-12.2.0.jre11.jar`.
* **connectionProperties**: A semicolon-separated list of key-value pairs for the JDBC connection. For example, `key1=value1;key2=value2`.
* **connectionInitSql**: A list of SQL statements to execute when a new connection is established. For example, `["SET TIME ZONE UTC"]`.
* **jdbcType**: Specifies the type of JDBC source. An appropriate default driver will be packaged. For example, `mssql`.
* **location**: The name of the database table to read data from. For example, `public.my_table`.
* **readQuery**: The SQL query to execute on the source to extract data. For example, `SELECT * FROM my_table WHERE status = 'active'`.
* **partitionColumn**: The name of a numeric column that will be used for partitioning the data. For example, `id`.
* **numPartitions**: The number of partitions to create for parallel reading. For example, `10`.
* **fetchSize**: The number of rows to fetch per database call. It should ONLY be used if the default value throws memory errors. For example, `50000`.
* **disableAutoCommit**: Whether to disable auto-commit on read. For example, `True`.
* **outputParallelization**: If true, the resulting PCollection will be reshuffled. For example, `True`.
* **configProperties**: A map of properties to pass to the Hadoop Configuration. For example, `{"fs.gs.impl": "com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem"}`.
* **drop**: A list of field names to drop. Mutually exclusive with 'keep' and 'only'. For example, `["field_to_drop_1", "field_to_drop_2"]`.
* **keep**: A list of field names to keep. Mutually exclusive with 'drop' and 'only'. For example, `["field_to_keep_1", "field_to_keep_2"]`.
* **only**: The name of a single field to write. Mutually exclusive with 'keep' and 'drop'. For example, `my_record_field`.
* **partitionFields**: A list of fields and transforms for partitioning, e.g., ['day(ts)', 'category']. For example, `["day(ts)", "bucket(id, 4)"]`.
* **tableProperties**: A map of Iceberg table properties to set when the table is created. For example, `{"commit.retry.num-retries": "2"}`.



## Getting Started

### Requirements

* Java 17
* Maven
* [gcloud CLI](https://cloud.google.com/sdk/gcloud), and execution of the
following commands:
* `gcloud auth login`
* `gcloud auth application-default login`

:star2: Those dependencies are pre-installed if you use Google Cloud Shell!

[![Open in Cloud Shell](http://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/editor?cloudshell_git_repo=https%3A%2F%2Fgithub.com%2FGoogleCloudPlatform%2FDataflowTemplates.git&cloudshell_open_in_editor=yaml/src/main/java/com/google/cloud/teleport/templates/yaml/SqlServerToIcebergYaml.java)

### Templates Plugin

This README provides instructions using
the [Templates Plugin](https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/contributor-docs/code-contributions.md#templates-plugin).

#### Validating the Template

This template has a validation command that is used to check code quality.

```shell
mvn clean install -PtemplatesValidate \
-DskipTests -am \
-pl yaml
```

### Building Template

This template is a Flex Template, meaning that the pipeline code will be
containerized and the container will be executed on Dataflow. Please
check [Use Flex Templates](https://cloud.google.com/dataflow/docs/guides/templates/using-flex-templates)
and [Configure Flex Templates](https://cloud.google.com/dataflow/docs/guides/templates/configuring-flex-templates)
for more information.

#### Staging the Template

If the plan is to just stage the template (i.e., make it available to use) by
the `gcloud` command or Dataflow "Create job from template" UI,
the `-PtemplatesStage` profile should be used:

```shell
export PROJECT=<my-project>
export BUCKET_NAME=<bucket-name>
export ARTIFACT_REGISTRY_REPO=<region>-docker.pkg.dev/$PROJECT/<repo>

mvn clean package -PtemplatesStage \
-DskipTests \
-DprojectId="$PROJECT" \
-DbucketName="$BUCKET_NAME" \
-DartifactRegistry="$ARTIFACT_REGISTRY_REPO" \
-DstagePrefix="templates" \
-DtemplateName="SqlServer_To_Iceberg_Yaml" \
-f yaml
```

The `-DartifactRegistry` parameter can be specified to set the artifact registry repository of the Flex Templates image.
If not provided, it defaults to `gcr.io/<project>`.

The command should build and save the template to Google Cloud, and then print
the complete location on Cloud Storage:

```
Flex Template was staged! gs://<bucket-name>/templates/flex/SqlServer_To_Iceberg_Yaml
```

The specific path should be copied as it will be used in the following steps.

#### Running the Template

**Using the staged template**:

You can use the path above run the template (or share with others for execution).

To start a job with the template at any time using `gcloud`, you are going to
need valid resources for the required parameters.

Provided that, the following command line can be used:

```shell
export PROJECT=<my-project>
export BUCKET_NAME=<bucket-name>
export REGION=us-central1
export TEMPLATE_SPEC_GCSPATH="gs://$BUCKET_NAME/templates/flex/SqlServer_To_Iceberg_Yaml"

### Required
export JDBC_URL=<jdbcUrl>
export TABLE=<table>
export CATALOG_NAME=<catalogName>
export CATALOG_PROPERTIES=<catalogProperties>

### Optional
export USERNAME=<username>
export PASSWORD=<password>
export DRIVER_CLASS_NAME=com.microsoft.sqlserver.jdbc.SQLServerDriver
export DRIVER_JARS=<driverJars>
export CONNECTION_PROPERTIES=<connectionProperties>
export CONNECTION_INIT_SQL=<connectionInitSql>
export JDBC_TYPE=mssql
export LOCATION=<location>
export READ_QUERY=<readQuery>
export PARTITION_COLUMN=<partitionColumn>
export NUM_PARTITIONS=<numPartitions>
export FETCH_SIZE=<fetchSize>
export DISABLE_AUTO_COMMIT=<disableAutoCommit>
export OUTPUT_PARALLELIZATION=<outputParallelization>
export CONFIG_PROPERTIES=<configProperties>
export DROP=<drop>
export KEEP=<keep>
export ONLY=<only>
export PARTITION_FIELDS=<partitionFields>
export TABLE_PROPERTIES=<tableProperties>

gcloud dataflow flex-template run "sqlserver-to-iceberg-yaml-job" \
--project "$PROJECT" \
--region "$REGION" \
--template-file-gcs-location "$TEMPLATE_SPEC_GCSPATH" \
--parameters "jdbcUrl=$JDBC_URL" \
--parameters "username=$USERNAME" \
--parameters "password=$PASSWORD" \
--parameters "driverClassName=$DRIVER_CLASS_NAME" \
--parameters "driverJars=$DRIVER_JARS" \
--parameters "connectionProperties=$CONNECTION_PROPERTIES" \
--parameters "connectionInitSql=$CONNECTION_INIT_SQL" \
--parameters "jdbcType=$JDBC_TYPE" \
--parameters "location=$LOCATION" \
--parameters "readQuery=$READ_QUERY" \
--parameters "partitionColumn=$PARTITION_COLUMN" \
--parameters "numPartitions=$NUM_PARTITIONS" \
--parameters "fetchSize=$FETCH_SIZE" \
--parameters "disableAutoCommit=$DISABLE_AUTO_COMMIT" \
--parameters "outputParallelization=$OUTPUT_PARALLELIZATION" \
--parameters "table=$TABLE" \
--parameters "catalogName=$CATALOG_NAME" \
--parameters "catalogProperties=$CATALOG_PROPERTIES" \
--parameters "configProperties=$CONFIG_PROPERTIES" \
--parameters "drop=$DROP" \
--parameters "keep=$KEEP" \
--parameters "only=$ONLY" \
--parameters "partitionFields=$PARTITION_FIELDS" \
--parameters "tableProperties=$TABLE_PROPERTIES"
```

For more information about the command, please check:
https://cloud.google.com/sdk/gcloud/reference/dataflow/flex-template/run


**Using the plugin**:

Instead of just generating the template in the folder, it is possible to stage
and run the template in a single command. This may be useful for testing when
changing the templates.

```shell
export PROJECT=<my-project>
export BUCKET_NAME=<bucket-name>
export REGION=us-central1

### Required
export JDBC_URL=<jdbcUrl>
export TABLE=<table>
export CATALOG_NAME=<catalogName>
export CATALOG_PROPERTIES=<catalogProperties>

### Optional
export USERNAME=<username>
export PASSWORD=<password>
export DRIVER_CLASS_NAME=com.microsoft.sqlserver.jdbc.SQLServerDriver
export DRIVER_JARS=<driverJars>
export CONNECTION_PROPERTIES=<connectionProperties>
export CONNECTION_INIT_SQL=<connectionInitSql>
export JDBC_TYPE=mssql
export LOCATION=<location>
export READ_QUERY=<readQuery>
export PARTITION_COLUMN=<partitionColumn>
export NUM_PARTITIONS=<numPartitions>
export FETCH_SIZE=<fetchSize>
export DISABLE_AUTO_COMMIT=<disableAutoCommit>
export OUTPUT_PARALLELIZATION=<outputParallelization>
export CONFIG_PROPERTIES=<configProperties>
export DROP=<drop>
export KEEP=<keep>
export ONLY=<only>
export PARTITION_FIELDS=<partitionFields>
export TABLE_PROPERTIES=<tableProperties>

mvn clean package -PtemplatesRun \
-DskipTests \
-DprojectId="$PROJECT" \
-DbucketName="$BUCKET_NAME" \
-Dregion="$REGION" \
-DjobName="sqlserver-to-iceberg-yaml-job" \
-DtemplateName="SqlServer_To_Iceberg_Yaml" \
-Dparameters="jdbcUrl=$JDBC_URL,username=$USERNAME,password=$PASSWORD,driverClassName=$DRIVER_CLASS_NAME,driverJars=$DRIVER_JARS,connectionProperties=$CONNECTION_PROPERTIES,connectionInitSql=$CONNECTION_INIT_SQL,jdbcType=$JDBC_TYPE,location=$LOCATION,readQuery=$READ_QUERY,partitionColumn=$PARTITION_COLUMN,numPartitions=$NUM_PARTITIONS,fetchSize=$FETCH_SIZE,disableAutoCommit=$DISABLE_AUTO_COMMIT,outputParallelization=$OUTPUT_PARALLELIZATION,table=$TABLE,catalogName=$CATALOG_NAME,catalogProperties=$CATALOG_PROPERTIES,configProperties=$CONFIG_PROPERTIES,drop=$DROP,keep=$KEEP,only=$ONLY,partitionFields=$PARTITION_FIELDS,tableProperties=$TABLE_PROPERTIES" \
-f yaml
```

## Terraform

Dataflow supports the utilization of Terraform to manage template jobs,
see [dataflow_flex_template_job](https://registry.terraform.io/providers/hashicorp/google/latest/docs/resources/dataflow_flex_template_job).

Terraform modules have been generated for most templates in this repository. This includes the relevant parameters
specific to the template. If available, they may be used instead of
[dataflow_flex_template_job](https://registry.terraform.io/providers/hashicorp/google/latest/docs/resources/dataflow_flex_template_job)
directly.

To use the autogenerated module, execute the standard
[terraform workflow](https://developer.hashicorp.com/terraform/intro/core-workflow):

```shell
cd v2/yaml/terraform/SqlServer_To_Iceberg_Yaml
terraform init
terraform apply
```

To use
[dataflow_flex_template_job](https://registry.terraform.io/providers/hashicorp/google/latest/docs/resources/dataflow_flex_template_job)
directly:

```terraform
provider "google-beta" {
project = var.project
}
variable "project" {
default = "<my-project>"
}
variable "region" {
default = "us-central1"
}

resource "google_dataflow_flex_template_job" "sqlserver_to_iceberg_yaml" {

provider = google-beta
container_spec_gcs_path = "gs://dataflow-templates-${var.region}/latest/flex/SqlServer_To_Iceberg_Yaml"
name = "sqlserver-to-iceberg-yaml"
region = var.region
parameters = {
jdbcUrl = "<jdbcUrl>"
table = "<table>"
catalogName = "<catalogName>"
catalogProperties = "<catalogProperties>"
# username = "<username>"
# password = "<password>"
# driverClassName = "com.microsoft.sqlserver.jdbc.SQLServerDriver"
# driverJars = "<driverJars>"
# connectionProperties = "<connectionProperties>"
# connectionInitSql = "<connectionInitSql>"
# jdbcType = "mssql"
# location = "<location>"
# readQuery = "<readQuery>"
# partitionColumn = "<partitionColumn>"
# numPartitions = "<numPartitions>"
# fetchSize = "<fetchSize>"
# disableAutoCommit = "<disableAutoCommit>"
# outputParallelization = "<outputParallelization>"
# configProperties = "<configProperties>"
# drop = "<drop>"
# keep = "<keep>"
# only = "<only>"
# partitionFields = "<partitionFields>"
# tableProperties = "<tableProperties>"
}
}
```
3 changes: 3 additions & 0 deletions yaml/pom.xml
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<scope>test</scope>
</dependency>
<dependency>
<groupId>com.microsoft.sqlserver</groupId>
<artifactId>mssql-jdbc</artifactId>
<version>${mssql-jdbc.version}</version>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>${mysql-connector-java.version}</version>
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