Can not create pipeline with spark job

[buithuhasvtech]

Hi there, I am trying to create a pipeline with Spark job (My code is based on this tutorial: https://docs.open-metadata.org/v1.3.x/connectors/ingestion/lineage/spark-lineage#spark-lineage-ingestion). The tables used in the spark job all exist in OM, the spark job has a finished status, and a pipeline service named My_pipeline_service has been created.

But I didn't see any pipeline. What can I do to create the pipeline with the spark job? I have only added metadata ingestion in the DB service, do I need to add any other type of ingestion to create the pipeline?

This is my code:
`from pyspark.sql import SparkSession
import sys
import calendar
import time
from pyspark.sql.types import StructType,StructField, StringType, IntegerType

ss_conf = {'database': 'test_db',
'password': 'my_pwd',
'port': '3306',
'host': 'my_host',
'ssl': 'false',
'username': 'my_username',
}

OM_JWT = "my_om_jwt"

spark = (
SparkSession.builder.master("local")
.appName("localTestApp").config(
"spark.jars",
"/thuvien/driver/singlestore-spark-connector_2.12-4.1.3-spark-3.3.0.jar,/thuvien/driver/mariadb-java-client-3.1.4.jar,/thuvien/driver/singlestore-jdbc-client-1.2.0.jar,/thuvien/driver/commons-dbcp2-2.9.0.jar,/thuvien/driver/commons-pool2-2.11.1.jar,/thuvien/driver/spray-json_2.10-1.2.5.jar,/thuvien/driver/openmetadata-spark-agent-1.0-beta.jar",
)
.config(
"spark.extraListeners",
"org.openmetadata.spark.agent.OpenMetadataSparkListener",
)
.config("spark.openmetadata.transport.hostPort", "my_hostPort")
.config("spark.openmetadata.transport.type", "openmetadata")
.config("spark.openmetadata.transport.jwtToken", OM_JWT)
.config(
"spark.openmetadata.transport.pipelineServiceName", "My_pipeline_service"
)
.config("spark.openmetadata.transport.pipelineName", "My_pipeline_name")
.config(
"spark.openmetadata.transport.pipelineDescription", "My ETL Pipeline"
)
.config(
"spark.openmetadata.transport.databaseServiceNames",
"analytic",
)
.config("spark.openmetadata.transport.timeout", "30")
.config("spark.openlineage.facets.disabled", "[spark_unknown;spark.logicalPlan]")
.getOrCreate()
)

sc = spark.sparkContext
sc.setLogLevel("INFO")
#conf ss
spark.conf.set("spark.datasource.singlestore.clientEndpoint",ss_conf['host'])
spark.conf.set("spark.datasource.singlestore.user", ss_conf['username'])
spark.conf.set("spark.datasource.singlestore.password", ss_conf['password'])

df1 = (spark
.read
.format("singlestore")
.option("spark.datasource.singlestore.disablePushdown", "true")
.option("enableParallelRead", "automatic")
.option("parallelRead.Features", "readFromAggregatorsMaterialized,readFromAggregators")
.option("paralledRead.repartition", "true")
.option("paralledRead.maxNumPartitions", 20)
.option("parallelRead.repartition.columns", "isdn_key")
.load("test_db.uc16_02_tkc_20240229"))

print(df1.count())

df1.na.fill(0)
.na.fill("")
.write
.mode("Overwrite")
.format("singlestore").save("test_db.uc16_02_tkc_20240229_new")

spark.stop()
`