Argthtjtr

I have created a source and a sink connector for kafka connect Confluent 5.0, to push two sqlserver tables to my datalake

Here is my SQLServer table schema :

CREATE TABLE MYBASE.dbo.TABLE1 (

id_field int IDENTITY(1,1) NOT NULL,

my_numericfield numeric(24,6) NULL,

time_field smalldatetime NULL,

CONSTRAINT PK_CBMARQ_F_COMPTEGA PRIMARY KEY (id_field)

) GO

My Cassandra schema :

create table TEST-TABLE1(my_numericfield decimal, id_field int, time_field timestamp, PRIMARY KEY (id_field));

Here is the source configuration, with a whitelist parameter :

{

"config":

{

    "connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector",

    "connection.url": "jdbc:sqlserver://localhost:1433;database=MYBASE",

    "connection.user": "admin",

    "connection.password": "password",

    "table.whitelist": "TABLE1, TABLE2",

    "mode": "timestamp+incrementing",

    "timestamp.column.name": "time_field",

    "incrementing.column.name": "id_field",

    "validate.non.null": "false",

    "topic.prefix": "TEST-",

    "tasks.max": "8",

    "numeric.mapping":"best_fit"

},

"name": "sqlserver-MYBASE-test"

}

Here is my sink connector :

{

"name": "s3-sink-MYBASE",

"config":

{

    "topics": "TEST-TABLE1, TEST_TABLE2",

    "topics.dir": "DATABASE_FULL",

    "s3.part.size": 5242880,

    "storage.class": "io.confluent.connect.s3.storage.S3Storage",

    "tasks.max": 8,

    "schema.compatibility": "NONE",

    "s3.region": "eu-central-1",

    "schema.generator.class": "io.confluent.connect.storage.hive.schema.DefaultSchemaGenerator",

    "connector.class": "io.confluent.connect.s3.S3SinkConnector",

    "partitioner.class": "io.confluent.connect.storage.partitioner.DefaultPartitioner",

    "format.class": "io.confluent.connect.s3.format.avro.AvroFormat",

    "s3.bucket.name": "mydatalake",

    "flush.size": 1,

    "transforms":"InsertSourceDetails",  

    "transforms.InsertSourceDetails.type":"org.apache.kafka.connect.transforms.InsertField$Value",

    "transforms.InsertSourceDetails.static.field":"DATABASE",

    "transforms.InsertSourceDetails.static.value":"MYBASE"

}

}

The problem is that some fields are typed NUMERIC in sqlserver, and kafka transforms them in BINARY when arrived in the datalake

Here is the schema_registry result :

{"type": "record",

"name": "TEST-TABLE1",

"fields": [

{

  "name": "my_numericfield",

  "type": [

    "null",

    {

      "type": "bytes",

      "scale": 6,

      "precision": 64,

      "connect.version": 1,

      "connect.parameters": {

        "scale": "6"

      },

      "connect.name": "org.apache.kafka.connect.data.Decimal",

      "logicalType": "decimal"

    }

  ],

  "default": null

},

{

  "name": "id_field",

  "type": "int"

},

{

  "name": "cbCreateur",

  "type": [

    "null",

    "string"

  ],

  "default": null

},

{

  "name": "time_field",

  "type": [

    "null",

    {

      "type": "long",

      "connect.version": 1,

      "connect.name": "org.apache.kafka.connect.data.Timestamp",

      "logicalType": "timestamp-millis"

    }

  ],

  "default": null

},

],

"connect.name": "TEST-TABLE1"}

Here is the spark script and result :

...: from pyspark.sql.functions import col 

...: AWS_ID='xxxxxxxxxxxxxxxxx'

...: AWS_KEY='xxxxxxxxxxxxxxxxx/'

...: sc._jsc.hadoopConfiguration().set("fs.s3a.access.key", AWS_ID)

...: sc._jsc.hadoopConfiguration().set("fs.s3a.secret.key", AWS_KEY)

...: sc._jsc.hadoopConfiguration().set("fs.s3a.endpoint", "s3.eu-central-1.amazonaws.com")

...: spark.conf.set('spark.cassandra.connection.host', 'localhost')

...: spark.conf.set('spark.cassandra.connection.port', 9042)

...: spark.conf.set('spark.cassandra.auth.username', 'cassandra')

...: spark.conf.set('spark.cassandra.auth.password', 'cassandra')

...: 

...: 



   :    F_TEST-TABLE1 = spark.read.format('com.databricks.spark.avro').load('s3a://mydatalake/DATABASE_FULL/TEST-TABLE1').drop('partition')

...:    DF_TEST-TABLE1 = F_TEST-TABLE1.toDF(*[c.lower() for c in TEST-TABLE1.columns])

...: 

...: 



: DF_TEST-TABLE1.printSchema()

root

 |-- my_numericfield: binary (nullable = true)

 |-- id_field: integer (nullable = true)

 |-- time_field: long (nullable = true)





: DF_TEST-TABLE1.createTempView("event")



: spark.sql("select * from event").show(1, False)

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

||my_numericfield|id_field|time_field    |

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

|[00]            | 5      |1542733800000 |

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

only showing top 1 row





: DF_TEST-TABLE1.write.format('org.apache.spark.sql.cassandra').options(keyspace='sage_full', table='f_test-table1').option('confirm.truncate', True).save(mode='overwrite')

18/11/22 08:29:05 ERROR Executor: Exception in task 1.0 in stage 2.0 (TID 3)

com.datastax.spark.connector.types.TypeConversionException: Cannot convert object [B@6d0d5743 of type class [B to java.lang.BigDecimal.

at com.datastax.spark.connector.types.TypeConverter$$anonfun$convert$1.apply(TypeConverter.scala:45)

at scala.PartialFunction$AndThen.applyOrElse(PartialFunction.scala:190)

I'm trying to cast the fields on the fly to match a numeric type (i.e. float), but i can't find a way to do it without knowing the field names in advance

With the whitelist parameter, the connector process the two tables without field description in the connector configuration

Is there a way to do the cast for all NUMERIC fields on the fly ?

Thanks for your help