Kafka connect Jdbc source connector data is stored as encoded string - apache-kafka

Question

I am new to kafka & exploring kafka connect in distributed mode. I have some issues which I have listed below.
Data from my oracle table is stored as encoded values in strings. (for example, one of my column which is an integer has value 60015 is stored as "AN+w").
If I use AVRO converter in worker configuration kafka connect throws the error "Invalid decimal scale 127 (greater than precision 64)".
Below is my configuration:
Worker Configuration:
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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# This file contains some of the configurations for the Kafka Connect distributed worker. This file is intended
# to be used with the examples, and some settings may differ from those used in a production system, especially
# the `bootstrap.servers` and those specifying replication factors.
# A list of host/port pairs to use for establishing the initial connection to the Kafka cluster.
bootstrap.servers=192.168.220.128:9092
# unique name for the cluster, used in forming the Connect cluster group. Note that this must not conflict with consumer group IDs
group.id=my-example-connect-cluster
# The converters specify the format of data in Kafka and how to translate it into Connect data. Every Connect user will
# need to configure these based on the format they want their data in when loaded from or stored into Kafka
key.converter=org.apache.kafka.connect.json.JsonConverter
value.converter=org.apache.kafka.connect.json.JsonConverter
# Converter-specific settings can be passed in by prefixing the Converter's setting with the converter we want to apply
# it to
key.converter.schemas.enable=true
value.converter.schemas.enable=false
# Topic to use for storing offsets. This topic should have many partitions and be replicated and compacted.
# Kafka Connect will attempt to create the topic automatically when needed, but you can always manually create
# the topic before starting Kafka Connect if a specific topic configuration is needed.
# Most users will want to use the built-in default replication factor of 3 or in some cases even specify a larger value.
# Since this means there must be at least as many brokers as the maximum replication factor used, we'd like to be able
# to run this example on a single-broker cluster and so here we instead set the replication factor to 1.
offset.storage.topic=connect-offsets-dm
offset.storage.replication.factor=1
#offset.storage.partitions=25
# Topic to use for storing connector and task configurations; note that this should be a single partition, highly replicated,
# and compacted topic. Kafka Connect will attempt to create the topic automatically when needed, but you can always manually create
# the topic before starting Kafka Connect if a specific topic configuration is needed.
# Most users will want to use the built-in default replication factor of 3 or in some cases even specify a larger value.
# Since this means there must be at least as many brokers as the maximum replication factor used, we'd like to be able
# to run this example on a single-broker cluster and so here we instead set the replication factor to 1.
config.storage.topic=connect-configs-dm
config.storage.replication.factor=1
# Topic to use for storing statuses. This topic can have multiple partitions and should be replicated and compacted.
# Kafka Connect will attempt to create the topic automatically when needed, but you can always manually create
# the topic before starting Kafka Connect if a specific topic configuration is needed.
# Most users will want to use the built-in default replication factor of 3 or in some cases even specify a larger value.
# Since this means there must be at least as many brokers as the maximum replication factor used, we'd like to be able
# to run this example on a single-broker cluster and so here we instead set the replication factor to 1.
status.storage.topic=connect-status-dm
status.storage.replication.factor=1
#status.storage.partitions=5
# Flush much faster than normal, which is useful for testing/debugging
offset.flush.interval.ms=10000
# These are provided to inform the user about the presence of the REST host and port configs
# Hostname & Port for the REST API to listen on. If this is set, it will bind to the interface used to listen to requests.
#rest.host.name=
rest.port=8083
# The Hostname & Port that will be given out to other workers to connect to i.e. URLs that are routable from other servers.
#rest.advertised.host.name=
#rest.advertised.port=
# Set to a list of filesystem paths separated by commas (,) to enable class loading isolation for plugins
# (connectors, converters, transformations). The list should consist of top level directories that include
# any combination of:
# a) directories immediately containing jars with plugins and their dependencies
# b) uber-jars with plugins and their dependencies
# c) directories immediately containing the package directory structure of classes of plugins and their dependencies
# Examples:
# plugin.path=/usr/local/share/java,/usr/local/share/kafka/plugins,/opt/connectors,
plugin.path=/home/bjanakiraman/Desktop/confluent-5.3.0/share/java
connect_plugin_path=/home/bjanakiraman/Desktop/confluent-5.3.0/share/java/kafka-connect-jdbc
Connect configuration:
{
"name": "test-oracle-jdbc-connector",
"config": {
"connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector",
"tasks.max": "1",
"connection.url": "MY-URL",
"connection.user": "username",
"connection.password": "password",
"mode": "incrementing",
"incrementing.column.name": "ID",
"topic.prefix": "test2-",
"name": "test-oracle-jdbc-connector",
"schema.pattern": "ABC",
"table.whitelist" : "TABLENAME"
}
}
Following is the full log error when I use AVRO converter in my connector:
org.apache.kafka.connect.errors.ConnectException: Tolerance exceeded in error handler
at org.apache.kafka.connect.runtime.errors.RetryWithToleranceOperator.execAndHandleError(RetryWithToleranceOperator.java:178)
at org.apache.kafka.connect.runtime.errors.RetryWithToleranceOperator.execute(RetryWithToleranceOperator.java:104)
at org.apache.kafka.connect.runtime.WorkerSourceTask.convertTransformedRecord(WorkerSourceTask.java:270)
at org.apache.kafka.connect.runtime.WorkerSourceTask.sendRecords(WorkerSourceTask.java:294)
at org.apache.kafka.connect.runtime.WorkerSourceTask.execute(WorkerSourceTask.java:229)
at org.apache.kafka.connect.runtime.WorkerTask.doRun(WorkerTask.java:177)
at org.apache.kafka.connect.runtime.WorkerTask.run(WorkerTask.java:227)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.IllegalArgumentException: Invalid decimal scale: 127 (greater than precision: 64)
at org.apache.avro.LogicalTypes$Decimal.validate(LogicalTypes.java:217)
at org.apache.avro.LogicalType.addToSchema(LogicalType.java:70)
at org.apache.avro.LogicalTypes$Decimal.addToSchema(LogicalTypes.java:182)
at io.confluent.connect.avro.AvroData.fromConnectSchema(AvroData.java:944)
at io.confluent.connect.avro.AvroData.addAvroRecordField(AvroData.java:1059)
at io.confluent.connect.avro.AvroData.fromConnectSchema(AvroData.java:900)
at io.confluent.connect.avro.AvroData.fromConnectSchema(AvroData.java:732)
at io.confluent.connect.avro.AvroData.fromConnectSchema(AvroData.java:726)
at io.confluent.connect.avro.AvroData.fromConnectData(AvroData.java:365)
at io.confluent.connect.avro.AvroConverter.fromConnectData(AvroConverter.java:80)
at org.apache.kafka.connect.runtime.WorkerSourceTask.lambda$convertTransformedRecord$2(WorkerSourceTask.java:270)
at org.apache.kafka.connect.runtime.errors.RetryWithToleranceOperator.execAndRetry(RetryWithToleranceOperator.java:128)
at org.apache.kafka.connect.runtime.errors.RetryWithToleranceOperator.execAndHandleError(RetryWithToleranceOperator.java:162)
... 11 more
Please Kindly help me in resolving this.

Answer 1

Check whether you have any columns of NUMBER type without defining any precision or scale. I got away this issue with changing my column data type to an NUMBER(38,0) which is INTEGER