I have the following code to run sql transformations in apache beam in direct runner on windows.
import apache_beam as beam
from apache_beam.transforms.sql import SqlTransform
with beam.Pipeline() as p:
pipe = (
p
|'hello' >> beam.Create([('SE',400),('SC',500)])
|'schema' >> beam.Map(lambda x: beam.Row(
state=x[0],
population=x[1]
))
)
sql = (
pipe
|'sql' >> SqlTransform('SELECT state, population FROM PCOLLECTION')
|'sql print' >> beam.Map(print)
)
And I get the following error:
File "c:\users\XXX\appdata\local\programs\python\python37\lib\subprocess.py", line 1306, in send_signal
raise ValueError("Unsupported signal: {}".format(sig))
ValueError: Unsupported signal: 2
I saw on youtube experts saying about Universal Local Runner, but I didn't find how to install it.
Anyone can help me?
Thank you in advance
The issue is now tracked in https://issues.apache.org/jira/browse/BEAM-12501.
I have no issue running your pipeline with source code built from Beam head. But I'm on Mac OS. What version of Beam did you use?
Could you try asking about it to the user#beam.apache.org? This might be an issue specific in the Windows system.
Related
I´ve been strugglin making this work, I know this is a cross-language transform and all of that and I installed the Java jdk on my pc (when I write java -version on cmd I get correct information and all of that) but when I am trying to make a simple pipeline work:
import apache_beam as beam
from apache_beam.io.external.kafka import ReadFromKafka
from apache_beam.options.pipeline_options import PipelineOptions
import os
os.environ['GOOGLE_APPLICATION_CREDENTIALS']='credentialsOld.json'
def main():
print('======================================================')
beam_options = PipelineOptions(runner='DataflowRunner',temp_location=temp_location,staging_location=staging_location,project=project,experiments=['use_runner_v2'],streaming=True)
with beam.Pipeline(options=beam_options) as p:
msgs = p | 'ReadKafka' >> ReadFromKafka(consumer_config={'bootstrap.servers':'xxxxx-xxxxx...','group_id':'testAB'},topics=['users'])
msgs | beam.FlatMap(print)
if __name__ == '__main__':
main()
I get this error: ValueError: Unsupported signal: 2
I have tried adding the parameter expansion_service= 'beam:external:java:kafka:read:v1' to the ReadFromKafka but then I get:
status = StatusCode.UNAVAILABLE
details = "DNS resolution failed for
beam:external:java:kafka:read:v1: UNKNOWN: OS Error"
Im working on a venv python enviroment if this info can be usefull and my kafka cluster is on confluent cloud.
Im also getting this runtime error:
RuntimeError: java.lang.RuntimeException: Failed to get dependencies of beam:transform:org.apache.beam:kafka_read_without_metadata:v1 from spec urn: "beam:transform:org.apache.beam:kafka_read_without_metadata:v1"
EDIT: Im getting the bootstrap server option from here
My mistake was that I was skippig the step where I have to start a expansion_service, I did that with this command
java -jar beam-sdks-java-io-expansion-service-2.37.0.jar 8088 --javaClassLookupAllowlistFile='*'
after downloading the beam-sdks-java-io-expansion-service-2.37.0.jar from https://mvnrepository.com/artifact/org.apache.beam/beam-sdks-java-io-expansion-service/2.36.0
and then specifying the port in expansion_service='localhost:8088'
Then I had two minor mistakes one was that I was using the JDK 18 and I think it wasnt compatible https://beam.apache.org/get-started/quickstart-java/ so I switched to JDK 17 and used python 3.8 instead of python 3.10
I have an Apache Beam pipeline which tries to write to Postgres after reading from BigQuery. The code uses JdbcIO connector and Dataflow runner.
The code is as follow :
output = (
pipeline
| 'ReadTable' >> beam.io.ReadFromBigQuery(query='', use_standard_sql=True)
| 'Write to jdbc' >> WriteToJdbc(
driver_class_name='org.postgresql.Driver',
jdbc_url='jdbc:postgresql://localhost:5432/db?currentSchema=public',
username='',
password='',
table_name='extract'
)
)
I am getting an error about URN coder:
Caused by: org.apache.beam.vendor.guava.v26_0_jre.com.google.common.util.concurrent.UncheckedExecutionException: java.lang.IllegalArgumentException: Unknown Coder URN beam:coder:pickled_python:v1. Known URNs: [beam:coder:avro:generic:v1, beam:coder:bytes:v1, beam:coder:bool:v1, beam:coder:string_utf8:v1, beam:coder:kv:v1, beam:coder:varint:v1, beam:coder:interval_window:v1, beam:coder:iterable:v1, beam:coder:timer:v1, beam:coder:length_prefix:v1, beam:coder:global_window:v1, beam:coder:windowed_value:v1, beam:coder:param_windowed_value:v1, beam:coder:double:v1, beam:coder:row:v1, beam:coder:sharded_key:v1]
I could not find what is the problem with that ?
The below code builds the pipeline and DAG is generated.
RuntimeError: NotImplementedError [while running 'generatedPtransform-438']Please let me know if there is any direct connector for mysql in python for beam.
from apache_beam.options.pipeline_options import PipelineOptions
from google.cloud import pubsub_v1
from google.cloud import bigquery
import mysql.connector
import apache_beam as beam
import logging
import argparse
import sys
import re
PROJECT="12344"
TOPIC = "projects/12344/topics/mytopic"
class insertfn(beam.Dofn):
def insertdata(self,data):
db_conn=mysql.connector.connect(host="localhost",user="abc",passwd="root",database="new")
db_cursor=db_conn.cursor()
emp_sql = " INSERT INTO emp(ename,eid,dept) VALUES (%s,%s,%s)"
db_cusror.executemany(emp_sql,(data[0],data[1],data[2]))
db_conn.commit()
print(db_cursor.rowcount,"record inserted")
class Split(beam.DoFn):
def process(self, data):
data = data.split(",")
return [{
'ename': data[0],
'eid': data[1],
'dept': data[2]
}]
def main(argv=None):
parser = argparse.ArgumentParser()
parser.add_argument("--input_topic")
parser.add_argument("--output")
known_args = parser.parse_known_args(argv)
p = beam.Pipeline(options=PipelineOptions())
(p
| 'ReadData' >> beam.io.ReadFromPubSub(topic=TOPIC).with_output_types(bytes)
| "Decode" >> beam.Map(lambda x: x.decode('utf-8'))
| 'ParseCSV' >> beam.ParDo(Split())
| 'WriteToMySQL' >> beam.ParDo(insertfn())
)
result = p.run()
result.wait_until_finish()
After our discussion in the comment section, I noticed that you are not using the proper commands to execute the DataFlow pipeline.
According to the documentation, there are mandatory flags which must be defined in order to run the pipeline in Dataflow Managed Service. These flags are described below:
job_name - The name of the Dataflow job being executed.
project - The ID of your Google Cloud project. runner - The pipeline
runner - that will parse your program and construct your pipeline. For
cloud execution, this must be DataflowRunner.
staging_location - A Cloud Storage path for Dataflow to stage code packages needed by workers executing the job.
temp_location - A Cloud Storage path for Dataflow to stage temporary job files created during the execution of the pipeline.
In addition to these flags, there are others you can use, in your case since you use a PubSub topic:
--input_topic: sets the input Pub/Sub topic to read messages from.
Therefore, an example to run a Dataflow pipeline would be as follows:
python RunPipelineDataflow.py \
--job_name=jobName\
--project=$PROJECT_NAME \
--runner=DataflowRunner \
--staging_location=gs://YOUR_BUCKET_NAME/AND_STAGING_DIRECTORY\
--temp_location=gs://$BUCKET_NAME/temp
--input_topic=projects/$PROJECT_NAME/topics/$TOPIC_NAME \
I would like to point the importance of using DataflowRunner, it allows you to use the Cloud Dataflow managed service, providing a fully managed service, autoscaling and dynamic work rebalancing. However, it is also possible to use DirectRunner which executes your pipeline in your machine, it is designed to validate the pipeline.
I'm trying to process the data streaming from Apache Kafka using the Python SDK for Apache Beam with the Flink runner. After running Kafka 2.4.0 and Flink 1.8.3, I follow these steps:
1) Compile and run Beam 2.16 with Flink 1.8 runner.
git clone --single-branch --branch release-2.16.0 https://github.com/apache/beam.git beam-2.16.0
cd beam-2.16.0
nohup ./gradlew :runners:flink:1.8:job-server:runShadow -PflinkMasterUrl=localhost:8081 &
2) Run the Python pipeline.
from apache_beam import Pipeline
from apache_beam.io.external.kafka import ReadFromKafka
from apache_beam.options.pipeline_options import PipelineOptions
if __name__ == '__main__':
with Pipeline(options=PipelineOptions([
'--runner=FlinkRunner',
'--flink_version=1.8',
'--flink_master_url=localhost:8081',
'--environment_type=LOOPBACK',
'--streaming'
])) as pipeline:
(
pipeline
| 'read' >> ReadFromKafka({'bootstrap.servers': 'localhost:9092'}, ['test']) # [BEAM-3788] ???
)
result = pipeline.run()
result.wait_until_finish()
3) Publish some data to Kafka.
bin/kafka-console-producer.sh --broker-list localhost:9092 --topic test
>{"hello":"world!"}
The Python script throws this error:
[flink-runner-job-invoker] ERROR org.apache.beam.runners.fnexecution.jobsubmission.JobInvocation - Error during job invocation BeamApp-USER-somejob. org.apache.flink.client.program.ProgramInvocationException: Job failed. (JobID: xxx)
at org.apache.flink.client.program.rest.RestClusterClient.submitJob(RestClusterClient.java:268)
at org.apache.flink.client.program.ClusterClient.run(ClusterClient.java:483)
at org.apache.beam.runners.flink.FlinkExecutionEnvironments$BeamFlinkRemoteStreamEnvironment.executeRemotely(FlinkExecutionEnvironments.java:360)
at org.apache.flink.streaming.api.environment.RemoteStreamEnvironment.execute(RemoteStreamEnvironment.java:310)
at org.apache.beam.runners.flink.FlinkStreamingPortablePipelineTranslator$StreamingTranslationContext.execute(FlinkStreamingPortablePipelineTranslator.java:173)
at org.apache.beam.runners.flink.FlinkPipelineRunner.runPipelineWithTranslator(FlinkPipelineRunner.java:104)
at org.apache.beam.runners.flink.FlinkPipelineRunner.run(FlinkPipelineRunner.java:80)
at org.apache.beam.runners.fnexecution.jobsubmission.JobInvocation.runPipeline(JobInvocation.java:78)
at org.apache.beam.vendor.guava.v26_0_jre.com.google.common.util.concurrent.TrustedListenableFutureTask$TrustedFutureInterruptibleTask.runInterruptibly(TrustedListenableFutureTask.java:125)
at org.apache.beam.vendor.guava.v26_0_jre.com.google.common.util.concurrent.InterruptibleTask.run(InterruptibleTask.java:57)
at org.apache.beam.vendor.guava.v26_0_jre.com.google.common.util.concurrent.TrustedListenableFutureTask.run(TrustedListenableFutureTask.java:78)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.flink.runtime.client.JobExecutionException: Job execution failed.
at org.apache.flink.runtime.jobmaster.JobResult.toJobExecutionResult(JobResult.java:146)
at org.apache.flink.client.program.rest.RestClusterClient.submitJob(RestClusterClient.java:265)
... 13 more
Caused by: java.lang.ClassCastException: org.apache.beam.sdk.io.kafka.KafkaRecord cannot be cast to [B
at org.apache.beam.sdk.coders.ByteArrayCoder.encode(ByteArrayCoder.java:41)
at org.apache.beam.sdk.coders.LengthPrefixCoder.encode(LengthPrefixCoder.java:56)
at org.apache.beam.sdk.values.ValueWithRecordId$ValueWithRecordIdCoder.encode(ValueWithRecordId.java:105)
at org.apache.beam.sdk.values.ValueWithRecordId$ValueWithRecordIdCoder.encode(ValueWithRecordId.java:81)
at org.apache.beam.sdk.util.WindowedValue$FullWindowedValueCoder.encode(WindowedValue.java:578)
at org.apache.beam.sdk.util.WindowedValue$FullWindowedValueCoder.encode(WindowedValue.java:529)
at org.apache.beam.sdk.util.CoderUtils.encodeToSafeStream(CoderUtils.java:82)
at org.apache.beam.sdk.util.CoderUtils.encodeToByteArray(CoderUtils.java:66)
at org.apache.beam.sdk.util.CoderUtils.encodeToByteArray(CoderUtils.java:51)
at org.apache.beam.sdk.util.CoderUtils.clone(CoderUtils.java:141)
at org.apache.beam.runners.flink.translation.types.CoderTypeSerializer.copy(CoderTypeSerializer.java:67)
at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:577)
at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:554)
at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:534)
at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:718)
at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:696)
at org.apache.flink.streaming.api.operators.StreamSourceContexts$ManualWatermarkContext.processAndCollect(StreamSourceContexts.java:305)
at org.apache.flink.streaming.api.operators.StreamSourceContexts$WatermarkContext.collect(StreamSourceContexts.java:394) at org.apache.beam.runners.flink.translation.wrappers.streaming.io.UnboundedSourceWrapper.emitElement(UnboundedSourceWrapper.java:341)
at org.apache.beam.runners.flink.translation.wrappers.streaming.io.UnboundedSourceWrapper.run(UnboundedSourceWrapper.java:283)
at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:93)
at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:57)
at org.apache.flink.streaming.runtime.tasks.SourceStreamTask.run(SourceStreamTask.java:97)
at org.apache.flink.streaming.runtime.tasks.StoppableSourceStreamTask.run(StoppableSourceStreamTask.java:45)
at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:302)
at org.apache.flink.runtime.taskmanager.Task.run(Task.java:711)
... 1 more
ERROR:root:java.lang.ClassCastException: org.apache.beam.sdk.io.kafka.KafkaRecord cannot be cast to [B
[flink-runner-job-invoker] INFO org.apache.beam.runners.fnexecution.artifact.BeamFileSystemArtifactRetrievalService - Manifest at/tmp/artifacts0k1mnin0/somejob/MANIFEST has 0 artifact locations
[flink-runner-job-invoker] INFO org.apache.beam.runners.fnexecution.artifact.BeamFileSystemArtifactStagingService - Removed dir /tmp/artifacts0k1mnin0/job_somejob/
Traceback (most recent call last):
File "main.py", line 40, in <module>
run()
File "main.py", line 37, in run
result.wait_until_finish()
File "/home/USER/beam/lib/python3.5/site-packages/apache_beam/runners/portability/portable_runner.py", line 439, in wait_until_finish self._job_id, self._state, self._last_error_message()))
RuntimeError: Pipeline BeamApp-USER-somejob failed in state FAILED: java.lang.ClassCastException: org.apache.beam.sdk.io.kafka.KafkaRecord cannot be cast to [B
I tried other deserializers available in Kafka but they did not work: Couldn't infer Coder from class org.apache.kafka.common.serialization.StringDeserializer. This error is originating from this piece of code.
Am I doing something wrong?
Disclaimer: this is my first encounter with Apache Beam project.
It seems that Kafka consumer support is quite fresh thing in Beam (at least in Python interface) according to this JIRA. Apparently, it seems that there is still problem with FlinkRunner combined with this new API. Even though your code is technically correct it will not run correctly on Flink. There is a patch available which seems more like a quickfix than final solution to me. It requires recompilation and thus is not something I would propose using on production. If you are just getting started with technology and don't want to be blocked then feel free to try it out.
I am very new for Apache Airflow Usage, currently using Airflow1.10.4 with Python 2.7 support.
I need to trigger a spark job via Airflow DAG, so using 'DataProcSparkOperator'. But facing exception
AttributeError: 'DataProcSparkOperator' object has no attribute 'dataproc_spark_jars'
Code snippet:
from airflow.contrib.operators.dataproc_operator import DataProcSparkOperator
.
.
.
data_t1 = DataProcSparkOperator(
task_id='data_job',
job_name='extract_data',
cluster_name='cluster-a',
arguments=["{{ task_instance.xcom_pull(task_ids='puller') }}","gs://data-bucket/dailydata"],
main_jar='gs://data-bucket/spark_jar1/spark-read-5.0-SNAPSHOT-jar-with-dependencies.jar',
region="us-central",
dag=dag
)
Tried with main_jar / dataproc_spark_jars attribute (all possible ways)
However, I tried with other fixes suggested(as airflow.contrib.operators.dataproc_operator is deprecated in some versions), hence used below
from airflow.gcp.operators.dataproc import DataProcSparkOperator
again I am facing
Import Error: No module gcp.operators.dataproc..