I have created a NEWLINE DELIM JSON File in GCS. I have created an external table on top of the same JSON file as well and able to read data from BigQuery UI.
I want to access the external table data using PySpark and create a dataframe and then run the same job from Dataproc. Below is the code snippet I've written:
#!/usr/bin/python
import sys
import json
from pyspark.sql.functions import udf, lit, when, date_sub
from pyspark.sql.types import ArrayType, IntegerType, StructType, StructField, StringType, BooleanType, DateType
from pyspark import SparkContext, SparkConf, SQLContext
from pyspark.sql import SparkSession
from pyspark.sql import Row
from datetime import datetime
TargetTableUri=sys.argv[1]
spark = SparkSession \
.builder \
.master('yarn') \
.appName('spark-bigquery-demo') \
.getOrCreate()
bucket1 = "gs://first-bucket-arpan/output1"
spark.conf.set('temporaryGcsBucket', bucket1)
src_tbl = spark.read.format('bigquery') \
.option('table', 'turing-thought-277215:first_dataset.ext_employee_details') \
.load()
src_tbl.createOrReplaceTempView('src_tbl')
src_tbl_df = spark.sql( 'SELECT EMPID,EMPNAME,STREETADRESS,REGION,STATE,COUNTRY FROM src_tbl' )
src_tbl_df.show()
src_tbl_df.print_schema()
While running the job from dataproc cluster, getting below error: ": java.lang.UnsupportedOperationException: The type of table turing-thought-277215.first_dataset.ext_employee_details is currently not supported: EXTERNAL"
Is BigQuery External Table not supported in PySpark for BigQuery Connector? Below is the complete log.
20/08/13 16:44:25 INFO org.spark_project.jetty.util.log: Logging initialized @4863ms
20/08/13 16:44:25 INFO org.spark_project.jetty.server.Server: jetty-9.3.z-SNAPSHOT, build timestamp: unknown, git hash: unknown
20/08/13 16:44:25 INFO org.spark_project.jetty.server.Server: Started @5045ms
20/08/13 16:44:25 INFO org.spark_project.jetty.server.AbstractConnector: Started ServerConnector@5cf22b28{HTTP/1.1,[http/1.1]}{0.0.0.0:4040}
20/08/13 16:44:25 WARN org.apache.spark.scheduler.FairSchedulableBuilder: Fair Scheduler configuration file not found so jobs will be scheduled in FIFO order. To use fair scheduling, configure pools in fairscheduler.xml or set spark.scheduler.allocation.file to a file that contains the configuration.
20/08/13 16:44:27 INFO org.apache.hadoop.yarn.client.RMProxy: Connecting to ResourceManager at my-dataproc-cluster-m/10.148.0.40:8032
20/08/13 16:44:27 INFO org.apache.hadoop.yarn.client.AHSProxy: Connecting to Application History server at my-dataproc-cluster-m/10.148.0.40:10200
20/08/13 16:44:31 INFO org.apache.hadoop.yarn.client.api.impl.YarnClientImpl: Submitted application application_1596957621647_0022
Traceback (most recent call last):
File "/tmp/job-scd2curation-6/scdtype2curation.py", line 25, in <module>
.option('table', 'turing-thought-277215:first_dataset.ext_employee_details') \
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/readwriter.py", line 172, in load
File "/usr/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 63, in deco
File "/usr/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o59.load.
: java.lang.UnsupportedOperationException: The type of table turing-thought-277215.first_dataset.ext_employee_details is currently not supported: EXTERNAL
at com.google.cloud.spark.bigquery.BigQueryRelationProvider.createRelationInternal(BigQueryRelationProvider.scala:83)
at com.google.cloud.spark.bigquery.BigQueryRelationProvider.createRelation(BigQueryRelationProvider.scala:40)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:341)
at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:239)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:227)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:164)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
spark.read.json.load(...)
. – David Rabinowitz