Dataproc:使用PySpark从BigQuery读取和写入数据时出错

Question

我正试图从Dataproc Workbench内的用户管理的Jupyter Notebook实例读取一些BigQuery数据(ID:my-project.mydatabase.mytable[原始名称受保护](。我正在尝试的灵感来自于此，更具体地说，代码是(请阅读一些关于代码本身的附加评论(：

from pyspark.sql import SparkSession
from pyspark.sql.functions import udf, col
from pyspark.sql.types import IntegerType, ArrayType, StringType
from google.cloud import bigquery
# UPDATE (2022-08-10): BQ conector added
spark = SparkSession.builder.appName('SpacyOverPySpark') 
.config('spark.jars.packages', 'com.google.cloud.spark:spark-bigquery-with-dependencies_2.12:0.24.2') 
.getOrCreate()
# ------------------ IMPORTING DATA FROM BIG QUERY --------------------------
# UPDATE (2022-08-10): This line now runs...
df = spark.read.format('bigquery').option('table', 'my-project.mydatabase.mytable').load()
# But imports the whole table, which could become expensive and not optimal
print("DataFrame shape: ", (df.count(), len(df.columns)) # 109M records & 9 columns; just need 1M records and one column: "posting"
# I tried the following, BUT with NO success:
# sql = """
# SELECT `posting`
# FROM `mentor-pilot-project.indeed.indeed-data-clean`
# LIMIT 1000000
# """
# df = spark.read.format("bigquery").load(sql)
# print("DataFrame shape: ", (df.count(), len(df.columns)))
# ------- CONTINGENCY PLAN: IMPORTING DATA FROM CLOUD STORAGE ---------------
# This section WORKS (just to enable the following sections)
# HINT: This dataframe contains 1M rows of text, under a single column: "posting"
df = spark.read.csv("gs://hidden_bucket/1M_samples.csv", header=True)
# ---------------------- EXAMPLE CUSTOM PROCESSING --------------------------
# Example Python UDF Python
def split_text(text:str) -> list:
return text.split()
# Turning Python UDF into Spark UDF
textsplitUDF = udf(lambda z: split_text(z), ArrayType(StringType()))
# "Applying" a UDF on a Spark Dataframe (THIS WORKS OK)
df.withColumn("posting_split", textsplitUDF(col("posting")))
# ------------------ EXPORTING DATA TO BIG QUERY ----------------------------
# UPDATE (2022-08-10) The code causing the error:
# df.write.format('bigquery') 
#   .option('table', 'wordcount_dataset.wordcount_output') 
#   .save()
# has been replace by a code that successfully stores data in BQ:
df.write 
.format('bigquery') 
.option("temporaryGcsBucket", "my_temp_bucket_name") 
.mode("overwrite") 
.save("my-project.mynewdatabase.mytable")

当使用SQL查询从BigQuery读取数据时，触发的错误为：

Py4JJavaError: An error occurred while calling o195.load.
: com.google.cloud.spark.bigquery.repackaged.com.google.inject.ProvisionException: Unable to provision, see the following errors:
1) Error in custom provider, java.lang.IllegalArgumentException: 'dataset' not parsed or provided.
at com.google.cloud.spark.bigquery.SparkBigQueryConnectorModule.provideSparkBigQueryConfig(SparkBigQueryConnectorModule.java:65)
while locating com.google.cloud.spark.bigquery.SparkBigQueryConfig
1 error
at com.google.cloud.spark.bigquery.repackaged.com.google.inject.internal.InternalProvisionException.toProvisionException(InternalProvisionException.java:226)
at com.google.cloud.spark.bigquery.repackaged.com.google.inject.internal.InjectorImpl$1.get(InjectorImpl.java:1097)
at com.google.cloud.spark.bigquery.repackaged.com.google.inject.internal.InjectorImpl.getInstance(InjectorImpl.java:1131)
at com.google.cloud.spark.bigquery.BigQueryRelationProvider.createRelationInternal(BigQueryRelationProvider.scala:75)
at com.google.cloud.spark.bigquery.BigQueryRelationProvider.createRelation(BigQueryRelationProvider.scala:46)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:332)
at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:242)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:230)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:197)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:750)
Caused by: java.lang.IllegalArgumentException: 'dataset' not parsed or provided.
at com.google.cloud.bigquery.connector.common.BigQueryUtil.lambda$parseTableId$2(BigQueryUtil.java:153)
at java.util.Optional.orElseThrow(Optional.java:290)
at com.google.cloud.bigquery.connector.common.BigQueryUtil.parseTableId(BigQueryUtil.java:153)
at com.google.cloud.spark.bigquery.SparkBigQueryConfig.from(SparkBigQueryConfig.java:237)
at com.google.cloud.spark.bigquery.SparkBigQueryConnectorModule.provideSparkBigQueryConfig(SparkBigQueryConnectorModule.java:67)
at com.google.cloud.spark.bigquery.SparkBigQueryConnectorModule$$FastClassByGuice$$db983008.invoke(<generated>)
at com.google.cloud.spark.bigquery.repackaged.com.google.inject.internal.ProviderMethod$FastClassProviderMethod.doProvision(ProviderMethod.java:264)
at com.google.cloud.spark.bigquery.repackaged.com.google.inject.internal.ProviderMethod.doProvision(ProviderMethod.java:173)
at com.google.cloud.spark.bigquery.repackaged.com.google.inject.internal.InternalProviderInstanceBindingImpl$CyclicFactory.provision(InternalProviderInstanceBindingImpl.java:185)
at com.google.cloud.spark.bigquery.repackaged.com.google.inject.internal.InternalProviderInstanceBindingImpl$CyclicFactory.get(InternalProviderInstanceBindingImpl.java:162)
at com.google.cloud.spark.bigquery.repackaged.com.google.inject.internal.ProviderToInternalFactoryAdapter.get(ProviderToInternalFactoryAdapter.java:40)
at com.google.cloud.spark.bigquery.repackaged.com.google.inject.internal.SingletonScope$1.get(SingletonScope.java:168)
at com.google.cloud.spark.bigquery.repackaged.com.google.inject.internal.InternalFactoryToProviderAdapter.get(InternalFactoryToProviderAdapter.java:39)
at com.google.cloud.spark.bigquery.repackaged.com.google.inject.internal.InjectorImpl$1.get(InjectorImpl.java:1094)
... 18 more

将数据写入BigQuery时，错误为：

Py4JJavaError: An error occurred while calling o167.save.
: java.lang.ClassNotFoundException: Failed to find data source: bigquery. Please find packages at http://spark.apache.org/third-party-projects.html

更新：(2022-09-10(向BigQuery写入数据时的错误已经解决，请参阅上面的代码以及下面的注释部分。

我做错了什么？

Answer 1

讨论中发现的要点：

1. 通过spark.jars=<gcs-uri>或spark.jars.packages=com.google.cloud.spark:spark-bigquery-with-dependencies_<scala-version>:<version>添加BigQuery连接器作为依赖项。

2. 以<project>.<dataset>.<table>格式指定正确的表名。

3. 数据帧写入程序的默认模式是errorifexists。当写入不存在的表时，数据集必须存在，表将自动创建。向现有表写入时，需要在df.write.mode(<mode>)...save()中将模式设置为"append"或"overwrite"。

4. 写入BQ表时，执行

   a( 直接写入(自0.26.0起支持(

   df.write 
   .format("bigquery") 
   .option("writeMethod", "direct") 
   .save("dataset.table")
   

   b( 或间接写入

   df.write 
   .format("bigquery") 
   .option("temporaryGcsBucket","some-bucket") 
   .save("dataset.table")
   

   请参阅此文档。

5. 通过SQL查询读取BigQuery时，添加强制属性viewsEnabled=true和materializationDataset=<dataset>:

   spark.conf.set("viewsEnabled","true")
   spark.conf.set("materializationDataset","<dataset>")
   sql = """
   SELECT tag, COUNT(*) c
   FROM (
   SELECT SPLIT(tags, '|') tags
   FROM `bigquery-public-data.stackoverflow.posts_questions` a
   WHERE EXTRACT(YEAR FROM creation_date)>=2014
   ), UNNEST(tags) tag
   GROUP BY 1
   ORDER BY 2 DESC
   LIMIT 10
   """
   df = spark.read.format("bigquery").load(sql)
   df.show()
   

   请参阅此文档。