[英]Reading and writing data from BigQuery, using PySpark: ERROR `Failed to find data source: bigquery`
I am trying to read some BigQuery data, (ID: my-project.mydatabase.mytable
[original names protected]) from a user-managed Jupyter Notebook instance, inside Dataproc Workbench.我正在尝试从Dataproc Workbench 内的用户管理的 Jupyter Notebook 实例中读取一些 BigQuery 数据(ID: my-project.mydatabase.mytable
[原始名称受保护])。 What I am trying is inspired in this , and more specifically, the code is (please read some additional comments, on the code itself):我正在尝试的灵感来自this ,更具体地说,代码是(请阅读一些关于代码本身的附加注释):
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")
When reading data from BigQuery, using a SQL query, the error triggered is:从 BigQuery 读取数据时,使用 SQL 查询,触发的错误是:
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
When writing data to BigQuery, the error is:将数据写入 BigQuery 时,错误是:
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
UPDATE: (2022-09-10) The error when writing data to BigQuery has been solved, please refer to the code above, as well as the comment section below. UPDATE: (2022-09-10) BigQuery 写入数据的错误已解决,请参考上面的代码,以及下面的评论部分。
What am I doing wrong?我究竟做错了什么?
Key points found during the discussion:讨论中发现的关键点:
Add the BigQuery connector as a dependency through spark.jars=<gcs-uri>
or spark.jars.packages=com.google.cloud.spark:spark-bigquery-with-dependencies_<scala-version>:<version>
.通过spark.jars=<gcs-uri>
或spark.jars.packages=com.google.cloud.spark:spark-bigquery-with-dependencies_<scala-version>:<version>
将 BigQuery 连接器添加为依赖项。
Specify the correct table name in <project>.<dataset>.<table>
format.以<project>.<dataset>.<table>
格式指定正确的表名称。
When writing to an existing BQ table, add mode "append"
or "overwrite"
in df.write.mode(<mode>)...save()
.写入现有 BQ 表时,在df.write.mode(<mode>)...save()
中添加模式"append"
或"overwrite"
。
When writing to a BQ table, do either写入 BQ 表时,请执行以下任一操作
a) direct write a) 直接写
df.write \.format("bigquery") \.option("writeMethod", "direct") \.save("dataset.table")
b) or indirect write b) 或间接写入
df.write \.format("bigquery") \.option("temporaryGcsBucket","some-bucket") \.save("dataset.table")
When reading from BigQuery through a SQL query, add mandatory properties viewsEnabled=true
and materializationDataset=<dataset>
:通过 SQL 查询从 BigQuery 读取时,添加必需属性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()
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