I am new to spark and delta-lake and trying to do one POC with pyspark and using minio as delta-lake's storage backend. However, I am getting error that
Class org.apache.hadoop.fs.s3a.S3AFileSystem not found
I have added the jar in python code and assuming it'll download the required jar on runtime. I am not able to understand where I am doing wrong.
Can someone please help me out ?
Thanks
ENV
OS: Windows 11
Spark: Apache Spark 3.3.1
Java Version: openjdk version "11.0.13" 2021-10-19
Python Version: 3.9.13
Python packages: pyspark 3.2.3, delta-spark 2.0.2
CODE
import pyspark
from delta import *
builder = pyspark.sql.SparkSession.builder.appName("MyApp") \
.config("spark.jars.packages", "org.apache.hadoop:hadoop-aws:3.3.1") \
.config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \
.config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") \
.config("spark.hadoop.fs.s3a.access.key", <my key>) \
.config("spark.hadoop.fs.s3a.secret.key", <my secret>) \
.config("spark.hadoop.fs.s3a.endpoint", <my endpoint>) \
.config("spark.databricks.delta.retentionDurationCheck.enabled", "false")
spark = configure_spark_with_delta_pip(builder).getOrCreate()
spark.conf.set("spark.hadoop.fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
data = spark.range(0, 5)
data.write.format("delta").save("s3a://<my bucket>/delta-lake/demo")
df = spark.read.format("delta").load("tmp/delta-table")
df.show()
OUTPUT
WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.apache.spark.unsafe.Platform (file:/C:/Spark/spark-3.2.3-bin-hadoop3.2/jars/spark-unsafe_2.12-3.2.3.jar) to constructor java.nio.DirectByteBuffer(long,int)
WARNING: Please consider reporting this to the maintainers of org.apache.spark.unsafe.Platform
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
:: loading settings :: url = jar:file:/C:/Spark/spark-3.2.3-bin-hadoop3.2/jars/ivy-2.5.0.jar!/org/apache/ivy/core/settings/ivysettings.xml
Ivy Default Cache set to: C:\Users\shari\.ivy2\cache
The jars for the packages stored in: C:\Users\shari\.ivy2\jars
io.delta#delta-core_2.12 added as a dependency
:: resolving dependencies :: org.apache.spark#spark-submit-parent-050502ed-ca85-4e4f-b7a3-ff69c12689d3;1.0
confs: [default]
found io.delta#delta-core_2.12;2.0.2 in central
found io.delta#delta-storage;2.0.2 in central
found org.antlr#antlr4-runtime;4.8 in central
found org.codehaus.jackson#jackson-core-asl;1.9.13 in central
:: resolution report :: resolve 173ms :: artifacts dl 0ms
:: modules in use:
io.delta#delta-core_2.12;2.0.2 from central in [default]
io.delta#delta-storage;2.0.2 from central in [default]
org.antlr#antlr4-runtime;4.8 from central in [default]
org.codehaus.jackson#jackson-core-asl;1.9.13 from central in [default]
---------------------------------------------------------------------
| | modules || artifacts |
| conf | number| search|dwnlded|evicted|| number|dwnlded|
---------------------------------------------------------------------
| default | 4 | 0 | 0 | 0 || 4 | 0 |
---------------------------------------------------------------------
:: retrieving :: org.apache.spark#spark-submit-parent-050502ed-ca85-4e4f-b7a3-ff69c12689d3
confs: [default]
0 artifacts copied, 4 already retrieved (0kB/0ms)
23/02/16 16:19:36 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Traceback (most recent call last):
File "D:\DLT\quin\quin-experian-elt\el\delta_test.py", line 19, in <module>
data.write.format("delta").save("s3a://quin-third-party-data-dev-1/delta-lake/demo")
File "C:\Users\shari\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\pyspark\sql\readwriter.py", line 740, in save
self._jwrite.save(path)
File "C:\Users\shari\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\py4j\java_gateway.py", line 1321, in __call__
return_value = get_return_value(
File "C:\Users\shari\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\pyspark\sql\utils.py", line 111, in deco
return f(*a, **kw)
File "C:\Users\shari\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\py4j\protocol.py", line 326, in get_return_value
raise Py4JJavaError(
py4j.protocol.Py4JJavaError: An error occurred while calling o61.save.
: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class org.apache.hadoop.fs.s3a.S3AFileSystem not found
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2667)
at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:3431)
at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:3466)
at org.apache.hadoop.fs.FileSystem.access$300(FileSystem.java:174)
at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:3574)
at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:3521)
at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:540)
at org.apache.hadoop.fs.Path.getFileSystem(Path.java:365)
at org.apache.spark.sql.delta.DeltaLog$.apply(DeltaLog.scala:620)
at org.apache.spark.sql.delta.DeltaLog$.forTable(DeltaLog.scala:530)
at org.apache.spark.sql.delta.sources.DeltaDataSource.createRelation(DeltaDataSource.scala:153)
at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:47)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:75)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:73)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:84)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.$anonfun$applyOrElse$1(QueryExecution.scala:97)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:97)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:93)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:481)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:82)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:481)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:267)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:263)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:457)
at org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:93)
at org.apache.spark.sql.execution.QueryExecution.commandExecuted$lzycompute(QueryExecution.scala:80)
at org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:78)
at org.apache.spark.sql.execution.QueryExecution.assertCommandExecuted(QueryExecution.scala:115)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:848)
at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:382)
at org.apache.spark.sql.DataFrameWriter.saveInternal(DataFrameWriter.scala:349)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:239)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:566)
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.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
at java.base/java.lang.Thread.run(Thread.java:829)
Caused by: java.lang.ClassNotFoundException: Class org.apache.hadoop.fs.s3a.S3AFileSystem not found
at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:2571)
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2665)
... 51 more
SUCCESS: The process with PID 6172 (child process of PID 24096) has been terminated.
SUCCESS: The process with PID 24096 (child process of PID 27152) has been terminated.
SUCCESS: The process with PID 27152 (child process of PID 25700) has been terminated.
The problem is most probably caused by the use of the configure_spark_with_delta_pip function that overrides spark.jars.packages option that you used for specifying Hadoop AWS dependency.
To fix it you need to specify that dependency as additional parameter to the same function:
builder = pyspark.sql.SparkSession.builder.appName("MyApp") \
.config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \
.config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") \
.config("spark.hadoop.fs.s3a.access.key", <my key>) \
.config("spark.hadoop.fs.s3a.secret.key", <my secret>) \
.config("spark.hadoop.fs.s3a.endpoint", <my endpoint>) \
.config("spark.databricks.delta.retentionDurationCheck.enabled", "false")
my_packages = ["org.apache.hadoop:hadoop-aws:3.3.1"]
spark = configure_spark_with_delta_pip(builder, extra_packages=my_packages).getOrCreate()
P.S. Check version compatibility of your Spark with Delta - see this compatibility matrix to find a correct version.