Error while reading TFRecords - buildReader is not supported for TFRECORD

Hi @junshi15

I have a problem same as #15. I’m trying to replicate the test examples shown in the README but I’m unable to do it because of this error. I’m using Scala 2.12 and Spark 3.0 with version 0.3.2 of spark-tfrecord.

With this installed, spark can write TFRecords as expected however can’t read the same tf-records it created.

I get this error message:

Py4JJavaError: An error occurred while calling o183.showString.
: java.lang.UnsupportedOperationException: buildReader is not supported for TFRECORD

Do you know a fix to this?

I’m using Python 3.7. Here’s my code (It’s mostly from this repo’s README)

import os
import sys
from pyspark.sql.types import *
from pyspark.sql import SparkSession


def build_session():
    sess_builder = SparkSession\
        .builder\
        .appName('tfrecordTest')

    # just needed to run locally
    os.environ['PYSPARK_PYTHON'] = sys.executable
    os.environ['PYSPARK_DRIVER_PYTHON'] = sys.executable

    config = {
        'spark.jars': '/data04/home/ben.davidson/code/ByteSpark/spark-tfrecord_2.12-0.3.2.jar',
    }
    for key in config:
        sess_builder.config(key, config[key])

    spark = sess_builder.getOrCreate()
    return spark


def get_data(spark):
    fields = [StructField("id", IntegerType()), StructField("IntegerCol", IntegerType()),
            StructField("LongCol", LongType()), StructField("FloatCol", FloatType()),
            StructField("DoubleCol", DoubleType()), StructField("VectorCol", ArrayType(DoubleType(), True)),
            StructField("StringCol", StringType())]
    schema = StructType(fields)
    test_rows = [[11, 1, 23, 10.0, 14.0, [1.0, 2.0], "r1"], [21, 2, 24, 12.0, 15.0, [2.0, 2.0], "r2"]]
    rdd = spark.sparkContext.parallelize(test_rows)
    df = spark.createDataFrame(rdd, schema)
    return df


if __name__ == '__main__':
    spark = build_session()
    df = get_data(spark)
    path = 'hdfs://harunava/user/ben.davidson/test/data'

    # works
    df.write.mode("overwrite").format("tfrecord").option("recordType", "Example").save(path)

    # breaks
    df = spark.read.format("tfrecord").option("recordType", "Example").load(path)
    df.show()

I get this error message:

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-7-31dc9d3fa2b3> in <module>
      1 df2 = spark.read.format("tfrecord").option("recordType", "Example").load(path)
----> 2 df2.show()

~/miniconda3/envs/ByteSpark/lib/python3.7/site-packages/pyspark/sql/dataframe.py in show(self, n, truncate, vertical)
    438         """
    439         if isinstance(truncate, bool) and truncate:
--> 440             print(self._jdf.showString(n, 20, vertical))
    441         else:
    442             print(self._jdf.showString(n, int(truncate), vertical))

~/miniconda3/envs/ByteSpark/lib/python3.7/site-packages/py4j/java_gateway.py in __call__(self, *args)
   1303         answer = self.gateway_client.send_command(command)
   1304         return_value = get_return_value(
-> 1305             answer, self.gateway_client, self.target_id, self.name)
   1306 
   1307         for temp_arg in temp_args:

~/miniconda3/envs/ByteSpark/lib/python3.7/site-packages/pyspark/sql/utils.py in deco(*a, **kw)
    129     def deco(*a, **kw):
    130         try:
--> 131             return f(*a, **kw)
    132         except py4j.protocol.Py4JJavaError as e:
    133             converted = convert_exception(e.java_exception)

~/miniconda3/envs/ByteSpark/lib/python3.7/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling o183.showString.
: java.lang.UnsupportedOperationException: buildReader is not supported for TFRECORD
	at org.apache.spark.sql.execution.datasources.FileFormat.buildReader(FileFormat.scala:116)
	at org.apache.spark.sql.execution.datasources.FileFormat.buildReaderWithPartitionValues(FileFormat.scala:137)
	at org.apache.spark.sql.execution.FileSourceScanExec.inputRDD$lzycompute(DataSourceScanExec.scala:535)
	at org.apache.spark.sql.execution.FileSourceScanExec.inputRDD(DataSourceScanExec.scala:525)
	at org.apache.spark.sql.execution.FileSourceScanExec.doExecute(DataSourceScanExec.scala:610)
	at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:180)
	at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:218)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:215)
	at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:176)
	at org.apache.spark.sql.execution.InputAdapter.inputRDD(WholeStageCodegenExec.scala:526)
...
...
...

Let me know if you know a fix to this, thanks! 😃

[GaganSD]

Right, I managed to find the bug, and it was indeed an issue with the environment settings.

We were using Spark 3.0.1 and not Spark 3.0(.0) (unlike what I mentioned above). I think this shows that this library isn’t compatible with the newer releases of Spark.

Upon searching online, I found out this also happens when pyspark version doesn’t match their installed spark version, or when Java’s version is 9.0+ as Spark doesn’t seem work well with the newer editions of java. Hope this helps someone if they run into this error.

Thanks for the reply! 😃

[junshi15]

Thanks for your question, @GaganSD .

I tested your script in pyspark shell, by using bin/pyspark --packages com.linkedin.sparktfrecord:spark-tfrecord_2.12:0.3.2

Then copied your code into the pyspark REPL. It worked for me.

I don’t know why you were seeing the error. My guess is that it has something to do your environmental settings.