Jason Ferrell created ZEPPELIN-4009:
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Summary: Large Numbers Truncated
Key: ZEPPELIN-4009
URL: https://issues.apache.org/jira/browse/ZEPPELIN-4009
Project: Zeppelin
Issue Type: Bug
Components: build
Affects Versions: 0.8.0
Environment: %pyspark
from pyspark.sql.functions import *
from pyspark.sql.types import *
sfTestValue = StructField("testValue",StringType(), True)
schemaTest = StructType([sfTestValue])
listTestValues = []
listTestValues.append(("4065453307562594031",))
listTestValues.append(("7659957277770523059",))
listTestValues.append(("1614560078712787995",))
dfTest = spark.createDataFrame(listTestValues, schemaTest)
dfTestExpanded = dfTest.selectExpr(\"testValue as idAsString",\"cast(testValue
as bigint) as idAsBigint",\"cast(testValue as long) as idAsLong")
dfTestExpanded.show() ##This will show three columns of data correctly.
dfTestExpanded.createOrReplaceGlobalTempView('testTable')
sqlContext.sql('select * from global_temp.testTable').show(3) ##shows truncated
values
Reporter: Jason Ferrell
(Copied from Apache Spark issue 26693 as it appears to be a Zeppelin issue
rather than Spark)
We have a process that takes a file dumped from an external API and formats it
for use in other processes. These API dumps are brought into Spark with all
fields read in as strings. One of the fields is a 19 digit visitor ID. Since
implementing Spark 2.4 a few weeks ago, we have noticed that dataframes read
the 19 digits correctly but any function in SQL appears to truncate the last
two digits and replace them with "00".
Our process is set up to convert these numbers to bigint, which worked before
Spark 2.4. We looked into data types, and the possibility of changing to a
"long" type with no luck. At that point we tried bringing in the string value
as is, with the same result. I've added code that should replicate the issue
with a few 19 digit test cases and demonstrating the type conversions I tried.
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