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https://issues.apache.org/jira/browse/SPARK-26325?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17276711#comment-17276711
]
Daniel Himmelstein commented on SPARK-26325:
--------------------------------------------
Here's the code from the original post, but using an RDD rather than JSON file
and applying [~maxgekk]'s suggestion to "try Z instead of 'Z'":
{code:python}
line = '{"time_field" : "2017-09-30 04:53:39.412496Z"}'
rdd = spark.sparkContext.parallelize([line])
(
spark.read
.option("timestampFormat", "yyyy-MM-dd HH:mm:ss.SSSSSSZ")
.json(path=rdd)
){code}
The output I get with pyspark 3.0.1 is `DataFrame[time_field: string]`. So it
looks like the issue remains.
I'd be interested if there are any examples where spark infers a timestamp from
a JSON string or whether timestampFormat does not work at all?
> Interpret timestamp fields in Spark while reading json (timestampFormat)
> ------------------------------------------------------------------------
>
> Key: SPARK-26325
> URL: https://issues.apache.org/jira/browse/SPARK-26325
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.4.0
> Reporter: Veenit Shah
> Priority: Major
>
> I am trying to read a pretty printed json which has time fields in it. I want
> to interpret the timestamps columns as timestamp fields while reading the
> json itself. However, it's still reading them as string when I {{printSchema}}
> E.g. Input json file -
> {code:java}
> [{
> "time_field" : "2017-09-30 04:53:39.412496Z"
> }]
> {code}
> Code -
> {code:java}
> df = spark.read.option("multiLine",
> "true").option("timestampFormat","yyyy-MM-dd
> HH:mm:ss.SSSSSS'Z'").json('path_to_json_file')
> {code}
> Output of df.printSchema() -
> {code:java}
> root
> |-- time_field: string (nullable = true)
> {code}
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