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https://issues.apache.org/jira/browse/SPARK-26325?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17277405#comment-17277405
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Daniel Himmelstein commented on SPARK-26325:
--------------------------------------------
h1. Solution in pyspark 3.0.1
Turns out there is an {{inferTimestamp }}option that must be enabled. >From the
spark [migration
guide|https://spark.apache.org/docs/latest/sql-migration-guide.html#upgrading-from-spark-sql-30-to-301]:
{quote}In Spark 3.0, JSON datasource and JSON function {{schema_of_json}} infer
TimestampType from string values if they match to the pattern defined by the
JSON option {{timestampFormat}}. Since version 3.0.1, the timestamp type
inference is disabled by default. Set the JSON option {{inferTimestamp}} to
{{true}} to enable such type inference.
{quote}
Surprised this would occur in a patch release and is not reflected yet in the
[latest
docs|https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/DataFrameReader.html].
But looks like it correlated with a major performance decrease so was turned
off by default:
[apache/spark#28966|https://github.com/apache/spark/pull/28966], SPARK-26325,
and SPARK-32130.
So in pyspark 3.0.1:
{code:python}
line = '{"time_field" : "2017-09-30 04:53:39.412496Z"}'
rdd = spark.sparkContext.parallelize([line])
(
spark.read
.option("inferTimestamp", "true")
.option("timestampFormat", "yyyy-MM-dd HH:mm:ss.SSSSSS'Z'")
.json(path=rdd)
){code}
Returns:
{code:java}
DataFrame[time_field: timestamp]
{code}
Yay!
> 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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