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https://issues.apache.org/jira/browse/SPARK-2870?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14156904#comment-14156904
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Nicholas Chammas commented on SPARK-2870:
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[~marmbrus] - A related feature that I think would be very important and useful
is the ability to infer a complete schema as described here, but do so by key.
i.e. something like {{inferSchemaByKey()}}
Say I have a large, single RDD of data that includes many different event
types. I want to key the RDD by event type and make a single pass over it to
get the schema for each event type. This would probably yield something like a
{{keyedSchemaRDD}} which I would want to register as multiple tables (one table
per key/schema) in one go.
Do you think this would be a useful feature? If so, should I track it in a
separate JIRA issue?
> Thorough schema inference directly on RDDs of Python dictionaries
> -----------------------------------------------------------------
>
> Key: SPARK-2870
> URL: https://issues.apache.org/jira/browse/SPARK-2870
> Project: Spark
> Issue Type: Improvement
> Components: PySpark, SQL
> Reporter: Nicholas Chammas
>
> h4. Background
> I love the {{SQLContext.jsonRDD()}} and {{SQLContext.jsonFile()}} methods.
> They process JSON text directly and infer a schema that covers the entire
> source data set.
> This is very important with semi-structured data like JSON since individual
> elements in the data set are free to have different structures. Matching
> fields across elements may even have different value types.
> For example:
> {code}
> {"a": 5}
> {"a": "cow"}
> {code}
> To get a queryable schema that covers the whole data set, you need to infer a
> schema by looking at the whole data set. The aforementioned
> {{SQLContext.json...()}} methods do this very well.
> h4. Feature Request
> What we need is for {{SQlContext.inferSchema()}} to do this, too.
> Alternatively, we need a new {{SQLContext}} method that works on RDDs of
> Python dictionaries and does something functionally equivalent to this:
> {code}
> SQLContext.jsonRDD(RDD[dict].map(lambda x: json.dumps(x)))
> {code}
> As of 1.0.2,
> [{{inferSchema()}}|http://spark.apache.org/docs/latest/api/python/pyspark.sql.SQLContext-class.html#inferSchema]
> just looks at the first element in the data set. This won't help much when
> the structure of the elements in the target RDD is variable.
> h4. Example Use Case
> * You have some JSON text data that you want to analyze using Spark SQL.
> * You would use one of the {{SQLContext.json...()}} methods, but you need to
> do some filtering on the data first to remove bad elements--basically, some
> minimal schema validation.
> * You deserialize the JSON objects to Python {{dict}} s and filter out the
> bad ones. You now have an RDD of dictionaries.
> * From this RDD, you want a SchemaRDD that captures the schema for the whole
> data set.
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