Hi,
This is going to sound complicated.
Taken as an individual JSON document, because its a self contained schema doc,
its structured. However there isn’t a persisting schema that has to be
consistent across multiple documents. So you can consider it semi structured.
If you’re parsing the JSON document and storing different attributes in
separate columns… you will have a major issue because its possible for a JSON
document to contain a new element that isn’t in your Parquet schema.
If you are going from JSON to parquet… you will probably be better off storing
a serialized version of the JSON doc and then storing highlighted attributes in
separate columns.
HTH
-Mike
> On Apr 23, 2018, at 1:46 PM, Lian Jiang <jiangok2...@gmail.com> wrote:
>
> Hi,
>
> I am using structured spark streaming which reads jsonl files and writes into
> parquet files. I am wondering what's the process if jsonl files schema change.
>
> Suppose jsonl files are generated in \jsonl folder and the old schema is {
> "field1": String}. My proposal is:
>
> 1. write the jsonl files with new schema (e.g. {"field1":String,
> "field2":Int}) into another folder \jsonl2
> 2. let spark job complete handling all data in \jsonl, then stop the spark
> streaming job.
> 3. use a spark script to convert the parquet files from old schema to new
> schema (e.g. add a new column with some default value for "field2").
> 4. upgrade and start the spark streaming job for handling the new schema
> jsonl files and parquet files.
>
> Is this process correct (best)? Thanks for any clue.
