This is a challenge when dealing with JSON. You can either force the data type
in the CTAS statement (likely better option) or deal with the data type change
in parquet table(s) by using CAST, etc. In the case of zip codes you need to
consider if it will be 5 digits or the extended 5-4 digits to decide if the
data type should be INT or VARCHAR.
Also look into the TYPEOF function, which you can use with CASE to deal with
these types of issues.
I prefer to deal with data issues as soon as possible in the pipeline, so the
tables you create are consistent and clean.
--Andries
On 2/23/18, 12:04 PM, "Lee, David" <david....@blackrock.com> wrote:
Using Drill's CTAS statements I've run into a schema inconsistency issue
and I'm not sure how to solve it..
CREATE TABLE name [ (column list) ] AS query;
If I have a directory called Cities which have JSON files which look like:
a.json:
{ "city":"San Francisco", "zip":"94105"}
{ "city":"San Jose", "zip":"94088"}
b.json:
{ "city":"Toronto ", "zip": null}
{ "city":"Montreal", "zip" null}
If I create a parquet file out of the Cities directory I will end up with
files called:
1_0_0.parquet through 1_5_1.parquet
Now I got a problem:
Most of the parquet files have a column type of char for zip.
Some of the parquet files have a column type of int for zip because the zip
value for a group of records was NULL..
This produces schema change errors later when trying to query the parquet
directory.
Is it possible for Drill to do a better job learning schemas across all
json files in a directory before creating parquet?
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