Nevermind. I figured this out. Was due to 'properties' being a reserved
keyword. I created a PR to fix the JSON doc on the drill site.
On Sat, Oct 7, 2023 at 1:46 PM Mike Beckerle <mbecke...@apache.org> wrote:
> Ok, after weeks of delay....
>
> That helps a great deal. You flatten the array of maps into a table of
> maps.
>
> I am confused still about when I must do square brackets versus dot
> notation: data['a'] vs. data.a
> The JSON documentation for Drill uses dot notation to reach into fields of
> a map.
>
> Ex: from the JSON doc:
>
> {
> "type": "FeatureCollection",
> "features": [
> {
> "type": "Feature",
> "properties":
> {
> "MAPBLKLOT": "0001001",
> "BLKLOT": "0001001",
> "BLOCK_NUM": "0001",
> "LOT_NUM": "001",
> ....
>
> The query uses SELECT features[0].properties.MAPBLKLOT, FROM ...
> Which is using dot notation where in your queries on my JSON you did not
> use dot notation.
>
> I tried revising the queries you wrote using the dot notation, and it was
> rejected. "no table named 'data'", but I'm not sure why.
>
> Ex:
>
> This works: (your original working query)
>
> SELECT data['a'], data['b'] FROM (select flatten(record) AS data from
> dfs.`/tmp/record.json`) WHERE data['b']['b1'] > 60.0;
>
> But this fails:
>
> SELECT data.a AS a, data.b AS b FROM (select flatten(record) AS data from
> dfs.`/tmp/record.json`) WHERE data.b.b1 > 60.0;
> Error: VALIDATION ERROR: From line 1, column 105 to line 1, column 108:
> Table 'data' not found
>
> But your sub-select defines 'data' as, I would assume, a table.
>
> Can you help me clarify this?
>
> [Error Id: 90c03b40-4f00-43b5-9de9-598102797b2f ] (state=,code=0)
> apache drill>
>
>
> On Mon, Sep 18, 2023 at 11:17 PM Charles Givre <cgi...@gmail.com> wrote:
>
>> Hi Mike,
>> Let me answer your question with some queries:
>>
>> >>> select * from dfs.test.`record.json`;
>>
>> +----------------------------------------------------------------------------------+
>> | record
>> |
>>
>> +----------------------------------------------------------------------------------+
>> |
>> [{"a":{"a1":5.0,"a2":6.0},"b":{}},{"a":{},"b":{"b1":55.0,"b2":66.0,"b3":77.0}},{"a":{"a1":7.0,"a2":8.0},"b":{}},{"a":{},"b":{"b1":77.0,"b2":88.0,"b3":99.0}}]
>> |
>>
>> +----------------------------------------------------------------------------------+
>>
>> Now... I can flatten that like this:
>>
>> >>> select flatten(record) AS data from dfs.test.`record.json`;
>> +----------------------------------------------+
>> | data |
>> +----------------------------------------------+
>> | {"a":{"a1":5.0,"a2":6.0},"b":{}} |
>> | {"a":{},"b":{"b1":55.0,"b2":66.0,"b3":77.0}} |
>> | {"a":{"a1":7.0,"a2":8.0},"b":{}} |
>> | {"a":{},"b":{"b1":77.0,"b2":88.0,"b3":99.0}} |
>> +----------------------------------------------+
>> 4 rows selected (0.298 seconds)
>>
>> You asked about filtering. For this, I broke it up into a subquery, but
>> here's how I did that:
>>
>> >>> SELECT data['a'], data['b']
>> 2..semicolon> FROM (select flatten(record) AS data from
>> dfs.test.`record.json`)
>> 3..semicolon> WHERE data['b']['b1'] > 60.0;
>> +--------+---------------------------------+
>> | EXPR$0 | EXPR$1 |
>> +--------+---------------------------------+
>> | {} | {"b1":77.0,"b2":88.0,"b3":99.0} |
>> +--------+---------------------------------+
>> 1 row selected (0.379 seconds)
>>
>> I did all this without the union data type.
>>
>> Does this make sense?
>> Best,
>> -- C
>>
>>
>> On Sep 13, 2023, at 11:08 AM, Mike Beckerle <mbecke...@apache.org> wrote:
>>
>> I'm thinking whether a first prototype of DFDL integration to Drill should
>> just use JSON.
>>
>> But please consider this JSON:
>>
>> { "record": [
>> { "a": { "a1":5, "a2":6 } },
>> { "b": { "b1":55, "b2":66, "b3":77 } }
>> { "a": { "a1":7, "a2":8 } },
>> { "b": { "b1":77, "b2":88, "b3":99 } }
>> ] }
>>
>> It corresponds to this text data file, parsed using Daffodil:
>>
>> 105062556677107082778899
>>
>> The file is a stream of records. The first byte is a tag value 1 for type
>> 'a' records, and 2 for type 'b' records.
>> The 'a' records are 2 fixed length fields, each 2 bytes long, named a1 and
>> a2. They are integers.
>> The 'b' records are 3 fixed length fields, each 2 bytes long, named b1,
>> b2,
>> and b3. They are integers.
>> This kind of format is very common, even textualized like this (from COBOL
>> programs for example)
>>
>> Can Drill query the JSON above to get (b1, b2) where b1 > 10 ?
>> (and ... does this require the experimental Union feature?)
>>
>> b1, b2
>> ---------
>> (55, 66)
>> (77, 88)
>>
>> I ask because in an XML Schema or DFDL schema choices with dozens of
>> 'branches' are very common.
>> Ex: schema for the above data:
>>
>> <element name="record" maxOccurs="unbounded">
>> <complexType>
>> <choice><!-- there are sub-record types, a, b,... there could be many
>> dozens of these -->
>> <element name="a">
>> <complexType>
>> <sequence>
>> ... many child elements let's say named a1, a2, ...
>> </sequence>
>> </complexType>
>> </element>
>> <element name="b">
>> <complexType>
>> <sequence>
>> ... many child elements let's say named b1, b2, b3
>> ...
>> </sequence>
>> </complexType>
>> </element>
>> </choice>
>> </complexType>
>> </element>
>>
>> To me XSD choice naturally requires a Union feature of some sort.
>> If that's expermental still in Drill ... what to do?
>>
>> On Sun, Aug 6, 2023 at 10:19 AM Charles S. Givre <
>> notificati...@github.com>
>> wrote:
>>
>> @mbeckerle <https://github.com/mbeckerle>
>> You've encountered another challenge that exists in Drill reading data
>> without a schema.
>> Let me explain a bit about this and I'm going to use the JSON reader as an
>> example. First Drill requires data to be homogeneous. Drill does have a
>> Union vector type which allows heterogeneous data however this is a bit
>> experimental and I wouldn't recommend using it. Also, it really just
>> shifts
>> schema inconsistencies to the user.
>>
>> For instance, let's say you have a column consisting of strings and
>> floats. What happens if you try to do something like this:
>>
>> SELECT sum(mixed_col)-- orSELECT.... ORDER BY mixed_col
>>
>> Remembering that Drill is distributed and if you have a column with the
>> same name and you try to do these operations, they will fail.
>>
>> Let's say we have data like this:
>>
>> [
>> {
>> 'col1': 'Hi there',
>> 'col2': 5.0
>> },
>> {
>> 'col1':True,
>> 'col2': 4,
>> 'col3': 'foo'
>> }
>> ]
>>
>> In older versions of Drill, this kind of data, this would throw all kinds
>> of SchemaChangeExceptions. However, in recent versions of Drill, @jnturton
>> <https://github.com/jnturton> submitted apache#2638
>> <https://github.com/apache/drill/pull/2638> which overhauled implicit
>> casting. What this meant for users is that col2 in the above would be
>> automatically cast to a FLOAT and col1 would be automatically cast to a
>> VARCHAR.
>>
>> However, when reading data the story is a little different. What we did
>> for the JSON reader was have several read modes. The least tolerant
>> attempts to infer all data types. This seems like a great idea in
>> practice,
>> however when you start actually using Drill with real data, you start
>> seeing the issues with this approach. The JSON reader has a few
>> configuration options that increase its tolerance for bad data. The next
>> level is readAllNumbersAsDouble which... as the name implies, reads all
>> numeric data as Doubles and does not attempt to infer ints vs floats. The
>> next options is allTextMode which reads all fields as VARCHAR. This
>> should be used when the data is so inconsistent that it cannot be read
>> with
>> either mode. These modes can be set globally, at the plugin level or at
>> query time.
>>
>> For the XML reader, I didn't add type inference because I figured the data
>> would be quite messy, however it wouldn't be that hard to add basically
>> the
>> same levels as the JSON reader.
>>
>> This fundamental issue exists in all the readers that read data without a
>> schema. My rationale for working on the XSD reader is that this will
>> enable
>> us to accurately read XML data with all the correct data types.
>>
>> —
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