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. > > — > Reply to this email directly, view it on GitHub > <https://github.com/cgivre/drill/pull/6#issuecomment-1666875922>, or > unsubscribe > < > https://github.com/notifications/unsubscribe-auth/AALUDAZZ6T6Z44AW44IKD2LXT6RVNANCNFSM6AAAAAA26ZZVQ4 > > > . > You are receiving this because you were mentioned.Message ID: > <cgivre/drill/pull/6/c1666875...@github.com> > > >