Hi Dobes,
You have a very interesting use case. Let's explore a bit further.
You are right that is should be possible to build a query engine on top of
Union/Variant (JSON object) types. MapR has done so with MapRDB. Sounds like
Mongo has also (I'll make a note to read up on Mongo.) Drill has implemented
some of these ideas in bits and pieces. However we've hit several problems due
to the SQL and columnar nature of Drill.
First, SQL is not designed for this use case: every operator and function has
to be rethought. (For example, a sort has see only INT values and has been
using an INT comparison. Now, the column becomes a DOUBLE, so we need to add a
DOUBLE comparison, DOUBLE/INT comparison, and logic to combine the two. Vectors
that were pure INT in previous sorted runs now have to change to become a UNION
of either INT or DOUBLE so we can merge runs of INTs with runs of DOUBLEs.)
Second, UNIONs are very inefficient with the extra storage, type checks and so
on. So, they need to be used only where needed else we violate the #1 concern
for any query engine: Performance. So, code complexity vastly increases.
Third, neither JDBC nor ODBC understand UNION (Variant). BI tools don't
understand UNION. Yet, these are Drill's primary clients. So, somebody has to
convert the UNION into something that xDBC can understand. Should this be
client code (which, I think, can't even be done for ODBC) or should Drill do
it? And, if we've only ever seen untyped nulls, we could not convert to the
correct type, even in principle, because we don't know it (unless we enforce
the CAST push-down from the other discussion.)
If Drill does the UNION-to-type conversion, why not do it early rather than
late to avoid all the complexity?
Sounds like you may live in a world where it is JSON in, JSON out, JSON-aware
clients and you need a JSON-aware query engine. That query engine cannot be
based on (standard) SQL. Might be based on SQL++ or a JSON-extended SQL which
does not try to work with xDBC.
Likely, elsewhere in your project, there is someone else (maybe it is you)
trying to convert runs of untyped JSON nulls into a typed Parquet file that
matches the case where those JSON fields have a type. Now you'd need to
convince Parquet, and all its consumers (Amazon Athena, say) to support
ambiguous types and unions. What about your ML group that now has to have
feature vectors of mixed types? Right about now a little voice might be saying,
"ah, maybe we're on the wrong track."
One could argue that the education market is looking for a simple solution: one
that uses standard (SQL-based) tools. Rather than having to reinvent the entire
stack to work around a flaw in JSON encoding (untyped nulls), it might be
better to fix the JSON bug so you can leverage the rich ecosystem of SQL-based
tools.
Can you explain a bit more the source and use of the data? Will either the
source or the user expect typed data? How might you handle type ambiguity in
parts of your app other than query?
Thanks,
- Paul
On Wednesday, February 26, 2020, 8:14:56 AM PST, Dobes Vandermeer
<[email protected]> wrote:
Hi Paul,
You can, of course, represent a JSON value as a union of the various types, and
your questions are already well answered in mongo and elsewhere.
Typically numeric operations (sum) will fail if values are not all numbers.
Numbers are promoted to double if they are not all double.
Group by can just compare each field separately and its presence or absence, no
problem there.
Conversion to jdbc can use whatever system is already used for objects. Any
variant not populated is null. If they want a more concise representation then
CAST will do a best effort conversion or fail.
For a chart you would CAST the data points to double.
I don't personally see any major roadblock there.
On Feb 25, 2020, 11:50 PM -0800, Paul Rogers <[email protected]>, wrote:
> Hi Dobes,
>
> Looking at the BSON spec [1], it seems that BSON works like JSON: a null
> isn't a null of some type, it is just null. So, if Drill sees, say, 10 nulls,
> and has to create a vector to store them, it doesn't know which type to use.
> In fact, if BSON worked any other way, there could not be a 1:1 translation
> between the two.
>
>
> This contrasts with the SQL model where a NULL is a NULL INT or a NULL
> VARCHAR; never just a NULL. Makes sense: all values in a SQL column must be
> of the same type so NULLs are of that column type.
>
> And here we have the fundamental conflict between SQL and JSON. If I have a
> field "c" in three JSON objects, JSON does not care if those three fields
> have the same type or wildly differing types (null, Integer, object, say.)
> SQL, however, gets its convenience from types being implicit and constant:
> the data dictionary gives the types so the user does not have to.
>
> Drill has a UNION type (called a "Variant" in some DBs) that can hold, say, a
> NULL a FLOAT8 and an object (MAP). Perfect! But, how do you sum, sort or
> group by this monster? How do you send the results to JDBC or ODBC? How to
> you chart a UNION?
>
>
> Trying to get SQL to act like JSON (or visa-versa) has been an ongoing
> conflict for the life of the Drill project (and, for me, with a BI tool
> before that.)
>
>
> The best we can do is to say that Drill works on the subset of JSON which
> represents a tabular structure (same types on fields of the same name across
> objects.) That is, Drill works if the JSON was created by the equivalent of a
> SQL query (with nested structure to "un-join" tables.) But, we still need to
> deal with untyped nulls.
>
> The "sample the first batch" approach only works if we can guarantee a
> non-null value appears in the first batch, always. I'd not bet on it. A query
> of one row ("Johnny's result from the test on Tuesday" when Johnny was absent
> and so "points" is null) will confuse the JDBC client programmed to expect a
> (possibly null) double.
>
>
> What we need is a way to know that "when column `points` does appear, it will
> be a FLOAT8". This is what the "provided schema" does: you can specify just
> the problematic columns. For files, we put the schema file in the table
> directory. This is very light-weight: when a conflict occurs, add a hint to
> make the problem go away.
>
> But, there is no directory in Mongo where we can stash a schema file. So,
> what we need is some other place to store that annotation. Maybe something
> associated with the storage plugin for something like Mongo: "for this Mongo
> server, `points` is FLOAT8." Or something fancier if, say, students,
> teachers, assignments and tests are all just objects (with some type field)
> in a single Mongo "table."
>
>
> Thinking a bit more broadly, I'd guess that the data comes from somewhere,
> perhaps a web form. If so, then the UI designer certainly knew what type she
> was gathering. If it is a DB extract, the original source had a type.
> Scantron? Has a type. PowerSchool/SchoolWires/Moodle/Whatever likely store
> data in a DB, so it has a type. If you ETL to Parquet for storage in S3,
> Parquet needs a type. So, somebody knows the type. We just have to let Drill
> in on the secret.
>
>
> Would some kind of hint-based model work for your use case? Some idea that
> would be better?
>
>
> Thanks,
> - Paul
>
> [1] http://bsonspec.org/spec.html
>
>
>
>
> On Tuesday, February 25, 2020, 10:17:56 PM PST, Dobes Vandermeer
> <[email protected]> wrote:
>
> Hi Paul,
>
> It seems to me that the type of the columns in a JSON file is "JSON" - e.g.
> map, array, number, string, null, or boolean. In mongodb it is "BSON", which
> adds dates, integers, and a few other things.
>
> Lacking further guidance from the user, I would expect drill to handle all
> JSON & BSON columns as if they could hold any of those types at any time. It
> definitely should not distinguish between integers and floats in JSON,
> because JSON does not have this distinction.
>
> I suppose this may seem like a pain, though; perhaps it blows up the
> algorithms drill uses. I'm still new to drill so I don't really understand
> all the implications of it. But I do know that this *is* the true data model
> of JSON & BSON. Trying to lockdown the schema will create impedance
> mismatches.
>
> Unless this reality is accepted then the pain will never end, I suspect.
>
>
> If the user does a CAST() on some values then the output of the CAST
> operation can be assumed to be specified type, or there will be an error.
> Perhaps there's some hope in that direction.
>
>
>
>
> On 2/25/2020 8:05:37 PM, Paul Rogers <[email protected]> wrote:
> Hi Dobes,
>
> You've run into the classic drawback of runtime schema inference: if Drill
> never sees a column value in its first sample, then it has no way to "predict
> the future" and guess what type will eventually show up. So, Drill guesses
> "nullable INT" which turns out to almost always be wrong.
>
> Some record readers pick the type on the very first row (a sample size of 1.)
> The newer JSON reader we're working on uses the first batch (a few thousand
> rows) as its sample size.
>
> Still, if you request "points", the reader is obligated to provide a column
> even if has to make something up. So, it makes up "nullable INT."
>
> This is the "black swan" problem of inductive reasoning: no matter how many
> empty values Drill sees, there could always be a non-empty value of some
> other type.
>
>
> Worse, one scan may see no value and choose "nullable INT" while another sees
> the actual value and chooses Float8. Now, some poor exchange receiver
> operator will see both types and have no clue what to do.
>
>
> This is why most DBs require a metastore (AKA data dictionary) to provide
> table descriptions. Instead of infering types, DBs define the types, often
> via the same spec that drives the generative process that created the data.
>
>
> Drill also has relatively new "provided schema" feature that helps with this
> issue in some (but not all) format plugins. But, it has not yet been added to
> Mongo (or any other storage plugin other than the file system plugin.)
>
> You could try a conditional cast: something like
>
> IF(sqlTypeOf(points) = `INT`, CAST(NULL AS FLOAT4), points)
>
> (I probably have the syntax a bit wrong.) This works if two different scans
> see the different types. But, it will fail if a single scan sees an empty
> value followed by a null value (which is exactly the case you describe)
> because the scan is trying to cope with the data before its even gotten to
> the Project operator where the IF would be applied.
>
> Sorry for the long post, but this is a difficult issue that has frustrated
> users for years. I recently posted a proposed solution design at [1] and
> would welcome feedback.
>
>
> Thanks,
> - Paul
>
> [1]
> https://github.com/paul-rogers/drill/wiki/Toward-a-Workable-Dynamic-Schema-Model
>
>
> On Tuesday, February 25, 2020, 5:27:01 PM PST, Dobes Vandermeer wrote:
>
> Hi,
>
>
> I was experimenting with the mongo storage system and I found that when I
> query a field that doesn't usually have any value, I get this error "You
> tried to write a Float8 type when you are using a ValueWriter of type
> NullableIntWriterImpl."
>
> Based on a bit of googling I found that this means drill has inferred the
> incorrect type for that field. I was hoping I could override the inferred
> type using CAST or something, but CAST didn't work. Is there a way to tell
> drill what type a field from mongodb is supposed to be?
>
> Example query:
>
> SELECT _id, CAST(points AS DOUBLE)
> FROM mongo.formative.answers AS answer
> WHERE answer.createdAt > DATE_SUB(current_timestamp, interval '1' day)
> LIMIT 100
>
> In this case "points" isn't set on every row, so I guess drill assumes it is
> "NullableInt" when really it is should be considered a double. We also have
> many boolean fields that are not set by default that we would want to query.
>
> What's the standard workaround for this case?
