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From Ron Cecchini <roncecch...@comcast.net>
Subject Re: Drill + Mongo
Date Fri, 06 Mar 2020 05:25:20 GMT
Thanks as always, Paul.

Well, technically Drill performs *better* than the (official) Presto, because the prestosql/presto
image's mongodb connector is broken at the current time...

I've been using the starburstdata/presto image because it's apparently more stable.  I'm still
a little confused how they achieve that because I *thought* they were building from the same
(prestosql) code base, with just some  configuration changes, but I think they must've modified
code as well.

Anyway, as for what kind of query, it's just a fairly vanilla query with a few range tests.

The (cleaned up and sanitized) Mongo query looks something like:

{ "$and" :
  [ 
    { "id" : { "$lt"  : 1000 } },
    { "d"  : { "$gte" : { "$date" : 1578000000000 } } },
    { "d"  : { "$lte" : { "$date" : 1578200000000 } } },
    { "x"  : { "$gte" : 1.0 } },
    { "x"  : { "$lte" : 4.0 } },
    { "y"  : { "$gte" : 5.0 } },
    { "y"  : { "$lte" : 8.0 } }
  ]
}

and then the SQL becomes something simple like:

SELECT * FROM mongodb.<schema>."<table.name>"
WHERE ( id < 1000 )
  AND ( d BETWEEN '2020-01-01T12:00:00Z' AND '2020-01-02T12:00:00Z' )
  AND ( x BETWEEN 1.0 AND 4.0 )
  AND ( y BETWEEN 5.0 AND 8.0 )
OFFSET 0 LIMIT 5000

> On March 5, 2020 at 2:31 AM Paul Rogers <par0328@yahoo.com.INVALID> wrote:
> 
> 
> Hi Ron,
> 
> Sounds like the good news is that Drill is about as good as Presto when querying Mongo.
Sounds like the bad news is that both are equally deficient. On the other hand, the other
good news is that better performance is just a matter of adding additional planning rules
(with perhaps some Mongo metadata.)
> 
> 
> The Wikipedia page for Mongo [1] suggests several features that Mongo (Simba) is probably
using in their own JDBC driver, but which Drill probably does not use:
> 
> * Primary and secondary indices
> * Field, range query, and regular-expression searches
> * User-defined JavaScript functions
> * Three ways to perform aggregation: the aggregation pipeline, the map-reduce function,
and single-purpose aggregation methods.
> 
> My guess is that the Mongo JDBC driver does thorough planning to exploit each of the
above functions, while Drill may use only a few. We already noted other weaknesses in the
filter push-down code for the Drill Mongo plugin. Seems fixable if we can put in the effort.
> 
> 
> Seems Mongo provides a Simba JDBC driver, which is proprietary, so no source code is
available we could use as a "cheat sheet" to see what's what.
> 
> 
> Just out of curiosity, what is the query that works well with the Mongo JDBC driver,
but poorly with Drill?
> 
> Anybody know more about how Mongo works and what Drill might be missing?
> 
> 
> Thanks,
> - Paul
> 
> [1] https://en.wikipedia.org/wiki/MongoDB
> 
> 
> 
>  
> 
>     On Wednesday, March 4, 2020, 9:28:44 PM PST, Ron Cecchini <roncecchini@comcast.net>
wrote:  
>  
>  Hi, guys.
> 
> This is actually more of a Mongo question than a Drill-specific question as it also applies
to Presto + Mongo, and the vanilla Mongo shell as well.
> 
> I'm asking here, though, because, well, I'm curious, and because you're the database
geniuses...
> 
> So, I essentially get why a NoSQL database, in general, wouldn't be as performant as
a SQL one at "relational" things.  From what I gather, there are denormalization and optimization
techniques and tricks you can use to speed up a Mongo query and so forth, but my question
is:
> 
> Why is it that any Drill/Presto + Mongo CLI or JDBC query against a large collection
(100-200 million documents) that includes even a single WHERE clause, or the Mongo equivalent
query made via Mongo shell, basically never returns and has to be killed, whereas the same
(Mongo equivalent) query against the same collection made via *Mongo's* JDBC driver takes
only a second or two?
> 
> Is the Mongo JDBC using some indexing that the others aren't?  (But how would that explain
Mongo shell's non-performance...  Why doesn't Mongo shell just make a JDBC call to the db...)
> 
> Thank you in advance for educating me.
> 
> Ron
>

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