Dear all,
Right on time for Christmas, I am happy to share news with you on the latest
release of the free and open source Rumble, 1.4.2 Willow Oak.
Rumble is a JSONiq engine with which you can query nested, heterogeneous data
(e.g., JSON) on Spark, but it carefully hides Spark's RDDs and DataFrames from
the user for a productive experience.
Since the last announcement on this list (Rumble 1.1), the following is new:
1. Types
Type expressions are all supported: cast as, castable as, treat as, instance
of, typeswitch. Item types are checked in parallel if the input sequence is big.
Rumble supports new types (you will recognize standard XML Schema types): date,
time, dateTime, hexBinary, base64Binary, duration, and more.
Grouping and ordering on these new types is supported (even in parallel, with
DataFrames below the hood).
2. Functions
User-defined functions are supported. This means you can define your own
functions.
Recursion works out of the box.
declare function fibonacci($i) {
if($i le 2) then 1 else fibonacci($i - 1) + fibonacci($i - 2)
};
for $i in 1 to 20
return fibonacci($i)
Higher-order functions are also supported (this follows the XQuery 3.0
standard). Functions can be passed as values to expressions and other functions
and dynamically called. And they also get automatically serialized, shipped to
the Spark cluster, deserialized and called if you build big sequences of
functions.
let $x := function($x as integer) as integer { $x * $x }
return $x(4)
Type-checking is made on all function parameters and returned values (if types
are provided), and sequence types are automatically checked in parallel if
sequences are large.
3. Parquet
We started adding other formats that have a similar data model to JSON, for
example with parquet-file(), you can open... Parquet files. This will nicely
map it to a sequence of objects that can then be queried in parallel. More
formats will follow.
It is also possible, for convenience, to open small, local JSON files spread
over multiple lines with json-doc(). (json-file() requires one object per line
and is meant for the parallelization over large files on HDFS, S3 or local
drive).
4. Bugfixes and enhancements
Many small things that our students found have been fixed: variables bound by
an outer FLWOR is visible in inner FLWOR expressions as well. We throw more
user-friendly exceptions if you nest a big FLWOR inside a big FLWOR (which, for
obvious reasons, will "break" on the cluster). The FLWOR count clause is more
stable (that one was not easy to get to work on top of DataFrames).
5. Predicates
position() and last() are supported in predicates. And predicates are executed
in parallel on big sequences. It is very easy to get a subsequence from a big
sequence (this otherwise requires more effort to do in Java or Scala)
json-file("file.json")[1]
json-file("file.json")[position() ge 10 and position() le last() - 20]
6. Counting optimizations
Rumble auto-detects when a non-grouping variable is only counted, and will
spontaneously get rid of all the items early, to only keep the count. This
significantly improves performance of such queries:
for $object in json-file("file.json")
group by $country := $object.country
return { "country" : $country, "count" : count($object) }
And finally, for those interested in the nitty gritty, we uploaded a paper
here: https://arxiv.org/pdf/1910.11582.pdf
Enjoy!
Kind regards and happy slide into 2020,
Ghislain
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