Can you give an example of the SQL syntax you are using for tumbling windows? Does it use GROUP BY and FLOOR, as in https://github.com/apache/incubator-calcite/blob/master/doc/STREAM.md#tumbling-windows <https://github.com/apache/incubator-calcite/blob/master/doc/STREAM.md#tumbling-windows>?
What do you mean by “tumbling window size”? You can easily deduce that "FLOOR(rowtime TO HOUR)” covers an hour range of the rowtime column, but to compute the number of rows or bytes you’d have to make assumptions about data rates and then you’d only get an estimate. Regarding re-issuing totals to incorporate late arrivals. It sounds useful, but you’ll have to be careful that it doesn’t screw up other operators downstream. Imagine that you have an aggregate followed by another aggregate that rolls it up. If the downstream operator isn’t expecting duplicates then it may double-count. I think it may be OK if the stream defines a primary key, specifies that there may be duplicates and the duplicates will be compacted. But in short, we need more metadata, because the consumer is a dumb operator not a smart human. Do you have a URL for Yi’s design doc? By the way, I am just about to check in a patch for https://issues.apache.org/jira/browse/CALCITE-704 <https://issues.apache.org/jira/browse/CALCITE-704> “FILTER clause for aggregate functions”. I think it would be really useful for streaming queries, because you can’t afford to re-run the query for a subset of the data. Samza-sql should get this virtually for free when it gets the next Calcite release. > On Apr 28, 2015, at 7:40 AM, Milinda Pathirage <mpath...@umail.iu.edu> wrote: > > Hi Julian, > > I am working on tumbling windows and hoping to have a look at other types of > window aggregates next. I was trying to extract the window spec out from the > aggregate operator (for tumbling window) and figure out that its impossible > to infer tumbling window size from date time expressions or from an > expression over any other type of monotonic field (such as row number for > tuple based windows). So we were thinking of implementing aggregates like we > normally implement stream aggregate in standard SQL (assuming group by fields > are sorted) but with support for handling out of order arrivals. One > difference in this method compared to stream aggregate from SQL is that an > input row(s) can contribute to multiple outputs due to late arrivals. My plan > is to emit the first result for tumbling window aggregate when we see a new > tuple from the next window and emit result again if we get a tuple for an old > window. We'll have a window closing policy where we will not handle tuples > arriving after the window timeout. Yi's window operator design document > contains most of the details required. What do you think about this approach > to implement tumbling windows? We highly appreciate your feedback on this. > > Thanks > Milinda > > On Mon, Apr 27, 2015 at 6:15 PM, Julian Hyde <jul...@hydromatic.net > <mailto:jul...@hydromatic.net>> wrote: > Milinda, > > I have seen your work adding initial streaming SQL to Samza. Good stuff. > > Which types of query are you thinking of doing next? > > As of calcite-1.2, the streaming extensions are in Calcite’s master branch. > (See https://github.com/apache/incubator-calcite/blob/master/doc/STREAM.md > <https://github.com/apache/incubator-calcite/blob/master/doc/STREAM.md>.) We > are a couple of weeks away from the next Calcite release. If you need some > work done in Calcite, now would be a good time. > > Julian > > > > > -- > Milinda Pathirage > > PhD Student | Research Assistant > School of Informatics and Computing | Data to Insight Center > Indiana University > > twitter: milindalakmal > skype: milinda.pathirage > blog: http://milinda.pathirage.org <http://milinda.pathirage.org/>
