Hi Juho,
a query with an OVER aggregation should emit exactly one row for each input
row.
Does your comment on "isn't catching all distinct values" mean that this is
not the case?
You can combine tumbling windows and over aggregates also by nesting
queries as shown below:
SELECT
s_aid1,
s_cid,
first_seen,
MIN(first_seen) OVER (PARTITION BY s_aid1, s_cid ORDER BY rowtime RANGE
BETWEEN INTERVAL '1' HOUR PRECEDING AND CURRENT ROW) AS first_seen_1h,
processdate,
tumble_start,
tumble_end
FROM (
SELECT
s_aid1,
s_cid,
TS_MIN(rowtime) AS first_seen,
CAST(DATE_FORMAT(TUMBLE_START(rowtime, INTERVAL '10' SECOND),
'%Y%m%d/%H/%i/%S') AS VARCHAR) AS processdate,
TUMBLE_START(rowtime, INTERVAL '10' SECOND) AS tumble_start,
TUMBLE_END(rowtime, INTERVAL '10' SECOND) AS tumble_end,
TUMBLE_ROWTIME(rowtime, INTERVAL '10' SECOND) AS rowtime
FROM events
WHERE s_aid1 IS NOT NULL
GROUP BY
s_aid1,
s_cid,
TUMBLE(rowtime, INTERVAL '10' SECOND)
)
Early triggering is not yet supported for SQL queries.
Best, Fabian
2018-02-27 15:20 GMT+01:00 Juho Autio <juho.au...@rovio.com>:
> Thanks for the hint! For some reason it isn't catching all distinct values
> (even though it's a much simpler way than what I initially tried and seems
> good in that sense). First of all, isn't this like a sliding window:
> "rowtime RANGE BETWEEN INTERVAL '1' HOUR PRECEDING AND CURRENT ROW"?
>
> My use case needs a tumbling window. I tried adding PARTITION BY
> additionally with DATE_FORMAT(rowtime, '%Y%m%d%H') to achieve the same
> result as with a tumbling window; this resulted in slightly more distinct
> values, but was still missing some! Would there by some nice way to create
> a tumbling window right in the RANGE condition instead?
>
> As a disclaimer I have to say we seem to be fine using a simple window
> _without_ any early triggering. But of course it would be nice to
> understand how early triggering could be enabled in a simple & scalable way.
>
> Cheers,
> Juho
>
> On Mon, Feb 19, 2018 at 1:44 PM, Fabian Hueske <fhue...@gmail.com> wrote:
>
>> Hi Juho,
>>
>> sorry for the late response. I found time to look into this issue.
>> I agree, that the start and end timestamps of the HOP window should be 1
>> hour apart from each other. I tried to reproduce the issue, but was not
>> able to do so.
>> Can you maybe open a JIRA and provide a simple test case (collection data
>> source, no Kafka) that reproduces the issue?
>>
>> Regarding the task that you are trying to solve, have you looked into
>> OVER windows?
>>
>> The following query would count for each record, how often a record with
>> the same ID combination was observed in the last hour based on its
>> timestamp:
>>
>> SELECT
>> s_aid1,
>> s_cid,
>> COUNT(*) OVER (PARTITION BY s_aid1, s_cid ORDER BY rowtime RANGE
>> BETWEEN INTERVAL '1' HOUR PRECEDING AND CURRENT ROW) AS occurrence,
>> rowtime
>> FROM events
>> WHERE s_aid1 IS NOT NULL
>>
>> If occurrence is 1, the current record is the only record within the last
>> 1 hour with the combination of aid and cid .
>> The query does not batch the stream by 10 seconds, but rather produces
>> the results in real-time. If the batching is not required, you should be
>> good by adding a filter on occurrence = 1.
>> Otherwise, you could add the filter and wrap it by 10 secs tumbling
>> window.
>>
>> Hope this helps,
>> Fabian
>>
>>
>> 2018-02-14 15:30 GMT+01:00 Juho Autio <juho.au...@rovio.com>:
>>
>>> I'm joining a tumbling & hopping window in Flink 1.5-SNAPSHOT. The
>>> result is unexpected. Am I doing something wrong? Maybe this is just not a
>>> supported join type at all? Any way here goes:
>>>
>>> I first register these two tables:
>>>
>>> 1. new_ids: a tumbling window of seen ids within the last 10 seconds:
>>>
>>> SELECT
>>> s_aid1,
>>> s_cid,
>>> TS_MIN(rowtime) AS first_seen,
>>> CAST(DATE_FORMAT(TUMBLE_START(rowtime, INTERVAL '10' SECOND),
>>> '%Y%m%d/%H/%i/%S') AS VARCHAR) AS processdate,
>>> TUMBLE_START(rowtime, INTERVAL '10' SECOND) AS tumble_start,
>>> TUMBLE_END(rowtime, INTERVAL '10' SECOND) AS tumble_end
>>> FROM events
>>> WHERE s_aid1 IS NOT NULL
>>> GROUP BY
>>> s_aid1,
>>> s_cid,
>>> TUMBLE(rowtime, INTERVAL '10' SECOND)
>>>
>>> 2. seen_ids: a sliding window of seen ids 1 hour backwards, 10 second
>>> hop:
>>>
>>> SELECT
>>> s_aid1,
>>> s_cid,
>>> TS_MIN(rowtime) AS first_seen,
>>> CAST(HOP_START(rowtime, INTERVAL '10' SECOND, INTERVAL '1' HOUR) AS
>>> DATE) AS processdate,
>>> HOP_START(rowtime, INTERVAL '10' SECOND, INTERVAL '1' HOUR) AS
>>> HOP_start,
>>> HOP_END(rowtime, INTERVAL '10' SECOND, INTERVAL '1' HOUR) AS HOP_end
>>> FROM events
>>> WHERE s_aid1 IS NOT NULL
>>> GROUP BY
>>> s_aid1,
>>> s_cid,
>>> HOP(rowtime, INTERVAL '10' SECOND, INTERVAL '1' HOUR)
>>>
>>> If I write the results of the "seen_ids" table, the difference between
>>> HOP_start and HOP_end is always 1 hour, as expected.
>>>
>>> Then I register another query that joins the 2 tables:
>>>
>>> unique_ids (mostly including fields for debugging - what I need is the
>>> unique, new combinations of s_cid x s_aid1):
>>>
>>> SELECT
>>> new_ids.s_cid,
>>> new_ids.s_aid1,
>>> new_ids.processdate AS processdate,
>>> seen_ids.processdate AS seen_ids_processdate,
>>> new_ids.first_seen AS new_ids_first_seen,
>>> seen_ids.first_seen AS seen_ids_first_seen,
>>> tumble_start,
>>> HOP_start,
>>> tumble_end,
>>> HOP_end
>>> FROM new_ids, seen_ids
>>> WHERE new_ids.s_cid = seen_ids.s_cid
>>> AND new_ids.s_aid1 = seen_ids.s_aid1
>>> AND (new_ids.first_seen <= seen_ids.first_seen OR seen_ids.first_seen
>>> IS NULL)
>>>
>>> I print the results of this table, and surprisingly the HOP_start &
>>> HOP_end are only separated by 10 seconds. Is this a bug?
>>>
>>> {
>>> "s_cid": "appsimulator_236e5fb7",
>>> "s_aid1": "L1GENe52d723b-b563-492f-942d-3dc1a31d7e26",
>>>
>>> "seen_ids_processdate": "2018-02-14",
>>>
>>> "seen_ids_first_seen": "2018-02-14 11:37:59.0",
>>> "new_ids_first_seen": "2018-02-14 11:34:33.0",
>>> "tumble_start": "2018-02-14 11:34:30.0",
>>> "tumble_end": "2018-02-14 11:34:40.0",
>>>
>>> "HOP_start": "2018-02-14 11:37:50.0",
>>> "HOP_end": "2018-02-14 11:38:00.0"
>>> }
>>>
>>> What I'm trying to do is exclude the id from the current "new_ids"
>>> window if it was already seen before (within the 1 hour scope of
>>> "seen_ids"), but that doesn't work either. This example result row also
>>> shows that "seen_ids.first_seen" is bigger than it should be.
>>>
>>>
>>> Even if I can find a fix to this to get what I need, this strategy seems
>>> overly complicated. If anyone can suggest a better way, I'd be glad to
>>> hear. If this was a batch job, it could be defined simply as:
>>>
>>> SELECT DISTINCT s_cid, s_aid1, DATE_FORMAT(rowtime, '%Y%m%d/%H')
>>>
>>> + when streaming this query, the new distinct values should be written
>>> out every 10 seconds (ONLY the new ones - within that wrapping 1 hour
>>> window). So far I haven't been able to figure out how to do that in a
>>> simple way with Flink.
>>>
>>>
>>> *) TS_MIN is a custom function, but it's just a mapping of Flink's
>>> MinAggFunction:
>>>
>>> import java.sql.Timestamp
>>>
>>> import com.rovio.ds.flink.common.udaf.ImplicitOrdering.ordered
>>>
>>> import org.apache.flink.api.common.typeinfo.SqlTimeTypeInfo
>>> import org.apache.flink.table.functions.aggfunctions.MaxAggFunction
>>> import org.apache.flink.table.functions.aggfunctions.MinAggFunction
>>>
>>> object TimestampAggFunctions {
>>>
>>> trait TimestampAggFunction {
>>> def getInitValue = null
>>> def getValueTypeInfo = SqlTimeTypeInfo.TIMESTAMP
>>> }
>>>
>>> class TimestampMinAggFunction extends MinAggFunction[Timestamp] with
>>> TimestampAggFunction
>>> class TimestampMaxAggFunction extends MaxAggFunction[Timestamp] with
>>> TimestampAggFunction
>>>
>>> }
>>>
>>> // Registered with:
>>> tableEnv.registerFunction("TS_MIN", new TimestampMinAggFunction());
>>>
>>>
>>
>
