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https://issues.apache.org/jira/browse/BEAM-2953?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16248750#comment-16248750
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ASF GitHub Bot commented on BEAM-2953:
--------------------------------------

GitHub user rezarokni opened a pull request:

    https://github.com/apache/beam/pull/4116

    [BEAM-2953] Part 1 of Multipart advanced timeseries examples

    Hi @kennknowles 
    This is part 1 of the multipart example dealing with backfill via a 
heartbeat of timeseries data.
    The update is in multiple parts to allow the PR to be easier to review.
    Cheers

You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/rezarokni/beam beam-2953

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/beam/pull/4116.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #4116
    
----
commit 81b6df2eb4c154739b9d6a18ead1c73af4faf16f
Author: rarokni <[email protected]>
Date:   2017-09-26T17:10:11Z

    Started beam-2953 timeseries branch
    This is part 1 of the multipart example dealing with backfill via a 
heartbeat of timeseries data.
    The update is in multiple parts to allow the PR to be easier to review.

----


> Create more advanced Timeseries processing examples using state API
> -------------------------------------------------------------------
>
>                 Key: BEAM-2953
>                 URL: https://issues.apache.org/jira/browse/BEAM-2953
>             Project: Beam
>          Issue Type: Improvement
>          Components: examples-java
>    Affects Versions: 2.1.0
>            Reporter: Reza ardeshir rokni
>            Assignee: Reuven Lax
>            Priority: Minor
>
> As described in the phase 1 portion of this solution outline:
> https://cloud.google.com/solutions/correlating-time-series-dataflow
> BEAM can be used to build out some very interesting pre-processing stages for 
> time series data. Some examples that will be useful:
> - Downsampling time series based on simple AVG, MIN, MAX
> - Creating a value for each time window using generatesequence as a seed 
> - Loading the value of a downsample with the previous value (used in FX with 
> previous close being brought into current open value) 
> This will show some concrete examples of keyed state as well as the use of 
> combiners. 
> The samples can also be used to show how you can create a ordered list of 
> values per key from a unbounded topic which has multiple time series keys. 



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