Re: CoGroupByKey question
Hi Eugene, that seemed to be the problem! Thanks a lot! Patrick Eugene Kirpichov wrote: You typically get this compile error when you have a raw type (e.g. PCollection instead of PCollection) sneaking somewhere. Perhaps tempPCollection or humPCollection is declared as a PCollection without a type? On Thu, Feb 8, 2018 at 2:53 AM Steiner Patrick mailto:patr...@steiner-buchholz.de>> wrote: Hi all, I'm trying to combine two PCollections> via CoGroupByKey When adapting the sample code final TupleTag t1 = new TupleTag<>(); final TupleTag t2 = new TupleTag<>(); PCollection> coGbkResultCollection = KeyedPCollectionTuple.of(t1, pt1) .and(t2, pt2) .apply(CoGroupByKey.create()); I do get final TupleTag tempTuple = new TupleTag<>(); final TupleTag humTuple = new TupleTag<>(); PCollection> coGbkResultCollection = KeyedPCollectionTuple.of(tempTuple, tempPCollection) .and(humTuple, humPCollection) .apply(CoGroupByKey.create()); which results in Type mismatch: cannot convert from POutput to PCollection> I guess it's a simple, stupid thing I'm either missing or not understanding, when combining my two PCollections to one. Any advice is appreciated Patrick
CoGroupByKey question
Hi all, I'm trying to combine two PCollections> via CoGroupByKey When adapting the sample code final TupleTag t1 = new TupleTag<>(); final TupleTag t2 = new TupleTag<>(); PCollection> coGbkResultCollection = KeyedPCollectionTuple.of(t1, pt1) .and(t2, pt2) .apply(CoGroupByKey.create()); I do get final TupleTag tempTuple = new TupleTag<>(); final TupleTag humTuple = new TupleTag<>(); PCollection> coGbkResultCollection = KeyedPCollectionTuple.of(tempTuple, tempPCollection) .and(humTuple, humPCollection) .apply(CoGroupByKey.create()); which results in Type mismatch: cannot convert from POutput to PCollection> I guess it's a simple, stupid thing I'm either missing or not understanding, when combining my two PCollections to one. Any advice is appreciated Patrick
Re: Calculate Average over a Window
Thanks for the pointer to 'CoGroupByKey', which makes perfect sense and
so does the usage of Mean.
Still I have to bother you, as I'm still probably lacking some basic
understanding on how ideal messages/data-structures are supposed to look
like in a Beam Pipeline.
This is what I want to happen for each element in my original
PCollection ( read from PubSub )
original message -> "{ humidity=42.71, temp=20.71}"
final message -> "deviceID":{timestamp=1516549776,
humidity=42.71, temp=20.71, avg_hum=, avg_temp=}"
Where I'm stuck is how to calculate the average ( Mean ) for each
datapoint in my original message?
As you pointed out the usage of 'CoGroupByKey' I assume I have to do
something like in the following visualization?
https://github.com/PatrickSteiner/Google_Cloud_IoT_Demo/blob/master/pictures/Mean_flow.png
Am I on the right path?
Thanks
Patrick
Kenneth Knowles wrote:
It looks like what you want is to join your input stream with the
computed averages. It might look something like this:
PCollection inputEvents =
...apply(Window.into(SlidingWindows))
PCollection> avgTemps =
inputEvents.apply(Mean.perKey())
I don't want to recreate all of the docs on this thread, so I will
just point to CoGroupByKey [1] that you would use to join these on
DeviceId; they pattern looks something like this, where I've left out
lots of boilerplate:
PCollection> joined = ...
PCollection> result =
joined.apply(ParDo.of())
Kenn
[1] https://beam.apache.org/documentation/programming-guide/#cogroupbykey
On Fri, Jan 26, 2018 at 6:23 AM, Steiner Patrick
mailto:patr...@steiner-buchholz.de>> wrote:
Hi Kenn,
thanks again for responding.
Let me try to explain better what I'm looking for. For simplicity
reason let's take a more simple example.
Message 1 "4711" : "{temp=10}" will become "4711" : "{temp=10,
avg_temp=10}"
Message 2 "4711" : "{temp=12}" will become "4711" : "{temp=12,
avg_temp=11}"
Message 3 "4711" : "{temp=14}" will become "4711" : "{temp=14,
avg_temp=12}"
Message 4 "4711" : "{temp=10}" will become "4711" : "{temp=10,
avg_temp=11.5}"
So for each incoming message I would like to append the current
windows "avg_temp" and all this with a sliding window.
So if we would say that the window is 2 seconds and we receive one
message per second, my sample would change to
Message 1 "4711" : "{temp=10}" will become "4711" : "{temp=10,
avg_temp=10}"
Message 2 "4711" : "{temp=12}" will become "4711" : "{temp=12,
avg_temp=11}"
Message 3 "4711" : "{temp=14}" will become "4711" : "{temp=14,
avg_temp=13}"
Message 4 "4711" : "{temp=10}" will become "4711" : "{temp=10,
avg_temp=12}"
Does this explain what I plan?
Thanks again for your help
Patrick
Kenneth Knowles wrote:
On Thu, Jan 25, 2018 at 3:31 AM, Steiner Patrick
mailto:patr...@steiner-buchholz.de>> wrote:
Hi Kenn, all,
you are right, sliding windows seems to be exactly what I
need. Thanks for that pointer.
Where I'm still in need of expert advise is how to structure
the data within my PCollection. From PubSub I do read all
data as a JSON-String
Format 1: "{ humidity=42.71, temp=20.71}"
currently I'm just extending the JSON-String with the
deviceID of the sender and the timestamp
Format 2: "{timestamp=1516549776, deviceID=esp8266_D608CF,
humidity=42.71, temp=20.71}"
I guess it would make sense to take the deviceID as "Key" to
a Key/Value pair, so I can Group by "deviceID"?
Format 3: "esp8266_D608CF" : "{timestamp=1516549776,
humidity=42.71, temp=20.71}"
Yup, this is the right basic set up for just about anything
you'll want to do.
What I'm still "missing" is an idea how to apply "Mean" to
each "humidity" and "temp", so that as a result I can create
something like
Format 4: "esp8266_D608CF" : "{timestamp=1516549776,
humidity=42.71, temp=20.71, avg_hum=,
avg_temp=}"
Do you just want to calculate the averages and have one record
output summarizing the device/window? Or do you want to keep all
the original records and annotate them with the avg for their
Re: Calculate Average over a Window
Hi Kenn,
thanks again for responding.
Let me try to explain better what I'm looking for. For simplicity reason
let's take a more simple example.
Message 1 "4711" : "{temp=10}" will become "4711" : "{temp=10,
avg_temp=10}"
Message 2 "4711" : "{temp=12}" will become "4711" : "{temp=12,
avg_temp=11}"
Message 3 "4711" : "{temp=14}" will become "4711" : "{temp=14,
avg_temp=12}"
Message 4 "4711" : "{temp=10}" will become "4711" : "{temp=10,
avg_temp=11.5}"
So for each incoming message I would like to append the current windows
"avg_temp" and all this with a sliding window.
So if we would say that the window is 2 seconds and we receive one
message per second, my sample would change to
Message 1 "4711" : "{temp=10}" will become "4711" : "{temp=10,
avg_temp=10}"
Message 2 "4711" : "{temp=12}" will become "4711" : "{temp=12,
avg_temp=11}"
Message 3 "4711" : "{temp=14}" will become "4711" : "{temp=14,
avg_temp=13}"
Message 4 "4711" : "{temp=10}" will become "4711" : "{temp=10,
avg_temp=12}"
Does this explain what I plan?
Thanks again for your help
Patrick
Kenneth Knowles wrote:
On Thu, Jan 25, 2018 at 3:31 AM, Steiner Patrick
mailto:patr...@steiner-buchholz.de>> wrote:
Hi Kenn, all,
you are right, sliding windows seems to be exactly what I need.
Thanks for that pointer.
Where I'm still in need of expert advise is how to structure the
data within my PCollection. From PubSub I do read all data as a
JSON-String
Format 1: "{ humidity=42.71, temp=20.71}"
currently I'm just extending the JSON-String with the deviceID of
the sender and the timestamp
Format 2: "{timestamp=1516549776, deviceID=esp8266_D608CF,
humidity=42.71, temp=20.71}"
I guess it would make sense to take the deviceID as "Key" to a
Key/Value pair, so I can Group by "deviceID"?
Format 3: "esp8266_D608CF" : "{timestamp=1516549776,
humidity=42.71, temp=20.71}"
Yup, this is the right basic set up for just about anything you'll
want to do.
What I'm still "missing" is an idea how to apply "Mean" to each
"humidity" and "temp", so that as a result I can create something like
Format 4: "esp8266_D608CF" : "{timestamp=1516549776,
humidity=42.71, temp=20.71, avg_hum=, avg_temp=}"
Do you just want to calculate the averages and have one record output
summarizing the device/window? Or do you want to keep all the original
records and annotate them with the avg for their window, in other
words basically doing the first calculation and then joining it with
your original stream?
Kenn
Happy to take advise or pointer into the right direction.
Thanks
Patrick
Kenneth Knowles wrote:
Hi Patrick,
It sounds like you want to read about event-time windowing:
https://beam.apache.org/documentation/programming-guide/#windowing
<https://beam.apache.org/documentation/programming-guide/#windowing>
In particular, when you say "the last 5 minutes" I would ask what
the point of reference is. Your needs may be served by fixed or
sliding windows of five minutes. These are included with the SDK,
and documented here:
https://beam.apache.org/documentation/programming-guide/#provided-windowing-functions
<https://beam.apache.org/documentation/programming-guide/#provided-windowing-functions>
Hope that gets you started,
Kenn
On Sun, Jan 21, 2018 at 8:29 AM, Steiner Patrick
mailto:patr...@steiner-buchholz.de>> wrote:
Hi,
I'm in the process of porting work that I have done based on
JBoss Technology ( ActiveMQ, Drools, etc ) to GCloud.
The scenario is a simple IoT example with devices sending
their values via MQTT to PubSub and getting received by
Dataflow for processing.
So far, while learning GCloud features from scratch, I was
able to receive the data and write it to a BigQuery Table.
It's all documented at
https://github.com/PatrickSteiner/Google_Cloud_IoT_Demo
<https://github.com/PatrickSteiner/Google_Cloud_IoT_Demo>
What's working is, that I receive a JSON String ( e.g. {
humidity=42.71, temp=20.71} ) via PubSub and extend
it to {timestamp=1516549776, deviceID=esp8266_D608CF,
humidity=42.71, temp=20.71} via DataFlow.
Where I have no clue is the fol
Re: Calculate Average over a Window
Hi Kenn, all,
you are right, sliding windows seems to be exactly what I need. Thanks
for that pointer.
Where I'm still in need of expert advise is how to structure the data
within my PCollection. From PubSub I do read all data as a JSON-String
Format 1: "{ humidity=42.71, temp=20.71}"
currently I'm just extending the JSON-String with the deviceID of the
sender and the timestamp
Format 2: "{timestamp=1516549776, deviceID=esp8266_D608CF,
humidity=42.71, temp=20.71}"
I guess it would make sense to take the deviceID as "Key" to a Key/Value
pair, so I can Group by "deviceID"?
Format 3: "esp8266_D608CF" : "{timestamp=1516549776, humidity=42.71,
temp=20.71}"
What I'm still "missing" is an idea how to apply "Mean" to each
"humidity" and "temp", so that as a result I can create something like
Format 4: "esp8266_D608CF" : "{timestamp=1516549776, humidity=42.71,
temp=20.71, avg_hum=, avg_temp=}"
Happy to take advise or pointer into the right direction.
Thanks
Patrick
Kenneth Knowles wrote:
Hi Patrick,
It sounds like you want to read about event-time windowing:
https://beam.apache.org/documentation/programming-guide/#windowing
In particular, when you say "the last 5 minutes" I would ask what the
point of reference is. Your needs may be served by fixed or sliding
windows of five minutes. These are included with the SDK, and
documented here:
https://beam.apache.org/documentation/programming-guide/#provided-windowing-functions
Hope that gets you started,
Kenn
On Sun, Jan 21, 2018 at 8:29 AM, Steiner Patrick
mailto:patr...@steiner-buchholz.de>> wrote:
Hi,
I'm in the process of porting work that I have done based on JBoss
Technology ( ActiveMQ, Drools, etc ) to GCloud.
The scenario is a simple IoT example with devices sending their
values via MQTT to PubSub and getting received by Dataflow for
processing.
So far, while learning GCloud features from scratch, I was able to
receive the data and write it to a BigQuery Table. It's all
documented at
https://github.com/PatrickSteiner/Google_Cloud_IoT_Demo
<https://github.com/PatrickSteiner/Google_Cloud_IoT_Demo>
What's working is, that I receive a JSON String ( e.g. {
humidity=42.71, temp=20.71} ) via PubSub and extend it to
{timestamp=1516549776, deviceID=esp8266_D608CF,
humidity=42.71, temp=20.71} via DataFlow.
Where I have no clue is the following: I want to calculate for
every "deviceID" the average value for "humidity" and "temp" for
the last 5 minutes.
Currently my simple Pipeline is
p.apply(PubsubIO.readMessagesWithAttributes().fromTopic(options.getPubSubTopic()))
.apply(ParDo.of(new FormatMessageAsTableRowFn()))
.apply(BigQueryIO.writeTableRows().to(tableSpec.toString())
.withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_APPEND)
.withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_NEVER));
Anyone an advice or pointer to docu how I need to proceed?
Patrick
Calculate Average over a Window
Hi,
I'm in the process of porting work that I have done based on JBoss
Technology ( ActiveMQ, Drools, etc ) to GCloud.
The scenario is a simple IoT example with devices sending their values
via MQTT to PubSub and getting received by Dataflow for processing.
So far, while learning GCloud features from scratch, I was able to
receive the data and write it to a BigQuery Table. It's all documented
at https://github.com/PatrickSteiner/Google_Cloud_IoT_Demo
What's working is, that I receive a JSON String ( e.g. {
humidity=42.71, temp=20.71} ) via PubSub and extend it to
{timestamp=1516549776, deviceID=esp8266_D608CF, humidity=42.71,
temp=20.71} via DataFlow.
Where I have no clue is the following: I want to calculate for every
"deviceID" the average value for "humidity" and "temp" for the last 5
minutes.
Currently my simple Pipeline is
p.apply(PubsubIO.readMessagesWithAttributes().fromTopic(options.getPubSubTopic()))
.apply(ParDo.of(new FormatMessageAsTableRowFn()))
.apply(BigQueryIO.writeTableRows().to(tableSpec.toString())
.withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_APPEND)
.withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_NEVER));
Anyone an advice or pointer to docu how I need to proceed?
Patrick
