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From 李明伟 <kramer2...@126.com>
Subject Re:Re: Re: Re: How big the spark stream window could be ?
Date Tue, 10 May 2016 02:12:03 GMT
Hi Mich


I added some more infor (the spark-env.sh setting and top command output in that thread.)
Can you help to check pleas?


Regards
Mingwei






At 2016-05-09 23:45:19, "Mich Talebzadeh" <mich.talebzadeh@gmail.com> wrote:

I had a look at the thread.


This is what you have which I gather a standalone box in other words one worker node


bin/spark-submit   --master spark://ES01:7077 --executor-memory 4G --num-executors 1 --total-executor-cores
1 ./latest5min.py 1>a.log 2>b.log


But what I don't understand why is using 80% of your RAM as opposed to 25% of it (4GB/16GB)
right?


Where else have you set up these parameters for example in $SPARK_HOME/con/spark-env.sh?


Can you send the output of /usr/bin/free and top


HTH



Dr Mich Talebzadeh

 

LinkedIn  https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw

 

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On 9 May 2016 at 16:19, 李明伟 <kramer2009@126.com> wrote:

Thanks for all the information guys. 


I wrote some code to do the test. Not using window. So only calculating data for each batch
interval. I set the interval to 30 seconds also reduce the size of data to about 30 000 lines
of csv.
Means my code should calculation on 30 000 lines of CSV in 30 seconds. I think it is not a
very heavy workload. But my spark stream code still crash.


I send another post to the user list here http://apache-spark-user-list.1001560.n3.nabble.com/Why-I-have-memory-leaking-for-such-simple-spark-stream-code-td26904.html

Is it possible for you to have a look please? Very appreciate.






At 2016-05-09 17:49:22, "Saisai Shao" <sai.sai.shao@gmail.com> wrote:

Pease see the inline comments.




On Mon, May 9, 2016 at 5:31 PM, Ashok Kumar <ashok34668@yahoo.com> wrote:

Thank you.


So If I create spark streaming then


The streams will always need to be cached? It cannot be stored in persistent storage
You don't need to cache the stream explicitly if you don't have specific requirement, Spark
will do it for you depends on different streaming sources (Kafka or socket).
The stream data cached will be distributed among all nodes of Spark among executors
As I understand each Spark worker node has one executor that includes cache. So the streaming
data is distributed among these work node caches. For example if I have 4 worker nodes each
cache will have a quarter of data (this assumes that cache size among worker nodes is the
same.)
Ideally, it will distributed evenly across the executors, also this is target for tuning.
Normally it depends on several conditions like receiver distribution, partition distribution.
 


The issue raises if the amount of streaming data does not fit into these 4 caches? Will the
job crash?



On Monday, 9 May 2016, 10:16, Saisai Shao <sai.sai.shao@gmail.com> wrote:




No, each executor only stores part of data in memory (it depends on how the partition are
distributed and how many receivers you have). 


For WindowedDStream, it will obviously cache the data in memory, from my understanding you
don't need to call cache() again.


On Mon, May 9, 2016 at 5:06 PM, Ashok Kumar <ashok34668@yahoo.com> wrote:

hi,


so if i have 10gb of streaming data coming in does it require 10gb of memory in each node?


also in that case why do we need using


dstream.cache()


thanks



On Monday, 9 May 2016, 9:58, Saisai Shao <sai.sai.shao@gmail.com> wrote:




It depends on you to write the Spark application, normally if data is already on the persistent
storage, there's no need to be put into memory. The reason why Spark Streaming has to be stored
in memory is that streaming source is not persistent source, so you need to have a place to
store the data.


On Mon, May 9, 2016 at 4:43 PM, 李明伟 <kramer2009@126.com> wrote:

Thanks.
What if I use batch calculation instead of stream computing? Do I still need that much memory?
For example, if the 24 hour data set is 100 GB. Do I also need a 100GB RAM to do the one time
batch calculation ?






At 2016-05-09 15:14:47, "Saisai Shao" <sai.sai.shao@gmail.com> wrote:

For window related operators, Spark Streaming will cache the data into memory within this
window, in your case your window size is up to 24 hours, which means data has to be in Executor's
memory for more than 1 day, this may introduce several problems when memory is not enough.


On Mon, May 9, 2016 at 3:01 PM, Mich Talebzadeh <mich.talebzadeh@gmail.com> wrote:

ok terms for Spark Streaming


"Batch interval" is the basic interval at which the system with receive the data in batches.
This is the interval set when creating a StreamingContext. For example, if you set the batch
interval as 300 seconds, then any input DStream will generate RDDs of received data at 300
seconds intervals.
A window operator is defined by two parameters -
- WindowDuration / WindowsLength - the length of the window
- SlideDuration / SlidingInterval - the interval at which the window will slide or move forward




Ok so your batch interval is 5 minutes. That is the rate messages are coming in from the source.


Then you have these two params


// window length - The duration of the window below that must be multiple of batch interval
n in = > StreamingContext(sparkConf, Seconds(n))
val windowLength = x =  m * n
// sliding interval - The interval at which the window operation is performed in other words
data is collected within this "previous interval'
val slidingInterval =  y l x/y = even number


Both the window length and the slidingInterval duration must be multiples of the batch interval,
as received data is divided into batches of duration "batch interval".


If you want to collect 1 hour data then windowLength =  12 * 5 * 60 seconds
If you want to collect 24 hour data then windowLength =  24 * 12 * 5 * 60


You sliding window should be set to batch interval = 5 * 60 seconds. In other words that where
the aggregates and summaries come for your report.


What is your data source here?


HTH




Dr Mich Talebzadeh
 
LinkedIn  https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
 
http://talebzadehmich.wordpress.com
 


On 9 May 2016 at 04:19, kramer2009@126.com<kramer2009@126.com> wrote:
We have some stream data need to be calculated and considering use spark
stream to do it.

We need to generate three kinds of reports. The reports are based on

1. The last 5 minutes data
2. The last 1 hour data
3. The last 24 hour data

The frequency of reports is 5 minutes.

After reading the docs, the most obvious way to solve this seems to set up a
spark stream with 5 minutes interval and two window which are 1 hour and 1
day.


But I am worrying that if the window is too big for one day and one hour. I
do not have much experience on spark stream, so what is the window length in
your environment?

Any official docs talking about this?




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