I actually provided them in submit command here:
nohup ./bin/spark-submit --master spark://ES01:7077 --executor-memory 4G
--num-executors 1 --total-executor-cores 1 --conf
"spark.storage.memoryFraction=0.2" ./mycode.py1>a.log 2>b.log &
At 2016-05-10 21:19:06, "Mich Talebzadeh" <mich.talebza...@gmail.com> wrote:
Hi Mingwei,
In your Spark conf setting what are you providing for these parameters. Are you
capping them?
For example
val conf = new SparkConf().
setAppName("AppName").
setMaster("local[2]").
set("spark.executor.memory", "4G").
set("spark.cores.max", "2").
set("spark.driver.allowMultipleContexts", "true")
val sc = new SparkContext(conf)
I assume you are running in standalone mode so each worker/aka slave grabs all
the available cores and allocates the remaining memory on each host. Do not
provide these in
Do not provide new values for these parameter meaning overwrite them in
${SPARK_HOME}/bin/spark-submit --
HTH
Dr Mich Talebzadeh
LinkedIn
https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
http://talebzadehmich.wordpress.com
On 10 May 2016 at 03:12, 李明伟 <kramer2...@126.com> wrote:
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.talebza...@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
http://talebzadehmich.wordpress.com
On 9 May 2016 at 16:19, 李明伟 <kramer2...@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.s...@gmail.com> wrote:
Pease see the inline comments.
On Mon, May 9, 2016 at 5:31 PM, Ashok Kumar <ashok34...@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.s...@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 <ashok34...@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.s...@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 <kramer2...@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.s...@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.talebza...@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, kramer2...@126.com<kramer2...@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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