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From 李明伟 <>
Subject Re:Re: Re: Re: Re: Re: How big the spark stream window could be ?
Date Wed, 11 May 2016 02:01:53 GMT
Hi Mich

From the ps command. I can find four process. 10409 is the master and 10603 is the worker.
12420 is the driver program and 12578 should be the executor (worker). Am I right? 
So you mean the 12420 is actually running both the driver and the worker role?

[root@ES01 ~]# ps -awx | grep spark | grep java
10409 ?        Sl     1:40 java -cp /opt/spark-1.6.0-bin-hadoop2.6/conf/:/opt/spark-1.6.0-bin-hadoop2.6/lib/spark-assembly-1.6.0-hadoop2.6.0.jar:/opt/spark-1.6.0-bin-hadoop2.6/lib/datanucleus-api-jdo-3.2.6.jar:/opt/spark-1.6.0-bin-hadoop2.6/lib/datanucleus-rdbms-3.2.9.jar:/opt/spark-1.6.0-bin-hadoop2.6/lib/datanucleus-core-3.2.10.jar:/opt/hadoop-2.6.2/etc/hadoop/
-Xms4G -Xmx4G -XX:MaxPermSize=256m org.apache.spark.deploy.master.Master --ip ES01 --port
7077 --webui-port 8080
10603 ?        Sl     6:00 java -cp /opt/spark-1.6.0-bin-hadoop2.6/conf/:/opt/spark-1.6.0-bin-hadoop2.6/lib/spark-assembly-1.6.0-hadoop2.6.0.jar:/opt/spark-1.6.0-bin-hadoop2.6/lib/datanucleus-api-jdo-3.2.6.jar:/opt/spark-1.6.0-bin-hadoop2.6/lib/datanucleus-rdbms-3.2.9.jar:/opt/spark-1.6.0-bin-hadoop2.6/lib/datanucleus-core-3.2.10.jar:/opt/hadoop-2.6.2/etc/hadoop/
-Xms4G -Xmx4G -XX:MaxPermSize=256m org.apache.spark.deploy.worker.Worker --webui-port 8081
12420 ?        Sl     6:34 java -cp /opt/spark-1.6.0-bin-hadoop2.6/conf/:/opt/spark-1.6.0-bin-hadoop2.6/lib/spark-assembly-1.6.0-hadoop2.6.0.jar:/opt/spark-1.6.0-bin-hadoop2.6/lib/datanucleus-api-jdo-3.2.6.jar:/opt/spark-1.6.0-bin-hadoop2.6/lib/datanucleus-rdbms-3.2.9.jar:/opt/spark-1.6.0-bin-hadoop2.6/lib/datanucleus-core-3.2.10.jar:/opt/hadoop-2.6.2/etc/hadoop/
-Xms1g -Xmx1g -XX:MaxPermSize=256m org.apache.spark.deploy.SparkSubmit --master spark://ES01:7077
--conf --executor-memory 4G --num-executors 1 --total-executor-cores
1 /opt/flowSpark/sparkStream/
12578 ?        Sl    13:16 java -cp /opt/spark-1.6.0-bin-hadoop2.6/conf/:/opt/spark-1.6.0-bin-hadoop2.6/lib/spark-assembly-1.6.0-hadoop2.6.0.jar:/opt/spark-1.6.0-bin-hadoop2.6/lib/datanucleus-api-jdo-3.2.6.jar:/opt/spark-1.6.0-bin-hadoop2.6/lib/datanucleus-rdbms-3.2.9.jar:/opt/spark-1.6.0-bin-hadoop2.6/lib/datanucleus-core-3.2.10.jar:/opt/hadoop-2.6.2/etc/hadoop/
-Xms4096M -Xmx4096M -Dspark.driver.port=52931 -XX:MaxPermSize=256m org.apache.spark.executor.CoarseGrainedExecutorBackend
--driver-url spark://CoarseGrainedScheduler@ --executor-id 0 --hostname --cores 1 --app-id app-20160511080701-0013 --worker-url spark://Worker@

At 2016-05-11 09:03:21, "Mich Talebzadeh" <> wrote:


This is a standalone mode.

When you are running Spark in Standalone mode, you only have one worker that lives within
the driver JVM process that you start when you start spark-shell or spark-submit.

However, since driver-memory setting encapsulates the JVM, you will need to set the amount
of driver memory for any non-default value before starting JVM by providing the new value:

${SPARK_HOME}/bin/spark-submit --driver-memory 5g


Dr Mich Talebzadeh




On 11 May 2016 at 01:22, 李明伟 <> wrote:

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 ""  ./mycode.py1>a.log
2>b.log &

At 2016-05-10 21:19:06, "Mich Talebzadeh" <> 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().
               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  --


Dr Mich Talebzadeh




On 10 May 2016 at 03:12, 李明伟 <> wrote:

Hi Mich

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


At 2016-05-09 23:45:19, "Mich Talebzadeh" <> 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 ./ 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)

Where else have you set up these parameters for example in $SPARK_HOME/con/

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


Dr Mich Talebzadeh




On 9 May 2016 at 16:19, 李明伟 <> 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

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

At 2016-05-09 17:49:22, "Saisai Shao" <> wrote:

Pease see the inline comments.

On Mon, May 9, 2016 at 5:31 PM, Ashok Kumar <> 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
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 <> 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 <> wrote:


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



On Monday, 9 May 2016, 9:58, Saisai Shao <> 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 <> wrote:

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" <> 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 <> 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?


Dr Mich Talebzadeh

On 9 May 2016 at 04:19,<> 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

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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