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From Jörn Franke <>
Subject Re: Silly question about Yarn client vs Yarn cluster modes...
Date Wed, 22 Jun 2016 05:46:47 GMT
I would import data via sqoop and put it on HDFS. It has some mechanisms to handle the lack
of reliability by jdbc. 

Then you can process the data via Spark. You could also use jdbc rdd but I do not recommend
to use it, because you do not want to pull data all the time out of the database when you
execute your application. Furthermore, you have to handle connection interruptions, the multiple
serialization/deserialization efforts, if one executor crashes you have to repull some or
all of the data from the database etc

Within the cluster it does not make sense to me to pull data via jdbc from hive. All the benefits
such as data locality, reliability etc would be gone.

Hive supports different execution engines (TEZ, Spark), formats (Orc, parquet) and further
optimizations to make the analysis fast. It always depends on your use case.

> On 22 Jun 2016, at 05:47, Michael Segel <> wrote:
> Sorry, I think you misunderstood. 
> Spark can read from JDBC sources so to say using beeline as a way to access data is not
a spark application isn’t really true.  Would you say the same if you were pulling data
in to spark from Oracle or DB2? 
> There are a couple of different design patterns and use cases where data could be stored
in Hive yet your only access method is via a JDBC or Thift/Rest service.  Think also of compute
/ storage cluster implementations. 
> WRT to #2, not exactly what I meant, by exposing the data… and there are limitations
to the thift service…
>> On Jun 21, 2016, at 5:44 PM, ayan guha <> wrote:
>> 1. Yes, in the sense you control number of executors from spark application config.

>> 2. Any IO will be done from executors (never ever on driver, unless you explicitly
call collect()). For example, connection to a DB happens one for each worker (and used by
local executors). Also, if you run a reduceByKey job and write to hdfs, you will find a bunch
of files were written from various executors. What happens when you want to expose the data
to world: Spark Thrift Server (STS), which is a long running spark application (ie spark context)
which can serve data from RDDs. 
>> Suppose I have a data source… like a couple of hive tables and I access the tables
via beeline. (JDBC)  -  
>> This is NOT a spark application, and there is no RDD created. Beeline is just a jdbc
client tool. You use beeline to connect to HS2 or STS. 
>> In this case… Hive generates a map/reduce job and then would stream the result
set back to the client node where the RDD result set would be built.  -- 
>> This is never true. When you connect Hive from spark, spark actually reads hive metastore
and streams data directly from HDFS. Hive MR jobs do not play any role here, making spark
faster than hive. 
>> HTH....
>> Ayan
>>> On Wed, Jun 22, 2016 at 9:58 AM, Michael Segel <>
>>> Ok, its at the end of the day and I’m trying to make sure I understand the
locale of where things are running.
>>> I have an application where I have to query a bunch of sources, creating some
RDDs and then I need to join off the RDDs and some other lookup tables.
>>> Yarn has two modes… client and cluster.
>>> I get it that in cluster mode… everything is running on the cluster.
>>> But in client mode, the driver is running on the edge node while the workers
are running on the cluster.
>>> When I run a sparkSQL command that generates a new RDD, does the result set live
on the cluster with the workers, and gets referenced by the driver, or does the result set
get migrated to the driver running on the client? (I’m pretty sure I know the answer, but
its never safe to assume anything…)
>>> The follow up questions:
>>> 1) If I kill the  app running the driver on the edge node… will that cause
YARN to free up the cluster’s resources? (In cluster mode… that doesn’t happen) What
happens and how quickly?
>>> 1a) If using the client mode… can I spin up and spin down the number of executors
on the cluster? (Assuming that when I kill an executor any portion of the RDDs associated
with that executor are gone, however the spark context is still alive on the edge node? [again
assuming that the spark context lives with the driver.])
>>> 2) Any I/O between my spark job and the outside world… (e.g. walking through
the data set and writing out a data set to a file) will occur on the edge node where the driver
is located?  (This may seem kinda silly, but what happens when you want to expose the result
set to the world… ? )
>>> Now for something slightly different…
>>> Suppose I have a data source… like a couple of hive tables and I access the
tables via beeline. (JDBC)  In this case… Hive generates a map/reduce job and then would
stream the result set back to the client node where the RDD result set would be built.  I
realize that I could run Hive on top of spark, but that’s a separate issue. Here the RDD
will reside on the client only.  (That is I could in theory run this as a single spark instance.)
>>> If I were to run this on the cluster… then the result set would stream thru
the beeline gate way and would reside back on the cluster sitting in RDDs within each executor?
>>> I realize that these are silly questions but I need to make sure that I know
the flow of the data and where it ultimately resides.  There really is a method to my madness,
and if I could explain it… these questions really would make sense. ;-)
>>> TIA,
>>> -Mike
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>> -- 
>> Best Regards,
>> Ayan Guha

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