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From Mike Hynes <91m...@gmail.com>
Subject Re: Stages with non-arithmetic numbering & Timing metrics in event logs
Date Wed, 10 Jun 2015 18:41:44 GMT
Hi Imran,

Thank you again for your email.

I just want to ask one further question to clarify the implementation
of the shuffle block fetches. When you say that rather than sitting
idle, [the executor] will immediately start reading the local block, I
would guess that, in implementation, the executor is going to launch
concurrent threads to read both local and remote blocks, which it
seems to do in the initialize() method of
core/.../storage/ShuffleBlockFetcherIterator.scala. Is that the case
or would the Executor run all local fetch threads first?

The reason I ask is that if the slave machine on which the Executor is
running has some number of cores, c, then I  would have thought that
some of the threads launched would occupy some number, c_1, of the
cores and conduct the local reads (where c_1 <= c). The other threads
would occupy the other (c - c_1) cores' cycles until *all* necessary
blocks have been read, and depending on c and the number of blocks to
fetch so that none of the cores are idle if there are many blocks to
fetch. (I monitor the CPU utilization of our nodes throughout a job,
and generally find them under-utilized statistically speaking; that
is, their usage over the whole job is lower than expected, with short
burst of high usage, so I ask this question in a specific way for this
reason, since I can see trends in the probability density functions of
CPU utilization as the #partitions of our RDDs are increased).

ShuffleBlockFetcherIterator.scala:

  private[this] def initialize(): Unit = {
		...
    // Send out initial requests for blocks, up to our maxBytesInFlight
    while (fetchRequests.nonEmpty &&
      (bytesInFlight == 0 || bytesInFlight + fetchRequests.front.size
<= maxBytesInFlight)) {
      sendRequest(fetchRequests.dequeue())
    }
    val numFetches = remoteRequests.size - fetchRequests.size
    logInfo("Started " + numFetches + " remote fetches in" +
Utils.getUsedTimeMs(startTime))

    // Get Local Blocks
    fetchLocalBlocks()
    logDebug("Got local blocks in " + Utils.getUsedTimeMs(startTime))
  }
  private[this] def fetchLocalBlocks() {
    val iter = localBlocks.iterator
    while (iter.hasNext) {
      val blockId = iter.next()
      try {
        val buf = blockManager.getBlockData(blockId)
        shuffleMetrics.incLocalBlocksFetched(1)
        shuffleMetrics.incLocalBytesRead(buf.size)
        buf.retain()
        results.put(new SuccessFetchResult(blockId, 0, buf))
      } catch {
				...
      }
    }
  }

Obviously, I will have to sit down with core/.../network/nio/* and
core/.../shuffle/* and do my own homework on this, but from what I can
tell, the BlockDataManager relies on either
NioBlockTransferService.scala or the NettyBlockTransferService.scala
(which are set in SparkEnv.scala), both of which do the grunt work of
actually buffering and transferring the blocks' bytes. Finally, the
tasks in new stage for which the shuffle outputs have been fetched
will not commence until all of the block fetching threads (both local
and remote) have terminated.

Does the above paint an accurate picture? I would really appreciate
clarification on the concurrency, since I would like to determine why
our jobs have under-utilization and poor weak scaling efficiency.

I will cc this thread over to the dev list. I did not cc them in case
my previous question was trivial---I didn't want to spam the list
unnecessarily, since I do not see these kinds of questions posed there
frequently.

Thanks a bunch,
Mike


On 6/10/15, Imran Rashid <irashid@cloudera.com> wrote:
> Hi Mike,
>
> no, this is a good question, I can see how my response could be interpreted
> both ways.
>
> To be more precise:
> *nothing* is fetched until the shuffle-read stage starts.  So it is normal
> to see a spike in cluster bandwidth when that stage starts.  There is a
> hard-boundary between stages -- that is, spark never starts any tasks in
> one stage until *all* tasks in the dependent stages have been completed.
> (There has been on-and-off discussion about relaxing this, but IMO this is
> unlikely to change in the near future.)  So spark will wait for all of the
> tasks in the previous shuffle-write stage to finish, and then kick off a
> bunch of shuffle-read tasks in the next stage, leading to the spike you
> see.
>
> I was referring to the way blocks are fetched within one of those
> shuffle-read tasks.  One of those tasks is will probably going to need a
> bunch of different blocks, from many executors.  But some of the blocks it
> needs will probably exist locally.  So the task first sends out a request
> to fetch blocks remotely (leading to the spike), but rather than sitting
> idle, it will immediately start reading the local blocks.  Ideally, by the
> time its done reading the local blocks, some of the remote blocks have
> already been fetched, so no time is spent *waiting* for the remote reads.
> As the remote blocks get read, spark sends out more requests, trying to
> balance how much data needs to be buffered vs. preventing any waiting on
> remote reads (which can  be controlled by spark.reducer.maxSizeInFlight).
>
> Hope that clarifies things!
>
> btw, you sent this last question to just me -- I think its a good question,
> do you mind sending it to the list?  I figured that was accidental but
> wanted to check.
>
> Imran
>
> On Wed, Jun 10, 2015 at 12:20 AM, Mike Hynes <91mbbh@gmail.com> wrote:
>
>> Hi Imran,
>> One additional quick question---I just want to confirm that I fully
>> understand your comment that "blocks are fetched before they are
>> needed." Typically on our system, we see spikes in cluster bandwidth
>> (with ganglia) at stage boundaries, so I previously assumed that all
>> shuffle read occurred there. Do you mean that the blocks are fetched
>> by the shuffle read iterator, and hence when tasks occur afterwards
>> the necessary blocks have already been fetched?
>> Thanks---I am sorry if this is an obvious question, but I'd like to
>> understand this as precisely as possible.
>> Mike
>>
>> On 6/10/15, Mike Hynes <91mbbh@gmail.com> wrote:
>> > Ahhh---forgive my typo: what I mean is,
>> > (t2 - t1) >= (t_ser + t_deser + t_exec)
>> > is satisfied, empirically.
>> >
>> > On 6/10/15, Mike Hynes <91mbbh@gmail.com> wrote:
>> >> Hi Imran,
>> >>
>> >> Thank you for your email.
>> >>
>> >> In examing the condition (t2 - t1) < (t_ser + t_deser + t_exec), I
>> >> have found it to be true, although I have not included the
>> >> t_{wait_for_read} in this, since it is---so far as I can tell---been
>> >> either zero or negligible compared to the task time.
>> >>
>> >> Thanks,
>> >> Mike
>> >>
>> >> On 6/8/15, Imran Rashid <irashid@cloudera.com> wrote:
>> >>> Hi Mike,
>> >>>
>> >>> all good questions, let me take a stab at answering them:
>> >>>
>> >>> 1. Event Logs + Stages:
>> >>>
>> >>> Its normal for stages to get skipped if they are shuffle map stages,
>> >>> which
>> >>> get read multiple times.  Eg., here's a little example program I
>> >>> wrote
>> >>> earlier to demonstrate this: "d3" doesn't need to be re-shuffled
>> >>> since
>> >>> each
>> >>> time its read w/ the same partitioner.  So skipping stages in this
>> >>> way
>> >>> is
>> >>> a
>> >>> good thing:
>> >>>
>> >>> val partitioner = new org.apache.spark.HashPartitioner(10)
>> >>> val d3 = sc.parallelize(1 to 100).map { x => (x % 10) ->
>> >>> x}.partitionBy(partitioner)
>> >>> (0 until 5).foreach { idx =>
>> >>>   val otherData = sc.parallelize(1 to (idx * 100)).map{ x => (x %
10)
>> ->
>> >>> x}.partitionBy(partitioner)
>> >>>   println(idx + " ---> " + otherData.join(d3).count())
>> >>> }
>> >>>
>> >>> If you run this, f you look in the UI you'd see that all jobs except
>> for
>> >>> the first one have one stage that is skipped.  You will also see this
>> in
>> >>> the log:
>> >>>
>> >>> 15/06/08 10:52:37 INFO DAGScheduler: Parents of final stage:
>> >>> List(Stage
>> >>> 12,
>> >>> Stage 13)
>> >>>
>> >>> 15/06/08 10:52:37 INFO DAGScheduler: Missing parents: List(Stage 13)
>> >>>
>> >>> Admittedly that is not very clear, but that is sort of indicating to
>> you
>> >>> that the DAGScheduler first created stage 12 as a necessary step, and
>> >>> then
>> >>> later on changed its mind by realizing that everything it needed for
>> >>> stage
>> >>> 12 already existed, so there was nothing to do.
>> >>>
>> >>>
>> >>> 2. Extracting Event Log Information
>> >>>
>> >>> maybe you are interested in SparkListener ? Though unfortunately, I
>> >>> don't
>> >>> know of a good blog post describing it, hopefully the docs are clear
>> ...
>> >>>
>> >>> 3. Time Metrics in Spark Event Log
>> >>>
>> >>> This is a great question.  I *think* the only exception is that t_gc
>> >>> is
>> >>> really overlapped with t_exec.  So I think you should really expect
>> >>>
>> >>> (t2 - t1) < (t_ser + t_deser + t_exec)
>> >>>
>> >>> I am not 100% sure about this, though.  I'd be curious if that was
>> >>> constraint was ever violated.
>> >>>
>> >>>
>> >>> As for your question on shuffle read vs. shuffle write time -- I
>> >>> wouldn't
>> >>> necessarily expect the same stage to have times for both shuffle read
>> >>> &
>> >>> shuffle write -- in the simplest case, you'll have shuffle write
>> >>> times
>> >>> in
>> >>> one, and shuffle read times in the next one.  But even taking that
>> >>> into
>> >>> account, there is a difference in the way they work & are measured.
>> >>>  shuffle read operations are pipelined and the way we measure shuffle
>> >>> read,
>> >>> its just how much time is spent *waiting* for network transfer.  It
>> >>> could
>> >>> be that there is no (measurable) wait time b/c the next blocks are
>> >>> fetched
>> >>> before they are needed.  Shuffle writes occur in the normal task
>> >>> execution
>> >>> thread, though, so we (try to) measure all of it.
>> >>>
>> >>>
>> >>> On Sun, Jun 7, 2015 at 11:12 PM, Mike Hynes <91mbbh@gmail.com>
wrote:
>> >>>
>> >>>> Hi Patrick and Akhil,
>> >>>>
>> >>>> Thank you both for your responses. This is a bit of an extended
>> >>>> email,
>> >>>> but I'd like to:
>> >>>> 1. Answer your (Patrick) note about the "missing" stages since the
>> >>>> IDs
>> >>>> do (briefly) appear in the event logs
>> >>>> 2. Ask for advice/experience with extracting information from the
>> >>>> event logs in a columnar, delimiter-separated format.
>> >>>> 3. Ask about the time metrics reported in the event logs; currently,
>> >>>> the elapsed time for a task does not equal the sum of the times
for
>> >>>> its components
>> >>>>
>> >>>> 1. Event Logs + Stages:
>> >>>> =========================
>> >>>>
>> >>>> As I said before, In the spark logs (the log4j configurable ones
>> >>>> from
>> >>>> the driver), I only see references to some stages, where the stage
>> >>>> IDs
>> >>>> are not arithmetically increasing. In the event logs, however, I
>> >>>> will
>> >>>> see reference to *every* stage, although not all stages will have
>> >>>> tasks associated with them.
>> >>>>
>> >>>> For instance, to examine the actual stages that have tasks, you
can
>> >>>> see missing stages:
>> >>>> # grep -E '"Event":"SparkListenerTaskEnd"' app.log \
>> >>>> #               | grep -Eo '"Stage ID":[[:digit:]]+'  \
>> >>>> #               | sort -n|uniq | head -n 5
>> >>>> "Stage ID":0
>> >>>> "Stage ID":1
>> >>>> "Stage ID":10
>> >>>> "Stage ID":11
>> >>>> "Stage ID":110
>> >>>>
>> >>>> However, these "missing" stages *do* appear in the event logs as
>> >>>> Stage
>> >>>> IDs in the jobs submitted, i.e: for
>> >>>> # grep -E '"Event":"SparkListenerJobStart"' app.log | grep -Eo
>> >>>> 'Stage
>> >>>> IDs":\[.*\]' | head -n 5
>> >>>> Stage IDs":[0,1,2]
>> >>>> Stage IDs":[5,3,4]
>> >>>> Stage IDs":[6,7,8]
>> >>>> Stage IDs":[9,10,11]
>> >>>> Stage IDs":[12,13,14]
>> >>>>
>> >>>> I do not know if this amounts to a bug, since I am not familiar
with
>> >>>> the scheduler in detail. The stages have seemingly been created
>> >>>> somewhere in the DAG, but then have no associated tasks and never
>> >>>> appear again.
>> >>>>
>> >>>> 2. Extracting Event Log Information
>> >>>> ====================================
>> >>>> Currently we are running scalability tests, and are finding very
>> >>>> poor
>> >>>> scalability for certain block matrix algorithms. I would like to
>> >>>> have
>> >>>> finer detail about the communication time and bandwidth when data
is
>> >>>> transferred between nodes.
>> >>>>
>> >>>> I would really just like to have a file with nothing but task info
>> >>>> in
>> >>>> a format such as:
>> >>>> timestamp (ms), task ID, hostname, execution time (ms), GC time
>> >>>> (ms),
>> >>>> ...
>> >>>> 0010294, 1, slave-1, 503, 34, ...
>> >>>> 0010392, 2, slave-2, 543, 32, ...
>> >>>> and similarly for jobs/stages/rdd_memory/shuffle output/etc.
>> >>>>
>> >>>> I have extracted the relevant time fields from the spark event logs
>> >>>> with a sed script, but I wonder if there is an even more expedient
>> >>>> way. Unfortunately, I do not immediately see how to do this using
>> >>>> the
>> >>>> $SPARK_HOME/conf/metrics.properties file and haven't come across
a
>> >>>> blog/etc that describes this. Could anyone please comment on whether
>> >>>> or not a metrics configuation for this already exists?
>> >>>>
>> >>>> 3. Time Metrics in Spark Event Log
>> >>>> ==================================
>> >>>> I am confused about the times reported for tasks in the event log.
>> >>>> There are launch and finish timestamps given for each task (call
>> >>>> them
>> >>>> t1 and t2, respectively), as well as GC time (t_gc), execution time
>> >>>> (t_exec), and serialization times (t_ser, t_deser). However the
>> >>>> times
>> >>>> do not add up as I would have expected. I would imagine that the
>> >>>> elapsed time t2 - t1 would be slightly larger than the sum of the
>> >>>> component times. However, I can find many instances in the event
>> >>>> logs
>> >>>> where:
>> >>>> (t2 - t1) < (t_gc + t_ser + t_deser + t_exec)
>> >>>> The difference can be 500 ms or more, which is not negligible for
my
>> >>>> current execution times of ~5000 ms. I have attached a plot that
>> >>>> illustrates this.
>> >>>>
>> >>>> Regarding this, I'd like to ask:
>> >>>> 1. How exactly are these times are being measured?
>> >>>> 2. Should the sum of the component times equal the elapsed (clock)
>> >>>> time for the task?
>> >>>> 3. If not, which component(s) is(are) being excluded, and when do
>> >>>> they
>> >>>> occur?
>> >>>> 4. There are occasionally reported measurements for Shuffle Write
>> >>>> time, but not shuffle read time. Is there a method to determine
the
>> >>>> time required to shuffle data? Could this be done by look at delays
>> >>>> between the first task in a new stage and the last task in the
>> >>>> previous stage?
>> >>>>
>> >>>> Thank you very much for your time,
>> >>>> Mike
>> >>>>
>> >>>>
>> >>>> On 6/7/15, Patrick Wendell <pwendell@gmail.com> wrote:
>> >>>> > Hey Mike,
>> >>>> >
>> >>>> > Stage ID's are not guaranteed to be sequential because of the
way
>> the
>> >>>> > DAG scheduler works (only increasing). In some cases stage
ID
>> numbers
>> >>>> > are skipped when stages are generated.
>> >>>> >
>> >>>> > Any stage/ID that appears in the Spark UI is an actual stage,
so
>> >>>> > if
>> >>>> > you see ID's in there, but they are not in the logs, then let
us
>> know
>> >>>> > (that would be a bug).
>> >>>> >
>> >>>> > - Patrick
>> >>>> >
>> >>>> > On Sun, Jun 7, 2015 at 9:06 AM, Akhil Das
>> >>>> > <akhil@sigmoidanalytics.com>
>> >>>> > wrote:
>> >>>> >> Are you seeing the same behavior on the driver UI? (that
running
>> >>>> >> on
>> >>>> >> port
>> >>>> >> 4040), If you click on the stage id header you can sort
the
>> >>>> >> stages
>> >>>> >> based
>> >>>> >> on
>> >>>> >> IDs.
>> >>>> >>
>> >>>> >> Thanks
>> >>>> >> Best Regards
>> >>>> >>
>> >>>> >> On Fri, Jun 5, 2015 at 10:21 PM, Mike Hynes <91mbbh@gmail.com>
>> >>>> >> wrote:
>> >>>> >>>
>> >>>> >>> Hi folks,
>> >>>> >>>
>> >>>> >>> When I look at the output logs for an iterative Spark
program, I
>> >>>> >>> see
>> >>>> >>> that the stage IDs are not arithmetically numbered---that
is,
>> there
>> >>>> >>> are gaps between stages and I might find log information
about
>> >>>> >>> Stage
>> >>>> >>> 0, 1,2, 5, but not 3 or 4.
>> >>>> >>>
>> >>>> >>> As an example, the output from the Spark logs below
shows what I
>> >>>> >>> mean:
>> >>>> >>>
>> >>>> >>> # grep -rE "Stage [[:digit:]]+" spark_stderr  | grep
finished
>> >>>> >>> 12048:INFO:DAGScheduler:Stage 0 (mapPartitions at
>> >>>> >>> blockMap.scala:1444)
>> >>>> >>> finished in 7.820 s:
>> >>>> >>> 15994:INFO:DAGScheduler:Stage 1 (map at blockMap.scala:1810)
>> >>>> >>> finished
>> >>>> >>> in 3.874 s:
>> >>>> >>> 18291:INFO:DAGScheduler:Stage 2 (count at blockMap.scala:1179)
>> >>>> >>> finished in 2.237 s:
>> >>>> >>> 20121:INFO:DAGScheduler:Stage 4 (map at blockMap.scala:1817)
>> >>>> >>> finished
>> >>>> >>> in 1.749 s:
>> >>>> >>> 21254:INFO:DAGScheduler:Stage 5 (count at blockMap.scala:1180)
>> >>>> >>> finished in 1.082 s:
>> >>>> >>> 23422:INFO:DAGScheduler:Stage 7 (map at blockMap.scala:1810)
>> >>>> >>> finished
>> >>>> >>> in 2.078 s:
>> >>>> >>> 24773:INFO:DAGScheduler:Stage 8 (count at blockMap.scala:1188)
>> >>>> >>> finished in 1.317 s:
>> >>>> >>> 26455:INFO:DAGScheduler:Stage 10 (map at blockMap.scala:1817)
>> >>>> >>> finished
>> >>>> >>> in 1.638 s:
>> >>>> >>> 27228:INFO:DAGScheduler:Stage 11 (count at blockMap.scala:1189)
>> >>>> >>> finished in 0.732 s:
>> >>>> >>> 27494:INFO:DAGScheduler:Stage 14 (foreach at
>> >>>> >>> blockMap.scala:1302)
>> >>>> >>> finished in 0.192 s:
>> >>>> >>> 27709:INFO:DAGScheduler:Stage 17 (foreach at
>> >>>> >>> blockMap.scala:1302)
>> >>>> >>> finished in 0.170 s:
>> >>>> >>> 28018:INFO:DAGScheduler:Stage 20 (count at blockMap.scala:1201)
>> >>>> >>> finished in 0.270 s:
>> >>>> >>> 28611:INFO:DAGScheduler:Stage 23 (map at blockMap.scala:1355)
>> >>>> >>> finished
>> >>>> >>> in 0.455 s:
>> >>>> >>> 29598:INFO:DAGScheduler:Stage 24 (count at blockMap.scala:274)
>> >>>> >>> finished in 0.928 s:
>> >>>> >>> 29954:INFO:DAGScheduler:Stage 27 (map at blockMap.scala:1355)
>> >>>> >>> finished
>> >>>> >>> in 0.305 s:
>> >>>> >>> 30390:INFO:DAGScheduler:Stage 28 (count at blockMap.scala:275)
>> >>>> >>> finished in 0.391 s:
>> >>>> >>> 30452:INFO:DAGScheduler:Stage 32 (first at
>> >>>> >>> MatrixFactorizationModel.scala:60) finished in 0.028
s:
>> >>>> >>> 30506:INFO:DAGScheduler:Stage 36 (first at
>> >>>> >>> MatrixFactorizationModel.scala:60) finished in 0.023
s:
>> >>>> >>>
>> >>>> >>> Can anyone comment on this being normal behavior? Is
it
>> >>>> >>> indicative
>> >>>> >>> of
>> >>>> >>> faults causing stages to be resubmitted? I also cannot
find the
>> >>>> >>> missing stages in any stage's parent List(Stage x,
Stage y, ...)
>> >>>> >>>
>> >>>> >>> Thanks,
>> >>>> >>> Mike
>> >>>> >>>
>> >>>> >>>
>> >>>> >>> On 6/1/15, Reynold Xin <rxin@databricks.com>
wrote:
>> >>>> >>> > Thanks, René. I actually added a warning to the
new JDBC
>> >>>> reader/writer
>> >>>> >>> > interface for 1.4.0.
>> >>>> >>> >
>> >>>> >>> > Even with that, I think we should support throttling
JDBC;
>> >>>> >>> > otherwise
>> >>>> >>> > it's
>> >>>> >>> > too convenient for our users to DOS their production
database
>> >>>> servers!
>> >>>> >>> >
>> >>>> >>> >
>> >>>> >>> >   /**
>> >>>> >>> >    * Construct a [[DataFrame]] representing the
database table
>> >>>> >>> > accessible
>> >>>> >>> > via JDBC URL
>> >>>> >>> >    * url named table. Partitions of the table
will be
>> >>>> >>> > retrieved
>> >>>> >>> > in
>> >>>> >>> > parallel
>> >>>> >>> > based on the parameters
>> >>>> >>> >    * passed to this function.
>> >>>> >>> >    *
>> >>>> >>> > *   * Don't create too many partitions in parallel
on a large
>> >>>> cluster;
>> >>>> >>> > otherwise Spark might crash*
>> >>>> >>> > *   * your external database systems.*
>> >>>> >>> >    *
>> >>>> >>> >    * @param url JDBC database url of the form
>> >>>> >>> > `jdbc:subprotocol:subname`
>> >>>> >>> >    * @param table Name of the table in the external
database.
>> >>>> >>> >    * @param columnName the name of a column of
integral type
>> that
>> >>>> will
>> >>>> >>> > be
>> >>>> >>> > used for partitioning.
>> >>>> >>> >    * @param lowerBound the minimum value of `columnName`
used
>> >>>> >>> > to
>> >>>> >>> > decide
>> >>>> >>> > partition stride
>> >>>> >>> >    * @param upperBound the maximum value of `columnName`
used
>> >>>> >>> > to
>> >>>> >>> > decide
>> >>>> >>> > partition stride
>> >>>> >>> >    * @param numPartitions the number of partitions.
 the range
>> >>>> >>> > `minValue`-`maxValue` will be split
>> >>>> >>> >    *                      evenly into this many
partitions
>> >>>> >>> >    * @param connectionProperties JDBC database
connection
>> >>>> >>> > arguments,
>> >>>> a
>> >>>> >>> > list
>> >>>> >>> > of arbitrary string
>> >>>> >>> >    *                             tag/value. Normally
at least
>> >>>> >>> > a
>> >>>> "user"
>> >>>> >>> > and
>> >>>> >>> > "password" property
>> >>>> >>> >    *                             should be included.
>> >>>> >>> >    *
>> >>>> >>> >    * @since 1.4.0
>> >>>> >>> >    */
>> >>>> >>> >
>> >>>> >>> >
>> >>>> >>> > On Mon, Jun 1, 2015 at 1:54 AM, René Treffer
<
>> rtreffer@gmail.com>
>> >>>> >>> > wrote:
>> >>>> >>> >
>> >>>> >>> >> Hi,
>> >>>> >>> >>
>> >>>> >>> >> I'm using sqlContext.jdbc(uri, table, where).map(_
=>
>> >>>> >>> >> 1).aggregate(0)(_+_,_+_) on an interactive
shell (where
>> >>>> >>> >> "where"
>> >>>> >>> >> is
>> >>>> an
>> >>>> >>> >> Array[String] of 32 to 48 elements).  (The
code is tailored
>> >>>> >>> >> to
>> >>>> >>> >> your
>> >>>> >>> >> db,
>> >>>> >>> >> specifically through the where conditions,
I'd have otherwise
>> >>>> >>> >> post
>> >>>> >>> >> it)
>> >>>> >>> >> That should be the DataFrame API, but I'm
just trying to load
>> >>>> >>> >> everything
>> >>>> >>> >> and discard it as soon as possible :-)
>> >>>> >>> >>
>> >>>> >>> >> (1) Never do a silent drop of the values by
default: it kills
>> >>>> >>> >> confidence.
>> >>>> >>> >> An option sounds reasonable.  Some sort of
insight / log
>> >>>> >>> >> would
>> >>>> >>> >> be
>> >>>> >>> >> great.
>> >>>> >>> >> (How many columns of what type were truncated?
why?)
>> >>>> >>> >> Note that I could declare the field as string
via
>> >>>> >>> >> JdbcDialects
>> >>>> (thank
>> >>>> >>> >> you
>> >>>> >>> >> guys for merging that :-) ).
>> >>>> >>> >> I have quite bad experiences with silent drops
/ truncates of
>> >>>> columns
>> >>>> >>> >> and
>> >>>> >>> >> thus _like_ the strict way of spark. It causes
trouble but
>> >>>> >>> >> noticing
>> >>>> >>> >> later
>> >>>> >>> >> that your data was corrupted during conversion
is even worse.
>> >>>> >>> >>
>> >>>> >>> >> (2) SPARK-8004
>> https://issues.apache.org/jira/browse/SPARK-8004
>> >>>> >>> >>
>> >>>> >>> >> (3) One option would be to make it safe to
use, the other
>> option
>> >>>> >>> >> would
>> >>>> >>> >> be
>> >>>> >>> >> to document the behavior (s.th. like "WARNING:
this method
>> tries
>> >>>> >>> >> to
>> >>>> >>> >> load
>> >>>> >>> >> as many partitions as possible, make sure
your database can
>> >>>> >>> >> handle
>> >>>> >>> >> the
>> >>>> >>> >> load
>> >>>> >>> >> or load them in chunks and use union"). SPARK-8008
>> >>>> >>> >> https://issues.apache.org/jira/browse/SPARK-8008
>> >>>> >>> >>
>> >>>> >>> >> Regards,
>> >>>> >>> >>   Rene Treffer
>> >>>> >>> >>
>> >>>> >>> >
>> >>>> >>>
>> >>>> >>>
>> >>>> >>> --
>> >>>> >>> Thanks,
>> >>>> >>> Mike
>> >>>> >>>
>> >>>> >>>
>> ---------------------------------------------------------------------
>> >>>> >>> To unsubscribe, e-mail: dev-unsubscribe@spark.apache.org
>> >>>> >>> For additional commands, e-mail: dev-help@spark.apache.org
>> >>>> >>>
>> >>>> >>
>> >>>> >
>> >>>>
>> >>>>
>> >>>> --
>> >>>> Thanks,
>> >>>> Mike
>> >>>>
>> >>>>
>> >>>> ---------------------------------------------------------------------
>> >>>> To unsubscribe, e-mail: dev-unsubscribe@spark.apache.org
>> >>>> For additional commands, e-mail: dev-help@spark.apache.org
>> >>>>
>> >>>
>> >>
>> >>
>> >> --
>> >> Thanks,
>> >> Mike
>> >>
>> >
>> >
>> > --
>> > Thanks,
>> > Mike
>> >
>>
>>
>> --
>> Thanks,
>> Mike
>>
>


-- 
Thanks,
Mike

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