I would echo much of what Andrew has said.
I manage a small/medium sized cluster (48 cores, 512G ram, 512G disk
space dedicated to spark, data storage in separate HDFS shares). I've
been using spark since 0.7, and as with Andrew I've observed
significant and consistent improvements in stability (and in the
PySpark API) since then. I have run into some trouble with mesos, and
I have run into some trouble when working with data which is large
relative to the size of my cluster (e.g. 100G), but overall it's
worked well and our group is continuing to build on top of spark.
On Thu, Apr 10, 2014 at 9:24 AM, Andrew Ash <and...@andrewash.com> wrote:
> The biggest issue I've come across is that the cluster is somewhat unstable
> when under memory pressure. Meaning that if you attempt to persist an RDD
> that's too big for memory, even with MEMORY_AND_DISK, you'll often still get
> OOMs. I had to carefully modify some of the space tuning parameters and GC
> settings to get some jobs to even finish.
>
> The other issue I've observed is if you group on a key that is highly
> skewed, with a few massively-common keys and a long tail of rare keys, the
> one massive key can be too big for a single machine and again cause OOMs.
>
> I'm hopeful that off-heap caching (Tachyon) could fix some of these issues.
>
> Just my personal experience, but I've observed significant improvements in
> stability since even the 0.7.x days, so I'm confident that things will
> continue to get better as long as people report what they're seeing so it
> can get fixed.
>
> Andrew
>
>
> On Thu, Apr 10, 2014 at 4:08 PM, Alex Boisvert <alex.boisv...@gmail.com>
> wrote:
>>
>> I'll provide answers from our own experience at Bizo. We've been using
>> Spark for 1+ year now and have found it generally better than previous
>> approaches (Hadoop + Hive mostly).
>>
>>
>>
>> On Thu, Apr 10, 2014 at 7:11 AM, Andras Nemeth
>> <andras.nem...@lynxanalytics.com> wrote:
>>>
>>> I. Is it too much magic? Lots of things "just work right" in Spark and
>>> it's extremely convenient and efficient when it indeed works. But should we
>>> be worried that customization is hard if the built in behavior is not quite
>>> right for us? Are we to expect hard to track down issues originating from
>>> the black box behind the magic?
>>
>>
>> I think is goes back to understanding Spark's architecture, its design
>> constraints and the problems it explicitly set out to address. If the
>> solution to your problems can be easily formulated in terms of the
>> map/reduce model, then it's a good choice. You'll want your
>> "customizations" to go with (not against) the grain of the architecture.
>>
>>>
>>> II. Is it mature enough? E.g. we've created a pull request which fixes a
>>> problem that we were very surprised no one ever stumbled upon before. So
>>> that's why I'm asking: is Spark being already used in professional settings?
>>> Can one already trust it being reasonably bug free and reliable?
>>
>>
>> There are lots of ways to use Spark; and not all of the features are
>> necessarily at the same level of maturity. For instance, we put all the
>> jars on the main classpath so we've never run into the issue your pull
>> request addresses.
>>
>> We definitely use and rely on Spark on a professional basis. We have 5+
>> spark jobs running nightly on Amazon's EMR, slicing through GBs of data.
>> Once we got them working with the proper configuration settings, they have
>> been running reliability since.
>>
>> I would characterize our use of Spark as a "better Hadoop", in the sense
>> that we use it for batch processing only, no streaming yet. We're happy it
>> performs better than Hadoop but we don't require/rely on its memory caching
>> features. In fact, for most of our jobs it would simplify our lives if
>> Spark wouldn't cache so many things in memory since it would make
>> configuration/tuning a lot simpler and jobs would run successfully on the
>> first try instead of having to tweak things (# of partitions and such).
>>
>>> So, to the concrete issues. Sorry for the long mail, and let me know if I
>>> should break this out into more threads or if there is some other way to
>>> have this discussion...
>>>
>>> 1. Memory management
>>> The general direction of these questions is whether it's possible to take
>>> RDD caching related memory management more into our own hands as LRU
>>> eviction is nice most of the time but can be very suboptimal in some of our
>>> use cases.
>>> A. Somehow prioritize cached RDDs, E.g. mark some "essential" that one
>>> really wants to keep. I'm fine with going down in flames if I mark too much
>>> data essential.
>>> B. Memory "reflection": can you pragmatically get the memory size of a
>>> cached rdd and memory sizes available in total/per executor? If we could do
>>> this we could indirectly avoid automatic evictions of things we might really
>>> want to keep in memory.
>>> C. Evictions caused by RDD partitions on the driver. I had a setup with
>>> huge worker memory and smallish memory on the driver JVM. To my surprise,
>>> the system started to cache RDD partitions on the driver as well. As the
>>> driver ran out of memory I started to see evictions while there were still
>>> plenty of space on workers. This resulted in lengthy recomputations. Can
>>> this be avoided somehow?
>>> D. Broadcasts. Is it possible to get rid of a broadcast manually, without
>>> waiting for the LRU eviction taking care of it? Can you tell the size of a
>>> broadcast programmatically?
>>>
>>>
>>> 2. Akka lost connections
>>> We have quite often experienced lost executors due to akka exceptions -
>>> mostly connection lost or similar. It seems to happen when an executor gets
>>> extremely busy with some CPU intensive work. Our hypothesis is that akka
>>> network threads get starved and the executor fails to respond within timeout
>>> limits. Is this plausible? If yes, what can we do with it?
>>
>>
>> We've seen these as well. In our case, increasing the akka timeouts and
>> framesize helped a lot.
>>
>> e.g. spark.akka.{timeout, askTimeout, lookupTimeout, frameSize}
>>
>>>
>>>
>>> In general, these are scary errors in the sense that they come from the
>>> very core of the framework and it's hard to link it to something we do in
>>> our own code, and thus hard to find a fix. So a question more for the
>>> community: how often do you end up scratching your head about cases where
>>> spark
>>>
>>> magic doesn't work perfectly?
>>
>>
>> For us, this happens most often for jobs processing TBs of data (instead
>> of GBs)... which is frustrating of course because these jobs cost a lot more
>> in $$$ + time to run/debug/diagnose than smaller jobs.
>>
>> It means we have to comb the logs to understand what happened, interpret
>> stack traces, dump memory / object allocations, read Spark source to
>> formulate hypothesis about what went wrong and then trial + error to get to
>> a configuration that works. Again, if Spark had better defaults and more
>> conservative execution model (rely less on in-memory caching of RDDs and
>> associated metadata, keepings large communication buffers on the heap,
>> etc.), it would definitely simplify our lives.
>>
>> (Though I recognize that others might use Spark very differently and that
>> these defaults and conservative behavior might not please everybody.)
>>
>> Hopefully this is the kind of feedback you were looking for...
>>
>>>
>>> 3. Recalculation of cached rdds
>>> I see the following scenario happening. I load two RDDs A,B from disk,
>>> cache them and then do some jobs on them, at the very least a count on each.
>>> After these jobs are done I see on the storage panel that 100% of these RDDs
>>> are cached in memory.
>>>
>>> Then I create a third RDD C which is created by multiple joins and maps
>>> from A and B, also cache it and start a job on C. When I do this I still see
>>> A and B completely cached and also see C slowly getting more and more
>>> cached. This is all fine and good, but in the meanwhile I see stages running
>>> on the UI that point to code which is used to load A and B. How is this
>>> possible? Am I misunderstanding how cached RDDs should behave?
>>>
>>> And again the general question - how can one debug such issues?
>>>
>>> 4. Shuffle on disk
>>> Is it true - I couldn't find it in official docs, but did see this
>>> mentioned in various threads - that shuffle _always_ hits disk?
>>> (Disregarding OS caches.) Why is this the case? Are you planning to add a
>>> function to do shuffle in memory or are there some intrinsic reasons for
>>> this to be impossible?
>>>
>>>
>>> Sorry again for the giant mail, and thanks for any insights!
>>>
>>> Andras
>>>
>>>
>>
>
