Thanks a ton Ashishsanjay
From: Ashish Rangole <arang...@gmail.com>
To: Sanjay Subramanian <sanjaysubraman...@yahoo.com>
Cc: Krishna Sankar <ksanka...@gmail.com>; Sean Owen <so...@cloudera.com>;
Guillermo Ortiz <konstt2...@gmail.com>; user <user@spark.apache.org>
Sent: Sunday, November 23, 2014 11:03 AM
Subject: Re: Spark or MR, Scala or Java?
This being a very broad topic, a discussion can quickly get subjective. I'll
try not to deviate from my experiences and observations to keep this thread
useful to those looking for answers.
I have used Hadoop MR (with Hive, MR Java apis, Cascading and Scalding) as well
as Spark (since v 0.6) in Scala. I learnt Scala for using Spark. My
observations are below.
Spark and Hadoop MR:1. There doesn't have to be a dichotomy between Hadoop
ecosystem and Spark since Spark is a part of it.
2. Spark or Hadoop MR, there is no getting away from learning how partitioning,
input splits, and shuffle process work. In order to optimize performance,
troubleshoot and design software one must know these. I recommend reading first
6-7 chapters of "Hadoop The definitive Guide" book to develop initial
understanding. Indeed knowing a couple of divide and conquer algorithms is a
pre-requisite and I assume everyone on this mailing list is very familiar :)
3. Having used a lot of different APIs and layers of abstraction for Hadoop MR,
my experience progressing from MR Java API --> Cascading --> Scalding is that
each new API looks "simpler" than the previous one. However, Spark API and
abstraction has been simplest. Not only for me but those who I have seen start
with Hadoop MR or Spark first. It is easiest to get started and become
productive with Spark with the exception of Hive for those who are already
familiar with SQL. Spark's ease of use is critical for teams starting out with
Big Data.
4. It is also extremely simple to chain multi-stage jobs in Spark, you do it
without even realizing by operating over RDDs. In Hadoop MR, one has to handle
it explicitly.
5. Spark has built-in support for graph algorithms (including Bulk Synchronous
Parallel processing BSP algorithms e.g. Pregel), Machine Learning and Stream
processing. In Hadoop MR you need a separate library/Framework for each and it
is non-trivial to combine multiple of these in the same application. This is
huge!
6. In Spark one does have to learn how to configure the memory and other
parameters of their cluster. Just to be clear, similar parameters exist in MR
as well (e.g. shuffle memory parameters) but you don't *have* to learn about
tuning them until you have jobs with larger data size jobs. In Spark you learn
this by reading the configuration and tuning documentation followed by
experimentation. This is an area of Spark where things can be better.
Java or Scala : I knew Java already yet I learnt Scala when I came across
Spark. As others have said, you can get started with a little bit of Scala and
learn more as you progress. Once you have started using Scala for a few weeks
you would want to stay with it instead of going back to Java. Scala is arguably
more elegant and less verbose than Java which translates into higher developer
productivity and more maintainable code.
Myth: Spark is for in-memory processing *only*. This is a common beginner
misunderstanding.
Sanjay: Spark uses Hadoop API for performing I/O from file systems (local,
HDFS, S3 etc). Therefore you can use the same Hadoop InputFormat and
RecordReader with Spark that you use with Hadoop for your multi-line record
format. See SparkContext APIs. Just like Hadoop, you will need to make sure
that your files are split at record boundaries.
Hope this is helpful.
On Sun, Nov 23, 2014 at 8:35 AM, Sanjay Subramanian
<sanjaysubraman...@yahoo.com.invalid> wrote:
I am a newbie as well to Spark. Been Hadoop/Hive/Oozie programming extensively
before this. I use Hadoop(Java MR code)/Hive/Impala/Presto on a daily basis.
To get me jumpstarted into Spark I started this gitHub where there is
"IntelliJ-ready-To-run" code (simple examples of jon, sparksql etc) and I will
keep adding to that. I dont know scala and I am learning that too to help me
use Spark better.https://github.com/sanjaysubramanian/msfx_scala.git
Philosophically speaking its possibly not a good idea to take an either/or
approach to technology...Like its never going to be either RDBMS or NOSQL (If
the Cassandra behind FB shows 100 fewer likes instead of 1000 on you Photo a
day for some reason u may not be as upset...but if the Oracle/Db2 systems
behind Wells Fargo show $100 LESS in your account due to an database error, you
will be PANIC-ing).
So its the same case with Spark or Hadoop. I can speak for myself. I have a
usecase for processing old logs that are multiline (i.e. they have a
[begin_timestamp_logid] and [end_timestamp_logid] and have many lines in
between. In Java Hadoop I created custom RecordReaders to solve this. I still
dont know how to do this in Spark. Till that time I am possibly gonna run the
Hadoop code within Oozie in production.
Also my current task is evangelizing Big Data at my company. So the tech people
I can educate with Hadoop and Spark and they would learn that but not the
business intelligence analysts. They love SQL so I have to educate them using
Hive , Presto, Impala...so the question is what is your task or tasks ?
Sorry , a long non technical answer to your question...
Make sense ?
sanjay
From: Krishna Sankar <ksanka...@gmail.com>
To: Sean Owen <so...@cloudera.com>
Cc: Guillermo Ortiz <konstt2...@gmail.com>; user <user@spark.apache.org>
Sent: Saturday, November 22, 2014 4:53 PM
Subject: Re: Spark or MR, Scala or Java?
Adding to already interesting answers:
- "Is there any case where MR is better than Spark? I don't know what cases
I should be used Spark by MR. When is MR faster than Spark?"
- Many. MR would be better (am not saying faster ;o)) for
- Very large dataset,
- Multistage map-reduce flows,
- Complex map-reduce semantics
- Spark is definitely better for the classic iterative,interactive workloads.
- Spark is very effective for implementing the concepts of in-memory
datasets & real time analytics
- Take a look at the Lambda architecture
- Also checkout how Ooyala is using Spark in multiple layers &
configurations. They also have MR in many places
- In our case, we found Spark very effective for ELT - we would have used MR
earlier
- "I know Java, is it worth it to learn Scala for programming to Spark or
it's okay just with Java?"
- Java will work fine. Especially when Java 8 becomes the norm, we will get
back some of the elegance
- I, personally, like Scala & Python lot better than Java. Scala is a lot
more elegant, but compilations, IDE integration et al are still clunky
- One word of caution - stick with one language as much as
possible-shuffling between Java & Scala is not fun
Cheers & HTH<k/>
On Sat, Nov 22, 2014 at 8:26 AM, Sean Owen <so...@cloudera.com> wrote:
MapReduce is simpler and narrower, which also means it is generally lighter
weight, with less to know and configure, and runs more predictably. If you have
a job that is truly just a few maps, with maybe one reduce, MR will likely be
more efficient. Until recently its shuffle has been more developed and offers
some semantics the Spark shuffle does not.I suppose it integrates with tools
like Oozie, that Spark does not. I suggest learning enough Scala to use Spark
in Scala. The amount you need to know is not large.(Mahout MR based
implementations do not run on Spark and will not. They have been removed
instead.)On Nov 22, 2014 3:36 PM, "Guillermo Ortiz" <konstt2...@gmail.com>
wrote:
Hello,
I'm a newbie with Spark but I've been working with Hadoop for a while.
I have two questions.
Is there any case where MR is better than Spark? I don't know what
cases I should be used Spark by MR. When is MR faster than Spark?
The other question is, I know Java, is it worth it to learn Scala for
programming to Spark or it's okay just with Java? I have done a little
piece of code with Java because I feel more confident with it,, but I
seems that I'm missed something
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