Return-Path: X-Original-To: apmail-hadoop-mapreduce-user-archive@minotaur.apache.org Delivered-To: apmail-hadoop-mapreduce-user-archive@minotaur.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id 3482611BFF for ; Wed, 2 Jul 2014 23:11:01 +0000 (UTC) Received: (qmail 62464 invoked by uid 500); 2 Jul 2014 23:10:56 -0000 Delivered-To: apmail-hadoop-mapreduce-user-archive@hadoop.apache.org Received: (qmail 62188 invoked by uid 500); 2 Jul 2014 23:10:55 -0000 Mailing-List: contact user-help@hadoop.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: user@hadoop.apache.org Delivered-To: mailing list user@hadoop.apache.org Received: (qmail 62119 invoked by uid 99); 2 Jul 2014 23:10:55 -0000 Received: from athena.apache.org (HELO athena.apache.org) (140.211.11.136) by apache.org (qpsmtpd/0.29) with ESMTP; Wed, 02 Jul 2014 23:10:55 +0000 X-ASF-Spam-Status: No, hits=1.5 required=5.0 tests=HTML_MESSAGE,RCVD_IN_DNSWL_LOW,SPF_PASS X-Spam-Check-By: apache.org Received-SPF: pass (athena.apache.org: domain of yue.yuanyuan@gmail.com designates 209.85.217.172 as permitted sender) Received: from [209.85.217.172] (HELO mail-lb0-f172.google.com) (209.85.217.172) by apache.org (qpsmtpd/0.29) with ESMTP; Wed, 02 Jul 2014 23:10:42 +0000 Received: by mail-lb0-f172.google.com with SMTP id c11so8634490lbj.3 for ; Wed, 02 Jul 2014 16:10:21 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :content-type; bh=3utns5fin7ecYhxH/NKGq/uMQuNSLZ8LiSUYlZrTmts=; b=H7H256M0pmkdN4gvX7DtOcZj74XG5MSB8L/cDKmUTPkKssJJSQNfVyDyZzb5ALyMwm 4DlmMZBdafpMY8ig1wyGl94rpUc7NW8pPrO5ui1OtBPC/k7I6HX2/ugcD+SZ4wgS5SF8 +vWz7KlLExB2cjdrkGt0n4QqT76Pko0PWU/7IOpuFIoKTl51l7RTltMR1AOqCbj94kjC iOStO0RHWTahyBXM73HB6srrBucQv7RttoHD15XN3pAV6allvmG2sqnI0oe7nZN49USp 3PqLvohGy61cg8+BdJGd2YpJ4V0vOmJA6MfK5pu+40x+CPrVfKry+kHZsMI2Za8AUNRM i3rA== X-Received: by 10.112.164.146 with SMTP id yq18mr576197lbb.5.1404342621078; Wed, 02 Jul 2014 16:10:21 -0700 (PDT) MIME-Version: 1.0 Received: by 10.112.82.135 with HTTP; Wed, 2 Jul 2014 16:10:00 -0700 (PDT) In-Reply-To: References: From: Gavin Yue Date: Wed, 2 Jul 2014 19:10:00 -0400 Message-ID: Subject: Re: Big Data tech stack (was Spark vs. Storm) To: user@hadoop.apache.org Content-Type: multipart/alternative; boundary=001a1133bd18128e6a04fd3dff41 X-Virus-Checked: Checked by ClamAV on apache.org --001a1133bd18128e6a04fd3dff41 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Isn't this what Yarn or Mesos are trying to do? Separate the resources management and applications. Run whatever suitable above them. Spark also could run above yanr or mesos. Spark was designed for iteration intensive computing like Machine learning algorithms. Storm is quite different. It is not designed for big data stored in the hard disk. It is inspired by the stream data like tweets. On the other side, Map-Reduce/HDFS was initially designed to handle stored webpage to build up index. Hadoop is on the way to become a generic Big Data analysis framework. HontonWorks and Cloudera are trying to make it much easier on management and deployment. On Wed, Jul 2, 2014 at 4:25 PM, Adaryl "Bob" Wakefield, MBA < adaryl.wakefield@hotmail.com> wrote: > You know what I=E2=80=99m really trying to do? I=E2=80=99m trying to co= me up with a > best practice technology stack. There are so many freaking projects it is > overwhelming. If I were to walk into an organization that had no Big Data > capability, what mix of projects would be best to implement based on > performance, scalability and easy of use/implementation? So far I=E2=80= =99ve got: > Ubuntu > Hadoop > Cassandra (Seems to be the highest performing NoSQL database out there.) > Storm (maybe?) > Python (Easier than Java. Maybe that shouldn=E2=80=99t be a concern.) > Hive (For people to leverage their existing SQL skillset.) > > That would seem to cover transaction processing and warehouse storage and > the capability to do batch and real time analysis. What am I leaving out = or > what do I have incorrect in my assumptions? > > B. > > > > *From:* Stephen Boesch > *Sent:* Wednesday, July 02, 2014 3:07 PM > *To:* user@hadoop.apache.org > *Subject:* Re: Spark vs. Storm > > Spark Streaming discretizes the stream by configurable intervals of no > less than 500Milliseconds. Therefore it is not appropriate for true real > time processing.So if you need to capture events in the low 100's of > milliseonds range or less than stick with Storm (at least for now). > > If you can afford one second+ of latency then spark provides advantages o= f > interoperability with the other Spark components and capabilities. > > > 2014-07-02 12:59 GMT-07:00 Shahab Yunus : > >> Not exactly. There are of course major implementation differences and >> then some subtle and high level ones too. >> >> My 2-cents: >> >> Spark is in-memory M/R and it simulated streaming or real-time >> distributed process for large datasets by micro-batching. The gain in sp= eed >> and performance as opposed to batch paradigm is in-memory buffering or >> batching (and I am here being a bit naive/crude in explanation.) >> >> Storm on the other hand, supports stream processing even at a single >> record level (known as tuple in its lingo.) You can do micro-batching on >> top of it as well (using Trident API which is good for state maintenance >> too, if your BL requires that). This is more applicable where you want >> control to a single record level rather than set, collection or batch of >> records. >> >> Having said that, Spark Streaming is trying to simulate Storm's extreme >> granular approach but as far as I recall, it still is built on top of co= re >> Spark (basically another level of abstraction over core Spark constructs= .) >> >> So given this, you can pick the framework which is more attuned to your >> needs. >> >> >> On Wed, Jul 2, 2014 at 3:31 PM, Adaryl "Bob" Wakefield, MBA < >> adaryl.wakefield@hotmail.com> wrote: >> >>> Do these two projects do essentially the same thing? Is one better >>> than the other? >>> >> >> > > --001a1133bd18128e6a04fd3dff41 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Isn't this what Yarn or Mesos are trying to do?= =C2=A0 Separate the resources management and applications. Run whatever sui= table above them.=C2=A0 Spark also could run above yanr or mesos. Spark was= designed for iteration intensive computing like Machine learning algorithm= s.

Storm is quite different.=C2=A0 It is not designed for = big data stored in the hard disk. It is inspired by the stream data like tw= eets. On the other side,=C2=A0Map-Reduce/HDFS was=C2=A0initially designed= =C2=A0to handle stored webpage to build up index.=C2=A0

Hadoop is on the way to become a generic Big Data analy= sis framework. HontonWorks and Cloudera are trying to make it much easier o= n management and deployment.



On Wed, Jul 2, 2014 at 4:25 PM, Adaryl &= quot;Bob" Wakefield, MBA <adaryl.wakefield@hotmail.com= > wrote:
You know what I=E2=80=99m really trying to do? I=E2=80=99m trying to c= ome up with a best=20 practice technology stack. There are so many freaking projects it is=20 overwhelming. If I were to walk into an organization that had no Big Data= =20 capability, what mix of projects would be best to implement based on=20 performance, scalability and easy of use/implementation? So far I=E2=80=99v= e=20 got:
Ubuntu
Hadoop
Cassandra (Seems to be the highest performing NoSQL database out=20 there.)
Storm (maybe?)
Python (Easier than Java. Maybe that shouldn=E2=80=99t be a concern.)<= /div>
Hive (For people to leverage their existing SQL skillset.)
=C2=A0
That would seem to cover transaction processing and warehouse storage = and=20 the capability to do batch and real time analysis. What am I leaving out or= what=20 do I have incorrect in my assumptions?
=C2=A0
B.
=C2=A0
=C2=A0
=C2=A0
Sent: Wednesday, July 02, 2014 3:07 PM
Subject: Re: Spark vs. Storm
=C2=A0
Spark Streaming discretizes the stream by configurable int= ervals of=20 no less than 500Milliseconds. Therefore it is not appropriate for true real= time=20 processing.So if you need to capture events in the low 100's of millise= onds=20 range or less than stick with Storm (at least for now).=20
=C2=A0
If you can afford one second+ of latency then spark provides advantage= s of=20 interoperability with the other Spark components and capabilities.


2014-07-02 12:59 GMT-07:00 Shahab Yunus <s= hahab.yunus@gmail.com>:
Not exactly. There are of course=C2=A0 major implementat= ion=20 differences and then some subtle and high level ones too.=20
=C2=A0
My 2-cents:
=C2=A0
Spark is in-memory M/R and it simulated streaming or real-time=20 distributed process for large datasets by micro-batching. The gain in spe= ed=20 and performance as opposed to batch paradigm is in-memory buffering or=20 batching (and I am here being a bit naive/crude in explanation.)
=C2=A0
Storm on the other hand, supports stream processing even at a single= =20 record level (known as tuple in its lingo.) You can do micro-batching on = top=20 of it as well (using Trident API which is good for state maintenance too,= if=20 your BL requires that). This is more applicable where you want control to= a=20 single record level rather than set, collection or batch of records.
=C2=A0
Having said that, Spark Streaming is trying to simulate Storm's = extreme=20 granular approach but as far as I recall, it still is built on top of cor= e=20 Spark (basically another level of abstraction over core Spark=20 constructs.)
=C2=A0
So given this, you can pick the framework which is more attuned to y= our=20 needs.


On Wed, Jul 2, 2014 at 3:31 PM, Adaryl "B= ob" Wakefield,=20 MBA <adaryl.wakefield@hotmail.com> wrote:
Do these two projects do essentially the same thing? Is one better= than=20 the other?
=C2=A0
=C2=A0

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