This sound like a great idea but I wonder is we can get rid of Mahout DRM as a native format. If we have DataFrames (have they actually renamed SchemaRDD?) backed DRMs we ideally don’t need Mahout native DRMs or IndexedDatasets, right? This would be a huge step! If we get data interchangeability with MLlib its a win. If we get general row and column IDs that follow the data through math, its a win. Need to think through how to use a DataFrame in a streaming case, probably through some checkpointing of the window DStream—hmm.
On Feb 4, 2015, at 7:37 AM, Andrew Palumbo <ap....@outlook.com> wrote: On 02/03/2015 08:22 PM, Dmitriy Lyubimov wrote: > I'd suggest to consider this: remember all this talk about > language-integrated spark ql being basically dataframe manipulation DSL? > > so now Spark devs are noticing this generality as well and are actually > proposing to rename SchemaRDD into DataFrame and make it mainstream data > structure. (my "told you so" moment of sorts :) > > What i am getting at, i'd suggest to make DRM and Spark's newly renamed > DataFrame our two major structures. In particular, standardize on using > DataFrame for things that may include non-numerical data and require more > grace about column naming and manipulation. Maybe relevant to TF-IDF work > when it deals with non-matrix content. Sounds like a worthy effort to me. We'd be basically implementing an API at the math-scala level for SchemaRDD/Dataframe datastructures correct? On Tue, Feb 3, 2015 at 5:01 PM, Pat Ferrel <p...@occamsmachete.com> wrote: >> Seems like seq2sparse would be really easy to replace since it takes text >> files to start with, then the whole pipeline could be kept in rdds. The >> dictionaries and counts could be either in-memory maps or rdds for use with >> joins? This would get rid of sequence files completely from the pipeline. >> Item similarity uses in-memory maps but the plan is to make it more >> scalable using joins as an alternative with the same API allowing the user >> to trade-off footprint for speed. I think you're right- should be relatively easy. I've been looking at porting seq2sparse to the DSL for bit now and the stopper at the DSL level is that we don't have a distributed data structure for strings..Seems like getting a DataFrame implemented as Dmitriy mentioned above would take care of this problem. The other issue i'm a little fuzzy on is the distributed collocation mapping- it's a part of the seq2sparse code that I've not spent too much time in. I think that this would be very worthy effort as well- I believe seq2sparse is a particular strong mahout feature. I'll start another thread since we're now way off topic from the refactoring proposal. >> >> My use for TF-IDF is for row similarity and would take a DRM (actually >> IndexedDataset) and calculate row/doc similarities. It works now but only >> using LLR. This is OK when thinking of the items as tags or metadata but >> for text tokens something like cosine may be better. >> >> I’d imagine a downsampling phase that would precede TF-IDF using LLR a lot >> like how CF preferences are downsampled. This would produce an sparsified >> all-docs DRM. Then (if the counts were saved) TF-IDF would re-weight the >> terms before row similarity uses cosine. This is not so good for search but >> should produce much better similarities than Solr’s “moreLikeThis” and does >> it for all pairs rather than one at a time. >> >> In any case it can be used to do a create a personalized content-based >> recommender or augment a CF recommender with one more indicator type. >> >> On Feb 3, 2015, at 3:37 PM, Andrew Palumbo <ap....@outlook.com> wrote: >> >> >> On 02/03/2015 12:44 PM, Andrew Palumbo wrote: >>> On 02/03/2015 12:22 PM, Pat Ferrel wrote: >>>> Some issues WRT lower level Spark integration: >>>> 1) interoperability with Spark data. TF-IDF is one example I actually >> looked at. There may be other things we can pick up from their committers >> since they have an abundance. >>>> 2) wider acceptance of Mahout DSL. The DSL’s power was illustrated to >> me when someone on the Spark list asked about matrix transpose and an MLlib >> committer’s answer was something like “why would you want to do that?”. >> Usually you don’t actually execute the transpose but they don’t even >> support A’A, AA’, or A’B, which are core to what I work on. At present you >> pretty much have to choose between MLlib or Mahout for sparse matrix stuff. >> Maybe a half-way measure is some implicit conversions (ugh, I know). If the >> DSL could interchange datasets with MLlib, people would be pointed to the >> DSL for all of a bunch of “why would you want to do that?” features. MLlib >> seems to be algorithms, not math. >>>> 3) integration of Streaming. DStreams support most of the RDD >> interface. Doing a batch recalc on a moving time window would nearly fall >> out of DStream backed DRMs. This isn’t the same as incremental updates on >> streaming but it’s a start. >>>> Last year we were looking at Hadoop Mapreduce vs Spark, H2O, Flink >> faster compute engines. So we jumped. Now the need is for streaming and >> especially incrementally updated streaming. Seems like we need to address >> this. >>>> Andrew, regardless of the above having TF-IDF would be super >> helpful—row similarity for content/text would benefit greatly. >>> I will put a PR up soon. >> Just to clarify, I'll be porting over the (very simple) TF, TFIDF classes >> and Weight interface over from mr-legacy to math-scala. They're available >> now in spark-shell but won't be after this refactoring. These still >> require dictionary and a frequency count maps to vectorize incoming text- >> so they're more for use with the old MR seq2sparse and I don't think they >> can be used with Spark's HashingTF and IDF. I'll put them up soon. >> Hopefully they'll be of some use. >> >> On Feb 3, 2015, at 8:47 AM, Dmitriy Lyubimov <dlie...@gmail.com> wrote: >>>> But first I need to do massive fixes and improvements to the distributed >>>> optimizer itself. Still waiting on green light for that. >>>> On Feb 3, 2015 8:45 AM, "Dmitriy Lyubimov" <dlie...@gmail.com> wrote: >>>> >>>>> On Feb 3, 2015 7:20 AM, "Pat Ferrel" <p...@occamsmachete.com> wrote: >>>>>> BTW what level of difficulty would making the DSL run on MLlib Vectors >>>>> and RowMatrix be? Looking at using their hashing TF-IDF but it raises >>>>> impedance mismatch between DRM and MLlib RowMatrix. This would further >>>>> reduce artifact size by a bunch. >>>>> >>>>> Short answer, if it were possible, I'd not bother with Mahout code >> base at >>>>> all. The problem is it lacks sufficient flexibility semantics and >>>>> abstruction. Breeze is indefinitely better in that department but at >> the >>>>> time it was sufficiently worse on abstracting interoperability of >> matrices >>>>> with different structures. And mllib does not expose breeze. >>>>> >>>>> Looking forward toward hardware acellerated bolt-on work I just must >> say >>>>> after reading breeze code for some time I still have much clearer plan >> how >>>>> such back hybridization and cost calibration might work with current >> Mahout >>>>> math abstractions than with breeze. It is also more in line with my >> current >>>>> work tasks. >>>>> >>>>>> Also backing something like a DRM with DStreams. Periodic model recalc >>>>> with streams is maybe the first step towards truly streaming algos. >> Looking >>>>> at DStream -> DRM conversion for A’A, A’B, and AA’ in item and row >>>>> similarity. Attach Kafka and get evergreen models, if not incrementally >>>>> updating models. >>>>>> On Feb 2, 2015, at 4:54 PM, Dmitriy Lyubimov <dlie...@gmail.com> >> wrote: >>>>>> bottom line compile-time dependencies are satisfied with no extra >> stuff >>>>>> from mr-legacy or its transitives. This is proven by virtue of >>>>> successful >>>>>> compilation with no dependency on mr-legacy on the tree. >>>>>> >>>>>> Runtime sufficiency for no extra dependency is proven via running >> shell >>>>> or >>>>>> embedded tests (unit tests) which are successful too. This implies >>>>>> embedding and shell apis. >>>>>> >>>>>> Issue with guava is typical one. if it were an issue, i wouldn't be >> able >>>>> to >>>>>> compile and/or run stuff. Now, question is what do we do if drivers >> want >>>>>> extra stuff that is not found in Spark. >>>>>> >>>>>> Now, It is so nice not to depend on anything extra so i am hesitant to >>>>>> offer anything here. either shading or lib with opt-in dependency >> policy >>>>>> would suffice though, since it doesn't look like we'd have to have >> tons >>>>> of >>>>>> extra for drivers. >>>>>> >>>>>> >>>>>> >>>>>> On Sat, Jan 31, 2015 at 10:17 AM, Pat Ferrel <p...@occamsmachete.com> >>>>> wrote: >>>>>>> I vaguely remember there being a Guava version problem where the >>>>> version >>>>>>> had to be rolled back in one of the hadoop modules. The math-scala >>>>>>> IndexedDataset shouldn’t care about version. >>>>>>> >>>>>>> BTW It seems pretty easy to take out the option parser and replace >> with >>>>>>> match and tuples especially if we can extend the Scala App class. It >>>>> might >>>>>>> actually simplify things since I can then use several case classes to >>>>> hold >>>>>>> options (scopt needed one object), which in turn takes out all those >>>>> ugly >>>>>>> casts. I’ll take a look next time I’m in there. >>>>>>> >>>>>>> On Jan 30, 2015, at 4:07 PM, Dmitriy Lyubimov <dlie...@gmail.com> >>>>> wrote: >>>>>>> in 'spark' module it is overwritten with spark dependency, which also >>>>> comes >>>>>>> at the same version so happens. so should be fine with 1.1.x >>>>>>> >>>>>>> [INFO] --- maven-dependency-plugin:2.8:tree (default-cli) @ >>>>>>> mahout-spark_2.10 --- >>>>>>> [INFO] org.apache.mahout:mahout-spark_2.10:jar:1.0-SNAPSHOT >>>>>>> [INFO] +- org.apache.spark:spark-core_2.10:jar:1.1.0:compile >>>>>>> [INFO] | +- org.apache.hadoop:hadoop-client:jar:2.2.0:compile >>>>>>> [INFO] | | +- org.apache.hadoop:hadoop-common:jar:2.2.0:compile >>>>>>> [INFO] | | | +- commons-cli:commons-cli:jar:1.2:compile >>>>>>> [INFO] | | | +- org.apache.commons:commons-math:jar:2.1:compile >>>>>>> [INFO] | | | +- commons-io:commons-io:jar:2.4:compile >>>>>>> [INFO] | | | +- commons-logging:commons-logging:jar:1.1.3:compile >>>>>>> [INFO] | | | +- commons-lang:commons-lang:jar:2.6:compile >>>>>>> [INFO] | | | +- >>>>>>> commons-configuration:commons-configuration:jar:1.6:compile >>>>>>> [INFO] | | | | +- >>>>>>> commons-collections:commons-collections:jar:3.2.1:compile >>>>>>> [INFO] | | | | +- >> commons-digester:commons-digester:jar:1.8:compile >>>>>>> [INFO] | | | | | \- >>>>>>> commons-beanutils:commons-beanutils:jar:1.7.0:compile >>>>>>> [INFO] | | | | \- >>>>>>> commons-beanutils:commons-beanutils-core:jar:1.8.0:compile >>>>>>> [INFO] | | | +- org.apache.avro:avro:jar:1.7.4:compile >>>>>>> [INFO] | | | +- >> com.google.protobuf:protobuf-java:jar:2.5.0:compile >>>>>>> [INFO] | | | +- org.apache.hadoop:hadoop-auth:jar:2.2.0:compile >>>>>>> [INFO] | | | \- >>>>> org.apache.commons:commons-compress:jar:1.4.1:compile >>>>>>> [INFO] | | | \- org.tukaani:xz:jar:1.0:compile >>>>>>> [INFO] | | +- org.apache.hadoop:hadoop-hdfs:jar:2.2.0:compile >>>>>>> [INFO] | | +- >>>>>>> org.apache.hadoop:hadoop-mapreduce-client-app:jar:2.2.0:compile >>>>>>> [INFO] | | | +- >>>>>>> org.apache.hadoop:hadoop-mapreduce-client-common:jar:2.2.0:compile >>>>>>> [INFO] | | | | +- >>>>>>> org.apache.hadoop:hadoop-yarn-client:jar:2.2.0:compile >>>>>>> [INFO] | | | | | +- com.google.inject:guice:jar:3.0:compile >>>>>>> [INFO] | | | | | | +- javax.inject:javax.inject:jar:1:compile >>>>>>> [INFO] | | | | | | \- aopalliance:aopalliance:jar:1.0:compile >>>>>>> [INFO] | | | | | +- >>>>>>> >>>>>>> >> com.sun.jersey.jersey-test-framework:jersey-test-framework-grizzly2:jar:1.9:compile >>>>>>> [INFO] | | | | | | +- >>>>>>> >>>>>>> >> com.sun.jersey.jersey-test-framework:jersey-test-framework-core:jar:1.9:compile >>>>>>> [INFO] | | | | | | | +- >>>>>>> javax.servlet:javax.servlet-api:jar:3.0.1:compile >>>>>>> [INFO] | | | | | | | \- >>>>> com.sun.jersey:jersey-client:jar:1.9:compile >>>>>>> [INFO] | | | | | | \- >>>>> com.sun.jersey:jersey-grizzly2:jar:1.9:compile >>>>>>> [INFO] | | | | | | +- >>>>>>> org.glassfish.grizzly:grizzly-http:jar:2.1.2:compile >>>>>>> [INFO] | | | | | | | \- >>>>>>> org.glassfish.grizzly:grizzly-framework:jar:2.1.2:compile >>>>>>> [INFO] | | | | | | | \- >>>>>>> org.glassfish.gmbal:gmbal-api-only:jar:3.0.0-b023:compile >>>>>>> [INFO] | | | | | | | \- >>>>>>> org.glassfish.external:management-api:jar:3.0.0-b012:compile >>>>>>> [INFO] | | | | | | +- >>>>>>> org.glassfish.grizzly:grizzly-http-server:jar:2.1.2:compile >>>>>>> [INFO] | | | | | | | \- >>>>>>> org.glassfish.grizzly:grizzly-rcm:jar:2.1.2:compile >>>>>>> [INFO] | | | | | | +- >>>>>>> org.glassfish.grizzly:grizzly-http-servlet:jar:2.1.2:compile >>>>>>> [INFO] | | | | | | \- >>>>> org.glassfish:javax.servlet:jar:3.1:compile >>>>>>> [INFO] | | | | | +- com.sun.jersey:jersey-server:jar:1.9:compile >>>>>>> [INFO] | | | | | | +- asm:asm:jar:3.1:compile >>>>>>> [INFO] | | | | | | \- >> com.sun.jersey:jersey-core:jar:1.9:compile >>>>>>> [INFO] | | | | | +- com.sun.jersey:jersey-json:jar:1.9:compile >>>>>>> [INFO] | | | | | | +- >>>>> org.codehaus.jettison:jettison:jar:1.1:compile >>>>>>> [INFO] | | | | | | | \- stax:stax-api:jar:1.0.1:compile >>>>>>> [INFO] | | | | | | +- >>>>> com.sun.xml.bind:jaxb-impl:jar:2.2.3-1:compile >>>>>>> [INFO] | | | | | | | \- >>>>> javax.xml.bind:jaxb-api:jar:2.2.2:compile >>>>>>> [INFO] | | | | | | | \- >>>>>>> javax.activation:activation:jar:1.1:compile >>>>>>> [INFO] | | | | | | +- >>>>>>> org.codehaus.jackson:jackson-jaxrs:jar:1.8.3:compile >>>>>>> [INFO] | | | | | | \- >>>>>>> org.codehaus.jackson:jackson-xc:jar:1.8.3:compile >>>>>>> [INFO] | | | | | \- >>>>>>> com.sun.jersey.contribs:jersey-guice:jar:1.9:compile >>>>>>> [INFO] | | | | \- >>>>>>> org.apache.hadoop:hadoop-yarn-server-common:jar:2.2.0:compile >>>>>>> [INFO] | | | \- >>>>>>> org.apache.hadoop:hadoop-mapreduce-client-shuffle:jar:2.2.0:compile >>>>>>> [INFO] | | +- org.apache.hadoop:hadoop-yarn-api:jar:2.2.0:compile >>>>>>> [INFO] | | +- >>>>>>> org.apache.hadoop:hadoop-mapreduce-client-core:jar:2.2.0:compile >>>>>>> [INFO] | | | \- >>>>> org.apache.hadoop:hadoop-yarn-common:jar:2.2.0:compile >>>>>>> [INFO] | | +- >>>>>>> org.apache.hadoop:hadoop-mapreduce-client-jobclient:jar:2.2.0:compile >>>>>>> [INFO] | | \- >> org.apache.hadoop:hadoop-annotations:jar:2.2.0:compile >>>>>>> [INFO] | +- net.java.dev.jets3t:jets3t:jar:0.7.1:compile >>>>>>> [INFO] | | +- commons-codec:commons-codec:jar:1.3:compile >>>>>>> [INFO] | | \- commons-httpclient:commons-httpclient:jar:3.1:compile >>>>>>> [INFO] | +- org.apache.curator:curator-recipes:jar:2.4.0:compile >>>>>>> [INFO] | | +- >> org.apache.curator:curator-framework:jar:2.4.0:compile >>>>>>> [INFO] | | | \- >> org.apache.curator:curator-client:jar:2.4.0:compile >>>>>>> [INFO] | | \- org.apache.zookeeper:zookeeper:jar:3.4.5:compile >>>>>>> [INFO] | | \- jline:jline:jar:0.9.94:compile >>>>>>> [INFO] | +- >> org.eclipse.jetty:jetty-plus:jar:8.1.14.v20131031:compile >>>>>>> [INFO] | | +- >>>>>>> >> org.eclipse.jetty.orbit:javax.transaction:jar:1.1.1.v201105210645:compile >>>>>>> [INFO] | | +- >>>>> org.eclipse.jetty:jetty-webapp:jar:8.1.14.v20131031:compile >>>>>>> [INFO] | | | +- >>>>> org.eclipse.jetty:jetty-xml:jar:8.1.14.v20131031:compile >>>>>>> [INFO] | | | \- >>>>>>> org.eclipse.jetty:jetty-servlet:jar:8.1.14.v20131031:compile >>>>>>> [INFO] | | \- >>>>> org.eclipse.jetty:jetty-jndi:jar:8.1.14.v20131031:compile >>>>>>> [INFO] | | \- >>>>>>> >>>>>>> >> org.eclipse.jetty.orbit:javax.mail.glassfish:jar:1.4.1.v201005082020:compile >>>>>>> [INFO] | | \- >>>>>>> >> org.eclipse.jetty.orbit:javax.activation:jar:1.1.0.v201105071233:compile >>>>>>> [INFO] | +- >>>>> org.eclipse.jetty:jetty-security:jar:8.1.14.v20131031:compile >>>>>>> [INFO] | +- >> org.eclipse.jetty:jetty-util:jar:8.1.14.v20131031:compile >>>>>>> [INFO] | +- >>>>> org.eclipse.jetty:jetty-server:jar:8.1.14.v20131031:compile >>>>>>> [INFO] | | +- >>>>>>> org.eclipse.jetty.orbit:javax.servlet:jar:3.0.0.v201112011016:compile >>>>>>> [INFO] | | +- >>>>>>> org.eclipse.jetty:jetty-continuation:jar:8.1.14.v20131031:compile >>>>>>> [INFO] | | \- >>>>> org.eclipse.jetty:jetty-http:jar:8.1.14.v20131031:compile >>>>>>> [INFO] | | \- >>>>> org.eclipse.jetty:jetty-io:jar:8.1.14.v20131031:compile >>>>>>> [INFO] | +- com.google.guava:guava:jar:16.0:compile >>>>>>> d >>>>>>> >>>>>>> On Fri, Jan 30, 2015 at 4:03 PM, Dmitriy Lyubimov <dlie...@gmail.com >>>>>>> wrote: >>>>>>> >>>>>>>> looks like it is also requested by mahout-math, wonder what is using >>>>> it >>>>>>>> there. >>>>>>>> >>>>>>>> At very least, it needs to be synchronized to the one currently used >>>>> by >>>>>>>> spark. >>>>>>>> >>>>>>>> [INFO] --- maven-dependency-plugin:2.8:tree (default-cli) @ >>>>> mahout-hadoop >>>>>>>> --- >>>>>>>> [INFO] org.apache.mahout:mahout-hadoop:jar:1.0-SNAPSHOT >>>>>>>> *[INFO] +- org.apache.mahout:mahout-math:jar:1.0-SNAPSHOT:compile* >>>>>>>> [INFO] | +- org.apache.commons:commons-math3:jar:3.2:compile >>>>>>>> *[INFO] | +- com.google.guava:guava:jar:16.0:compile* >>>>>>>> [INFO] | \- com.tdunning:t-digest:jar:2.0.2:compile >>>>>>>> [INFO] +- >>>>> org.apache.mahout:mahout-math:test-jar:tests:1.0-SNAPSHOT:test >>>>>>>> [INFO] +- org.apache.hadoop:hadoop-client:jar:2.2.0:compile >>>>>>>> [INFO] | +- org.apache.hadoop:hadoop-common:jar:2.2.0:compile >>>>>>>> >>>>>>>> >>>>>>>> On Fri, Jan 30, 2015 at 7:52 AM, Pat Ferrel <p...@occamsmachete.com> >>>>>>> wrote: >>>>>>>>> Looks like Guava is in Spark. >>>>>>>>> >>>>>>>>> On Jan 29, 2015, at 4:03 PM, Pat Ferrel <p...@occamsmachete.com> >>>>> wrote: >>>>>>>>> IndexedDataset uses Guava. Can’t tell from sure but it sounds like >>>>> this >>>>>>>>> would not be included since I think it was taken from the mrlegacy >>>>> jar. >>>>>>>>> On Jan 25, 2015, at 10:52 AM, Dmitriy Lyubimov <dlie...@gmail.com> >>>>>>> wrote: >>>>>>>>> ---------- Forwarded message ---------- >>>>>>>>> From: "Pat Ferrel" <p...@occamsmachete.com> >>>>>>>>> Date: Jan 25, 2015 9:39 AM >>>>>>>>> Subject: Re: Codebase refactoring proposal >>>>>>>>> To: <dev@mahout.apache.org> >>>>>>>>> Cc: >>>>>>>>> >>>>>>>>>> When you get a chance a PR would be good. >>>>>>>>> Yes, it would. And not just for that. >>>>>>>>> >>>>>>>>>> As I understand it you are putting some class jars somewhere in >> the >>>>>>>>> classpath. Where? How? >>>>>>>>> /bin/mahout >>>>>>>>> >>>>>>>>> (Computes 2 different classpaths. See 'bin/mahout classpath' vs. >>>>>>>>> 'bin/mahout -spark'.) >>>>>>>>> >>>>>>>>> If i interpret current shell code there correctky, legacy path >> tries >>>>> to >>>>>>>>> use >>>>>>>>> examples assemblies if not packaged, or /lib if packaged. True >>>>>>> motivation >>>>>>>>> of that significantly predates 2010 and i suspect only Benson knows >>>>>>> whole >>>>>>>>> true intent there. >>>>>>>>> >>>>>>>>> The spark path, which is really a quick hack of the script, tries >> to >>>>> get >>>>>>>>> only selected mahout jars and locally instlalled spark classpath >>>>> which i >>>>>>>>> guess is just the shaded spark jar in recent spark releases. It >> also >>>>>>>>> apparently tries to include /libs/*, which is never compiled in >>>>>>> unpackaged >>>>>>>>> version, and now i think it is a bug it is included because >> /libs/* >>>>> is >>>>>>>>> apparently legacy packaging, and shouldnt be used in spark jobs >>>>> with a >>>>>>>>> wildcard. I cant beleive how lazy i am, i still did not find time >> to >>>>>>>>> understand mahout build in all cases. >>>>>>>>> >>>>>>>>> I am not even sure if packaged mahout will work with spark, >> honestly, >>>>>>>>> because of the /lib. Never tried that, since i mostly use >> application >>>>>>>>> embedding techniques. >>>>>>>>> >>>>>>>>> The same solution may apply to adding external dependencies and >>>>> removing >>>>>>>>> the assembly in the Spark module. Which would leave only one major >>>>> build >>>>>>>>> issue afaik. >>>>>>>>>> On Jan 24, 2015, at 11:53 PM, Dmitriy Lyubimov <dlie...@gmail.com >>>>>>>>> wrote: >>>>>>>>>> No, no PR. Only experiment on private. But i believe i >> sufficiently >>>>>>>>> defined >>>>>>>>>> what i want to do in order to gauge if we may want to advance it >>>>> some >>>>>>>>> time >>>>>>>>>> later. Goal is much lighter dependency for spark code. Eliminate >>>>>>>>> everything >>>>>>>>>> that is not compile-time dependent. (and a lot of it is thru >> legacy >>>>> MR >>>>>>>>> code >>>>>>>>>> which we of course don't use). >>>>>>>>>> >>>>>>>>>> Cant say i understand the remaining issues you are talking about >>>>>>> though. >>>>>>>>>> If you are talking about compiling lib or shaded assembly, no, >> this >>>>>>>>> doesn't >>>>>>>>>> do anything about it. Although point is, as it stands, the algebra >>>>> and >>>>>>>>>> shell don't have any external dependencies but spark and these 4 >>>>> (5?) >>>>>>>>>> mahout jars so they technically don't even need an assembly (as >>>>>>>>>> demonstrated). >>>>>>>>>> >>>>>>>>>> As i said, it seems driver code is the only one that may need some >>>>>>>>> external >>>>>>>>>> dependencies, but that's a different scenario from those i am >>>>> talking >>>>>>>>>> about. But i am relatively happy with having the first two working >>>>>>>>> nicely >>>>>>>>>> at this point. >>>>>>>>>> >>>>>>>>>> On Sat, Jan 24, 2015 at 9:06 AM, Pat Ferrel < >> p...@occamsmachete.com> >>>>>>>>> wrote: >>>>>>>>>>> +1 >>>>>>>>>>> >>>>>>>>>>> Is there a PR? You mention a "tiny mahout-hadoop” module. It >> would >>>>> be >>>>>>>>> nice >>>>>>>>>>> to see how you’ve structured that in case we can use the same >>>>> model to >>>>>>>>>>> solve the two remaining refactoring issues. >>>>>>>>>>> 1) external dependencies in the spark module >>>>>>>>>>> 2) no spark or h2o in the release artifacts. >>>>>>>>>>> >>>>>>>>>>> On Jan 23, 2015, at 6:45 PM, Shannon Quinn <squ...@gatech.edu> >>>>> wrote: >>>>>>>>>>> Also +1 >>>>>>>>>>> >>>>>>>>>>> iPhone'd >>>>>>>>>>> >>>>>>>>>>>> On Jan 23, 2015, at 18:38, Andrew Palumbo <ap....@outlook.com> >>>>>>> wrote: >>>>>>>>>>>> +1 >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> Sent from my Verizon Wireless 4G LTE smartphone >>>>>>>>>>>> >>>>>>>>>>>> <div>-------- Original message --------</div><div>From: Dmitriy >>>>>>>>> Lyubimov >>>>>>>>>>> <dlie...@gmail.com> </div><div>Date:01/23/2015 6:06 PM >>>>> (GMT-05:00) >>>>>>>>>>> </div><div>To: dev@mahout.apache.org </div><div>Subject: >> Codebase >>>>>>>>>>> refactoring proposal </div><div> >>>>>>>>>>>> </div> >>>>>>>>>>>> So right now mahout-spark depends on mr-legacy. >>>>>>>>>>>> I did quick refactoring and it turns out it only _irrevocably_ >>>>>>> depends >>>>>>>>> on >>>>>>>>>>>> the following classes there: >>>>>>>>>>>> >>>>>>>>>>>> MatrixWritable, VectorWritable, Varint/Varlong and >> VarintWritable, >>>>>>> and >>>>>>>>>>> ... >>>>>>>>>>>> *sigh* o.a.m.common.Pair >>>>>>>>>>>> >>>>>>>>>>>> So I just dropped those five classes into new a new tiny >>>>>>>>> mahout-hadoop >>>>>>>>>>>> module (to signify stuff that is directly relevant to >> serializing >>>>>>>>> thigns >>>>>>>>>>> to >>>>>>>>>>>> DFS API) and completely removed mrlegacy and its transients from >>>>>>> spark >>>>>>>>>>> and >>>>>>>>>>>> spark-shell dependencies. >>>>>>>>>>>> >>>>>>>>>>>> So non-cli applications (shell scripts and embedded api use) >>>>> actually >>>>>>>>>>> only >>>>>>>>>>>> need spark dependencies (which come from SPARK_HOME classpath, >> of >>>>>>>>> course) >>>>>>>>>>>> and mahout jars (mahout-spark, mahout-math(-scala), >> mahout-hadoop >>>>> and >>>>>>>>>>>> optionally mahout-spark-shell (for running shell)). >>>>>>>>>>>> >>>>>>>>>>>> This of course still doesn't address driver problems that want >> to >>>>>>>>> throw >>>>>>>>>>>> more stuff into front-end classpath (such as cli parser) but at >>>>> least >>>>>>>>> it >>>>>>>>>>>> renders transitive luggage of mr-legacy (and the size of >>>>>>>>> worker-shipped >>>>>>>>>>>> jars) much more tolerable. >>>>>>>>>>>> >>>>>>>>>>>> How does that sound? >>>>>>>>> >> >>
