On Tue, Jul 8, 2014 at 7:29 AM, Lizhengbing (bing, BIPA)
zhengbing...@huawei.com wrote:
1) I download the imdb data from
http://komarix.org/ac/ds/Blanc__Mel.txt.bz2 and use this data to test
LBFGS
2) I find the imdb data are zero-based-index data
Since the method is for parsing the
hi, when i create a table, i can point the cache strategy using shark.cache,
i think shark.cache=memory_only means data are managed by spark, and
data are in the same jvm with excutor; while shark.cache=tachyon
means data are managed by tachyon which is off heap, and data are not in
the same
See http://spark.apache.org/news/spark-mailing-lists-moving-to-apache.html
Cheers
On Jul 8, 2014, at 4:17 AM, Leon Zhang leonca...@gmail.com wrote:
This is the correct page: http://spark.apache.org/community.html
Cheers
On Jul 8, 2014, at 4:43 AM, Ted Yu yuzhih...@gmail.com wrote:
See http://spark.apache.org/news/spark-mailing-lists-moving-to-apache.html
Cheers
On Jul 8, 2014, at 4:17 AM, Leon Zhang leonca...@gmail.com wrote:
Tachyon should only be marginally less performant than memory_only, because
we mmap the data from Tachyon's ramdisk. We do not have to, say, transfer
the data over a pipe from Tachyon; we can directly read from the buffers in
the same way that Shark reads from its in-memory columnar format.
On
As Sean mentions, if you can change the data to the standard format, that's
probably a good idea. If you'd rather read the data raw, then writing your
own version of loadLibSVMFile - then you could make your own loader
function which is very similar to the existing one with a few characters
You are ignoring serde costs :-)
- Mridul
On Tue, Jul 8, 2014 at 8:48 PM, Aaron Davidson ilike...@gmail.com wrote:
Tachyon should only be marginally less performant than memory_only, because
we mmap the data from Tachyon's ramdisk. We do not have to, say, transfer
the data over a pipe from
Shark's in-memory format is already serialized (it's compressed and
column-based).
On Tue, Jul 8, 2014 at 9:50 AM, Mridul Muralidharan mri...@gmail.com
wrote:
You are ignoring serde costs :-)
- Mridul
On Tue, Jul 8, 2014 at 8:48 PM, Aaron Davidson ilike...@gmail.com wrote:
Tachyon should
Hi all,
MLlib currently has one clustering algorithm implementation, KMeans.
It would benefit from having implementations of other clustering
algorithms such as MiniBatch KMeans, Fuzzy C-Means, Hierarchical
Clustering, and Affinity Propagation.
I recently submitted a PR [1] for a MiniBatch
Hi All
I read somewhere that Cloudera announced Hive on Spark, since AmpLab already
have Shark. I was trying to understand is it rebranding of Shark or they are
planning something new altogether.
Please suggest.
--
Anish Sneh
Experience is the best teacher.
Hi All
My apologies for very basic question, do we have full support of data locality
in Spark MapReduce.
Please suggest.
--
Anish Sneh
Experience is the best teacher.
http://in.linkedin.com/in/anishsneh
This blog post probably clarifies a lot of things:
http://databricks.com/blog/2014/07/01/shark-spark-sql-hive-on-spark-and-the-future-of-sql-on-spark.html
On Tue, Jul 8, 2014 at 12:24 PM, anishs...@yahoo.co.in
anishs...@yahoo.co.in wrote:
Hi All
I read somewhere that Cloudera announced
I would say for bigdata applications the most useful would be hierarchical
k-means with back tracking and the ability to support k nearest centroids.
On Tue, Jul 8, 2014 at 10:54 AM, RJ Nowling rnowl...@gmail.com wrote:
Hi all,
MLlib currently has one clustering algorithm implementation,
Hi Anish,
Spark, like MapReduce, makes an effort to schedule tasks on the same nodes
and racks that the input blocks reside on.
-Sandy
On Tue, Jul 8, 2014 at 12:27 PM, anishs...@yahoo.co.in
anishs...@yahoo.co.in wrote:
Hi All
My apologies for very basic question, do we have full support
Thanks, Hector! Your feedback is useful.
On Tuesday, July 8, 2014, Hector Yee hector@gmail.com wrote:
I would say for bigdata applications the most useful would be hierarchical
k-means with back tracking and the ability to support k nearest centroids.
On Tue, Jul 8, 2014 at 10:54 AM, RJ
Hector, could you share the references for hierarchical K-means? thanks.
On Tue, Jul 8, 2014 at 1:01 PM, Hector Yee hector@gmail.com wrote:
I would say for bigdata applications the most useful would be hierarchical
k-means with back tracking and the ability to support k nearest centroids.
Having a common framework for clustering makes sense to me. While we
should be careful about what algorithms we include, having solid
implementations of minibatch clustering and hierarchical clustering seems
like a worthwhile goal, and we should reuse as much code and APIs as
reasonable.
On
No idea, never looked it up. Always just implemented it as doing k-means
again on each cluster.
FWIW standard k-means with euclidean distance has problems too with some
dimensionality reduction methods. Swapping out the distance metric with
negative dot or cosine may help.
Other more useful
sure. more interesting problem here is choosing k at each level. Kernel
methods seem to be most promising.
On Tue, Jul 8, 2014 at 1:31 PM, Hector Yee hector@gmail.com wrote:
No idea, never looked it up. Always just implemented it as doing k-means
again on each cluster.
FWIW standard
The scikit-learn implementation may be of interest:
http://scikit-learn.org/stable/modules/generated/sklearn.cluster.Ward.html#sklearn.cluster.Ward
It's a bottom up approach. The pair of clusters for merging are
chosen to minimize variance.
Their code is under a BSD license so it can be used
K doesn't matter much I've tried anything from 2^10 to 10^3 and the
performance
doesn't change much as measured by precision @ K. (see table 1
http://machinelearning.wustl.edu/mlpapers/papers/weston13). Although 10^3
kmeans did outperform 2^10 hierarchical SVD slightly in terms of the
metrics,
No was thinking more top-down:
assuming a distributed kmeans system already existing, recursively apply
the kmeans algorithm on data already partitioned by the previous level of
kmeans.
I haven't been much of a fan of bottom up approaches like HAC mainly
because they assume there is already a
If you're thinking along these lines, have a look at the DecisionTree
implementation in MLlib. It uses the same idea and is optimized to prevent
multiple passes over the data by computing several splits at each level of
tree building. The tradeoff is increased model state and computation per
pass
Yeah if one were to replace the objective function in decision tree with
minimizing the variance of the leaf nodes it would be a hierarchical
clusterer.
On Tue, Jul 8, 2014 at 2:12 PM, Evan R. Sparks evan.spa...@gmail.com
wrote:
If you're thinking along these lines, have a look at the
Hi all,
I was testing an addition to Catalyst today (reimplementing a Hive UDF) and ran
into some odd failures in the test suite. In particular, it seems that what
most of these have in common is that an array is spuriously reversed somewhere.
For example, the stddev tests in the
Is there a way to run the spark driver program without starting the
monitoring web UI in-process? I didn't see any config setting around it.
[cross-posted from hdfs-dev@hadoop, common-dev@hadoop]
We'd like to invite you to the
Consensus based replication in Hadoop: A deep dive
event that we are happy to hold in our San Ramon office on July 15th at noon.
We'd like to accommodate as many people as possible, but I think are
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