You can take a look at http://zeppelin.incubator.apache.org. it is a
notebook and graphic visual designer.
On Sun, Jan 11, 2015, 01:45 Cui Lin cui@hds.com wrote:
Thanks, Gaurav and Corey,
Probably I didn’t make myself clear. I am looking for best Spark
practice similar to Shiny for R,
HI,YaoPau：
You can set `spark.ui.port` to 0 ,the program will find a available port by
random
2015-01-11 16:38 GMT+08:00 YaoPau jonrgr...@gmail.com:
I have multiple Spark Streaming jobs running all day, and so when I run my
hourly batch job, I always get a java.net.BindException: Address
the question really is whether this is expected that the memory requirements
grow rapidly with the rank... as I would expect memory is rather O(1) problem
with dependency only on the size of input data.
if this is expected is there any rough formula to determine the required memory
based on ALS
I have multiple Spark Streaming jobs running all day, and so when I run my
hourly batch job, I always get a java.net.BindException: Address already in
use which starts at 4040 then goes to 4041, 4042, 4043 before settling at
4044.
That slows down my hourly job, and time is critical. Is there a
I do not recall seeing support for missing values.
Categorical values are encoded as 0.0, 1.0, 2.0, ... When training the
model you indicate which are interpreted as categorical with the
categoricalFeaturesInfo parameter, which maps feature offset to count
of distinct categorical values for the
I would expect the size of the user/item feature RDDs to grow linearly
with the rank, of course. They are cached, so that would drive cache
memory usage on the cluster.
This wouldn't cause executors to fail for running out of memory
though. In fact, your error does not show the task failing for
You just mean you want to divide the data set into N subsets, and do
that dividing by user, not make one model per user right?
I suppose you could filter the source RDD N times, and build a model
for each resulting subset. This can be parallelized on the driver. For
example let's say you divide
I have the same exception when I run the following example fromSpark SQL
Programming Guide - Spark 1.2.0 Documentation
| |
| | | | | |
| Spark SQL Programming Guide - Spark 1.2.0 DocumentationSpark SQL Programming
Guide Overview Getting Started Data Sources RDDs Inferring the Schema
On 1/11/15 1:40 PM, Nathan McCarthy wrote:
Thanks Cheng Michael! Makes sense. Appreciate the tips!
Idiomatic scala isn't performant. I’ll definitely start using while
loops or tail recursive methods. I have noticed this in the spark code
base.
I might try turning off columnar compression
If you set up a number of pools equal to the number of different priority
levels you want, make the relative weights of those pools very different,
and submit a job to the pool representing its priority, I think youll get
behavior equivalent to a priority queue. Try it and see.
If I'm
I think AvroWriteSupport class already saves avro schema as part of parquet
meta data. You can think of using parquet-mr
https://github.com/Parquet/parquet-mr directly.
Raghavendra
On Fri, Jan 9, 2015 at 10:32 PM, Jerry Lam chiling...@gmail.com wrote:
Hi Raghavendra,
This makes a lot of
Thank you Abhishek. That works.
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Cody,
While I might be able to improve the scheduling of my jobs by using a few
different pools with weights equal to, say, 1, 1e3 and 1e6, effectively
getting a small handful of priority classes. Still, this is really not
quite what I am describing. This is why my original post was on the dev
Cant speak to the internals of SparkSubmit and how to reproduce sans jvm,
guess would depend if you want/need to support various deployment
enviroments (stand-alone, mesos, yarn, etc)
If just need YARN, or looking at starting point, might want to look at
capabilities of YARN API:
Is there any plan to extend the data types that would be accepted by the Tree
models in Spark? e.g. Many models that we build contain a large number of
string-based categorical factors. Currently the only strategy is to map these
string values to integers, and store the mapping so the data can
It looks like to be similar (simpler) to the connected component
implementation in GraphX.
Have you checked that ?
I have questions though, in your example, the graph is a tree. What is the
behavior if it is a more general graph ?
Cheers,
Anwar Rizal.
On Mon, Jan 12, 2015 at 1:02 AM, dizzy5112
Hi Akhil, thank you for your response.
Actually we are first reading from cassandra and then writing back after
doing some processing. All the reader stages succeed with no error and many
writer stages also succeed but many fail as well.
Thanks
Ankur
On Sat, Jan 10, 2015 at 10:15 PM, Akhil Das
Hi all looking for some help in propagating some values in edges. What i want
to achieve (see diagram) is for each connected part of the graph assign an
incrementing value for each of the out links from the root node. This value
will restart again for the next part of the graph. ie node 1 has out
Is it possible to submit a Spark application to a cluster from a machine
that does not have Java installed?
My impression is that many, many more computers come with Python installed
by default than do with Java.
I want to write a command-line utility
Hi all,
Due to some reasons, I restarted Spark master node.
Before I restart it, there were some application running records at the
bottom of the master web page. But they are gone after I restart the master
node. The records include application name, running time, status, and so on.
I am sure
I tried two Spark stand-alone configurations:
SPARK_WORKER_CORES=1
SPARK_WORKER_MEMORY=1g
SPARK_WORKER_INSTANCES=6
spark.driver.memory 1g
spark.executor.memory 1g
spark.storage.memoryFraction 0.9
--total-executor-cores 60
In the second configuration (same as first, but):
SPARK_WORKER_CORES=6
this seems to have sorted it, awesome, thanks for great help.Antony.
On Sunday, 11 January 2015, 13:02, Sean Owen so...@cloudera.com wrote:
I would expect the size of the user/item feature RDDs to grow linearly
with the rank, of course. They are cached, so that would drive cache
Hi Anders,
Have you checked your NodeManager logs to make sure YARN isn't killing
executors for exceeding memory limits?
-Sandy
On Tue, Jan 6, 2015 at 8:20 AM, Anders Arpteg arp...@spotify.com wrote:
Hey,
I have a job that keeps failing if too much data is processed, and I can't
see how to
Dear List,
What are common approaches for addressing over a union of tables / RDDs?
E.g. suppose I have a collection of log files in HDFS, one log file per
day, and I want to compute the sum of some field over a date range in SQL.
Using log schema, I can read each as a distinct SchemaRDD, but I
Hi,
I found out that SparkSQL supports only a relatively small subset of SQL
dialect currently.
I would like to know the roadmap for the coming releases.
And, are you focusing more on popularizing the 'Hive on Spark' SQL dialect
or the Spark SQL dialect?
Rgds
--
Niranda
I see, can you paste the piece of code? Its probably because you are
exceeding the number of connection that are specified in the
property rpc_max_threads. Make sure you close all the connections properly.
Thanks
Best Regards
On Mon, Jan 12, 2015 at 7:45 AM, Ankur Srivastava
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