m/about-aws/whats-new/2017/12/amazon-
> redshift-introduces-late-materialization-for-faster-query-processing/)
>
> Thanks..
>
>
> On Mar 18, 2018 8:09 PM, "nguyen duc Tuan" wrote:
>
>> Hi @CPC,
>> Parquet is column storage format, so if you want to read
Hi @CPC,
Parquet is column storage format, so if you want to read data from only one
column, you can do that without accessing all of your data. Spark SQL
consists of a query optimizer ( see
https://databricks.com/blog/2015/04/13/deep-dive-into-spark-sqls-catalyst-optimizer.html),
so it will optimi
In genernal, RDD, which is the central concept of Spark, is just
deffinition of how to get data and process data. Each partition of RDD
defines how to get/process each partition of data. A series of
transformation will transform every partitions of data from previous RDD. I
give you very easy examp
Hi Chapman,
You can use "cluster by" to do what you want.
https://deepsense.io/optimize-spark-with-distribute-by-and-cluster-by/
2017-06-24 17:48 GMT+07:00 Saliya Ekanayake :
> I haven't worked with datasets but would this help https://stackoverflow.
> com/questions/37513667/how-to-create-a-spark
e it supports timestamp-based index.
2017-03-28 5:24 GMT+07:00 Soumitra Johri :
> Hi, did you guys figure it out?
>
> Thanks
> Soumitra
>
> On Sun, Mar 5, 2017 at 9:51 PM nguyen duc Tuan
> wrote:
>
>> Hi everyone,
>> We are deploying kafka cluster for ingesting strea
Hi everyone,
We are deploying kafka cluster for ingesting streaming data. But sometimes,
some of nodes on the cluster have troubles (node dies, kafka daemon is
killed...). However, Recovering data in Kafka can be very slow. It takes
serveral hours to recover from disaster. I saw a slide here sugges
ache.org/jira/browse/SPARK-18082). It could be there
>> are some implementation details that would make a difference here.
>>
>> By the way, what is the join threshold you use in approx join?
>>
>> Could you perhaps create a JIRA ticket with the details in order to t
nLSH is not yet in Spark master (see
>> https://issues.apache.org/jira/browse/SPARK-18082). It could be there
>> are some implementation details that would make a difference here.
>>
>> By the way, what is the join threshold you use in approx join?
>>
>> Could you pe
After all, I switched back to LSH implementation that I used before (
https://github.com/karlhigley/spark-neighbors ). I can run on my dataset
now. If someone has any suggestion, please tell me.
Thanks.
2017-02-12 9:25 GMT+07:00 nguyen duc Tuan :
> Hi Timur,
> 1) Our data is transfor
GMT+07:00 Nick Pentreath :
> What other params are you using for the lsh transformer?
>
> Are the issues occurring during transform or during the similarity join?
>
>
> On Fri, 10 Feb 2017 at 05:46, nguyen duc Tuan
> wrote:
>
>> hi Das,
>> In general, I will
Debasish Das :
> If it is 7m rows and 700k features (or say 1m features) brute force row
> similarity will run fine as well...check out spark-4823...you can compare
> quality with approximate variant...
> On Feb 9, 2017 2:55 AM, "nguyen duc Tuan" wrote:
>
>> Hi everyone,
Hi everyone,
Since spark 2.1.0 introduces LSH (
http://spark.apache.org/docs/latest/ml-features.html#locality-sensitive-hashing),
we want to use LSH to find approximately nearest neighbors. Basically, We
have dataset with about 7M rows. we want to use cosine distance to meassure
the similarity betw
The simplest solution that I found: using an browser extension which do
that for you :D. For example, if you are using Chrome, you can use this
extension:
https://chrome.google.com/webstore/detail/easy-auto-refresh/aabcgdmkeabbnleenpncegpcngjpnjkc/related?hl=en
An other way, but a bit more manually
Hi jeremycod,
If you want to find top N nearest neighbors for all users using exact top-k
algorithm for all users, I recommend using the same approach as as used in
Mllib :
https://github.com/apache/spark/blob/85d6b0db9f5bd425c36482ffcb1c3b9fd0fcdb31/mllib/src/main/scala/org/apache/spark/mllib/rec
About spark issue that you refer to, it's is not related to your problem :D
In this case, you only have to to is using withColumn function. For example:
import org.apache.spark.sql.functions._
val getRange = udf((x: Int) => get price range code ...)
val priceRange = resultPrice.withColumn("range",
Stream 'result' is empty or not
> and based on the result, perform some operation on input1Pair DStream and
> input2Pair RDD.
>
>
> On Mon, Jun 20, 2016 at 7:05 PM, nguyen duc tuan
> wrote:
>
>> Hi Praseetha,
>> In order to check if DStream is emp
Hi Praseetha,
In order to check if DStream is empty or not, using isEmpty method is
correct. I think the problem here is calling
input1Pair.lefOuterJoin(input2Pair).
I guess input1Pair rdd comes from above transformation. You should do it on
DStream instead. In this case, do any transformation with
You should set both PYSPARK_DRIVER_PYTHON and PYSPARK_PYTHON the path to
your python interpreter.
2016-06-02 20:32 GMT+07:00 Bhupendra Mishra :
> did not resolved. :(
>
> On Thu, Jun 2, 2016 at 3:01 PM, Sergio Fernández
> wrote:
>
>>
>> On Thu, Jun 2, 2016 at 9:59 AM, Bhupendra Mishra <
>> bh
Spark will load the whole dataset.
The sampling action can be viewed as an filter. The real implementation can
be more complicate, but I give you the idea by simple implementation.
val rand = new Random();
val subRdd = rdd.filter(x => rand.nextDouble() <= 0.3)
To prevent recomputing data, you c
.foreachRDD(process_rdd) <<< As you can see here, no variable to
> store the output from foreachRDD. My target is to get (pred, text) pair and
> then use
>
> Whatever it is, the output from "extract_feature" is not what I want.
> I will be more than happy if you please co
t to
> another place/context, like db,file etc.
> I could use that but seems like over head of having another hop.
> I wanted to make it simple and light.
>
>
> On Tuesday, 31 May 2016, nguyen duc tuan wrote:
>
>> How about using foreachRDD ? I think this is much bett
# Retrun will be tuple of (score,'original text')
> return predictions
>
>
> Hope, it will help somebody who is facing same problem.
> If anybody has better idea, please post it here.
>
> -Obaid
>
> On Mon, May 30, 2016 at 8:43 PM, nguyen duc tuan
&g
How about this ?
def extract_feature(rf_model, x):
text = getFeatures(x).split(',')
fea = [float(i) for i in text]
prediction = rf_model.predict(fea)
return (prediction, x)
output = texts.map(lambda x: extract_feature(rf_model, x))
2016-05-30 14:17 GMT+07:00 obaidul karim :
> Hi,
>
> Anybody has
Take a look in here:
http://stackoverflow.com/questions/33424445/is-there-a-way-to-checkpoint-apache-spark-dataframes
So all you have to do create a checkpoint for a dataframe is as follow:
df.rdd.checkpoint
df.rdd.count // or any action
2016-05-25 8:43 GMT+07:00 naliazheli <754405...@qq.com>:
>
There's no *RowSimilarity *method in RowMatrix class. You have to transpose
your matrix to use that method. However, when the number of rows is large,
this approach is still very slow.
Try to use approximate nearest neighbor (ANN) methods instead such as LSH.
There are several implements of LSH on
Try to use Dataframe instead of RDD.
Here's an introduction to Dataframe:
https://databricks.com/blog/2015/02/17/introducing-dataframes-in-spark-for-large-scale-data-science.html
2016-05-06 21:52 GMT+07:00 pratik gawande :
> Thanks Shao for quick reply. I will look into how pyspark jobs are
> exe
what does the WebUI show? What do you see when you click on "stderr" and
"stdout" links ? These links must contain stdoutput and stderr for each
executor.
About your custom logging in executor, are you sure you checked "${spark.
yarn.app.container.log.dir}/spark-app.log"
Actual location of this fil
These are executor's logs, not the driver logs. To see this log files, you
have to go to executor machines where tasks is running. To see what you
will print to stdout or stderr you can either go to the executor machines
directly (will store in "stdout" and "stderr" files somewhere in the
executor
oint ids would).
>
> Here's an implementation of that idea in the context of finding nearest
> neighbors:
>
> https://github.com/karlhigley/spark-neighbors/blob/master/src/main/scala/com/github/karlhigley/spark/neighbors/ANNModel.scala#L33-L34
>
> Best,
> Karl
>
Hi all,
Currently, I'm working on implementing LSH on spark. The problem leads to
follow problem. I have an RDD[(Int, Int)] stores all pairs of ids of
vectors need to compute distance and an other RDD[(Int, Vector)] stores all
vectors with their ids. Can anyone suggest an efficiency way to compute
Maybe the problem is the data itself. For example, the first dataframe
might has common keys in only one part of the second dataframe. I think you
can verify if you are in this situation by repartition one dataframe and
join it. If this is the true reason, you might see the result distributed
more
That's very useful information.
The reason for weird problem is because of the non-determination of RDD
before applying randomSplit.
By caching RDD, we can make RDD become deterministic and so problem is
solved.
Thank you for your help.
2016-02-21 11:12 GMT+07:00 Ted Yu :
> Have you looked at:
>
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