PST or est ?
> On Jan 19, 2017, at 21:55, ayan guha wrote:
>
> Sure...we will wait :) :)
>
> Just kidding
>
>> On Fri, Jan 20, 2017 at 4:48 PM, Manohar753
>> wrote:
>> Get Outlook for Android
>> Happiest Minds Disclaimer
>> This message is for the sole use of the intended recipient(s) a
Skymind you could try. It is java
I never test though.
> On Sep 30, 2016, at 7:30 PM, janardhan shetty wrote:
>
> Hi,
>
> Are there any good libraries which can be used for scala deep learning models
> ?
> How can we integrate tensorflow with scala ML ?
Any shuffling?
> On Sep 3, 2016, at 5:50 AM, Сергей Романов wrote:
>
> Same problem happens with CSV data file, so it's not parquet-related either.
>
>
> Welcome to
> __
> / __/__ ___ _/ /__
> _\ \/ _ \/ _ `/ __/ '_/
>/__ / .__/\_,_/_/ /_/\_\ vers
I tried both M4 and R3. R3 is slightly more expensive, but has larger
memory.
If you doing a lot of in-memory staff, like Join. I recommend R3.
Otherwise M4 is fine. Also I remember M4 is EBS instance, so you have to
pay for additional EBS cost as well.
On Fri, Aug 26, 2016 at 10:29 AM, Sa
I am trying to output RDD to disk by
rdd.coleasce(1).saveAsTextFile("/foo")
It outputs to foo folder with a file with name: Part-0.
Is there a way I could directly save the file as /foo/somename ?
Thanks.
This is useful:)
Thank you for sharing.
> On Jul 29, 2016, at 1:30 PM, Jean Georges Perrin wrote:
>
> Sorry if this looks like a shameless self promotion, but some of you asked me
> to say when I'll have my Java recipes for Apache Spark updated. It's done
> here: http://jgp.net/2016/07/22
anrbJd6zP6AcPCCdOABUrV8Pw
>
> http://talebzadehmich.wordpress.com
>
>
>> On 11 June 2016 at 22:26, Gavin Yue wrote:
>> The standalone mode is against Yarn mode or Mesos mode, which means spark
>> uses Yarn or Mesos as cluster managements.
>>
>>
The standalone mode is against Yarn mode or Mesos mode, which means spark
uses Yarn or Mesos as cluster managements.
Local mode is actually a standalone mode which everything runs on the
single local machine instead of remote clusters.
That is my understanding.
On Sat, Jun 11, 2016 at 12:40 PM,
wrote:
> Hi,
>
> I think if we try to see why is Query 2 faster than Query 1 then all the
> answers will be given without beating around the bush. That is the right
> way to find out what is happening and why.
>
>
> Regards,
> Gourav
>
> On Thu, Jun 9, 2016 at 11:19
Could you print out the sql execution plan? My guess is about broadcast join.
> On Jun 9, 2016, at 07:14, Gourav Sengupta wrote:
>
> Hi,
>
> Query1 is almost 25x faster in HIVE than in SPARK. What is happening here and
> is there a way we can optimize the queries in SPARK without the obviou
If not reading the whole dataset, how do you know the total number of records?
If not knowing total number, how do you choose 30%?
> On May 31, 2016, at 00:45, pbaier wrote:
>
> Hi all,
>
> I have to following use case:
> I have around 10k of jsons that I want to use for learning.
> The json
For logs file I would suggest save as gziped text file first. After
aggregation, convert them into parquet by merging a few files.
> On May 19, 2016, at 22:32, Deng Ching-Mallete wrote:
>
> IMO, it might be better to merge or compact the parquet files instead of
> keeping lots of small fil
Hey,
Want to try the NLP on the spark. Could anyone recommend any easy to run
NLP open source lib on spark?
Also is there any recommended semantic network?
Thanks a lot.
n-tiers or layers is mainly for separate a big problem into pieces smaller
problem. So it is always valid.
Just for different application, it means different things.
Speaking of offline analytics, or big data eco-world, there are numerous
way of slicing the problem into different tier/layer. Yo
It is a separate project based on my understanding. I am currently evaluating
it right now.
> On Mar 29, 2016, at 16:17, Michael Segel wrote:
>
>
>
>> Begin forwarded message:
>>
>> From: Michael Segel
>> Subject: Re: Spark and N-tier architecture
>> Date: March 29, 2016 at 4:16:44 PM MS
I recommend you provide more information. Using inverted index certainly speed
up the query time if hitting the index, but it would take longer to create and
insert.
Is the source code not available at this moment?
Thanks
Gavin
> On Feb 22, 2016, at 20:27, 开心延年 wrote:
>
> if apache enjo
This sqlContext is one instance of hive context, do not be confused by the
name.
> On Feb 16, 2016, at 12:51, Prabhu Joseph wrote:
>
> Hi All,
>
> On creating HiveContext in spark-shell, fails with
>
> Caused by: ERROR XSDB6: Another instance of Derby may have already booted the
> datab
Found the answer. It is the block size.
Thanks.
On Wed, Feb 3, 2016 at 5:05 PM, Gavin Yue wrote:
> I am doing a simple count like:
>
> sqlContext.read.parquet("path").count
>
> I have only 5000 parquet files. But generate over 2 tasks.
>
> Each parquet fil
I am doing a simple count like:
sqlContext.read.parquet("path").count
I have only 5000 parquet files. But generate over 2 tasks.
Each parquet file is converted from one gz text file.
Please give some advice.
Thanks
Has anyone used Ignite in production system ?
On Mon, Jan 11, 2016 at 11:44 PM, Jörn Franke wrote:
> You can look at ignite as a HDFS cache or for storing rdds.
>
> > On 11 Jan 2016, at 21:14, Dmitry Goldenberg
> wrote:
> >
> > We have a bunch of Spark jobs deployed and a few large resource f
wrote:
> Could you use "coalesce" to reduce the number of partitions?
>
>
> Shixiong Zhu
>
>
> On Mon, Jan 11, 2016 at 12:21 AM, Gavin Yue
> wrote:
>
>> Here is more info.
>>
>> The job stuck at:
>> INFO cluster.YarnScheduler: Adding task
u saw "3000 jobs" failed. Were you writing each Parquet
> file with an individual job? (Usually people use
> write.partitionBy(...).parquet(...) to write multiple Parquet files.)
>
> Cheng
>
>
> On 1/10/16 10:12 PM, Gavin Yue wrote:
>
>> Hey,
>>
>> I
spark.network.timeout from 120s to 600s. It sometimes
works.
Each task is a parquet file. I could not repartition due to out of GC
problem.
Is there any way I could to improve the performance?
Thanks,
Gavin
On Sun, Jan 10, 2016 at 1:51 AM, Gavin Yue wrote:
> Hey,
>
> I have 10 days data, each
Hey,
I am trying to convert a bunch of json files into parquet, which would
output over 7000 parquet files. But tthere are too many files, so I want
to repartition based on id to 3000.
But I got the error of GC problem like this one:
https://mail-archives.apache.org/mod_mbox/spark-user/201512.mb
Hey,
I have 10 days data, each day has a parquet directory with over 7000
partitions.
So when I union 10 days and do a count, then it submits over 70K tasks.
Then the job failed silently with one container exit with code 1. The
union with like 5, 6 days data is fine.
In the spark-shell, it just
So I tried to set the parquet compression codec to lzo, but hadoop does not
have the lzo natives, while lz4 does included.
But I could set the code to lz4, it only accepts lzo.
Any solution here?
Thank,
Gavin
On Sat, Jan 9, 2016 at 12:09 AM, Gavin Yue wrote:
> I saw in the document,
aster than GZIP and smaller than Snappy.
>
> Cheers
>
> On Fri, Jan 8, 2016 at 7:56 PM, Gavin Yue wrote:
>
>> Thank you .
>>
>> And speaking of compression, is there big difference on performance
>> between gzip and snappy? And why parquet is using gzip by defau
vin:
> Which release of hbase did you play with ?
>
> HBase has been evolving and is getting more stable.
>
> Cheers
>
> On Fri, Jan 8, 2016 at 6:29 PM, Gavin Yue wrote:
>
>> I used to maintain a HBase cluster. The experience with it was not happy.
>>
>> I just
ber of partitions to see if the
>> event has already come (and no gurantee it is full proof, but lead to
>> unnecessary loading in most cases).
>>
>> On Sat, Jan 9, 2016 at 12:56 PM, Gavin Yue
>> wrote:
>>
>>> Hey,
>>> Thank you for the answer. I
s small, spark can
> get it done faster than having everything in one run.
> - I think using groupBy (userId, timestamp) might be better than
> distinct. I guess distinct() will compare every field.
>
>
> On Fri, Jan 8, 2016 at 2:31 PM, Gavin Yue wrote:
>
&
And the most frequent operation I am gonna do is find the UserID who have
some events, then retrieve all the events associted with the UserID.
In this case, how should I partition to speed up the process?
Thanks.
On Fri, Jan 8, 2016 at 2:29 PM, Gavin Yue wrote:
> hey Ted,
>
> Event
by date ?
>
> Can you dedup within partitions ?
>
> Cheers
>
> On Fri, Jan 8, 2016 at 2:10 PM, Gavin Yue wrote:
>
>> I tried on Three day's data. The total input is only 980GB, but the
>> shuffle write Data is about 6.2TB, then the job failed during shuff
this process?
Thanks.
On Fri, Jan 8, 2016 at 2:04 PM, Gavin Yue wrote:
> Hey,
>
> I got everyday's Event table and want to merge them into a single Event
> table. But there so many duplicates among each day's data.
>
> I use Parquet as the data source. What I am do
Hey,
I got everyday's Event table and want to merge them into a single Event
table. But there so many duplicates among each day's data.
I use Parquet as the data source. What I am doing now is
EventDay1.unionAll(EventDay2).distinct().write.parquet("a new parquet
file").
Each day's Event is sto
I am trying to read json files following the example:
val path = "examples/src/main/resources/jsonfile"val people =
sqlContext.read.json(path)
I have 1 Tb size files in the path. It took 1.2 hours to finish the
reading to infer the schema.
But I already know the schema. Could I make this proces
I found that in 1.6 dataframe could do repartition.
Should I still need to do orderby first or I just have to repartition?
On Tue, Jan 5, 2016 at 9:25 PM, Gavin Yue wrote:
> I tried the Ted's solution and it works. But I keep hitting the JVM out
> of memory problem.
> And gr
t%20Aggregator.html
>
> On Tue, Jan 5, 2016 at 2:59 PM, Ted Yu wrote:
>
>> Something like the following:
>>
>> val zeroValue = collection.mutable.Set[String]()
>>
>> val aggredated = data.aggregateByKey (zeroValue)((set, v) => set += v,
>> (setOne, setTwo
Hey,
For example, a table df with two columns
id name
1 abc
1 bdf
2 ab
2 cd
I want to group by the id and concat the string into array of string. like
this
id
1 [abc,bdf]
2 [ab, cd]
How could I achieve this in dataframe? I stuck on df.groupBy("id"). ???
Thanks
I have json files which contains timestamped events. Each event associate
with a user id.
Now I want to group by user id. So converts from
Event1 -> UserIDA;
Event2 -> UserIDA;
Event3 -> UserIDB;
To intermediate storage.
UserIDA -> (Event1, Event2...)
UserIDB-> (Event3...)
Then I will label po
utside the
> cluster,
> and the job will run on yarn, similar as hadoop job is already done, could
> you
> confirm it could exactly work for spark...
>
> Do you mean that I would print those variables on linux command side?
>
> Best Regards,
> Zhiliang
>
>
>
&
Print out your env variables and check first
Sent from my iPhone
> On Sep 25, 2015, at 18:43, Zhiliang Zhu wrote:
>
> Hi All,
>
> I would like to submit spark job on some another remote machine outside the
> cluster,
> I also copied hadoop/spark conf files under the remote machine, then hado
> Thanks
> Best Regards
>
> On Fri, Sep 25, 2015 at 1:58 AM, Gavin Yue wrote:
>
>> Running Spark app over Yarn 2.7
>>
>> Here is my sparksubmit setting:
>> --master yarn-cluster \
>> --num-executors 100 \
>> --executor-cores 3 \
>> --ex
Running Spark app over Yarn 2.7
Here is my sparksubmit setting:
--master yarn-cluster \
--num-executors 100 \
--executor-cores 3 \
--executor-memory 20g \
--driver-memory 20g \
--driver-cores 2 \
But the executor cores setting is not working. It always assigns only one
vcore to one containe
I am trying to parse quite a lot large json files.
At the beginning, I am doing like this
textFile(path).map(parseJson(line)).count()
For each file(800 - 900 Mb), it would take roughtly 1 min to finish.
I then changed the code tl
val rawData = textFile(path)
rawData.cache()
rawData.count()
ra
For a large dataset, I want to filter out something and then do the
computing intensive work.
What I am doing now:
Data.filter(somerules).cache()
Data.count()
Data.map(timeintensivecompute)
But this sometimes takes unusually long time due to cache missing and
recalculation.
So I changed to thi
Spark SQL?
>
> On Fri, Aug 28, 2015 at 12:12 PM, Sabarish Sasidharan <
> sabarish.sasidha...@manthan.com> wrote:
>
>> I see that you are not reusing the same mapper instance in the Scala
>> snippet.
>>
>> Regards
>> Sab
>>
>> On Fri, Aug
> Regards
> Sab
> On 28-Aug-2015 7:29 am, "Gavin Yue" wrote:
>
>> Hey
>>
>> I am using the Json4s-Jackson parser coming with spark and parsing
>> roughly 80m records with totally size 900mb.
>>
>> But the speed is slow. It took my 50 node
Hey
I am using the Json4s-Jackson parser coming with spark and parsing roughly 80m
records with totally size 900mb.
But the speed is slow. It took my 50 nodes(16cores cpu,100gb mem) roughly
30mins to parse Json to use spark sql.
Jackson has the benchmark saying parsing should be ms level.
Hey,
I have a RDD[(String,Boolean)]. I want to keep all Boolean: True rows and
randomly keep some Boolean:false rows. And hope in the final result, the
negative ones could be 10 times more than positive ones.
What would be most efficient way to do this?
Thanks,
Hey,
I am testing the StreamingLinearRegressionWithSGD following the tutorial.
It works, but I could not output the prediction results. I tried the
saveAsTextFile, but it only output _SUCCESS to the folder.
I am trying to check the prediction results and use
BinaryClassificationMetrics to get
I got the same problem when I upgrade from 1.3.1 to 1.4.
The same Conf has been used, 1.3 works, but 1.4UI does not work.
So I added the
yarn.resourcemanager.webapp.address
:8088
yarn.resourcemanager.hostname
To yarn-site.xml. The problem solved.
Spark 1.4 +
> On Jun 14, 2015, at 02:10, ayan guha wrote:
>
> Can you do dedupe process locally for each file first and then globally?
> Also I did not fully get the logic of the part inside reducebykey. Can you
> kindly explain?
>
>> On 14 Jun 2015 13:58, "Gavin Yue"
I have 10 folder, each with 6000 files. Each folder is roughly 500GB. So
totally 5TB data.
The data is formatted as key t/ value. After union, I want to remove
the duplicates among keys. So each key should be unique and has only one
value.
Here is what I am doing.
folders = Array("folder1"
I have 10 folder, each with 6000 files. Each folder is roughly 500GB. So
totally 5TB data.
The data is formatted as key t/ value. After union, I want to remove the
duplicates among keys. So each key should be unique and has only one value.
Here is what I am doing.
folders = Array("folde
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