My issue is posted here on stack-overflow. What am I doing wrong here?
http://stackoverflow.com/questions/28689186/facing-error-while-extending-scala-class-with-product-interface-to-overcome-limi
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I have three tables with the following schema:
case class *date_d*(WID: Int, CALENDAR_DATE: java.sql.Timestamp,
DATE_STRING: String, DAY_OF_WEEK: String, DAY_OF_MONTH: Int, DAY_OF_YEAR:
Int, END_OF_MONTH_FLAG: String, YEARWEEK: Int, CALENDAR_MONTH: String,
MONTH_NUM: Int, YEARMONTH: Int, QUARTER:
Can you please post how did you overcome this issue.
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Thank you Alessandro :)
On Tue, Mar 3, 2015 at 10:03 AM, whitebread [via Apache Spark User List] <
ml-node+s1001560n2188...@n3.nabble.com> wrote:
> Anu,
>
> 1) I defined my class Header as it follows:
>
> case class Header(timestamp: java.sql.Timestamp, c_ip: String,
> c
I have a query that's like:
Could you help in providing me pointers as to how to start to optimize it
w.r.t. spark sql:
sqlContext.sql("
SELECT dw.DAY_OF_WEEK, dw.HOUR, avg(dw.SDP_USAGE) AS AVG_SDP_USAGE
FROM (
SELECT sdp.WID, DAY_OF_WEEK, HOUR, SUM(INTERVAL_VALUE) AS
SDP_USAGE
Spark Version - 1.1.0
Scala - 2.10.4
I have loaded following type data from a parquet file, stored in a schemaRDD
[7654321,2015-01-01 00:00:00.007,0.49,THU]
Since, in spark version 1.1.0, parquet format doesn't support saving
timestamp valuues, I have saved the timestamp data as string. Can you
*I am not clear if spark sql supports HIve on Spark when spark is run as a
service in CDH 5.2? *
Can someone please clarify this. If this is possible, how what configuration
changes have I to make to import hive context in spark shell as well as to
be able to do a spark-submit for the job to be ru
I have a schema RDD with thw following Schema :
scala> mainRDD.printSchema
root
|-- COL1: integer (nullable = false)
|-- COL2: integer (nullable = false)
|-- COL3: string (nullable = true)
|-- COL4: double (nullable = false)
|-- COL5: string (nullable = true)
Now, I transform the mainRDD l
Hi All
I would like to measure Bytes Read and Peak Memory Usage for a Spark SQL
Query.
Please clarify if Bytes Read = aggregate size of all RDDs ??
All my RDDs are in memory and 0B spill to disk.
And I am clueless how to measure Peak Memory Usage.
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Hi Alessandro
Could you specify which query were you able to run successfully?
1. sqlContext.sql("SELECT * FROM Logs as l where l.timestamp = '2012-10-08
16:10:36' ").collect
OR
2. sqlContext.sql("SELECT * FROM Logs as l where cast(l.timestamp as string)
= '2012-10-08 16:10:36.0').collect
I
emsets().collect()print(result)*
Understood that it is a warning, but just wanted to know in detail
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Anu
r z h k p
z y x w v u t s
s x o n r
x z y m t s q e
z
x z y r q t p
from pyspark.mllib.fpm import FPGrowth
import pyspark
from pyspark.context import SparkContext
from pyspark.sql.
ava:3230)
at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:113)
at
java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93*
Please let me know how to resolve this ?
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Anu
Hi,
I have tried all possible way to unsubscripted from this group. Can anyone
help?
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Anu
I am sending this Unsubscribe mail for last few months! It never happens!
If anyone can help us to unsubscribe it wil be really helpful!
On Tue, Oct 30, 2018 at 3:27 PM Mohan Palavancha
wrote:
>
>
I have already send minimum 10 times! Today also I have send one!
On Tue, Oct 30, 2018 at 3:51 PM Biplob Biswas
wrote:
> You need to send the email to user-unsubscr...@spark.apache.org and not
> to the usergroup.
>
> Thanks & Regards
> Biplob Biswas
>
>
> On Tue, Oc
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