In the http://spark.apache.org/downloads.html page,We cann't download the
newest Spark release.
At 2014-12-19 17:55:29,"Sean Owen" wrote:
>Tag 1.2.0 is older than 1.2.0-rc2. I wonder if it just didn't get
>updated. I assume it's going to be 1.2.0-rc2 plus a few commits
>related to the r
Hi all, Today download Spark source from http://spark.apache.org/downloads.html
page, and I use
./make-distribution.sh --tgz -Phadoop-2.2 -Pyarn -DskipTests
-Dhadoop.version=2.2.0 -Phive
to build the release, but I encountered an exception as follow:
[INFO] --- build-helper-maven-plugin:1.
I want to get removed RDD from windows as follow, The old RDDs will removed
from current window,
// _
// | previous window _|___
// |___| current window| --> Time
// |___
I want to get removed RDD from windows as follow, The old RDDs will removed
from current window,
// _
// | previous window _|___
// |___| current window| --> Time
// |___
Hi,
When I recovery from checkpoint in yarn-cluster mode using Spark Streaming,
I found it will reuse the application id (In my case is
application_1428664056212_0016) before falied to write spark eventLog, But now
my application id is application_1428664056212_0017,then spark write eventLog
application_1428664056212_0017 not application_1428664056212_0016.
At 2015-04-20 11:46:12,"Sean Owen" wrote:
>This is why spark.hadoop.validateOutputSpecs exists, really:
>https://spark.apache.org/docs/latest/configuration.html
>
>On Mon, Apr 20, 2015 at 3:40 AM, wyphao.2007 wr
Hi everyone, I am using val messages = KafkaUtils.createDirectStream[String,
String, StringDecoder, StringDecoder](ssc, kafkaParams, topicsSet) to read data
from kafka(1k/second), and store the data in windows,the code snippets as
follow:val windowedStreamChannel =
streamChannel.combin
Best Regards
>
>On Tue, Apr 28, 2015 at 7:35 AM, wyphao.2007 wrote:
>
>> Hi everyone, I am using val messages =
>> KafkaUtils.createDirectStream[String, String, StringDecoder,
>> StringDecoder](ssc, kafkaParams, topicsSet) to read data from
>> kafka(1k/second
Hi, Dear developer, I am using Spark Streaming to read data from kafka, the
program already run about 120 hours, but today the program failed because of
driver's OOM as follow:
Container [pid=49133,containerID=container_1429773909253_0050_02_01] is
running beyond physical memory limits. Cu
-dc5aa97c2554.png
At 2015-04-29 18:48:33,zhangxiongfei wrote:
The mount of memory that the driver consumes depends on your program logic,did
you try to collect the result of Spark job?
At 2015-04-29 18:42:04, "wyphao.2007" wrote:
Hi, Dear developer, I am using Spark Streaming to
Hi,
I am test checkpoint to understand how it works, My code as following:
scala> val data = sc.parallelize(List("a", "b", "c"))
data: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[0] at
parallelize at :15
scala> sc.setCheckpointDir("/tmp/checkpoint")
15/11/25 18:09:07 WARN spark
I am test checkpoint to understand how it works, My code as following:
scala> val data = sc.parallelize(List("a", "b", "c"))
data: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[0] at
parallelize at :15
scala> sc.setCheckpointDir("/tmp/checkpoint")
15/11/25 18:09:07 WARN spark.Sp
Spark 1.5.2.
在 2015-11-26 13:19:39,"张志强(旺轩)" 写道:
What’s your spark version?
发件人: wyphao.2007 [mailto:wyphao.2...@163.com]
发送时间: 2015年11月26日 10:04
收件人: user
抄送:dev@spark.apache.org
主题: Spark checkpoint problem
I am test checkpoint to understand how it works, My code as following:
Hi,I want to know how to use jdbcRDD in JAVA not scala, trying to figure out
the last parameter in the constructor of jdbcRDD
thanks
Hi, When I building spark with maven, but failed, the error message is as
following. I didn't found the satisfactory solution by google. Anyone can
help me? Thank you!
INFO]
[INFO] Reactor Summary:
[INFO]
[INFO] Spark
Hi, When I run spark job on yarn,and the job finished success,but I found
there are some error logs in the logfile as follow(the red color text):
14/09/17 18:25:03 INFO ui.SparkUI: Stopped Spark web UI at
http://sparkserver2.cn:63937
14/09/17 18:25:03 INFO scheduler.DAGScheduler: Stopping DAGS
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