[mllib] Is there any bugs to divide a Breeze sparse vectors at Spark v1.3.0-rc3?
Hi all,
Is there any bugs to divide a Breeze sparse vector at Spark v1.3.0-rc3? When
I tried to divide a sparse vector at Spark v1.3.0-rc3, I got a wrong result
if the target vector has any zero values.
Spark v1.3.0-rc3 depends on Breeze v0.11.1. And Breeze v0.11.1 seems to have
any bugs to divide a sparse vector by a scalar value. When dividing a breeze
sparse vector which has any zero values, the result seems to be a zero
vector. However, we can run the same code on Spark v1.2.x.
However, there is no problem to multiply a breeze sparse vector. I asked the
breeze community this problem on the below issue.
https://github.com/scalanlp/breeze/issues/382
For example,
```
test(dividing a breeze spark vector) {
val vec = Vectors.sparse(6, Array(0, 4), Array(0.0, 10.0)).toBreeze
val n = 60.0
val answer1 = vec :/ n
val answer2 = vec.toDenseVector :/ n
println(vec)
println(answer1)
println(answer2)
assert(answer1.toDenseVector === answer2)
}
SparseVector((0,0.0), (4,10.0))
SparseVector()
DenseVector(0.0, 0.0, 0.0, 0.0, 0.1, 0.0)
DenseVector(0.0, 0.0, 0.0, 0.0, 0.0, 0.0) did not equal DenseVector(0.0,
0.0, 0.0, 0.0, 0.1, 0.0)
org.scalatest.exceptions.TestFailedException: DenseVector(0.0, 0.0, 0.0,
0.0, 0.0, 0.0) did not equal DenseVector(0.0, 0.0, 0.0, 0.0,
0.1, 0.0)
```
Thanks,
Yu Ishikawa
-
-- Yu Ishikawa
--
View this message in context:
http://apache-spark-developers-list.1001551.n3.nabble.com/mllib-Is-there-any-bugs-to-divide-a-Breeze-sparse-vectors-at-Spark-v1-3-0-rc3-tp11056.html
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Re: [mllib] Is there any bugs to divide a Breeze sparse vectors at Spark v1.3.0-rc3?
David Hall who is a breeze creator told me that it's a bug. So, I made a jira ticket about this issue. We need to upgrade breeze from 0.11.1 to 0.11.2 or later in order to fix the bug, when the new version of breeze will be released. [SPARK-6341] Upgrade breeze from 0.11.1 to 0.11.2 or later - ASF JIRA https://issues.apache.org/jira/browse/SPARK-6341 Thanks, Yu Ishikawa - -- Yu Ishikawa -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/mllib-Is-there-any-bugs-to-divide-a-Breeze-sparse-vectors-at-Spark-v1-3-0-rc3-tp11056p11058.html Sent from the Apache Spark Developers List mailing list archive at Nabble.com. - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
Re: [mllib] Is there any bugs to divide a Breeze sparse vectors at Spark v1.3.0-rc3?
It's a bug in breeze's side. Once David fixes it and publishes it to
maven, we can upgrade to breeze 0.11.2. Please file a jira ticket for
this issue. thanks.
Sincerely,
DB Tsai
---
Blog: https://www.dbtsai.com
On Sun, Mar 15, 2015 at 12:45 AM, Yu Ishikawa
yuu.ishikawa+sp...@gmail.com wrote:
Hi all,
Is there any bugs to divide a Breeze sparse vector at Spark v1.3.0-rc3? When
I tried to divide a sparse vector at Spark v1.3.0-rc3, I got a wrong result
if the target vector has any zero values.
Spark v1.3.0-rc3 depends on Breeze v0.11.1. And Breeze v0.11.1 seems to have
any bugs to divide a sparse vector by a scalar value. When dividing a breeze
sparse vector which has any zero values, the result seems to be a zero
vector. However, we can run the same code on Spark v1.2.x.
However, there is no problem to multiply a breeze sparse vector. I asked the
breeze community this problem on the below issue.
https://github.com/scalanlp/breeze/issues/382
For example,
```
test(dividing a breeze spark vector) {
val vec = Vectors.sparse(6, Array(0, 4), Array(0.0, 10.0)).toBreeze
val n = 60.0
val answer1 = vec :/ n
val answer2 = vec.toDenseVector :/ n
println(vec)
println(answer1)
println(answer2)
assert(answer1.toDenseVector === answer2)
}
SparseVector((0,0.0), (4,10.0))
SparseVector()
DenseVector(0.0, 0.0, 0.0, 0.0, 0.1, 0.0)
DenseVector(0.0, 0.0, 0.0, 0.0, 0.0, 0.0) did not equal DenseVector(0.0,
0.0, 0.0, 0.0, 0.1, 0.0)
org.scalatest.exceptions.TestFailedException: DenseVector(0.0, 0.0, 0.0,
0.0, 0.0, 0.0) did not equal DenseVector(0.0, 0.0, 0.0, 0.0,
0.1, 0.0)
```
Thanks,
Yu Ishikawa
-
-- Yu Ishikawa
--
View this message in context:
http://apache-spark-developers-list.1001551.n3.nabble.com/mllib-Is-there-any-bugs-to-divide-a-Breeze-sparse-vectors-at-Spark-v1-3-0-rc3-tp11056.html
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Re: Wrong version on the Spark documentation page
When I enter http://spark.apache.org/docs/latest/ into Chrome address bar, I saw 1.3.0 Cheers On Sun, Mar 15, 2015 at 11:12 AM, Patrick Wendell pwend...@gmail.com wrote: Cheng - what if you hold shift+refresh? For me the /latest link correctly points to 1.3.0 On Sun, Mar 15, 2015 at 10:40 AM, Cheng Lian lian.cs@gmail.com wrote: It's still marked as 1.2.1 here http://spark.apache.org/docs/latest/ But this page is updated (1.3.0) http://spark.apache.org/docs/latest/index.html Cheng - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
Wrong version on the Spark documentation page
It's still marked as 1.2.1 here http://spark.apache.org/docs/latest/ But this page is updated (1.3.0) http://spark.apache.org/docs/latest/index.html Cheng - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
Re: Wrong version on the Spark documentation page
Cheng - what if you hold shift+refresh? For me the /latest link correctly points to 1.3.0 On Sun, Mar 15, 2015 at 10:40 AM, Cheng Lian lian.cs@gmail.com wrote: It's still marked as 1.2.1 here http://spark.apache.org/docs/latest/ But this page is updated (1.3.0) http://spark.apache.org/docs/latest/index.html Cheng - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
Re: broadcast hang out
Cross region as in different data centers ?
- Mridul
On Sun, Mar 15, 2015 at 8:08 PM, lonely Feb lonely8...@gmail.com wrote:
Hi all, i meet up with a problem that torrent broadcast hang out in my
spark cluster (1.2, standalone) , particularly serious when driver and
executors are cross-region. when i read the code of broadcast i found that
a sync block read here:
def fetchBlockSync(host: String, port: Int, execId: String, blockId:
String): ManagedBuffer = {
// A monitor for the thread to wait on.
val result = Promise[ManagedBuffer]()
fetchBlocks(host, port, execId, Array(blockId),
new BlockFetchingListener {
override def onBlockFetchFailure(blockId: String, exception:
Throwable): Unit = {
result.failure(exception)
}
override def onBlockFetchSuccess(blockId: String, data:
ManagedBuffer): Unit = {
val ret = ByteBuffer.allocate(data.size.toInt)
ret.put(data.nioByteBuffer())
ret.flip()
result.success(new NioManagedBuffer(ret))
}
})
Await.result(result.future, Duration.Inf)
}
it seems that fetchBlockSync method does not have a timeout limit but wait
forever ? Anybody can show me how to control the timeout here?
-
To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org
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Re: broadcast hang out
yes
2015-03-16 11:43 GMT+08:00 Mridul Muralidharan mri...@gmail.com:
Cross region as in different data centers ?
- Mridul
On Sun, Mar 15, 2015 at 8:08 PM, lonely Feb lonely8...@gmail.com wrote:
Hi all, i meet up with a problem that torrent broadcast hang out in my
spark cluster (1.2, standalone) , particularly serious when driver and
executors are cross-region. when i read the code of broadcast i found
that
a sync block read here:
def fetchBlockSync(host: String, port: Int, execId: String, blockId:
String): ManagedBuffer = {
// A monitor for the thread to wait on.
val result = Promise[ManagedBuffer]()
fetchBlocks(host, port, execId, Array(blockId),
new BlockFetchingListener {
override def onBlockFetchFailure(blockId: String, exception:
Throwable): Unit = {
result.failure(exception)
}
override def onBlockFetchSuccess(blockId: String, data:
ManagedBuffer): Unit = {
val ret = ByteBuffer.allocate(data.size.toInt)
ret.put(data.nioByteBuffer())
ret.flip()
result.success(new NioManagedBuffer(ret))
}
})
Await.result(result.future, Duration.Inf)
}
it seems that fetchBlockSync method does not have a timeout limit but
wait
forever ? Anybody can show me how to control the timeout here?
Re: broadcast hang out
Thx. But this method is in BlockTransferService.scala of spark which i can
not replace unless i rewrite the core code. I wonder if it is handled
somewhere already.
2015-03-16 11:27 GMT+08:00 Chester Chen ches...@alpinenow.com:
can you just replace Duration.Inf with a shorter duration ? how about
import scala.concurrent.duration._
val timeout = new Timeout(10 seconds)
Await.result(result.future, timeout.duration)
or
val timeout = new FiniteDuration(10, TimeUnit.SECONDS)
Await.result(result.future, timeout)
or simply
import scala.concurrent.duration._
Await.result(result.future, 10 seconds)
On Sun, Mar 15, 2015 at 8:08 PM, lonely Feb lonely8...@gmail.com wrote:
Hi all, i meet up with a problem that torrent broadcast hang out in my
spark cluster (1.2, standalone) , particularly serious when driver and
executors are cross-region. when i read the code of broadcast i found that
a sync block read here:
def fetchBlockSync(host: String, port: Int, execId: String, blockId:
String): ManagedBuffer = {
// A monitor for the thread to wait on.
val result = Promise[ManagedBuffer]()
fetchBlocks(host, port, execId, Array(blockId),
new BlockFetchingListener {
override def onBlockFetchFailure(blockId: String, exception:
Throwable): Unit = {
result.failure(exception)
}
override def onBlockFetchSuccess(blockId: String, data:
ManagedBuffer): Unit = {
val ret = ByteBuffer.allocate(data.size.toInt)
ret.put(data.nioByteBuffer())
ret.flip()
result.success(new NioManagedBuffer(ret))
}
})
Await.result(result.future, Duration.Inf)
}
it seems that fetchBlockSync method does not have a timeout limit but wait
forever ? Anybody can show me how to control the timeout here?
Re: [mllib] Is there any bugs to divide a Breeze sparse vectors at Spark v1.3.0-rc3?
snapshot is pushed. If you verify I'll publish the new artifacts. On Sun, Mar 15, 2015 at 1:14 AM, Yu Ishikawa yuu.ishikawa+sp...@gmail.com wrote: David Hall who is a breeze creator told me that it's a bug. So, I made a jira ticket about this issue. We need to upgrade breeze from 0.11.1 to 0.11.2 or later in order to fix the bug, when the new version of breeze will be released. [SPARK-6341] Upgrade breeze from 0.11.1 to 0.11.2 or later - ASF JIRA https://issues.apache.org/jira/browse/SPARK-6341 Thanks, Yu Ishikawa - -- Yu Ishikawa -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/mllib-Is-there-any-bugs-to-divide-a-Breeze-sparse-vectors-at-Spark-v1-3-0-rc3-tp11056p11058.html Sent from the Apache Spark Developers List mailing list archive at Nabble.com. - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
Re: SparkSQL 1.3.0 (RC3) failed to read parquet file generated by 1.1.1
Thanks!
On Sat, Mar 14, 2015 at 3:31 AM, Michael Armbrust mich...@databricks.com
wrote:
Here is the JIRA: https://issues.apache.org/jira/browse/SPARK-6315
On Thu, Mar 12, 2015 at 11:00 PM, Michael Armbrust mich...@databricks.com
wrote:
We are looking at the issue and will likely fix it for Spark 1.3.1.
On Thu, Mar 12, 2015 at 8:25 PM, giive chen thegi...@gmail.com wrote:
Hi all
My team has the same issue. It looks like Spark 1.3's sparkSQL cannot
read
parquet file generated by Spark 1.1. It will cost a lot of migration
work
when we wanna to upgrade Spark 1.3.
Is there anyone can help me?
Thanks
Wisely Chen
On Tue, Mar 10, 2015 at 5:06 PM, Pei-Lun Lee pl...@appier.com wrote:
Hi,
I found that if I try to read parquet file generated by spark 1.1.1
using
1.3.0-rc3 by default settings, I got this error:
com.fasterxml.jackson.core.JsonParseException: Unrecognized token
'StructType': was expecting ('true', 'false' or 'null')
at [Source: StructType(List(StructField(a,IntegerType,false))); line:
1,
column: 11]
at
com.fasterxml.jackson.core.JsonParser._constructError(JsonParser.java:1419)
at
com.fasterxml.jackson.core.base.ParserMinimalBase._reportError(ParserMinimalBase.java:508)
at
com.fasterxml.jackson.core.json.ReaderBasedJsonParser._reportInvalidToken(ReaderBasedJsonParser.java:2300)
at
com.fasterxml.jackson.core.json.ReaderBasedJsonParser._handleOddValue(ReaderBasedJsonParser.java:1459)
at
com.fasterxml.jackson.core.json.ReaderBasedJsonParser.nextToken(ReaderBasedJsonParser.java:683)
at
com.fasterxml.jackson.databind.ObjectMapper._initForReading(ObjectMapper.java:3105)
at
com.fasterxml.jackson.databind.ObjectMapper._readMapAndClose(ObjectMapper.java:3051)
at
com.fasterxml.jackson.databind.ObjectMapper.readValue(ObjectMapper.java:2161)
at
org.json4s.jackson.JsonMethods$class.parse(JsonMethods.scala:19)
at org.json4s.jackson.JsonMethods$.parse(JsonMethods.scala:44)
at
org.apache.spark.sql.types.DataType$.fromJson(dataTypes.scala:41)
at
org.apache.spark.sql.parquet.ParquetRelation2$$anonfun$readSchema$1$$anonfun$25.apply(newParquet.scala:675)
at
org.apache.spark.sql.parquet.ParquetRelation2$$anonfun$readSchema$1$$anonfun$25.apply(newParquet.scala:675)
this is how I save parquet file with 1.1.1:
sql(select 1 as a).saveAsParquetFile(/tmp/foo)
and this is the meta data of the 1.1.1 parquet file:
creator: parquet-mr version 1.4.3
extra: org.apache.spark.sql.parquet.row.metadata =
StructType(List(StructField(a,IntegerType,false)))
by comparison, this is 1.3.0 meta:
creator: parquet-mr version 1.6.0rc3
extra: org.apache.spark.sql.parquet.row.metadata =
{type:struct,fields:[{name:a,type:integer,nullable:t
[more]...
It looks like now ParquetRelation2 is used to load parquet file by
default
and it only recognizes JSON format schema but 1.1.1 schema was case
class
string format.
Setting spark.sql.parquet.useDataSourceApi to false will fix it, but I
don't know the differences.
Is this considered a bug? We have a lot of parquet files from 1.1.1,
should
we disable data source api in order to read them if we want to upgrade
to
1.3?
Thanks,
--
Pei-Lun
broadcast hang out
Hi all, i meet up with a problem that torrent broadcast hang out in my
spark cluster (1.2, standalone) , particularly serious when driver and
executors are cross-region. when i read the code of broadcast i found that
a sync block read here:
def fetchBlockSync(host: String, port: Int, execId: String, blockId:
String): ManagedBuffer = {
// A monitor for the thread to wait on.
val result = Promise[ManagedBuffer]()
fetchBlocks(host, port, execId, Array(blockId),
new BlockFetchingListener {
override def onBlockFetchFailure(blockId: String, exception:
Throwable): Unit = {
result.failure(exception)
}
override def onBlockFetchSuccess(blockId: String, data:
ManagedBuffer): Unit = {
val ret = ByteBuffer.allocate(data.size.toInt)
ret.put(data.nioByteBuffer())
ret.flip()
result.success(new NioManagedBuffer(ret))
}
})
Await.result(result.future, Duration.Inf)
}
it seems that fetchBlockSync method does not have a timeout limit but wait
forever ? Anybody can show me how to control the timeout here?
Re: broadcast hang out
can you just replace Duration.Inf with a shorter duration ? how about
import scala.concurrent.duration._
val timeout = new Timeout(10 seconds)
Await.result(result.future, timeout.duration)
or
val timeout = new FiniteDuration(10, TimeUnit.SECONDS)
Await.result(result.future, timeout)
or simply
import scala.concurrent.duration._
Await.result(result.future, 10 seconds)
On Sun, Mar 15, 2015 at 8:08 PM, lonely Feb lonely8...@gmail.com wrote:
Hi all, i meet up with a problem that torrent broadcast hang out in my
spark cluster (1.2, standalone) , particularly serious when driver and
executors are cross-region. when i read the code of broadcast i found that
a sync block read here:
def fetchBlockSync(host: String, port: Int, execId: String, blockId:
String): ManagedBuffer = {
// A monitor for the thread to wait on.
val result = Promise[ManagedBuffer]()
fetchBlocks(host, port, execId, Array(blockId),
new BlockFetchingListener {
override def onBlockFetchFailure(blockId: String, exception:
Throwable): Unit = {
result.failure(exception)
}
override def onBlockFetchSuccess(blockId: String, data:
ManagedBuffer): Unit = {
val ret = ByteBuffer.allocate(data.size.toInt)
ret.put(data.nioByteBuffer())
ret.flip()
result.success(new NioManagedBuffer(ret))
}
})
Await.result(result.future, Duration.Inf)
}
it seems that fetchBlockSync method does not have a timeout limit but wait
forever ? Anybody can show me how to control the timeout here?
Re: broadcast hang out
Anyone can help? Thanks a lot !
2015-03-16 11:45 GMT+08:00 lonely Feb lonely8...@gmail.com:
yes
2015-03-16 11:43 GMT+08:00 Mridul Muralidharan mri...@gmail.com:
Cross region as in different data centers ?
- Mridul
On Sun, Mar 15, 2015 at 8:08 PM, lonely Feb lonely8...@gmail.com wrote:
Hi all, i meet up with a problem that torrent broadcast hang out in my
spark cluster (1.2, standalone) , particularly serious when driver and
executors are cross-region. when i read the code of broadcast i found
that
a sync block read here:
def fetchBlockSync(host: String, port: Int, execId: String, blockId:
String): ManagedBuffer = {
// A monitor for the thread to wait on.
val result = Promise[ManagedBuffer]()
fetchBlocks(host, port, execId, Array(blockId),
new BlockFetchingListener {
override def onBlockFetchFailure(blockId: String, exception:
Throwable): Unit = {
result.failure(exception)
}
override def onBlockFetchSuccess(blockId: String, data:
ManagedBuffer): Unit = {
val ret = ByteBuffer.allocate(data.size.toInt)
ret.put(data.nioByteBuffer())
ret.flip()
result.success(new NioManagedBuffer(ret))
}
})
Await.result(result.future, Duration.Inf)
}
it seems that fetchBlockSync method does not have a timeout limit but
wait
forever ? Anybody can show me how to control the timeout here?
