I want to use ARIMA for a predictive model so that I can take time series
data (metrics) and perform a light anomaly detection. The time series data
is going to be bucketed to different time units (several minutes within
several hours, several hours within several days, several days within
several
Spark uses a SerializableWritable [1] to java serialize writable objects.
I've noticed (at least in Spark 1.2.1) that it breaks down with some
objects when Kryo is used instead of regular java serialization. Though it
is wrapping the actual AccumuloInputFormat (another example of something
you may
I would do sum square. This would allow you to keep an ongoing value as an
associative operation (in an aggregator) and then calculate the variance &
std deviation after the fact.
On Wed, Mar 25, 2015 at 10:28 PM, Haopu Wang wrote:
> Hi,
>
>
>
> I have a DataFrame object and I want to do types
Given the following scenario:
dstream.map(...).filter(...).window(...).foreachrdd()
When would the onBatchCompleted fire?
+1
On Tue, Mar 10, 2015 at 10:57 AM, David Medinets
wrote:
> +1
>
> On Tue, Mar 10, 2015 at 10:56 AM, Adam Fuchs wrote:
> > +1
> >
> > Adam
> > On Mar 10, 2015 2:48 AM, "Sean Busbey" wrote:
> >
> >> Hi Accumulo!
> >>
> >> This is the VOTE thread following our DISCUSS thread on establishing a
>
batching in new producer is
> per topic partition, the batch size it is controlled by both max batch
> size and linger time config.
>
> Jiangjie (Becket) Qin
>
> On 3/9/15, 10:10 AM, "Corey Nolet" wrote:
>
> >I'm curious what type of batching Kafka prod
+1 (non-binding)
- Verified signatures
- Built on Mac OS X and Fedora 21.
On Mon, Mar 9, 2015 at 11:01 PM, Krishna Sankar wrote:
> Excellent, Thanks Xiangrui. The mystery is solved.
> Cheers
>
>
>
> On Mon, Mar 9, 2015 at 3:30 PM, Xiangrui Meng wrote:
>
> > Krishna, I tested your linear regre
I'm new to Kafka and I'm trying to understand the version semantics. We
want to use Kafka w/ Spark but our version of Spark is tied to 0.8.0. We
were wondering what guarantees are made about backwards compatbility across
0.8.x.x. At first glance, given the 3 digits used for versions, I figured
0.8.
I'm curious what type of batching Kafka producers do at the socket layer.
For instance, if I have a partitioner that round robin's n messages to a
different partition, am I guaranteed to get n different messages sent over
the socket or is there some micro-batching going on underneath?
I am trying
Thanks for taking this on Ted!
On Sat, Feb 28, 2015 at 4:17 PM, Ted Yu wrote:
> I have created SPARK-6085 with pull request:
> https://github.com/apache/spark/pull/4836
>
> Cheers
>
> On Sat, Feb 28, 2015 at 12:08 PM, Corey Nolet wrote:
>
>> +1 to a better def
me-consuming jobs. Imagine if there was an
> automatic partition reconfiguration function that automagically did that...
>
>
> On Tue, Feb 24, 2015 at 3:20 AM, Corey Nolet wrote:
>
>> I *think* this may have been related to the default memory overhead
>> setting being too lo
+1 to a better default as well.
We were working find until we ran against a real dataset which was much
larger than the test dataset we were using locally. It took me a couple
days and digging through many logs to figure out this value was what was
causing the problem.
On Sat, Feb 28, 2015 at 11:
tively be listening to a
> partition.
>
> Yes, my understanding is that multiple receivers in one group are the
> way to consume a topic's partitions in parallel.
>
> On Sat, Feb 28, 2015 at 12:56 AM, Corey Nolet wrote:
> > Looking @ [1], it seems to recommend pull f
Looking @ [1], it seems to recommend pull from multiple Kafka topics in
order to parallelize data received from Kafka over multiple nodes. I notice
in [2], however, that one of the createConsumer() functions takes a
groupId. So am I understanding correctly that creating multiple DStreams
with the s
:31 AM, Zhan Zhang
> wrote:
> > Currently in spark, it looks like there is no easy way to know the
> > dependencies. It is solved at run time.
> >
> > Thanks.
> >
> > Zhan Zhang
> >
> > On Feb 26, 2015, at 4:20 PM, Corey Nolet wrote:
> >
> > Ted. That one I know. It was the dependency part I was curious about
>
xt has this method:
>* Return information about what RDDs are cached, if they are in mem or
> on disk, how much space
>* they take, etc.
>*/
> @DeveloperApi
> def getRDDStorageInfo: Array[RDDInfo] = {
>
> Cheers
>
> On Thu, Feb 26, 2015 at 4:00 PM, Corey Nolet
.map().()
> rdd1.count
> future { rdd1.saveAsHasoopFile(...) }
> future { rdd2.saveAsHadoopFile(…)]
>
> In this way, rdd1 will be calculated once, and two saveAsHadoopFile will
> happen concurrently.
>
> Thanks.
>
> Zhan Zhang
>
>
>
> On Feb 26, 2015
d be the behavior and myself and all my coworkers
expected.
On Thu, Feb 26, 2015 at 6:26 PM, Corey Nolet wrote:
> I should probably mention that my example case is much over simplified-
> Let's say I've got a tree, a fairly complex one where I begin a series of
> jobs at the ro
partition of rdd1 even when the rest is ready.
>
> That is probably usually a good idea in almost all cases. That much, I
> don't know how hard it is to implement. But I speculate that it's
> easier to deal with it at that level than as a function of the
> dependency gr
and trigger the execution
> if there is no shuffle dependencies in between RDDs.
>
> Thanks.
>
> Zhan Zhang
> On Feb 26, 2015, at 1:28 PM, Corey Nolet wrote:
>
> > Let's say I'm given 2 RDDs and told to store them in a sequence file and
> they have the fo
I see the "rdd.dependencies()" function, does that include ALL the
dependencies of an RDD? Is it safe to assume I can say
"rdd2.dependencies.contains(rdd1)"?
On Thu, Feb 26, 2015 at 4:28 PM, Corey Nolet wrote:
> Let's say I'm given 2 RDDs and told to store t
Let's say I'm given 2 RDDs and told to store them in a sequence file and
they have the following dependency:
val rdd1 = sparkContext.sequenceFile().cache()
val rdd2 = rdd1.map()
How would I tell programmatically without being the one who built rdd1 and
rdd2 whether or not rdd2 depend
ll see tomorrow-
but i have a suspicion this may have been the cause of the executors being
killed by the application master.
On Feb 23, 2015 5:25 PM, "Corey Nolet" wrote:
> I've got the opposite problem with regards to partitioning. I've got over
> 6000 partitions for s
t; too few in the beginning, the problems seems to decrease. Also, increasing
> spark.akka.askTimeout and spark.core.connection.ack.wait.timeout
> significantly (~700 secs), the problems seems to almost disappear. Don't
> wont to celebrate yet, still long way left before the job complet
t;> fraction of the Executor heap will be used for your user code vs the
>> shuffle vs RDD caching with the spark.storage.memoryFraction setting.
>>
>> On Sat, Feb 21, 2015 at 2:58 PM, Petar Zecevic
>> wrote:
>>
>>>
>>> Could you try to
x Parquet filter push-down
> SPARK-5310 SPARK-5166 Update SQL programming guide for 1.3
> SPARK-5183 SPARK-5180 Document data source API
> SPARK-3650 Triangle Count handles reverse edges incorrectly
> SPARK-3511 Create a RELEASE-NOTES.txt file in the repo
>
>
> On Mon, Feb 23, 20
This vote was supposed to close on Saturday but it looks like no PMCs voted
(other than the implicit vote from Patrick). Was there a discussion offline
to cut an RC2? Was the vote extended?
On Mon, Feb 23, 2015 at 6:59 AM, Robin East wrote:
> Running ec2 launch scripts gives me the following err
I'm experiencing the same issue. Upon closer inspection I'm noticing that
executors are being lost as well. Thing is, I can't figure out how they are
dying. I'm using MEMORY_AND_DISK_SER and i've got over 1.3TB of memory
allocated for the application. I was thinking perhaps it was possible that
a s
+1 (non-binding)
- Verified signatures using [1]
- Built on MacOSX Yosemite
- Built on Fedora 21
Each build was run with and Hadoop-2.4 version with yarn, hive, and
hive-thriftserver profiles
I am having trouble getting all the tests passing on a single run on both
machines but we have this same
The Apache Accumulo project is happy to announce its 1.6.2 release.
Version 1.6.2 is the most recent bug-fix release in its 1.6.x release line.
This version includes numerous bug fixes as well as a performance
improvement over previous versions. Existing users of 1.6.x are encouraged
to upgrade to
Forwarding to dev.
-- Forwarded message --
From: Corey Nolet
Date: Wed, Feb 18, 2015 at 12:25 PM
Subject: [ANNOUNCE] Apache Accumulo 1.6.2 Released
To: u...@accumulo.apache.org, annou...@apache.org
The Apache Accumulo project is happy to announce its 1.6.2 release.
Version
The Apache Accumulo project is happy to announce its 1.6.2 release.
Version 1.6.2 is the most recent bug-fix release in its 1.6.x release line.
This version includes numerous bug fixes as well as a performance
improvement over previous versions. Existing users of 1.6.x are encouraged
to upgrade to
k we're all good.
>
>
> Keith Turner wrote:
>
>> Corey thanks for doing this release. I took a look at the release notes
>> on
>> staging, looks good.
>>
>>
>>
>> On Wed, Feb 11, 2015 at 8:52 AM, Corey Nolet wrote:
>>
>>
Niranda,
I'm not sure if I'd say Spark's use of Jetty to expose its UI monitoring
layer constitutes a use of "two web servers in a single product". Hadoop
uses Jetty as well as do many other applications today that need embedded
http layers for serving up their monitoring UI to users. This is comp
We've been using commons configuration to pull our properties out of
properties files and system properties (prioritizing system properties over
others) and we add those properties to our spark conf explicitly and we use
ArgoPartser to get the command line argument for which property file to
load.
Billie took on the user manual last time. I'm still not sure how to build
the website output for that.
On Sun, Feb 15, 2015 at 8:58 AM, Corey Nolet wrote:
> Josh- I'm terribly busy this weekend but I am going to tackle the release
> notes, publishing the artifacts to the websi
sh Elser wrote:
> Great work, Corey!
>
> What else do we need to do? Release notes? Do you have the
> javadoc/artifact deployments under control?
>
>
> Corey Nolet wrote:
>
>> The vote is now closed. The release of Apache Accumulo 1.6.2 RC5 has been
>> accepted wi
n 1.6.2.Because of ACCUMULO-3597, I was not
> able to get a long randomwalk run. The bug happened shortly after
> starting the test. I killed the deadlocked tserver and everything started
> running again.
>
>
>
> On Wed, Feb 11, 2015 at 8:52 AM, Corey Nolet wrote:
&g
Nevermind- I think I may have had a schema-related issue (sometimes
booleans were represented as string and sometimes as raw booleans but when
I populated the schema one or the other was chosen.
On Fri, Feb 13, 2015 at 8:03 PM, Corey Nolet wrote:
> Here are the results of a few different
Here are the results of a few different SQL strings (let's assume the
schemas are valid for the data types used):
SELECT * from myTable where key1 = true -> no filters are pushed to my
PrunedFilteredScan
SELECT * from myTable where key1 = true and key2 = 5 -> 1 filter (key2) is
pushed to my Prune
This doesn't seem to have helped.
On Fri, Feb 13, 2015 at 2:51 PM, Michael Armbrust
wrote:
> Try using `backticks` to escape non-standard characters.
>
> On Fri, Feb 13, 2015 at 11:30 AM, Corey Nolet wrote:
>
>> I don't remember Oracle ever enforcing that I couldn&
72 hours after
time at which the RC5 was announced, which was 2pm UTC on Wednesday,
February 11th.
That would make the vote close on Saturday, February 14th at 2pm UTC (9am
EST, 6am PT)
On Fri, Feb 13, 2015 at 1:38 PM, Corey Nolet wrote:
> Thanks Josh for your verification. Just a reminder tha
I don't remember Oracle ever enforcing that I couldn't include a $ in a
column name, but I also don't thinking I've ever tried.
When using sqlContext.sql(...), I have a "SELECT * from myTable WHERE
locations_$homeAddress = '123 Elm St'"
It's telling me $ is invalid. Is this a bug?
* Verified NOTICE in native.tar.gz
>
>
> Corey Nolet wrote:
>
>>Devs,
>>
>> Please consider the following candidate for Apache Accumulo 1.6.2
>>
>> Branch: 1.6.2-rc5
>> SHA1: 42943a1817434f1f32e9f0224941aa2fff162e74
>>
Ok. I just verified that this is the case with a little test:
WHERE (a = 'v1' and b = 'v2')PrunedFilteredScan passes down 2 filters
WHERE(a = 'v1' and b = 'v2') or (a = 'v3') PrunedFilteredScan passes down 0
filters
On Fri, Feb 13, 2015
tDate).toDate
> }.getOrElse()
> val end = filters.find {
> case LessThan("end", endDate: String) => DateTime.parse(endDate).toDate
> }.getOrElse()
>
> ...
>
> Filters are advisory, so you can ignore ones that aren't start/end.
>
> Michael
>
> On
I have a temporal data set in which I'd like to be able to query using
Spark SQL. The dataset is actually in Accumulo and I've already written a
CatalystScan implementation and RelationProvider[1] to register with the
SQLContext so that I can apply my SQL statements.
With my current implementation
ng all the
data to a single partition (no matter what window I set) and it seems to
lock up my jobs. I waited for 15 minutes for a stage that usually takes
about 15 seconds and I finally just killed the job in yarn.
On Thu, Feb 12, 2015 at 4:40 PM, Corey Nolet wrote:
> So I tried this:
>
>
group should need to fit.
>
> On Wed, Feb 11, 2015 at 2:56 PM, Corey Nolet wrote:
>
>> Doesn't iter still need to fit entirely into memory?
>>
>> On Wed, Feb 11, 2015 at 5:55 PM, Mark Hamstra
>> wrote:
>>
>>> rdd.mapPartitions { iter =
I was able to get this working by extending KryoRegistrator and setting the
"spark.kryo.registrator" property.
On Thu, Feb 12, 2015 at 12:31 PM, Corey Nolet wrote:
> I'm trying to register a custom class that extends Kryo's Serializer
> interface. I can
I'm trying to register a custom class that extends Kryo's Serializer
interface. I can't tell exactly what Class the registerKryoClasses()
function on the SparkConf is looking for.
How do I register the Serializer class?
Doesn't iter still need to fit entirely into memory?
On Wed, Feb 11, 2015 at 5:55 PM, Mark Hamstra
wrote:
> rdd.mapPartitions { iter =>
> val grouped = iter.grouped(batchSize)
> for (group <- grouped) { ... }
> }
>
> On Wed, Feb 11, 2015 at 2:44 PM, Corey Nolet
I think the word "partition" here is a tad different than the term
"partition" that we use in Spark. Basically, I want something similar to
Guava's Iterables.partition [1], that is, If I have an RDD[People] and I
want to run an algorithm that can be optimized by working on 30 people at a
time, I'd
Devs,
Please consider the following candidate for Apache Accumulo 1.6.2
Branch: 1.6.2-rc5
SHA1: 42943a1817434f1f32e9f0224941aa2fff162e74
Staging Repository:
https://repository.apache.org/content/repositories/orgapacheaccumulo-1024/
Source tarball:
https://repository.apache.
> w/ agitation, ran for 26 hrs and wrote 21 billion entries.
>
> <https://issues.apache.org/jira/browse/ACCUMULO-3576>
>
> On Thu, Feb 5, 2015 at 11:00 PM, Corey Nolet wrote:
>
> > Devs,
> >
> > Please consider the fo
I am able to get around the problem by doing a map and getting the Event
out of the EventWritable before I do my collect. I think I'll do that for
now.
On Tue, Feb 10, 2015 at 6:04 PM, Corey Nolet wrote:
> I am using an input format to load data from Accumulo [1] in to a Spark
> RD
I am using an input format to load data from Accumulo [1] in to a Spark
RDD. It looks like something is happening in the serialization of my output
writable between the time it is emitted from the InputFormat and the time
it reaches its destination on the driver.
What's happening is that the resul
e included in RC4.
>
>
> --
> Christopher L Tubbs II
> http://gravatar.com/ctubbsii
>
> On Thu, Feb 5, 2015 at 11:00 PM, Corey Nolet wrote:
>
> > Devs,
> >
> > Please consider the following candidate for Apache Accumulo 1.6.2
> >
> > B
Devs,
Please consider the following candidate for Apache Accumulo 1.6.2
Branch: 1.6.2-rc4
SHA1: 0649982c2e395852ce2e4408d283a40d6490a980
Staging Repository:
https://repository.apache.org/content/repositories/orgapacheaccumulo-1022/
Source tarball:
https://repository.apache.
Here's another lightweight example of running a SparkContext in a common
java servlet container: https://github.com/calrissian/spark-jetty-server
On Thu, Feb 5, 2015 at 11:46 AM, Charles Feduke
wrote:
> If you want to design something like Spark shell have a look at:
>
> http://zeppelin-project.
our effort.
>
> -Eric
>
> On Fri, Jan 30, 2015 at 10:36 AM, Keith Turner wrote:
>
> > On Thu, Jan 29, 2015 at 7:27 PM, Corey Nolet wrote:
> >
> > > > However I am seeing ACCUMULO-3545[1] that
> > > I need to investigate.
> > >
> > >
My mistake Marcello, I was looking at the wrong message. That reply was
meant for bo yang.
On Feb 4, 2015 4:02 PM, "Marcelo Vanzin" wrote:
> Hi Corey,
>
> On Wed, Feb 4, 2015 at 12:44 PM, Corey Nolet wrote:
> >> Another suggestion is to build Spark by yourself.
>
[
https://issues.apache.org/jira/browse/ACCUMULO-3549?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14305913#comment-14305913
]
Corey Nolet commented on ACCUMULO-3549:
---
So we're comfortable with th
ith Spark 1.1 and earlier you'd get
>> Guava 14 from Spark, so still a problem for you).
>>
>> Right now, the option Markus mentioned
>> (spark.yarn.user.classpath.first) can be a workaround for you, since
>> it will place your app's jars before Yarn's on the classpath.
>>
>
replaced volume does not appear in
instance.volumes
Thanks,
Corey Nolet
.org/jira/browse/SPARK-2996 - only works for YARN).
>> Also thread at
>> http://apache-spark-user-list.1001560.n3.nabble.com/netty-on-classpath-when-using-spark-submit-td18030.html
>> .
>>
>> HTH,
>> Markus
>>
>> On 02/03/2015 11:20 PM, Corey Nolet wrot
I'm having a really bad dependency conflict right now with Guava versions
between my Spark application in Yarn and (I believe) Hadoop's version.
The problem is, my driver has the version of Guava which my application is
expecting (15.0) while it appears the Spark executors that are working on
my R
Congrats guys!
On Tue, Feb 3, 2015 at 7:01 PM, Evan Chan wrote:
> Congrats everyone!!!
>
> On Tue, Feb 3, 2015 at 3:17 PM, Timothy Chen wrote:
> > Congrats all!
> >
> > Tim
> >
> >
> >> On Feb 4, 2015, at 7:10 AM, Pritish Nawlakhe <
> prit...@nirvana-international.com> wrote:
> >>
> >> Congrats
We have a series of spark jobs which run in succession over various cached
datasets, do small groups and transforms, and then call
saveAsSequenceFile() on them.
Each call to save as a sequence file appears to have done its work, the
task says it completed in "xxx.x seconds" but then it pauses
tests. I had one IT that failed on me from the source
> build which we can fix later -- things are looking good otherwise from my
> testing.
>
> Thanks for working through this Corey, and Keith for finding bugs :)
>
>
> Corey Nolet wrote:
>
>>Devs,
>>
>>
e/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
On Wed, Jan 28, 2015 at 9:16 AM, Corey Nolet wrote:
> I'm looking @ the ShuffledRDD code and it looks like there is a method
> setKeyOrdering()- is this guaranteed to order everything in the partition?
> I'm on S
t;https://mail.google.com/mail/?view=cm&fs=1&tf=1&to=ctubb...@apache.org
> >>
> > wrote:
> >
> > > Does it matter that this was built with Java 1.7.0_25? Is that going to
> > > cause issues running in a 1.6 JRE?
> > >
> > >
> > > --
I'll start on an RC4 but leave this open for awhile in case any more issues
like pop up like this.
On Jan 28, 2015 5:24 PM, "Keith Turner" wrote:
> -1 because of ACCUMULO-3541
>
> On Wed, Jan 28, 2015 at 2:38 AM, Corey Nolet wrote:
>
> > Devs,
> >
I'm looking @ the ShuffledRDD code and it looks like there is a method
setKeyOrdering()- is this guaranteed to order everything in the partition?
I'm on Spark 1.2.0
On Wed, Jan 28, 2015 at 9:07 AM, Corey Nolet wrote:
> In all of the soutions I've found thus far, sorting h
y-spark-one-spark-job
>
> On Wed, Jan 28, 2015 at 12:51 AM, Corey Nolet wrote:
>
>> I need to be able to take an input RDD[Map[String,Any]] and split it into
>> several different RDDs based on some partitionable piece of the key
>> (groups) and then send each partition to
Devs,
Please consider the following candidate for Apache Accumulo 1.6.2
Branch: 1.6.2-rc3
SHA1: 3a6987470c1e5090a2ca159614a80f0fa50393bf
Staging Repository:
https://repository.apache.org/content/repositories/orgapacheaccumulo-1021/
Source tarball:
https://repository.apache.
51 AM, Corey Nolet wrote:
> I need to be able to take an input RDD[Map[String,Any]] and split it into
> several different RDDs based on some partitionable piece of the key
> (groups) and then send each partition to a separate set of files in
> different folders in HDFS.
>
> 1
I need to be able to take an input RDD[Map[String,Any]] and split it into
several different RDDs based on some partitionable piece of the key
(groups) and then send each partition to a separate set of files in
different folders in HDFS.
1) Would running the RDD through a custom partitioner be the
I've read that this is supposed to be a rather significant optimization to
the shuffle system in 1.1.0 but I'm not seeing much documentation on
enabling this in Yarn. I see github classes for it in 1.2.0 and a property
"spark.shuffle.service.enabled" in the spark-defaults.conf.
The code mentions t
ting
---
Basic build with unit tests.
Thanks,
Corey Nolet
ting
---
Basic build with unit tests.
Thanks,
Corey Nolet
ests.
Thanks,
Corey Nolet
ests.
Thanks,
Corey Nolet
ttps://reviews.apache.org/r/30280/diff/
Testing
---
Basic build with unit tests.
Thanks,
Corey Nolet
mulo/core/util/HadoopCompatUtil.java
PRE-CREATION
examples/simple/src/main/java/org/apache/accumulo/examples/simple/mapreduce/TeraSortIngest.java
1b8cbaf
Diff: https://reviews.apache.org/r/30280/diff/
Testing
---
Basic build with unit tests.
Thanks,
Corey Nolet
I believe Josh just committed a fix for the missing license header.
On Mon, Jan 26, 2015 at 1:24 PM, Mike Drob wrote:
>
> ---
> This is an automatically generated e-mail. To reply, visit:
> https://reviews.apache.org/r/30252/#review69636
>
Christopher,
I see what I did in regards to the commit hash- I based the rc2 branch off
of the branch I ran the maven release plugin from instead of basing it off
the tag which was created.
On Sun, Jan 25, 2015 at 3:38 PM, Corey Nolet wrote:
> Forwarding discussions to dev.
> On Jan 25
Forwarding discussions to dev.
On Jan 25, 2015 3:22 PM, "Josh Elser" wrote:
> plus, I don't think it's valid to call this vote on the user list :)
>
> Corey Nolet wrote:
>
>> -1 for backwards compatibility issues described.
>>
>> -1
>>
&
mulo/1.6.1_to_1.6.2/compat_report.html
* 1.6.2 -> 1.6.1 (under a semver patch increment, this should be just as
strong an assertion as the reverse)
http://people.apache.org/~busbey/compat_reports/accumulo/1.6.2_to_1.6.1/compat_report.html
On Fri, Jan 23, 2015 at 8:02 PM, Corey Nolet wrote:
---
This is an automatically generated e-mail. To reply, visit:
https://reviews.apache.org/r/30252/#review69571
---
Ship it!
Ship It!
- Corey Nolet
On Jan. 25, 2015, 9:38 a.m
g/r/30252/#comment114283>
Good. I'll add this to the release documentation I've been working on.
- Corey Nolet
On Jan. 25, 2015, 9:38 a.m., Sean Busbey wrote:
>
> ---
> This is an automatically generated e-mail.
Devs,
Please consider the following candidate for Apache Accumulo 1.6.2
Branch: 1.6.2-rc2
SHA1: 34987b4c8b4d896bbf2d26be8e70f70976614c0f
Staging Repository:
https://repository.apache.org/content/repositories/orgapacheaccumulo-1020/
Source tarball:
https://repository.apache.
5 at 11:56 PM, Josh Elser wrote:
>
> > I think we used to have instruction lying around that described how to
> use
> > https://github.com/lvc/japi-compliance-checker (not like that has any
> > influence on what Sean used, though :D)
> >
> >
> > Corey Nolet
gt; On Wed, Jan 21, 2015 at 7:50 PM, Corey Nolet wrote:
>
> > > I did notice something strange reviewing this RC. It appears the
> staging
> > > repo doesn't have hash files for the detached GPG signatures
> (*.asc.md5,
> > > *.asc.sha1). That's new.
> I did notice something strange reviewing this RC. It appears the staging
> repo doesn't have hash files for the detached GPG signatures (*.asc.md5,
> *.asc.sha1). That's new. Did you do something special regarding this,
> Corey? Or maybe this is just a change with mvn, or maybe it's a change
with
onality to the public API.
> >
>
> nice catch
>
> -1
>
>
> >
> > On Tue, Jan 20, 2015 at 11:18 PM, Corey Nolet
> wrote:
> >
> > > Devs,
> > >
> > > Please consider the following candidate for Apache Accum
Let's say I have 2 formats for json objects in the same file
schema1 = { "location": "12345 My Lane" }
schema2 = { "location":{"houseAddres":"1234 My Lane"} }
>From my tests, it looks like the current inferSchema() function will end up
with only StructField("location", StringType).
What would be
Devs,
Please consider the following candidate for Apache Accumulo 1.6.2
Branch: 1.6.2-rc1
SHA1: 533d93adb17e8b27c5243c97209796f66c6b8b2d
Staging Repository:
https://repository.apache.org/content/repositories/orgapacheaccumulo-1018/
Source tarball:
https://repository.apach
lumes' is called and a replaced volume appears in instance.volumes.
Also verified that the error does not appear when 'bin/accumulo init
--add-volumes' is called and the replaced volume does not appear in
instance.volumes
Thanks,
Corey Nolet
---------
On Jan. 16, 2015, 5:06 a.m., Corey Nolet wrote:
>
> ---
> This is an automatically generated e-mail. To reply, visit:
> https://reviews.apache.org/r/29959/
>
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