+1
Mainly tested ML/Graph/R. Perf tests from Tim Hunter showed minor speedups
from 1.6 for common ML algorithms.
On Thu, Jul 21, 2016 at 9:41 AM, Ricardo Almeida <
ricardo.alme...@actnowib.com> wrote:
> +1 (non binding)
>
> Tested PySpark Core, DataFrame/SQL, MLlib and Streaming on a standalone
> cluster
>
> On 21 July 2016 at 05:24, Reynold Xin <r...@databricks.com> wrote:
>
>> +1
>>
>>
>> On Wednesday, July 20, 2016, Krishna Sankar <ksanka...@gmail.com> wrote:
>>
>>> +1 (non-binding, of course)
>>>
>>> 1. Compiled OS X 10.11.5 (El Capitan) OK Total time: 24:07 min
>>> mvn clean package -Pyarn -Phadoop-2.7 -DskipTests
>>> 2. Tested pyspark, mllib (iPython 4.0)
>>> 2.0 Spark version is 2.0.0
>>> 2.1. statistics (min,max,mean,Pearson,Spearman) OK
>>> 2.2. Linear/Ridge/Lasso Regression OK
>>> 2.3. Classification : Decision Tree, Naive Bayes OK
>>> 2.4. Clustering : KMeans OK
>>> Center And Scale OK
>>> 2.5. RDD operations OK
>>> State of the Union Texts - MapReduce, Filter,sortByKey (word count)
>>> 2.6. Recommendation (Movielens medium dataset ~1 M ratings) OK
>>> Model evaluation/optimization (rank, numIter, lambda) with
>>> itertools OK
>>> 3. Scala - MLlib
>>> 3.1. statistics (min,max,mean,Pearson,Spearman) OK
>>> 3.2. LinearRegressionWithSGD OK
>>> 3.3. Decision Tree OK
>>> 3.4. KMeans OK
>>> 3.5. Recommendation (Movielens medium dataset ~1 M ratings) OK
>>> 3.6. saveAsParquetFile OK
>>> 3.7. Read and verify the 3.6 save(above) - sqlContext.parquetFile,
>>> registerTempTable, sql OK
>>> 3.8. result = sqlContext.sql("SELECT
>>> OrderDetails.OrderID,ShipCountry,UnitPrice,Qty,Discount FROM Orders INNER
>>> JOIN OrderDetails ON Orders.OrderID = OrderDetails.OrderID") OK
>>> 4.0. Spark SQL from Python OK
>>> 4.1. result = sqlContext.sql("SELECT * from people WHERE State = 'WA'")
>>> OK
>>> 5.0. Packages
>>> 5.1. com.databricks.spark.csv - read/write OK (--packages
>>> com.databricks:spark-csv_2.10:1.4.0)
>>> 6.0. DataFrames
>>> 6.1. cast,dtypes OK
>>> 6.2. groupBy,avg,crosstab,corr,isNull,na.drop OK
>>> 6.3. All joins,sql,set operations,udf OK
>>> [Dataframe Operations very fast from 11 secs to 3 secs, to 1.8 secs, to
>>> 1.5 secs! Good work !!!]
>>> 7.0. GraphX/Scala
>>> 7.1. Create Graph (small and bigger dataset) OK
>>> 7.2. Structure APIs - OK
>>> 7.3. Social Network/Community APIs - OK
>>> 7.4. Algorithms : PageRank of 2 datasets, aggregateMessages() - OK
>>>
>>> Cheers
>>> <k/>
>>>
>>> On Tue, Jul 19, 2016 at 7:35 PM, Reynold Xin <r...@databricks.com>
>>> wrote:
>>>
>>>> Please vote on releasing the following candidate as Apache Spark
>>>> version 2.0.0. The vote is open until Friday, July 22, 2016 at 20:00 PDT
>>>> and passes if a majority of at least 3 +1 PMC votes are cast.
>>>>
>>>> [ ] +1 Release this package as Apache Spark 2.0.0
>>>> [ ] -1 Do not release this package because ...
>>>>
>>>>
>>>> The tag to be voted on is v2.0.0-rc5
>>>> (13650fc58e1fcf2cf2a26ba11c819185ae1acc1f).
>>>>
>>>> This release candidate resolves ~2500 issues:
>>>> https://s.apache.org/spark-2.0.0-jira
>>>>
>>>> The release files, including signatures, digests, etc. can be found at:
>>>> http://people.apache.org/~pwendell/spark-releases/spark-2.0.0-rc5-bin/
>>>>
>>>> Release artifacts are signed with the following key:
>>>> https://people.apache.org/keys/committer/pwendell.asc
>>>>
>>>> The staging repository for this release can be found at:
>>>> https://repository.apache.org/content/repositories/orgapachespark-1195/
>>>>
>>>> The documentation corresponding to this release can be found at:
>>>> http://people.apache.org/~pwendell/spark-releases/spark-2.0.0-rc5-docs/
>>>>
>>>>
>>>> =================================
>>>> How can I help test this release?
>>>> =================================
>>>> If you are a Spark user, you can help us test this release by taking an
>>>> existing Spark workload and running on this release candidate, then
>>>> reporting any regressions from 1.x.
>>>>
>>>> ==========================================
>>>> What justifies a -1 vote for this release?
>>>> ==========================================
>>>> Critical bugs impacting major functionalities.
>>>>
>>>> Bugs already present in 1.x, missing features, or bugs related to new
>>>> features will not necessarily block this release. Note that historically
>>>> Spark documentation has been published on the website separately from the
>>>> main release so we do not need to block the release due to documentation
>>>> errors either.
>>>>
>>>>
>>>
>
