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From Denny Lee <denny.g....@gmail.com>
Subject Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
Date Fri, 04 Sep 2015 05:13:31 GMT
+1

Distinct count test is blazing fast - awesome!,

On Thu, Sep 3, 2015 at 8:21 PM Krishna Sankar <ksankar42@gmail.com> wrote:

> +?
>
> 1. Compiled OSX 10.10 (Yosemite) OK Total time: 26:09 min
>      mvn clean package -Pyarn -Phadoop-2.6 -DskipTests
> 2. Tested pyspark, mllib
> 2.1. statistics (min,max,mean,Pearson,Spearman) OK
> 2.2. Linear/Ridge/Laso Regression OK
> 2.3. Decision Tree, Naive Bayes OK
> 2.4. 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 4.3 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_2.11:1.2.0-s_2.11 didn’t work. But
> com.databricks:spark-csv_2.11:1.2.0 worked)
> 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
>
> Two Problems:
>
> 1. The synthetic column names are lowercase ( i.e. now ‘sum(OrderPrice)’;
> previously ‘SUM(OrderPrice)’, now ‘avg(Total)’; previously 'AVG(Total)').
> So programs that depend on the case of the synthetic column names would
> fail.
> 2. orders_3.groupBy("Year","Month").sum('Total').show()
>     fails with the error ‘java.io.IOException: Unable to acquire 4194304
> bytes of memory’
>     orders_3.groupBy("CustomerID","Year").sum('Total').show() - fails with
> the same error
>     Is this a known bug ?
> Cheers
> <k/>
> P.S: Sorry for the spam, forgot Reply All
>
> On Tue, Sep 1, 2015 at 1:41 PM, Reynold Xin <rxin@databricks.com> wrote:
>
>> Please vote on releasing the following candidate as Apache Spark version
>> 1.5.0. The vote is open until Friday, Sep 4, 2015 at 21:00 UTC and passes
>> if a majority of at least 3 +1 PMC votes are cast.
>>
>> [ ] +1 Release this package as Apache Spark 1.5.0
>> [ ] -1 Do not release this package because ...
>>
>> To learn more about Apache Spark, please see http://spark.apache.org/
>>
>>
>> The tag to be voted on is v1.5.0-rc3:
>>
>> https://github.com/apache/spark/commit/908e37bcc10132bb2aa7f80ae694a9df6e40f31a
>>
>> The release files, including signatures, digests, etc. can be found at:
>> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-bin/
>>
>> Release artifacts are signed with the following key:
>> https://people.apache.org/keys/committer/pwendell.asc
>>
>> The staging repository for this release (published as 1.5.0-rc3) can be
>> found at:
>> https://repository.apache.org/content/repositories/orgapachespark-1143/
>>
>> The staging repository for this release (published as 1.5.0) can be found
>> at:
>> https://repository.apache.org/content/repositories/orgapachespark-1142/
>>
>> The documentation corresponding to this release can be found at:
>> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-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.
>>
>>
>> ================================================
>> What justifies a -1 vote for this release?
>> ================================================
>> This vote is happening towards the end of the 1.5 QA period, so -1 votes
>> should only occur for significant regressions from 1.4. Bugs already
>> present in 1.4, minor regressions, or bugs related to new features will not
>> block this release.
>>
>>
>> ===============================================================
>> What should happen to JIRA tickets still targeting 1.5.0?
>> ===============================================================
>> 1. It is OK for documentation patches to target 1.5.0 and still go into
>> branch-1.5, since documentations will be packaged separately from the
>> release.
>> 2. New features for non-alpha-modules should target 1.6+.
>> 3. Non-blocker bug fixes should target 1.5.1 or 1.6.0, or drop the target
>> version.
>>
>>
>> ==================================================
>> Major changes to help you focus your testing
>> ==================================================
>>
>> As of today, Spark 1.5 contains more than 1000 commits from 220+
>> contributors. I've curated a list of important changes for 1.5. For the
>> complete list, please refer to Apache JIRA changelog.
>>
>> RDD/DataFrame/SQL APIs
>>
>> - New UDAF interface
>> - DataFrame hints for broadcast join
>> - expr function for turning a SQL expression into DataFrame column
>> - Improved support for NaN values
>> - StructType now supports ordering
>> - TimestampType precision is reduced to 1us
>> - 100 new built-in expressions, including date/time, string, math
>> - memory and local disk only checkpointing
>>
>> DataFrame/SQL Backend Execution
>>
>> - Code generation on by default
>> - Improved join, aggregation, shuffle, sorting with cache friendly
>> algorithms and external algorithms
>> - Improved window function performance
>> - Better metrics instrumentation and reporting for DF/SQL execution plans
>>
>> Data Sources, Hive, Hadoop, Mesos and Cluster Management
>>
>> - Dynamic allocation support in all resource managers (Mesos, YARN,
>> Standalone)
>> - Improved Mesos support (framework authentication, roles, dynamic
>> allocation, constraints)
>> - Improved YARN support (dynamic allocation with preferred locations)
>> - Improved Hive support (metastore partition pruning, metastore
>> connectivity to 0.13 to 1.2, internal Hive upgrade to 1.2)
>> - Support persisting data in Hive compatible format in metastore
>> - Support data partitioning for JSON data sources
>> - Parquet improvements (upgrade to 1.7, predicate pushdown, faster
>> metadata discovery and schema merging, support reading non-standard legacy
>> Parquet files generated by other libraries)
>> - Faster and more robust dynamic partition insert
>> - DataSourceRegister interface for external data sources to specify short
>> names
>>
>> SparkR
>>
>> - YARN cluster mode in R
>> - GLMs with R formula, binomial/Gaussian families, and elastic-net
>> regularization
>> - Improved error messages
>> - Aliases to make DataFrame functions more R-like
>>
>> Streaming
>>
>> - Backpressure for handling bursty input streams.
>> - Improved Python support for streaming sources (Kafka offsets, Kinesis,
>> MQTT, Flume)
>> - Improved Python streaming machine learning algorithms (K-Means, linear
>> regression, logistic regression)
>> - Native reliable Kinesis stream support
>> - Input metadata like Kafka offsets made visible in the batch details UI
>> - Better load balancing and scheduling of receivers across cluster
>> - Include streaming storage in web UI
>>
>> Machine Learning and Advanced Analytics
>>
>> - Feature transformers: CountVectorizer, Discrete Cosine transformation,
>> MinMaxScaler, NGram, PCA, RFormula, StopWordsRemover, and VectorSlicer.
>> - Estimators under pipeline APIs: naive Bayes, k-means, and isotonic
>> regression.
>> - Algorithms: multilayer perceptron classifier, PrefixSpan for sequential
>> pattern mining, association rule generation, 1-sample Kolmogorov-Smirnov
>> test.
>> - Improvements to existing algorithms: LDA, trees/ensembles, GMMs
>> - More efficient Pregel API implementation for GraphX
>> - Model summary for linear and logistic regression.
>> - Python API: distributed matrices, streaming k-means and linear models,
>> LDA, power iteration clustering, etc.
>> - Tuning and evaluation: train-validation split and multiclass
>> classification evaluator.
>> - Documentation: document the release version of public API methods
>>
>>
>>
>

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