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From Krishna Sankar <ksanka...@gmail.com>
Subject Re: [VOTE] Release Apache Spark 1.5.0 (RC3)
Date Fri, 04 Sep 2015 21:22:49 GMT
Yin,
   It is the
https://github.com/xsankar/global-bd-conf/blob/master/004-Orders.ipynb.
Cheers
<k/>

On Fri, Sep 4, 2015 at 9:58 AM, Yin Huai <yhuai@databricks.com> wrote:

> Hi Krishna,
>
> Can you share your code to reproduce the memory allocation issue?
>
> Thanks,
>
> Yin
>
> On Fri, Sep 4, 2015 at 8:00 AM, Krishna Sankar <ksankar42@gmail.com>
> wrote:
>
>> Thanks Tom.  Interestingly it happened between RC2 and RC3.
>> Now my vote is +1/2 unless the memory error is known and has a workaround.
>>
>> Cheers
>> <k/>
>>
>>
>> On Fri, Sep 4, 2015 at 7:30 AM, Tom Graves <tgraves_cs@yahoo.com> wrote:
>>
>>> The upper/lower case thing is known.
>>> https://issues.apache.org/jira/browse/SPARK-9550
>>> I assume it was decided to be ok and its going to be in the release
>>> notes  but Reynold or Josh can probably speak to it more.
>>>
>>> Tom
>>>
>>>
>>>
>>> On Thursday, September 3, 2015 10:21 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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