I've seen similar tar file warnings and in my case it was because I was using the default tar on a Macbook. Using gnu-tar from brew made the warnings go away.
Thanks Shivaram On Fri, Aug 28, 2015 at 2:37 PM, Luciano Resende <luckbr1...@gmail.com> wrote: > The binary archives seems to be having some issues, which seems consistent > on few of the different ones (different versions of hadoop) that I tried. > > tar -xvf spark-1.5.0-bin-hadoop2.6.tgz > > x spark-1.5.0-bin-hadoop2.6/lib/spark-examples-1.5.0-hadoop2.6.0.jar > x spark-1.5.0-bin-hadoop2.6/lib/spark-assembly-1.5.0-hadoop2.6.0.jar > x spark-1.5.0-bin-hadoop2.6/lib/spark-1.5.0-yarn-shuffle.jar > x spark-1.5.0-bin-hadoop2.6/README.md > tar: copyfile unpack > (spark-1.5.0-bin-hadoop2.6/python/test_support/sql/orc_partitioned/SUCCESS.crc) > failed: No such file or directory > > tar tzf spark-1.5.0-bin-hadoop2.3.tgz | grep SUCCESS.crc > spark-1.5.0-bin-hadoop2.3/python/test_support/sql/orc_partitioned/._SUCCESS.crc > > This seems similar to a problem Avro release was having recently. > > > On Tue, Aug 25, 2015 at 9:28 PM, Reynold Xin <r...@databricks.com> wrote: >> >> Please vote on releasing the following candidate as Apache Spark version >> 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 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-rc2: >> >> https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a >> >> The release files, including signatures, digests, etc. can be found at: >> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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-rc2) can be >> found at: >> https://repository.apache.org/content/repositories/orgapachespark-1141/ >> >> The staging repository for this release (published as 1.5.0) can be found >> at: >> https://repository.apache.org/content/repositories/orgapachespark-1140/ >> >> The documentation corresponding to this release can be found at: >> http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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 >> > > > > -- > Luciano Resende > http://people.apache.org/~lresende > http://twitter.com/lresende1975 > http://lresende.blogspot.com/ --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
