+1. Tested on Yarn with Hadoop 2.6.
A few of the things tested: pyspark, hive integration, aux shuffle handler,
history server, basic submit cli behavior, distributed cache behavior, cluster
and client mode...
Tom
On Tuesday, September 1, 2015 3:42 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, 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
