Author: pwendell
Date: Fri Mar 13 17:15:41 2015
New Revision: 1666516
URL: http://svn.apache.org/r1666516
Log:
Incorrect link in Kafka API in 1.3 release notes
Modified:
spark/releases/_posts/2015-03-13-spark-release-1-3-0.md
spark/site/releases/spark-release-1-3-0.html
Modified: spark/releases/_posts/2015-03-13-spark-release-1-3-0.md
URL:
http://svn.apache.org/viewvc/spark/releases/_posts/2015-03-13-spark-release-1-3-0.md?rev=1666516&r1=1666515&r2=1666516&view=diff
==============================================================================
--- spark/releases/_posts/2015-03-13-spark-release-1-3-0.md (original)
+++ spark/releases/_posts/2015-03-13-spark-release-1-3-0.md Fri Mar 13 17:15:41
2015
@@ -28,7 +28,7 @@ In this release Spark SQL [graduates fro
In this release Spark MLlib introduces several new algorithms: latent
Dirichlet allocation (LDA) for [topic
modeling](https://issues.apache.org/jira/browse/SPARK-1405), [multinomial
logistic regression](https://issues.apache.org/jira/browse/SPARK-2309) for
multiclass classification, [Gaussian mixture model
(GMM)](https://issues.apache.org/jira/browse/SPARK-5012) and [power iteration
clustering](https://issues.apache.org/jira/browse/SPARK-4259) for clustering,
[FP-growth](https://issues.apache.org/jira/browse/SPARK-4001) for frequent
pattern mining, and [block matrix
abstraction](https://issues.apache.org/jira/browse/SPARK-4409) for distributed
linear algebra. Initial support has been added for [model
import/export](https://issues.apache.org/jira/browse/SPARK-4587) in
exchangeable format, which will be expanded in future versions to cover more
model types in Java/Python/Scala. The implementations of k-means and ALS
receive [updates](https://issues.apache.org/jira/browse/SPARK-3424, h
ttps://issues.apache.org/jira/browse/SPARK-3541) that lead to significant
performance gain. PySpark now supports the [ML pipeline
API](https://issues.apache.org/jira/browse/SPARK-4586) added in Spark 1.2, and
[gradient boosted trees](https://issues.apache.org/jira/browse/SPARK-5094) and
[Gaussian mixture model](https://issues.apache.org/jira/browse/SPARK-5012).
Finally, the ML pipeline API has been ported to support the new DataFrames
abstraction.
### Spark Streaming
-Spark 1.3 introduces a new [*direct* Kafka
API](https://issues.apache.org/jira/browse/SPARK-6946)
([docs](http://spark.apache.org/docs/1.3.0/streaming-kafka-integration.html))
which enables exactly-once delivery without the use of write ahead logs. It
also adds a [Python Kafka
API](https://issues.apache.org/jira/browse/SPARK-5047) along with
infrastructure for additional Python APIâs in future releases. An online
version of [logistic
regression](https://issues.apache.org/jira/browse/SPARK-4979) and the ability
to read [binary records](https://issues.apache.org/jira/browse/SPARK-4969) have
also been added. For stateful operations, support has been added for loading of
an [initial state RDD](https://issues.apache.org/jira/browse/SPARK-3660).
Finally, the streaming programming guide has been updated to include
information about SQL and DataFrame operations within streaming applications,
and important clarifications to the fault-tolerance semantics.
+Spark 1.3 introduces a new [*direct* Kafka
API](https://issues.apache.org/jira/browse/SPARK-4964)
([docs](http://spark.apache.org/docs/1.3.0/streaming-kafka-integration.html))
which enables exactly-once delivery without the use of write ahead logs. It
also adds a [Python Kafka
API](https://issues.apache.org/jira/browse/SPARK-5047) along with
infrastructure for additional Python APIâs in future releases. An online
version of [logistic
regression](https://issues.apache.org/jira/browse/SPARK-4979) and the ability
to read [binary records](https://issues.apache.org/jira/browse/SPARK-4969) have
also been added. For stateful operations, support has been added for loading of
an [initial state RDD](https://issues.apache.org/jira/browse/SPARK-3660).
Finally, the streaming programming guide has been updated to include
information about SQL and DataFrame operations within streaming applications,
and important clarifications to the fault-tolerance semantics.
### GraphX
GraphX adds a handful of utility functions in this release, including
conversion into a [canonical edge
graph](https://issues.apache.org/jira/browse/SPARK-4917).
Modified: spark/site/releases/spark-release-1-3-0.html
URL:
http://svn.apache.org/viewvc/spark/site/releases/spark-release-1-3-0.html?rev=1666516&r1=1666515&r2=1666516&view=diff
==============================================================================
--- spark/site/releases/spark-release-1-3-0.html (original)
+++ spark/site/releases/spark-release-1-3-0.html Fri Mar 13 17:15:41 2015
@@ -187,7 +187,7 @@
<p>In this release Spark MLlib introduces several new algorithms: latent
Dirichlet allocation (LDA) for <a
href="https://issues.apache.org/jira/browse/SPARK-1405";>topic modeling</a>, <a
href="https://issues.apache.org/jira/browse/SPARK-2309";>multinomial logistic
regression</a> for multiclass classification, <a
href="https://issues.apache.org/jira/browse/SPARK-5012";>Gaussian mixture model
(GMM)</a> and <a href="https://issues.apache.org/jira/browse/SPARK-4259";>power
iteration clustering</a> for clustering, <a
href="https://issues.apache.org/jira/browse/SPARK-4001";>FP-growth</a> for
frequent pattern mining, and <a
href="https://issues.apache.org/jira/browse/SPARK-4409";>block matrix
abstraction</a> for distributed linear algebra. Initial support has been added
for <a href="https://issues.apache.org/jira/browse/SPARK-4587";>model
import/export</a> in exchangeable format, which will be expanded in future
versions to cover more model types in Java/Python/Scala. The implementations of
k-mea
ns and ALS receive <a href="https://issues.apache.org/jira/browse/SPARK-3424,
https://issues.apache.org/jira/browse/SPARK-3541";>updates</a> that lead to
significant performance gain. PySpark now supports the <a
href="https://issues.apache.org/jira/browse/SPARK-4586";>ML pipeline API</a>
added in Spark 1.2, and <a
href="https://issues.apache.org/jira/browse/SPARK-5094";>gradient boosted
trees</a> and <a
href="https://issues.apache.org/jira/browse/SPARK-5012";>Gaussian mixture
model</a>. Finally, the ML pipeline API has been ported to support the new
DataFrames abstraction.</p>
<h3 id="spark-streaming">Spark Streaming</h3>
-<p>Spark 1.3 introduces a new <a
href="https://issues.apache.org/jira/browse/SPARK-6946";><em>direct</em> Kafka
API</a> (<a
href="http://spark.apache.org/docs/1.3.0/streaming-kafka-integration.html";>docs</a>)
which enables exactly-once delivery without the use of write ahead logs. It
also adds a <a href="https://issues.apache.org/jira/browse/SPARK-5047";>Python
Kafka API</a> along with infrastructure for additional Python APIâs in future
releases. An online version of <a
href="https://issues.apache.org/jira/browse/SPARK-4979";>logistic regression</a>
and the ability to read <a
href="https://issues.apache.org/jira/browse/SPARK-4969";>binary records</a> have
also been added. For stateful operations, support has been added for loading of
an <a href="https://issues.apache.org/jira/browse/SPARK-3660";>initial state
RDD</a>. Finally, the streaming programming guide has been updated to include
information about SQL and DataFrame operations within streaming applications,
and important clari
fications to the fault-tolerance semantics. </p>
+<p>Spark 1.3 introduces a new <a
href="https://issues.apache.org/jira/browse/SPARK-4964";><em>direct</em> Kafka
API</a> (<a
href="http://spark.apache.org/docs/1.3.0/streaming-kafka-integration.html";>docs</a>)
which enables exactly-once delivery without the use of write ahead logs. It
also adds a <a href="https://issues.apache.org/jira/browse/SPARK-5047";>Python
Kafka API</a> along with infrastructure for additional Python APIâs in future
releases. An online version of <a
href="https://issues.apache.org/jira/browse/SPARK-4979";>logistic regression</a>
and the ability to read <a
href="https://issues.apache.org/jira/browse/SPARK-4969";>binary records</a> have
also been added. For stateful operations, support has been added for loading of
an <a href="https://issues.apache.org/jira/browse/SPARK-3660";>initial state
RDD</a>. Finally, the streaming programming guide has been updated to include
information about SQL and DataFrame operations within streaming applications,
and important clari
fications to the fault-tolerance semantics. </p>
<h3 id="graphx">GraphX</h3>
<p>GraphX adds a handful of utility functions in this release, including
conversion into a <a
href="https://issues.apache.org/jira/browse/SPARK-4917";>canonical edge
graph</a>.</p>
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