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| -rw-r--r-- | releases/_posts/2015-03-13-spark-release-1-3-0.md | 2 | ||||
| -rw-r--r-- | site/releases/spark-release-1-3-0.html | 2 |
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diff --git a/releases/_posts/2015-03-13-spark-release-1-3-0.md b/releases/_posts/2015-03-13-spark-release-1-3-0.md index 6c0a53288..a7c2a7924 100644 --- a/releases/_posts/2015-03-13-spark-release-1-3-0.md +++ b/releases/_posts/2015-03-13-spark-release-1-3-0.md @@ -28,7 +28,7 @@ In this release Spark SQL [graduates from an alpha project](https://issues.apach 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, https://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 API’s 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 API’s 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). diff --git a/site/releases/spark-release-1-3-0.html b/site/releases/spark-release-1-3-0.html index a104b053a..4fa5fba8e 100644 --- a/site/releases/spark-release-1-3-0.html +++ b/site/releases/spark-release-1-3-0.html @@ -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-means 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 API’s 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 clarifications 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 API’s 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 clarifications 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> |