Incorrect link in Kafka API in 1.3 release notes

1 files changed, 1 insertions, 1 deletions

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).