[Update] Update spark version in pyspark-notebook and all-spark-notebook README

all-spark-notebook/README.md

@@ -11,8 +11,8 @@
 * Scala 2.11.x
 * pyspark, pandas, matplotlib, scipy, seaborn, scikit-learn pre-installed for Python
 * ggplot2, rcurl preinstalled for R
-* Spark 2.0.2 with Hadoop 2.7 for use in local mode or to connect to a cluster of Spark workers
-* Mesos client 0.25 binary that can communicate with a Mesos master
+* Spark 2.1.1 with Hadoop 2.7 for use in local mode or to connect to a cluster of Spark workers
+* Mesos client 1.2 binary that can communicate with a Mesos master
 * spylon-kernel
 * Unprivileged user `jovyan` (uid=1000, configurable, see options) in group `users` (gid=100) with ownership over `/home/jovyan` and `/opt/conda`
 * [tini](https://github.com/krallin/tini) as the container entrypoint and [start-notebook.sh](../base-notebook/start-notebook.sh) as the default command
@@ -124,8 +124,8 @@ conf = pyspark.SparkConf()
 # point to mesos master or zookeeper entry (e.g., zk://10.10.10.10:2181/mesos)
 conf.setMaster("mesos://10.10.10.10:5050")
 # point to spark binary package in HDFS or on local filesystem on all slave
-# nodes (e.g., file:///opt/spark/spark-2.0.2-bin-hadoop2.7.tgz)
-conf.set("spark.executor.uri", "hdfs://10.10.10.10/spark/spark-2.0.2-bin-hadoop2.7.tgz")
+# nodes (e.g., file:///opt/spark/spark-2.1.1-bin-hadoop2.7.tgz)
+conf.set("spark.executor.uri", "hdfs://10.10.10.10/spark/spark-2.1.1-bin-hadoop2.7.tgz")
 # set other options as desired
 conf.set("spark.executor.memory", "8g")
 conf.set("spark.core.connection.ack.wait.timeout", "1200")
@@ -157,10 +157,10 @@ library(SparkR)
 # point to mesos master or zookeeper entry (e.g., zk://10.10.10.10:2181/mesos)\
 # as the first argument
 # point to spark binary package in HDFS or on local filesystem on all slave
-# nodes (e.g., file:///opt/spark/spark-2.0.2-bin-hadoop2.7.tgz) in sparkEnvir
+# nodes (e.g., file:///opt/spark/spark-2.1.1-bin-hadoop2.7.tgz) in sparkEnvir
 # set other options in sparkEnvir
 sc <- sparkR.session("mesos://10.10.10.10:5050", sparkEnvir=list(
-    spark.executor.uri="hdfs://10.10.10.10/spark/spark-2.0.2-bin-hadoop2.7.tgz",
+    spark.executor.uri="hdfs://10.10.10.10/spark/spark-2.1.1-bin-hadoop2.7.tgz",
     spark.executor.memory="8g"
     )
 )
@@ -183,7 +183,7 @@ The Apache Toree kernel automatically creates a `SparkContext` when it starts ba
 For instance, to pass information about a Mesos master, Spark binary location in HDFS, and an executor options, you could start the container like so:
 
 `docker run -d -p 8888:8888 -e SPARK_OPTS '--master=mesos://10.10.10.10:5050 \
-    --spark.executor.uri=hdfs://10.10.10.10/spark/spark-2.0.2-bin-hadoop2.7.tgz \
+    --spark.executor.uri=hdfs://10.10.10.10/spark/spark-2.1.1-bin-hadoop2.7.tgz \
     --spark.executor.memory=8g' jupyter/all-spark-notebook`
 
 Note that this is the same information expressed in a notebook in the Python case above. Once the kernel spec has your cluster information, you can test your cluster in an Apache Toree notebook like so:

pyspark-notebook/README.md

@@ -70,8 +70,8 @@ conf = pyspark.SparkConf()
 # point to mesos master or zookeeper entry (e.g., zk://10.10.10.10:2181/mesos)
 conf.setMaster("mesos://10.10.10.10:5050")
 # point to spark binary package in HDFS or on local filesystem on all slave
-# nodes (e.g., file:///opt/spark/spark-2.0.2-bin-hadoop2.7.tgz)
-conf.set("spark.executor.uri", "hdfs://10.122.193.209/spark/spark-2.0.2-bin-hadoop2.7.tgz")
+# nodes (e.g., file:///opt/spark/spark-2.1.1-bin-hadoop2.7.tgz)
+conf.set("spark.executor.uri", "hdfs://10.122.193.209/spark/spark-2.1.1-bin-hadoop2.7.tgz")
 # set other options as desired
 conf.set("spark.executor.memory", "8g")
 conf.set("spark.core.connection.ack.wait.timeout", "1200")