workaround for #7628

StartHere.ipynb

@@ -86,7 +86,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   },
   "toc": {
    "base_numbering": 1,

doc/scala/Flint.ipynb

@@ -13,7 +13,9 @@
     "Unlike `DataFrame` and `Dataset`, Flint's `TimeSeriesRDD`s can leverage the existing ordering properties of datasets at rest and the fact that almost all data manipulations and analysis over these datasets respect their temporal ordering properties.\n",
     "It differs from other time series efforts in Spark in its ability to efficiently compute across panel data or on large scale high frequency data.\n",
     "\n",
-    "This example uses `prices.csv` file from [Kaggle](https://www.kaggle.com/dgawlik/nyse). For it to work you need to get it and put it in `/tmp/prices.csv`."
+    "This example uses `prices.csv` file from [Kaggle](https://www.kaggle.com/dgawlik/nyse). For it to work you need to get it and put it in `/tmp/prices.csv`.\n",
+    "\n",
+    "The `io.netty` lines are a workaround for a temporary upstream problem, see [#7628](https://github.com/twosigma/beakerx/issues/7628)."
    ]
   },
   {
@@ -23,7 +25,10 @@
    "outputs": [],
    "source": [
     "%%classpath add mvn\n",
-    "com.github.twosigma flint master-SNAPSHOT\n",
+    "io.netty netty-all 4.1.25.Final\n",
+    "io.netty netty-buffer 4.1.25.Final\n",
+    "io.netty netty-common 4.1.25.Final\n",
+    "com.github.twosigma flint master-b560b000bc-1\n",
     "org.apache.spark spark-sql_2.11 2.2.1\n",
     "org.apache.spark spark-mllib_2.11 2.2.1"
    ]
@@ -178,7 +183,7 @@
    "outputs": [],
    "source": [
     "// Calculate logarithm of a column\n",
-    "val logVolumeRdd = pricesRdd.addColumns(\"logVolume\" -> DoubleType -> { row => Math.log(row.getAs[Double](\"volume\")) })\n",
+    "val logVolumeRdd = pricesRdd.addColumns(\"logVolume\" -> DoubleType -> { row => scala.math.log(row.getAs[Double](\"volume\")) })\n",
     "preview(pricesRdd)"
    ]
   },
@@ -189,7 +194,7 @@
    "outputs": [],
    "source": [
     "// Raise a column to an exponent\n",
-    "val squaredVolumeRdd = pricesRdd.addColumns(\"squaredVolume\" -> DoubleType -> { row => Math.pow(row.getAs[Double](\"volume\"), 2) })\n",
+    "val squaredVolumeRdd = pricesRdd.addColumns(\"squaredVolume\" -> DoubleType -> { row => scala.math.pow(row.getAs[Double](\"volume\"), 2) })\n",
     "preview(squaredVolumeRdd)"
    ]
   },
@@ -339,8 +344,8 @@
     "// Compute the Z score across an interval\n",
     "val zScoreRdd = pricesRdd.addColumnsForCycle(\"volumeZScore\" -> DoubleType -> { rows: Seq[Row] =>\n",
     "    val mean = rows.map(_.getAs[Double](\"volume\")).sum / rows.size\n",
-    "    val stddev = Math.sqrt(rows.map { row =>\n",
-    "        Math.pow(row.getAs[Double](\"close\") - mean, 2)\n",
+    "    val stddev = scala.math.sqrt(rows.map { row =>\n",
+    "        scala.math.pow(row.getAs[Double](\"close\") - mean, 2)\n",
     "    }.sum ) / (rows.size - 1)\n",
     "    rows.map { row =>\n",
     "        row -> (row.getAs[Double](\"close\") - mean) / stddev\n",

doc/scala/SparkUI.ipynb

@@ -8,7 +8,9 @@
     "\n",
     "BeakerX has a Spark magic that provides deeper integration with Spark.  It provides a GUI dialog for connecting to a cluster, a progress meter that shows how your job is working and links to the regular Spark UI, and it forwards kernel interrupt messages onto the cluster so you can stop a job without leaving the notebook, and it automatically displays Datasets using an interactive widget.  Finally, it automatically closes the Spark session when the notebook is closed.\n",
     "\n",
-    "It is compatible with Spark version 2.x."
+    "It is compatible with Spark version 2.x.\n",
+    "\n",
+    "The `io.netty` and flint lines are a workaround for a temporary upstream problem, see [#7628](https://github.com/twosigma/beakerx/issues/7628)."
    ]
   },
   {
@@ -20,7 +22,11 @@
    "outputs": [],
    "source": [
     "%%classpath add mvn\n",
-    "org.apache.spark spark-sql_2.11 2.3.1"
+    "io.netty netty-all 4.1.25.Final\n",
+    "io.netty netty-buffer 4.1.25.Final\n",
+    "io.netty netty-common 4.1.25.Final\n",
+    "com.github.twosigma flint master-b560b000bc-1\n",
+    "org.apache.spark spark-sql_2.11 2.2.1"
    ]
   },
   {