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Diffstat (limited to 'docs/hardware-provisioning.md')
-rw-r--r-- | docs/hardware-provisioning.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/docs/hardware-provisioning.md b/docs/hardware-provisioning.md index e5f054cb14..790220500a 100644 --- a/docs/hardware-provisioning.md +++ b/docs/hardware-provisioning.md @@ -43,7 +43,7 @@ rest for the operating system and buffer cache. How much memory you will need will depend on your application. To determine how much your application uses for a certain dataset size, load part of your dataset in a Spark RDD and use the -Storage tab of Spark's monitoring UI (`http://<driver-node>:3030`) to see its size in memory. +Storage tab of Spark's monitoring UI (`http://<driver-node>:4040`) to see its size in memory. Note that memory usage is greatly affected by storage level and serialization format -- see the [tuning guide](tuning.html) for tips on how to reduce it. @@ -59,7 +59,7 @@ In our experience, when the data is in memory, a lot of Spark applications are n Using a **10 Gigabit** or higher network is the best way to make these applications faster. This is especially true for "distributed reduce" applications such as group-bys, reduce-bys, and SQL joins. In any given application, you can see how much data Spark shuffles across the network -from the application's monitoring UI (`http://<driver-node>:3030`). +from the application's monitoring UI (`http://<driver-node>:4040`). # CPU Cores |