[SPARK-18236] Reduce duplicate objects in Spark UI and HistoryServer

## What changes were proposed in this pull request?

When profiling heap dumps from the HistoryServer and live Spark web UIs, I found a large amount of memory being wasted on duplicated objects and strings. This patch's changes remove most of this duplication, resulting in over 40% memory savings for some benchmarks.

- **Task metrics** (6441f0624dfcda9c7193a64bfb416a145b5aabdf): previously, every `TaskUIData` object would have its own instances of `InputMetricsUIData`, `OutputMetricsUIData`, `ShuffleReadMetrics`, and `ShuffleWriteMetrics`, but for many tasks these metrics are irrelevant because they're all zero. This patch changes how we construct these metrics in order to re-use a single immutable "empty" value for the cases where these metrics are empty.
- **TaskInfo.accumulables** (ade86db901127bf13c0e0bdc3f09c933a093bb76): Previously, every `TaskInfo` object had its own empty `ListBuffer` for holding updates from named accumulators. Tasks which didn't use named accumulators still paid for the cost of allocating and storing this empty buffer. To avoid this overhead, I changed the `val` with a mutable buffer into a `var` which holds an immutable Scala list, allowing tasks which do not have named accumulator updates to share the same singleton `Nil` object.
- **String.intern() in JSONProtocol** (7e05630e9a78c455db8c8c499f0590c864624e05): in the HistoryServer, executor hostnames and ids are deserialized from JSON, leading to massive duplication of these string objects. By calling `String.intern()` on the deserialized values we can remove all of this duplication. Since Spark now requires Java 7+ we don't have to worry about string interning exhausting the permgen (see http://java-performance.info/string-intern-in-java-6-7-8/).

## How was this patch tested?

I ran

```
sc.parallelize(1 to 100000, 100000).count()
```

in `spark-shell` with event logging enabled, then loaded that event log in the HistoryServer, performed a full GC, and took a heap dump. According to YourKit, the changes in this patch reduced memory consumption by roughly 28 megabytes (or 770k Java objects):

![image](https://cloud.githubusercontent.com/assets/50748/19953276/4f3a28aa-a129-11e6-93df-d7fa91396f66.png)

Here's a table illustrating the drop in objects due to deduplication (the drop is <100k for some objects because some events were dropped from the listener bus; this is a separate, existing bug that I'll address separately after CPU-profiling):

![image](https://cloud.githubusercontent.com/assets/50748/19953290/6a271290-a129-11e6-93ad-b825f1448886.png)

Author: Josh Rosen <joshrosen@databricks.com>

Closes #15743 from JoshRosen/spark-ui-memory-usage.

1 files changed, 4 insertions, 1 deletions

diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 350b144f82..12f7ed202b 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -86,7 +86,10 @@ object MimaExcludes {
       // [SPARK-18034] Upgrade to MiMa 0.1.11 to fix flakiness.
       ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.param.shared.HasAggregationDepth.aggregationDepth"),
       ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.param.shared.HasAggregationDepth.getAggregationDepth"),
-      ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.param.shared.HasAggregationDepth.org$apache$spark$ml$param$shared$HasAggregationDepth$_setter_$aggregationDepth_=")
+      ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.param.shared.HasAggregationDepth.org$apache$spark$ml$param$shared$HasAggregationDepth$_setter_$aggregationDepth_="),
+
+      // [SPARK-18236] Reduce duplicate objects in Spark UI and HistoryServer
+      ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.scheduler.TaskInfo.accumulables")
     )
   }