[SPARK-16351][SQL] Avoid per-record type dispatch in JSON when writing

## What changes were proposed in this pull request?

Currently, `JacksonGenerator.apply` is doing type-based dispatch for each row to write appropriate values.
It might not have to be done like this because the schema is already kept.

So, appropriate writers can be created first according to the schema once, and then apply them to each row. This approach is similar with `CatalystWriteSupport`.

This PR corrects `JacksonGenerator` so that it creates all writers for the schema once and then applies them to each row rather than type dispatching for every row.

Benchmark was proceeded with the codes below:

```scala
test("Benchmark for JSON writer") {
  val N = 500 << 8
  val row =
    """{"struct":{"field1": true, "field2": 92233720368547758070},
      "structWithArrayFields":{"field1":[4, 5, 6], "field2":["str1", "str2"]},
      "arrayOfString":["str1", "str2"],
      "arrayOfInteger":[1, 2147483647, -2147483648],
      "arrayOfLong":[21474836470, 9223372036854775807, -9223372036854775808],
      "arrayOfBigInteger":[922337203685477580700, -922337203685477580800],
      "arrayOfDouble":[1.2, 1.7976931348623157E308, 4.9E-324, 2.2250738585072014E-308],
      "arrayOfBoolean":[true, false, true],
      "arrayOfNull":[null, null, null, null],
      "arrayOfStruct":[{"field1": true, "field2": "str1"}, {"field1": false}, {"field3": null}],
      "arrayOfArray1":[[1, 2, 3], ["str1", "str2"]],
      "arrayOfArray2":[[1, 2, 3], [1.1, 2.1, 3.1]]
     }"""
  val df = spark.sqlContext.read.json(spark.sparkContext.parallelize(List.fill(N)(row)))
  val benchmark = new Benchmark("JSON writer", N)
  benchmark.addCase("writing JSON file", 10) { _ =>
    withTempPath { path =>
      df.write.format("json").save(path.getCanonicalPath)
    }
  }
  benchmark.run()
}
```

This produced the results below

- **Before**

```
JSON writer:                             Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------
writing JSON file                             1675 / 1767          0.1       13087.5       1.0X
```

- **After**

```
JSON writer:                             Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------
writing JSON file                             1597 / 1686          0.1       12477.1       1.0X
```

In addition, I ran this benchmark 10 times for each and calculated the average elapsed time as below:

| **Before** | **After**|
|---------------|------------|
|17478ms  |16669ms |

It seems roughly ~5% is improved.

## How was this patch tested?

Existing tests should cover this.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #14028 from HyukjinKwon/SPARK-16351.

1 files changed, 0 insertions, 3 deletions

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
index 6c72019702..a09f61aba9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
@@ -21,10 +21,7 @@ import java.io.{File, StringWriter}
 import java.nio.charset.StandardCharsets
 import java.sql.{Date, Timestamp}
 
-import scala.collection.JavaConverters._
-
 import com.fasterxml.jackson.core.JsonFactory
-import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{Path, PathFilter}
 import org.apache.hadoop.io.SequenceFile.CompressionType
 import org.apache.hadoop.io.compress.GzipCodec