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19 | 19 | // $example on:schema_merging$
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20 | 20 | import java.io.Serializable;
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21 | 21 | import java.util.ArrayList;
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| 22 | +import java.util.Arrays; |
22 | 23 | import java.util.List;
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23 | 24 | // $example off:schema_merging$
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24 | 25 |
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25 | 26 | // $example on:basic_parquet_example$
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| 27 | +import org.apache.spark.api.java.JavaRDD; |
| 28 | +import org.apache.spark.api.java.JavaSparkContext; |
26 | 29 | import org.apache.spark.api.java.function.MapFunction;
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27 | 30 | import org.apache.spark.sql.Encoders;
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28 | 31 | // $example on:schema_merging$
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@@ -213,6 +216,19 @@ private static void runJsonDatasetExample(SparkSession spark) {
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213 | 216 | // +------+
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214 | 217 | // |Justin|
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215 | 218 | // +------+
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| 219 | + |
| 220 | + // Alternatively, a DataFrame can be created for a JSON dataset represented by |
| 221 | + // an RDD[String] storing one JSON object per string. |
| 222 | + List<String> jsonData = Arrays.asList( |
| 223 | + "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}"); |
| 224 | + JavaRDD<String> anotherPeopleRDD = new JavaSparkContext(spark.sparkContext()).parallelize(jsonData); |
| 225 | + Dataset anotherPeople = spark.read().json(anotherPeopleRDD); |
| 226 | + anotherPeople.show(); |
| 227 | + // +---------------+----+ |
| 228 | + // | address|name| |
| 229 | + // +---------------+----+ |
| 230 | + // |[Columbus,Ohio]| Yin| |
| 231 | + // +---------------+----+ |
216 | 232 | // $example off:json_dataset$
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217 | 233 | }
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218 | 234 |
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