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author | Yin Huai <huai@cse.ohio-state.edu> | 2014-10-09 14:57:27 -0700 |
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committer | Michael Armbrust <michael@databricks.com> | 2014-10-09 14:57:27 -0700 |
commit | 1c7f0ab302de9f82b1bd6da852d133823bc67c66 (patch) | |
tree | b59cbdd05cd7fdc532da7c24333c6917a57a361b /sql/core/src/main | |
parent | 1faa1135a3fc0acd89f934f01a4a2edefcb93d33 (diff) | |
download | spark-1c7f0ab302de9f82b1bd6da852d133823bc67c66.tar.gz spark-1c7f0ab302de9f82b1bd6da852d133823bc67c66.tar.bz2 spark-1c7f0ab302de9f82b1bd6da852d133823bc67c66.zip |
[SPARK-3339][SQL] Support for skipping json lines that fail to parse
This PR aims to provide a way to skip/query corrupt JSON records. To do so, we introduce an internal column to hold corrupt records (the default name is `_corrupt_record`. This name can be changed by setting the value of `spark.sql.columnNameOfCorruptRecord`). When there is a parsing error, we will put the corrupt record in its unparsed format to the internal column. Users can skip/query this column through SQL.
* To query those corrupt records
```
-- For Hive parser
SELECT `_corrupt_record`
FROM jsonTable
WHERE `_corrupt_record` IS NOT NULL
-- For our SQL parser
SELECT _corrupt_record
FROM jsonTable
WHERE _corrupt_record IS NOT NULL
```
* To skip corrupt records and query regular records
```
-- For Hive parser
SELECT field1, field2
FROM jsonTable
WHERE `_corrupt_record` IS NULL
-- For our SQL parser
SELECT field1, field2
FROM jsonTable
WHERE _corrupt_record IS NULL
```
Generally, it is not recommended to change the name of the internal column. If the name has to be changed to avoid possible name conflicts, you can use `sqlContext.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, <new column name>)` or `sqlContext.sql(SET spark.sql.columnNameOfCorruptRecord=<new column name>)`.
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes #2680 from yhuai/corruptJsonRecord and squashes the following commits:
4c9828e [Yin Huai] Merge remote-tracking branch 'upstream/master' into corruptJsonRecord
309616a [Yin Huai] Change the default name of corrupt record to "_corrupt_record".
b4a3632 [Yin Huai] Merge remote-tracking branch 'upstream/master' into corruptJsonRecord
9375ae9 [Yin Huai] Set the column name of corrupt json record back to the default one after the unit test.
ee584c0 [Yin Huai] Provide a way to query corrupt json records as unparsed strings.
Diffstat (limited to 'sql/core/src/main')
4 files changed, 46 insertions, 18 deletions
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala index f6f4cf3b80..07e6e2eccd 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala @@ -35,6 +35,7 @@ private[spark] object SQLConf { val PARQUET_BINARY_AS_STRING = "spark.sql.parquet.binaryAsString" val PARQUET_CACHE_METADATA = "spark.sql.parquet.cacheMetadata" val PARQUET_COMPRESSION = "spark.sql.parquet.compression.codec" + val COLUMN_NAME_OF_CORRUPT_RECORD = "spark.sql.columnNameOfCorruptRecord" // This is only used for the thriftserver val THRIFTSERVER_POOL = "spark.sql.thriftserver.scheduler.pool" @@ -131,6 +132,9 @@ private[sql] trait SQLConf { private[spark] def inMemoryPartitionPruning: Boolean = getConf(IN_MEMORY_PARTITION_PRUNING, "false").toBoolean + private[spark] def columnNameOfCorruptRecord: String = + getConf(COLUMN_NAME_OF_CORRUPT_RECORD, "_corrupt_record") + /** ********************** SQLConf functionality methods ************ */ /** Set Spark SQL configuration properties. */ diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index 35561cac3e..014e1e2826 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -195,9 +195,12 @@ class SQLContext(@transient val sparkContext: SparkContext) */ @Experimental def jsonRDD(json: RDD[String], schema: StructType): SchemaRDD = { + val columnNameOfCorruptJsonRecord = columnNameOfCorruptRecord val appliedSchema = - Option(schema).getOrElse(JsonRDD.nullTypeToStringType(JsonRDD.inferSchema(json, 1.0))) - val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema) + Option(schema).getOrElse( + JsonRDD.nullTypeToStringType( + JsonRDD.inferSchema(json, 1.0, columnNameOfCorruptJsonRecord))) + val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord) applySchema(rowRDD, appliedSchema) } @@ -206,8 +209,11 @@ class SQLContext(@transient val sparkContext: SparkContext) */ @Experimental def jsonRDD(json: RDD[String], samplingRatio: Double): SchemaRDD = { - val appliedSchema = JsonRDD.nullTypeToStringType(JsonRDD.inferSchema(json, samplingRatio)) - val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema) + val columnNameOfCorruptJsonRecord = columnNameOfCorruptRecord + val appliedSchema = + JsonRDD.nullTypeToStringType( + JsonRDD.inferSchema(json, samplingRatio, columnNameOfCorruptJsonRecord)) + val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord) applySchema(rowRDD, appliedSchema) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala index c006c4330f..f8171c3be3 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala @@ -148,8 +148,12 @@ class JavaSQLContext(val sqlContext: SQLContext) extends UDFRegistration { * It goes through the entire dataset once to determine the schema. */ def jsonRDD(json: JavaRDD[String]): JavaSchemaRDD = { - val appliedScalaSchema = JsonRDD.nullTypeToStringType(JsonRDD.inferSchema(json.rdd, 1.0)) - val scalaRowRDD = JsonRDD.jsonStringToRow(json.rdd, appliedScalaSchema) + val columnNameOfCorruptJsonRecord = sqlContext.columnNameOfCorruptRecord + val appliedScalaSchema = + JsonRDD.nullTypeToStringType( + JsonRDD.inferSchema(json.rdd, 1.0, columnNameOfCorruptJsonRecord)) + val scalaRowRDD = + JsonRDD.jsonStringToRow(json.rdd, appliedScalaSchema, columnNameOfCorruptJsonRecord) val logicalPlan = LogicalRDD(appliedScalaSchema.toAttributes, scalaRowRDD)(sqlContext) new JavaSchemaRDD(sqlContext, logicalPlan) @@ -162,10 +166,14 @@ class JavaSQLContext(val sqlContext: SQLContext) extends UDFRegistration { */ @Experimental def jsonRDD(json: JavaRDD[String], schema: StructType): JavaSchemaRDD = { + val columnNameOfCorruptJsonRecord = sqlContext.columnNameOfCorruptRecord val appliedScalaSchema = Option(asScalaDataType(schema)).getOrElse( - JsonRDD.nullTypeToStringType(JsonRDD.inferSchema(json.rdd, 1.0))).asInstanceOf[SStructType] - val scalaRowRDD = JsonRDD.jsonStringToRow(json.rdd, appliedScalaSchema) + JsonRDD.nullTypeToStringType( + JsonRDD.inferSchema( + json.rdd, 1.0, columnNameOfCorruptJsonRecord))).asInstanceOf[SStructType] + val scalaRowRDD = JsonRDD.jsonStringToRow( + json.rdd, appliedScalaSchema, columnNameOfCorruptJsonRecord) val logicalPlan = LogicalRDD(appliedScalaSchema.toAttributes, scalaRowRDD)(sqlContext) new JavaSchemaRDD(sqlContext, logicalPlan) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala index fbc2965e61..61ee960aad 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala @@ -22,6 +22,7 @@ import scala.collection.convert.Wrappers.{JMapWrapper, JListWrapper} import scala.math.BigDecimal import java.sql.Timestamp +import com.fasterxml.jackson.core.JsonProcessingException import com.fasterxml.jackson.databind.ObjectMapper import org.apache.spark.rdd.RDD @@ -35,16 +36,19 @@ private[sql] object JsonRDD extends Logging { private[sql] def jsonStringToRow( json: RDD[String], - schema: StructType): RDD[Row] = { - parseJson(json).map(parsed => asRow(parsed, schema)) + schema: StructType, + columnNameOfCorruptRecords: String): RDD[Row] = { + parseJson(json, columnNameOfCorruptRecords).map(parsed => asRow(parsed, schema)) } private[sql] def inferSchema( json: RDD[String], - samplingRatio: Double = 1.0): StructType = { + samplingRatio: Double = 1.0, + columnNameOfCorruptRecords: String): StructType = { require(samplingRatio > 0, s"samplingRatio ($samplingRatio) should be greater than 0") val schemaData = if (samplingRatio > 0.99) json else json.sample(false, samplingRatio, 1) - val allKeys = parseJson(schemaData).map(allKeysWithValueTypes).reduce(_ ++ _) + val allKeys = + parseJson(schemaData, columnNameOfCorruptRecords).map(allKeysWithValueTypes).reduce(_ ++ _) createSchema(allKeys) } @@ -274,7 +278,9 @@ private[sql] object JsonRDD extends Logging { case atom => atom } - private def parseJson(json: RDD[String]): RDD[Map[String, Any]] = { + private def parseJson( + json: RDD[String], + columnNameOfCorruptRecords: String): RDD[Map[String, Any]] = { // According to [Jackson-72: https://jira.codehaus.org/browse/JACKSON-72], // ObjectMapper will not return BigDecimal when // "DeserializationFeature.USE_BIG_DECIMAL_FOR_FLOATS" is disabled @@ -289,12 +295,16 @@ private[sql] object JsonRDD extends Logging { // For example: for {"key": 1, "key":2}, we will get "key"->2. val mapper = new ObjectMapper() iter.flatMap { record => - val parsed = mapper.readValue(record, classOf[Object]) match { - case map: java.util.Map[_, _] => scalafy(map).asInstanceOf[Map[String, Any]] :: Nil - case list: java.util.List[_] => scalafy(list).asInstanceOf[Seq[Map[String, Any]]] - } + try { + val parsed = mapper.readValue(record, classOf[Object]) match { + case map: java.util.Map[_, _] => scalafy(map).asInstanceOf[Map[String, Any]] :: Nil + case list: java.util.List[_] => scalafy(list).asInstanceOf[Seq[Map[String, Any]]] + } - parsed + parsed + } catch { + case e: JsonProcessingException => Map(columnNameOfCorruptRecords -> record) :: Nil + } } }) } |