apache-spark – avro类型的createDataFrame中的无限递归

2023年10月8日 192次阅读

我在这个例子中从createDataFrame调用中获取了StackOverflowError.它起源于涉及
java类型推理的scala代码,它在无限循环中调用自身.

final EventParser parser = new EventParser();
JavaRDD<Event> eventRDD = sc.textFile(path)
        .map(new Function<String, Event>()
{
    public Event call(String line) throws Exception
    {
        Event event = parser.parse(line);
        log.info("event: "+event.toString());
        return event;
    }
});
log.info("eventRDD:" + eventRDD.toDebugString());

DataFrame df = sqlContext.createDataFrame(eventRDD, Event.class);
df.show();

堆栈跟踪的底部如下所示：

at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
at org.apache.spark.sql.catalyst.JavaTypeInference$.org$apache$spark$sql$catalyst$JavaTypeInference$$inferDataType(JavaTypeInference.scala:102)
at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$2.apply(JavaTypeInference.scala:104)
at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$2.apply(JavaTypeInference.scala:102)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)

这看起来类似于http://apache-spark-developers-list.1001551.n3.nabble.com/Stackoverflow-in-createDataFrame-td11791.html中报告的错误,但我使用的是Spark 1.4.1,这比修复此错误的时间晚.

Event类由avro从此avsc生成.它确实包含双字段和长字段,据报道它会导致问题,但用字符串替换double不会改变症状.

{
    "namespace": "mynamespace", 
    "type": "record", 
    "name": "Event", 
    "fields": [
        { "name": "ts", "type": "double", "doc": "Timestamp"},
        { "name": "uid", "type": "string", "doc": "Unique ID of Connection"},
        { "name": "idorigh", "type": "string", "doc": "Originating endpoint’s IP address (AKA ORIG)"},
        { "name": "idorigp", "type": "int", "doc": "Originating endpoint’s TCP/UDP port (or ICMP code)"},
        { "name": "idresph", "type": "string", "doc": "Responding endpoint’s IP address (AKA RESP)"},
        { "name": "idrespp", "type": "int", "doc": "Responding endpoint’s TCP/UDP port (or ICMP code)"},
        { "name": "proto", "type": "string", "doc": "Transport layer protocol of connection"},
        { "name": "service", "type": "string", "doc": "Dynamically detected application protocol, if any"},
        { "name": "duration", "type": "double", "doc": "Time of last packet seen – time of first packet seen"},
        { "name": "origbytes", "type": "int", "doc": "Originator payload bytes; from sequence numbers if TCP"},
        { "name": "respbytes", "type": "int", "doc": "Responder payload bytes; from sequence numbers if TCP"},
        { "name": "connstate", "type": "string", "doc": "Connection state (see conn.log:conn_state table)"},
        { "name": "localorig", "type": "boolean", "doc": "If conn originated locally T; if remotely F."},
        { "name": "localresp", "type": "boolean", "doc": "empty, always unset"},
        { "name": "missedbytes", "type": "int", "doc": "Number of missing bytes in content gaps"},
        { "name": "history", "type": "string", "doc": "Connection state history (see conn.log:history table)"},
        { "name": "origpkts", "type": [ "int", "null"], "doc": "Number of ORIG packets"},
        { "name": "origipbytes", "type": [ "int", "null"], "doc": "Number of RESP IP bytes (via IP total_length header field)"},
        { "name": "resppkts", "type": [ "int", "null"], "doc": "Number of RESP packets"},
        { "name": "respipbytes", "type": [ "int", "null"], "doc": "Number of RESP IP bytes (via IP total_length header field)"},
        { "name": "tunnelparents", "type": [ "string", "null"], "doc": "If tunneled, connection UID of encapsulating parent (s)"},
        { "name": "origcc", "type": ["string", "null"], "doc": "ORIG GeoIP Country Code"},
        { "name": "respcc", "type": ["string", "null"], "doc": "RESP GeoIP Country Code"}
    ]
}

有人可以建议吗？谢谢！

最佳答案 在spark-avro项目中正在开展工作来解决这个问题,请参阅：
https://github.com/databricks/spark-avro/pull/217和

https://github.com/databricks/spark-avro/pull/216

合并后,应该有一个函数将Avro对象的RDD转换为DataSet(Rows的DataSet等效于DataFrame),而不会在生成的类中使用getSchema()函数的循环引用问题.

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