avro类型的createDataFrame中的无限递归

Question

我从本例中的createDataFrame调用中获得了一个StackOverflow Error。它起源于涉及java类型推理的scala代码，该代码在无限循环中调用自己。

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)

这与中报告的错误类似http://apache-spark-developers-list.1001551.n3.nabble.com/Stackoverflow-in-createDataFrame-td11791.html但我使用的是Spark 1.4.1，它比修复这个bug时要晚。

事件类由avro从此avsc生成。它确实包含双精度字段和长字段，据报告这会导致问题，但用字符串替换双精度字段并不会改变症状。

{
    "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"}
    ]
}

有人能提供建议吗？谢谢

Answer 1

spark avro项目正在开展工作来解决这个问题，请参见：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转换为数据集（行数据集相当于数据帧），而不存在生成类中getSchema（）函数的循环引用问题。