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这篇文章给大家分享的是有关Spark存储Parquet数据到Hive时如何对map、array、struct字段类型进行处理的内容。小编觉得挺实用的，因此分享给大家做个参考，一起跟随小编过来看看吧。
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为了更好的说明导致问题的原因、现象以及解决方案，首先看下述示例：
-- 创建存储格式为parquet的Hive非分区表
CREATE EXTERNAL TABLE `t1`(
`id` STRING,
`map_col` MAP,
`arr_col` ARRAY,
`struct_col` STRUCT)
STORED AS PARQUET
LOCATION '/home/spark/test/tmp/t1';
-- 创建存储格式为parquet的Hive分区表
CREATE EXTERNAL TABLE `t2`(
`id` STRING,
`map_col` MAP,
`arr_col` ARRAY,
`struct_col` STRUCT)
PARTITIONED BY (`dt` STRING)
STORED AS PARQUET
LOCATION '/home/spark/test/tmp/t2';
insert into table t1 values(1,map(),array('1,1,1'),named_struct('A','1','B','1'));
insert into table t2 partition(dt='20200101')
t1表正常执行，但对t2执行上述insert语句时，报如下异常：
Caused by: parquet.io.ParquetEncodingException: empty fields are illegal, the field should be ommited completely instead
at parquet.io.MessageColumnIO$MessageColumnIORecordConsumer.endField(MessageColumnIO.java:244)
at org.apache.hadoop.hive.ql.io.parquet.write.DataWritableWriter.writeMap(DataWritableWriter.java:241)
at org.apache.hadoop.hive.ql.io.parquet.write.DataWritableWriter.writeValue(DataWritableWriter.java:116)
at org.apache.hadoop.hive.ql.io.parquet.write.DataWritableWriter.writeGroupFields(DataWritableWriter.java:89)
at org.apache.hadoop.hive.ql.io.parquet.write.DataWritableWriter.write(DataWritableWriter.java:60)
... 23 moret1和t2从建表看唯一的区别就是t1不是分区表而t2是分区表，仅仅从报错信息是无法看出表分区产生这种问题的原因，看看源码是做了哪些不同的处理（这里为了方便，笔者这里直接给出分析这个问题的源码思路图）：
从抛出的异常信息empty fields are illegal，关键看empty fields在哪里抛出，做了哪些处理，这要看MessageColumnIO中startField和endField是做了哪些处理：
public void startField(String field, int index) {
try {
if (MessageColumnIO.DEBUG) {
this.log("startField(" + field + ", " + index + ")");
}
this.currentColumnIO = ((GroupColumnIO)this.currentColumnIO).getChild(index);
//MessageColumnIO中，startField方法中首先会将emptyField设置为true
this.emptyField = true;
if (MessageColumnIO.DEBUG) {
this.printState();
}
} catch (RuntimeException var4) {
throw new ParquetEncodingException("error starting field " + field + " at " + index, var4);
}
}
//endField方法中会针对emptyField是否为true来决定是否抛出异常
public void endField(String field, int index) {
if (MessageColumnIO.DEBUG) {
this.log("endField(" + field + ", " + index + ")");
}
this.currentColumnIO = this.currentColumnIO.getParent();
//如果到这里仍为true，则抛异常
if (this.emptyField) {
throw new ParquetEncodingException("empty fields are illegal, the field should be ommited completely instead");
} else {
this.fieldsWritten[this.currentLevel].markWritten(index);
this.r[this.currentLevel] = this.currentLevel == 0 ? 0 : this.r[this.currentLevel - 1];
if (MessageColumnIO.DEBUG) {
this.printState();
}
}
}针对map做处理的一些源码：
private void writeMap(final Object value, final MapObjectInspector inspector, final GroupType type) {
// Get the internal map structure (MAP_KEY_VALUE)
GroupType repeatedType = type.getType(0).asGroupType();
recordConsumer.startGroup();
recordConsumer.startField(repeatedType.getName(), 0);
Map mapValues = inspector.getMap(value);
Type keyType = repeatedType.getType(0);
String keyName = keyType.getName();
ObjectInspector keyInspector = inspector.getMapKeyObjectInspector();
Type valuetype = repeatedType.getType(1);
String valueName = valuetype.getName();
ObjectInspector valueInspector = inspector.getMapValueObjectInspector();
for (Map.Entry keyValue : mapValues.entrySet()) {
recordConsumer.startGroup();
if (keyValue != null) {
// write key element
Object keyElement = keyValue.getKey();
//recordConsumer此处对应的是MessageColumnIO中的MessageColumnIORecordConsumer
//查看其中的startField和endField的处理
recordConsumer.startField(keyName, 0);
//查看writeValue中对原始数据类型的处理，如int、boolean、varchar
writeValue(keyElement, keyInspector, keyType);
recordConsumer.endField(keyName, 0);
// write value element
Object valueElement = keyValue.getValue();
if (valueElement != null) {
//同上
recordConsumer.startField(valueName, 1);
writeValue(valueElement, valueInspector, valuetype);
recordConsumer.endField(valueName, 1);
}
}
recordConsumer.endGroup();
}
recordConsumer.endField(repeatedType.getName(), 0);
recordConsumer.endGroup();
}
private void writePrimitive(final Object value, final PrimitiveObjectInspector inspector) {
//value为null，则return
if (value == null) {
return;
}
switch (inspector.getPrimitiveCategory()) {
//PrimitiveCategory为VOID，则return
case VOID:
return;
case DOUBLE:
recordConsumer.addDouble(((DoubleObjectInspector) inspector).get(value));
break;
//下面是对double、boolean、float、byte、int等数据类型做的处理，这里不在贴出
....
这里只是以map为例，对于array、struct都有类似问题，看源码HiveFileFormat -> DataWritableWriter对这三者处理方式类似。类似的问题，在Hive的issue中https://issues.apache.org/jira/browse/HIVE-11625也有讨论。
1. 如果无法改变建表schema，或者存储时底层用的就是HiveFileFormat