spark到json/sparsevector（来自bluephantom）
在pyspark中使用ml。否则转换为scala。

%python
from pyspark.sql.types import StructType, StructField, DoubleType
from pyspark.ml.linalg import SparseVector, VectorUDT

temp_rdd = sc.parallelize([
    (0.0, SparseVector(4, {1: 1.0, 3: 5.5})),
    (1.0, SparseVector(4, {0: -1.0, 2: 0.5}))])

schema = StructType([
    StructField("label", DoubleType(), False),
    StructField("features", VectorUDT(), False)
])

df = temp_rdd.toDF(schema)
df.printSchema()
df.write.json("/FileStore/V.json")

df2 = spark.read.schema(schema).json("/FileStore/V.json")
df2.show()

读取时返回：

+-----+--------------------+
|label|            features|
+-----+--------------------+
|  1.0|(4,[0,2],[-1.0,0.5])|
|  0.0| (4,[1,3],[1.0,5.5])|
+-----+--------------------+

Spark至avro/avro2tf（自py-r）
本教程中介绍的avro2tf库似乎是直接利用avro的一个有趣的替代方法。结果，稀疏向量将被编码如下：

+---------------------+--------------------+
|genreFeatures_indices|genreFeatures_values|
+---------------------+--------------------+
|     [2, 4, 1, 8, 11]|[1.0, 1.0, 1.0, 1...|
|          [11, 10, 3]|     [1.0, 1.0, 1.0]|
|            [2, 4, 8]|     [1.0, 1.0, 1.0]|
|             [11, 10]|          [1.0, 1.0]|
|               [4, 8]|          [1.0, 1.0]|
|         [2, 4, 7, 3]|[1.0, 1.0, 1.0, 1.0]|