使用复杂类型查询 Spark SQL DataFrame [英] Querying Spark SQL DataFrame with complex types
问题描述
如何查询具有复杂类型(例如映射/数组)的 RDD?例如，当我写这个测试代码时:
How Can I query an RDD with complex types such as maps/arrays? for example, when I was writing this test code:
case class Test(name: String, map: Map[String, String])
val map = Map("hello" -> "world", "hey" -> "there")
val map2 = Map("hello" -> "people", "hey" -> "you")
val rdd = sc.parallelize(Array(Test("first", map), Test("second", map2)))
我认为语法应该是这样的:
I thought the syntax would be something like:
sqlContext.sql("SELECT * FROM rdd WHERE map.hello = world")
或
sqlContext.sql("SELECT * FROM rdd WHERE map[hello] = world")
但我明白
无法访问 MapType(StringType,StringType,true) 类型的嵌套字段
Can't access nested field in type MapType(StringType,StringType,true)
和
org.apache.spark.sql.catalyst.errors.package$TreeNodeException:未解析的属性
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Unresolved attributes
分别.
推荐答案
这取决于列的类型.让我们从一些虚拟数据开始:
It depends on a type of the column. Lets start with some dummy data:
import org.apache.spark.sql.functions.{udf, lit}
import scala.util.Try
case class SubRecord(x: Int)
case class ArrayElement(foo: String, bar: Int, vals: Array[Double])
case class Record(
an_array: Array[Int], a_map: Map[String, String],
a_struct: SubRecord, an_array_of_structs: Array[ArrayElement])
val df = sc.parallelize(Seq(
Record(Array(1, 2, 3), Map("foo" -> "bar"), SubRecord(1),
Array(
ArrayElement("foo", 1, Array(1.0, 2.0, 2.0)),
ArrayElement("bar", 2, Array(3.0, 4.0, 5.0)))),
Record(Array(4, 5, 6), Map("foz" -> "baz"), SubRecord(2),
Array(ArrayElement("foz", 3, Array(5.0, 6.0)),
ArrayElement("baz", 4, Array(7.0, 8.0))))
)).toDF
df.registerTempTable("df")
df.printSchema
// root
// |-- an_array: array (nullable = true)
// | |-- element: integer (containsNull = false)
// |-- a_map: map (nullable = true)
// | |-- key: string
// | |-- value: string (valueContainsNull = true)
// |-- a_struct: struct (nullable = true)
// | |-- x: integer (nullable = false)
// |-- an_array_of_structs: array (nullable = true)
// | |-- element: struct (containsNull = true)
// | | |-- foo: string (nullable = true)
// | | |-- bar: integer (nullable = false)
// | | |-- vals: array (nullable = true)
// | | | |-- element: double (containsNull = false)
数组 (
ArrayType) 列:Column.getItem方法
df.select($"an_array".getItem(1)).show // +-----------+ // |an_array[1]| // +-----------+ // | 2| // | 5| // +-----------+Hive 方括号语法:
Hive brackets syntax:
sqlContext.sql("SELECT an_array[1] FROM df").show // +---+ // |_c0| // +---+ // | 2| // | 5| // +---+一个 UDF
an UDF
val get_ith = udf((xs: Seq[Int], i: Int) => Try(xs(i)).toOption) df.select(get_ith($"an_array", lit(1))).show // +---------------+ // |UDF(an_array,1)| // +---------------+ // | 2| // | 5| // +---------------+除了上面列出的方法之外，Spark 还支持越来越多的对复杂类型进行操作的内置函数.值得注意的例子包括高阶函数，如
transform(SQL 2.4+、Scala 3.0+、PySpark/SparkR 3.1+):Additionally to the methods listed above Spark supports a growing list of built-in functions operating on complex types. Notable examples include higher order functions like
transform(SQL 2.4+, Scala 3.0+, PySpark / SparkR 3.1+):df.selectExpr("transform(an_array, x -> x + 1) an_array_inc").show // +------------+ // |an_array_inc| // +------------+ // | [2, 3, 4]| // | [5, 6, 7]| // +------------+ import org.apache.spark.sql.functions.transform df.select(transform($"an_array", x => x + 1) as "an_array_inc").show // +------------+ // |an_array_inc| // +------------+ // | [2, 3, 4]| // | [5, 6, 7]| // +------------+filter(SQL 2.4+, Scala 3.0+, Python/SparkR 3.1+)filter(SQL 2.4+, Scala 3.0+, Python / SparkR 3.1+)df.selectExpr("filter(an_array, x -> x % 2 == 0) an_array_even").show // +-------------+ // |an_array_even| // +-------------+ // | [2]| // | [4, 6]| // +-------------+ import org.apache.spark.sql.functions.filter df.select(filter($"an_array", x => x % 2 === 0) as "an_array_even").show // +-------------+ // |an_array_even| // +-------------+ // | [2]| // | [4, 6]| // +-------------+aggregate(SQL 2.4+、Scala 3.0+、PySpark/SparkR 3.1+):aggregate(SQL 2.4+, Scala 3.0+, PySpark / SparkR 3.1+):df.selectExpr("aggregate(an_array, 0, (acc, x) -> acc + x, acc -> acc) an_array_sum").show // +------------+ // |an_array_sum| // +------------+ // | 6| // | 15| // +------------+ import org.apache.spark.sql.functions.aggregate df.select(aggregate($"an_array", lit(0), (x, y) => x + y) as "an_array_sum").show // +------------+ // |an_array_sum| // +------------+ // | 6| // | 15| // +------------+数组处理函数(
array_*)，如array_distinct(2.4+):import org.apache.spark.sql.functions.array_distinct df.select(array_distinct($"an_array_of_structs.vals"(0))).show // +-------------------------------------------+ // |array_distinct(an_array_of_structs.vals[0])| // +-------------------------------------------+ // | [1.0, 2.0]| // | [5.0, 6.0]| // +-------------------------------------------+array_max(array_min，2.4+):import org.apache.spark.sql.functions.array_max df.select(array_max($"an_array")).show // +-------------------+ // |array_max(an_array)| // +-------------------+ // | 3| // | 6| // +-------------------+扁平化(2.4+)import org.apache.spark.sql.functions.flatten df.select(flatten($"an_array_of_structs.vals")).show // +---------------------------------+ // |flatten(an_array_of_structs.vals)| // +---------------------------------+ // | [1.0, 2.0, 2.0, 3...| // | [5.0, 6.0, 7.0, 8.0]| // +---------------------------------+arrays_zip(2.4+):import org.apache.spark.sql.functions.arrays_zip df.select(arrays_zip($"an_array_of_structs.vals"(0), $"an_array_of_structs.vals"(1))).show(false) // +--------------------------------------------------------------------+ // |arrays_zip(an_array_of_structs.vals[0], an_array_of_structs.vals[1])| // +--------------------------------------------------------------------+ // |[[1.0, 3.0], [2.0, 4.0], [2.0, 5.0]] | // |[[5.0, 7.0], [6.0, 8.0]] | // +--------------------------------------------------------------------+array_union(2.4+):import org.apache.spark.sql.functions.array_union df.select(array_union($"an_array_of_structs.vals"(0), $"an_array_of_structs.vals"(1))).show // +---------------------------------------------------------------------+ // |array_union(an_array_of_structs.vals[0], an_array_of_structs.vals[1])| // +---------------------------------------------------------------------+ // | [1.0, 2.0, 3.0, 4...| // | [5.0, 6.0, 7.0, 8.0]| // +---------------------------------------------------------------------+slice(2.4+):import org.apache.spark.sql.functions.slice df.select(slice($"an_array", 2, 2)).show // +---------------------+ // |slice(an_array, 2, 2)| // +---------------------+ // | [2, 3]| // | [5, 6]| // +---------------------+使用
Column.getField方法:
地图(MapType)列
df.select($"a_map".getField("foo")).show
// +----------+
// |a_map[foo]|
// +----------+
// | bar|
// | null|
// +----------+
使用 Hive 括号语法:
using Hive brackets syntax:
sqlContext.sql("SELECT a_map['foz'] FROM df").show
// +----+
// | _c0|
// +----+
// |null|
// | baz|
// +----+
使用带点语法的完整路径:
using a full path with dot syntax:
df.select($"a_map.foo").show
// +----+
// | foo|
// +----+
// | bar|
// |null|
// +----+
使用 UDF
using an UDF
val get_field = udf((kvs: Map[String, String], k: String) => kvs.get(k))
df.select(get_field($"a_map", lit("foo"))).show
// +--------------+
// |UDF(a_map,foo)|
// +--------------+
// | bar|
// | null|
// +--------------+
越来越多的 map_* 函数，例如 map_keys (2.3+)
Growing number of map_* functions like map_keys (2.3+)
import org.apache.spark.sql.functions.map_keys
df.select(map_keys($"a_map")).show
// +---------------+
// |map_keys(a_map)|
// +---------------+
// | [foo]|
// | [foz]|
// +---------------+
或 map_values (2.3+)
import org.apache.spark.sql.functions.map_values
df.select(map_values($"a_map")).show
// +-----------------+
// |map_values(a_map)|
// +-----------------+
// | [bar]|
// | [baz]|
// +-----------------+
请查看 SPARK-23899 以获取详细列表.
Please check SPARK-23899 for a detailed list.
struct (StructType) 列使用带点语法的完整路径:
struct (StructType) columns using full path with dot syntax:
使用 DataFrame API
with DataFrame API
df.select($"a_struct.x").show
// +---+
// | x|
// +---+
// | 1|
// | 2|
// +---+
使用原始 SQL
with raw SQL
sqlContext.sql("SELECT a_struct.x FROM df").show
// +---+
// | x|
// +---+
// | 1|
// | 2|
// +---+
structs 数组中的字段可以使用点语法、名称和标准 Column 方法访问:
fields inside array of structs can be accessed using dot-syntax, names and standard Column methods:
df.select($"an_array_of_structs.foo").show
// +----------+
// | foo|
// +----------+
// |[foo, bar]|
// |[foz, baz]|
// +----------+
sqlContext.sql("SELECT an_array_of_structs[0].foo FROM df").show
// +---+
// |_c0|
// +---+
// |foo|
// |foz|
// +---+
df.select($"an_array_of_structs.vals".getItem(1).getItem(1)).show
// +------------------------------+
// |an_array_of_structs.vals[1][1]|
// +------------------------------+
// | 4.0|
// | 8.0|
// +------------------------------+
可以使用 UDF 访问用户定义类型 (UDT) 字段.有关详细信息，请参阅 Spark SQL 引用 UDT 的属性.
注意事项:
- 根据 Spark 版本，其中一些方法只能与
HiveContext一起使用.UDF 应该与标准SQLContext和HiveContext的版本无关. 一般来说，嵌套值是二等公民.并非所有典型操作都支持嵌套字段.根据上下文，扁平化架构和/或分解集合可能会更好
- depending on a Spark version some of these methods can be available only with
HiveContext. UDFs should work independent of version with both standardSQLContextandHiveContext. generally speaking nested values are a second class citizens. Not all typical operations are supported on nested fields. Depending on a context it could be better to flatten the schema and / or explode collections
df.select(explode($"an_array_of_structs")).show
// +--------------------+
// | col|
// +--------------------+
// |[foo,1,WrappedArr...|
// |[bar,2,WrappedArr...|
// |[foz,3,WrappedArr...|
// |[baz,4,WrappedArr...|
// +--------------------+
点语法可以与通配符 (*) 结合以选择(可能是多个)字段，而无需明确指定名称:
Dot syntax can be combined with wildcard character (*) to select (possibly multiple) fields without specifying names explicitly:
df.select($"a_struct.*").show
// +---+
// | x|
// +---+
// | 1|
// | 2|
// +---+
JSON 列可以使用 get_json_object 和 from_json 函数进行查询.有关详细信息，请参阅如何使用 Spark DataFrames 查询 JSON 数据列?.
JSON columns can be queried using get_json_object and from_json functions. See How to query JSON data column using Spark DataFrames? for details.
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