Spark增量加载覆盖旧记录 [英] Spark incremental loading overwrite old record

问题描述

我需要使用 Spark (PySpark) 对表进行增量加载

示例如下:

第一天

id |价值-----------1 |美国广播公司2 |定义

第二天

id |价值-----------2 |编码器3 |xyz

预期结果

id |价值-----------1 |美国广播公司2 |编码器3 |xyz

这可以在关系数据库中轻松完成，
想知道这是否可以在 Spark 或其他转换工具中完成，例如快点?

解决方案

给你！第一个数据框:

 >>>list1 = [(1, 'abc'),(2,'def')]>>>olddf = spark.createDataFrame(list1, ['id', 'value'])>>>olddf.show();+---+-----+|ID|值|+---+-----+|1|abc||2|定义|+---+-----+

第二个数据框:

<预><代码>>>>list2 = [(2, 'cde'),(3,'xyz')]>>>newdf = spark.createDataFrame(list2, ['id', 'value'])>>>newdf.show();+---+-----+|ID|值|+---+-----+|2|编码||3|xyz|+---+-----+

现在使用全外连接连接和合并这两个数据名，并在选择时使用合并功能，并且可以用用户定义的值替换空值.

from pyspark.sql.functions import *>>>df = olddf.join(newdf, olddf.id == newdf.id,'full_outer').select(coalesce(olddf.id,newdf.id).alias("id"),coalesce(newdf.value,olddf.value).alias("value"))>>>df.show();+---+-----+|ID|值|+---+-----+|1|abc||3|xyz||2|编码|+---+-----+

我希望这能解决您的问题.:-)

I have a requirement to do the incremental loading to a table by using Spark (PySpark)

Here's the example:

Day 1

id | value
-----------
1  | abc
2  | def

Day 2

id | value
-----------
2  | cde
3  | xyz

Expected result

id | value
-----------
1  | abc
2  | cde
3  | xyz

This can be done easily in relational database,
Wondering whether this can be done in Spark or other transformational tool, e.g. Presto?

解决方案

Here you go! First Dataframe:

 >>> list1 = [(1, 'abc'),(2,'def')]
 >>> olddf = spark.createDataFrame(list1, ['id', 'value'])
 >>> olddf.show();
 +---+-----+
 | id|value|
 +---+-----+
 |  1|  abc|
 |  2|  def|
 +---+-----+

Second Dataframe:

>>> list2 = [(2, 'cde'),(3,'xyz')]
>>> newdf = spark.createDataFrame(list2, ['id', 'value'])
>>> newdf.show();
+---+-----+
| id|value|
+---+-----+
|  2|  cde|
|  3|  xyz|
+---+-----+

Now join and merge these two datafame using full outer join and use coalesce function while select and can replace the null values wih user defined values.

from pyspark.sql.functions import *

>>> df = olddf.join(newdf, olddf.id == newdf.id,'full_outer').select(coalesce(olddf.id,newdf.id).alias("id"),coalesce(newdf.value,olddf.value).alias("value"))
>>> df.show();
+---+-----+
| id|value|
+---+-----+
|  1|  abc|
|  3|  xyz|
|  2|  cde|
+---+-----+

I hope this should solve your problem. :-)

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