apache-sparkelasticsearchspark-structured-streamingelasticsearch-spark

How to transform array of JSONs to rows before writeStream to Elasticsearch?


Follow-up to this question

I have JSON streaming data in the format same as below

|  A    | B                                        |
|-------|------------------------------------------|
|  ABC  |  [{C:1, D:1}, {C:2, D:4}]                | 
|  XYZ  |  [{C:3, D :6}, {C:9, D:11}, {C:5, D:12}] |

I need to transform it to the format below

|   A   |  C  |  D   |
|-------|-----|------|
|  ABC  |  1  |  1   |
|  ABC  |  2  |  4   |
|  XYZ  |  3  |  6   |
|  XYZ  |  9  |  11  |
|  XYZ  |  5  |  12  | 

To achieve this performed the transformations as suggested to the previous question.

val df1 = df0.select($"A", explode($"B")).toDF("A", "Bn")

val df2 = df1.withColumn("SeqNum", monotonically_increasing_id()).toDF("A", "Bn", "SeqNum") 

val df3 = df2.select($"A", explode($"Bn"), $"SeqNum").toDF("A", "B", "C", "SeqNum")

val df4 = df3.withColumn("dummy", concat( $"SeqNum", lit("||"), $"A"))

val df5 = df4.select($"dummy", $"B", $"C").groupBy("dummy").pivot("B").agg(first($"C")) 

val df6 = df5.withColumn("A", substring_index(col("dummy"), "||", -1)).drop("dummy")

Now I need to save the data to a ElasticSearch.

 df6.writeStream
  .outputMode("complete")
  .format("es")
  .option("es.resource", "index/type")
  .option("es.nodes", "localhost")
  .option("es.port", 9200)
  .start()
  .awaitTermination()

I get an error that ElasticSearch doesn't support Append output mode. On Append mode it fails write to writeStream with aggregation cannot be done on Append mode. I was able to write to console on complete mode. How can I write the data to ElasticSearch now

Any help will be appreciated.


Solution

  • There is no need for pivot or aggregation here. If B column is indeed Array[Map[String, String]] (array<map<string, string>> in SQL types), all you need is a simple select or withColumn:

    df
      .withColumn("B", explode($"B"))
      .select($"A", $"B"("C") as "C", $"B"("D") as "D")