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.
apache-spark elasticsearch spark-structured-streaming elasticsearch-spark
edited Nov 25 '18 at 19:53
Jacek Laskowski
46.2k18137276
asked Nov 23 '18 at 11:42
Hasif SubairHasif Subair
1222318
add a comment |
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.
apache-spark elasticsearch spark-structured-streaming elasticsearch-spark
edited Nov 25 '18 at 19:53
Jacek Laskowski
46.2k18137276
asked Nov 23 '18 at 11:42
Hasif SubairHasif Subair
1222318
add a comment |
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.
apache-spark elasticsearch spark-structured-streaming elasticsearch-spark
edited Nov 25 '18 at 19:53
Jacek Laskowski
46.2k18137276
asked Nov 23 '18 at 11:42
Hasif SubairHasif Subair
1222318
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.
apache-spark elasticsearch spark-structured-streaming elasticsearch-spark
apache-spark elasticsearch spark-structured-streaming elasticsearch-spark
edited Nov 25 '18 at 19:53
Jacek Laskowski
46.2k18137276
asked Nov 23 '18 at 11:42
Hasif SubairHasif Subair
1222318
edited Nov 25 '18 at 19:53
Jacek Laskowski
46.2k18137276
asked Nov 23 '18 at 11:42
Hasif SubairHasif Subair
1222318
edited Nov 25 '18 at 19:53
Jacek Laskowski
46.2k18137276
edited Nov 25 '18 at 19:53
Jacek Laskowski
46.2k18137276
edited Nov 25 '18 at 19:53
Jacek Laskowski
46.2k18137276
46.2k18137276
asked Nov 23 '18 at 11:42
Hasif SubairHasif Subair
1222318
asked Nov 23 '18 at 11:42
Hasif SubairHasif Subair
1222318
asked Nov 23 '18 at 11:42
Hasif SubairHasif Subair
1222318
1222318
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
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", $"B"("D") as "D")
answered Nov 23 '18 at 11:54
user10465355user10465355
2,1192521
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
There is no need for pivot or aggregation here. If B column is indeed Array[Map[String, String]] (array<map<string, string>> in SQL types), all you need is a simple select or withColumn:
df
.withColumn("B", explode($"B"))
.select($"A", $"B"("C") as "C", $"B"("D") as "D")
answered Nov 23 '18 at 11:54
user10465355user10465355
2,1192521
add a comment |
There is no need for pivot or aggregation here. If B column is indeed Array[Map[String, String]] (array<map<string, string>> in SQL types), all you need is a simple select or withColumn:
df
.withColumn("B", explode($"B"))
.select($"A", $"B"("C") as "C", $"B"("D") as "D")
answered Nov 23 '18 at 11:54
user10465355user10465355
2,1192521
add a comment |
There is no need for pivot or aggregation here. If B column is indeed Array[Map[String, String]] (array<map<string, string>> in SQL types), all you need is a simple select or withColumn:
df
.withColumn("B", explode($"B"))
.select($"A", $"B"("C") as "C", $"B"("D") as "D")
answered Nov 23 '18 at 11:54
user10465355user10465355
2,1192521
There is no need for pivot or aggregation here. If B column is indeed Array[Map[String, String]] (array<map<string, string>> in SQL types), all you need is a simple select or withColumn:
df
.withColumn("B", explode($"B"))
.select($"A", $"B"("C") as "C", $"B"("D") as "D")
answered Nov 23 '18 at 11:54
user10465355user10465355
2,1192521
answered Nov 23 '18 at 11:54
user10465355user10465355
2,1192521
answered Nov 23 '18 at 11:54
user10465355user10465355
2,1192521
answered Nov 23 '18 at 11:54
user10465355user10465355
2,1192521
2,1192521
add a comment |
add a comment |
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