Spark structured streaming kafka convert JSON without schema (infer schema) - apache-spark

Question

I read Spark Structured Streaming doesn't support schema inference for reading Kafka messages as JSON. Is there a way to retrieve schema the same as Spark Streaming does:
val dataFrame = spark.read.json(rdd.map(_.value()))
dataFrame.printschema

Answer 1

Here is one possible way to do this:
Before you start streaming, get a small batch of the data from Kafka
Infer the schema from the small batch
Start streaming the data using the extracted schema.
The pseudo-code below illustrates this approach.
Step 1:
Extract a small (two records) batch from Kafka,
val smallBatch = spark.read.format("kafka")
.option("kafka.bootstrap.servers", "node:9092")
.option("subscribe", "topicName")
.option("startingOffsets", "earliest")
.option("endingOffsets", """{"topicName":{"0":2}}""")
.load()
.selectExpr("CAST(value AS STRING) as STRING").as[String].toDF()
Step 2:
Write the small batch to a file:
smallBatch.write.mode("overwrite").format("text").save("/batch")
This command writes the small batch into hdfs directory /batch. The name of the file that it creates is part-xyz*. So you first need to rename the file using hadoop FileSystem commands (see org.apache.hadoop.fs._ and org.apache.hadoop.conf.Configuration, here's an example https://stackoverflow.com/a/41990859) and then read the file as json:
val smallBatchSchema = spark.read.json("/batch/batchName.txt").schema
Here, batchName.txt is the new name of the file and smallBatchSchema contains the schema inferred from the small batch.
Finally, you can stream the data as follows (Step 3):
val inputDf = spark.readStream.format("kafka")
.option("kafka.bootstrap.servers", "node:9092")
.option("subscribe", "topicName")
.option("startingOffsets", "earliest")
.load()
val dataDf = inputDf.selectExpr("CAST(value AS STRING) as json")
.select( from_json($"json", schema=smallBatchSchema).as("data"))
.select("data.*")
Hope this helps!

Answer 2

It is possible using this construct:
myStream = spark.readStream.schema(spark.read.json("my_sample_json_file_as_schema.json").schema).json("my_json_file")..
How can this be? Well, as the spark.read.json("..").schema returns exactly a wanted inferred schema, you can use this returned schema as an argument for the mandatory schema parameter of spark.readStream
What I did was to specify a one-liner sample-json as input for inferring the schema stuff so it does not unnecessary take up memory. In case your data changes, simply update your sample-json.
Took me a while to figure out (constructing StructTypes and StructFields by hand was pain in the ..), therefore I'll be happy for all upvotes :-)

Answer 3

It is not possible. Spark Streaming supports limited schema inference in development with spark.sql.streaming.schemaInference set to true:
By default, Structured Streaming from file based sources requires you to specify the schema, rather than rely on Spark to infer it automatically. This restriction ensures a consistent schema will be used for the streaming query, even in the case of failures. For ad-hoc use cases, you can reenable schema inference by setting spark.sql.streaming.schemaInference to true.
but it cannot be used to extract JSON from Kafka messages and DataFrameReader.json doesn't support streaming Datasets as arguments.
You have to provide schema manually How to read records in JSON format from Kafka using Structured Streaming?

Answer 4

It is possible to convert JSON to a DataFrame without having to manually type the schema, if that is what you meant to ask.
Recently I ran into a situation where I was receiving massively long nested JSON packets via Kafka, and manually typing the schema would have been both cumbersome and error-prone.
With a small sample of the data and some trickery you can provide the schema to Spark2+ as follows:
val jsonstr = """ copy paste a representative sample of data here"""
val jsondf = spark.read.json(Seq(jsonstr).toDS) //jsondf.schema has the nested json structure we need
val event = spark.readStream.format..option...load() //configure your source
val eventWithSchema = event.select($"value" cast "string" as "json").select(from_json($"json", jsondf.schema) as "data").select("data.*")
Now you can do whatever you want with this val as you would with Direct Streaming. Create temp view, run SQL queries, whatever..

Answer 5

Taking Arnon's solution to the next step (since it's deprecated in spark's newer versions, and would require iterating the whole dataframe just for a type casting)
spark.read.json(df.as[String])
Anyways, as for now, it's still experimental.