I am trying to read data from Kafka using structured streaming. The data received from kafka is in json format.
My code is as follows:
in the code I use the from_json function to convert the json to a dataframe for further processing.
val **schema**: StructType = new StructType()
.add("time", LongType)
.add(id", LongType)
.add("properties",new StructType()
.add("$app_version", StringType)
.
.
)
val df: DataFrame = spark.readStream
.format("kafka")
.option("kafka.bootstrap.servers","...")
.option("subscribe","...")
.load()
.selectExpr("CAST(value AS STRING) as value")
.select(from_json(col("value"), **schema**))
My problem is that if the field is increased,
I can't stop the spark program to manually add these fields,
then how can I parse these fields dynamically, I tried schema_of_json(),
it can only take the first line to infer the field type and it not suitable for multi-level nested structures json data.
My problem is that if the field is increased, I can't stop the spark program to manually add these fields, then how can I parse these fields dynamically
It is not possible in Spark Structured Streaming (or even Spark SQL) out of the box. There are a couple of solutions though.
Changing Schema in Code and Resuming Streaming Query
You simply have to stop your streaming query, change the code to match the current schema, and resume it. It is possible in Spark Structured Streaming with data sources that support resuming from checkpoint. Kafka data source does support it.
User-Defined Function (UDF)
You could write a user-defined function (UDF) that would do this dynamic JSON parsing for you. That's also among the easiest options.
New Data Source (MicroBatchReader)
Another option is to create an extension to the built-in Kafka data source that would do the dynamic JSON parsing (similarly to Kafka deserializers). That requires a bit more development, but is certainly doable.
Related
I am using Spark/Scala to make an API Request and parse the response into a dataframe. Following is the sample JSON response I am using for testing purpose:
API Request/Response
However, I tried to use the following answer from StackOverflow to convert to JSON but the nested fields are not being processed. Is there any way to convert the JSON string to a dataframe with columns??
I think the problem is that the json that you have attached, if we read it as a df, it is giving a single row(and it is very huge) and hence spark might be truncating the result.
If this is what you want then you can try to use the spark property spark.debug.maxToStringFields to a higher value(default is 25)
spark.conf().set("spark.debug.maxToStringFields", 100)
However, if you want to process the Results from json, then it would be better to get it as data frame and then do the processing. Here is how you can do it
val results = JsonParser.parseString(<json content>).getAsJsonObject().get("Results").getAsJsonArray.toString
import spark.implicits._
val df = spark.read.json(Seq(results).toDS)
df.show(false)
What I'm trying to do is something similar to the Stackoverflow question here: basically converting .seq.gz JSON files to Parquet files with a proper schema defined.
I don't want to infer the schema, rather I would like to define my own, ideally having my Scala case classes so they can be reused as models by other jobs.
I'm not too sure whether I should deserialise my JSON into a case class and let toDS() to implicitly convert my data like below:
spark
.sequenceFile(input, classOf[IntWritable], classOf[Text])
.mapValues(
json => deserialize[MyClass](json.toString) // json to case class instance
)
.toDS()
.write.mode(SaveMode.Overwrite)
.parquet(outputFile)
...or rather use a Spark Data Frame schema instead, or even a Parquet schema. But I don't know how to do it though.
My objective is having full control over my models and possibly map JSON types (which is a poorer format) to Parquet types.
Thanks!
I'm new to Structured Streaming, and I'd like to know is there a way to specify Kafka value's schema like what we do in normal structured streaming jobs. The format in Kafka value is 50+ fields syslog-like csv, and manually splitting is painfully slow.
Here's the brief part of my code (see full gist here)
spark.readStream.format("kafka")
.option("kafka.bootstrap.servers", "myserver:9092")
.option("subscribe", "mytopic")
.load()
.select(split('value, """\^""") as "raw")
.select(ColumnExplode('raw, schema.size): _*) // flatten WrappedArray
.toDF(schema.fieldNames: _*) // apply column names
.select(fieldsWithTypeFix: _*) // cast column types from string
.select(schema.fieldNames.map(col): _*) // re-order columns, as defined in schema
.writeStream.format("console").start()
with no further operations, I can only achieve roughly 10MB/s throughput on a 24-core 128GB mem server. Would it help if I convert the syslog to JSON in prior? In that case I can use from_json with schema, and maybe it will be faster.
is there a way to specify Kafka value's schema like what we do in normal structured streaming jobs.
No. The so-called output schema for kafka external data source is fixed and cannot be changed ever. See this line.
Would it help if I convert the syslog to JSON in prior? In that case I can use from_json with schema, and maybe it will be faster.
I don't think so. I'd even say that CSV is a simpler text format than JSON (as there's simply a single separator usually).
Using split standard function is the way to go and think you can hardly get better performance since it's to split a row and take every element to build the final output.
I am new to Apache Spark.
My Scala code is consuming JSON messages as strings from a Kafka topic in Apache Spark.
Now I want to aggregate over a certain field in my JSON. What are my options?
You can put the JSON in a dataframe/dataset and perform the following aggregate operations.
groupBy
groupByKey
rollup
cube
Spark SQL can automatically infer the schema of a JSON dataset and load it as a Dataset[Row]. This conversion can be done using SparkSession.read.json() on either an RDD of String, or a JSON file.
val json_path = "dir/example.json"
val jsonDF = spark.read.json(json_path)
jsonDF.groupBy("col1").count().show()
I want to convert my nested json into csv ,i used
df.write.format("com.databricks.spark.csv").option("header", "true").save("mydata.csv")
But it can use to normal json but not nested json. Anyway that I can convert my nested json to csv?help will be appreciated,Thanks!
When you ask Spark to convert a JSON structure to a CSV, Spark can only map the first level of the JSON.
This happens because of the simplicity of the CSV files. It is just asigning a value to a name. That is why {"name1":"value1", "name2":"value2"...} can be represented as a CSV with this structure:
name1,name2, ...
value1,value2,...
In your case, you are converting a JSON with several levels, so Spark exception is saying that it cannot figure out how to convert such a complex structure into a CSV.
If you try to add only a second level to your JSON, it will work, but be careful. It will remove the names of the second level to include only the values in an array.
You can have a look at this link to see the example for json datasets. It includes an example.
As I have no information about the nature of the data, I can't say much more about it. But if you need to write the information as a CSV you will need to simplify the structure of your data.
Read json file in spark and create dataframe.
val path = "examples/src/main/resources/people.json"
val people = sqlContext.read.json(path)
Save the dataframe using spark-csv
people.write
.format("com.databricks.spark.csv")
.option("header", "true")
.save("newcars.csv")
Source :
read json
save to csv