Reading JSON with Apache Spark - `corrupt_record` - json

Question

I have a json file, nodes that looks like this:
[{"toid":"osgb4000000031043205","point":[508180.748,195333.973],"index":1}
,{"toid":"osgb4000000031043206","point":[508163.122,195316.627],"index":2}
,{"toid":"osgb4000000031043207","point":[508172.075,195325.719],"index":3}
,{"toid":"osgb4000000031043208","point":[508513,196023],"index":4}]
I am able to read and manipulate this record with Python.
I am trying to read this file in scala through the spark-shell.
From this tutorial, I can see that it is possible to read json via sqlContext.read.json
val vfile = sqlContext.read.json("path/to/file/nodes.json")
However, this results in a corrupt_record error:
vfile: org.apache.spark.sql.DataFrame = [_corrupt_record: string]
Can anyone shed some light on this error? I can read and use the file with other applications and I am confident it is not corrupt and sound json.

Answer 1

As Spark expects "JSON Line format" not a typical JSON format, we can tell spark to read typical JSON by specifying:
val df = spark.read.option("multiline", "true").json("<file>")

Answer 2

Spark cannot read JSON-array to a record on top-level, so you have to pass:
{"toid":"osgb4000000031043205","point":[508180.748,195333.973],"index":1}
{"toid":"osgb4000000031043206","point":[508163.122,195316.627],"index":2}
{"toid":"osgb4000000031043207","point":[508172.075,195325.719],"index":3}
{"toid":"osgb4000000031043208","point":[508513,196023],"index":4}
As it's described in the tutorial you're referring to:
Let's begin by loading a JSON file, where each line is a JSON object
The reasoning is quite simple. Spark expects you to pass a file with a lot of JSON-entities (entity per line), so it could distribute their processing (per entity, roughly saying).
To put more light on it, here is a quote form the official doc
Note that the file that is offered as a json file is not a typical
JSON file. Each line must contain a separate, self-contained valid
JSON object. As a consequence, a regular multi-line JSON file will
most often fail.
This format is called JSONL. Basically it's an alternative to CSV.

Answer 3

To read the multi-line JSON as a DataFrame:
val spark = SparkSession.builder().getOrCreate()
val df = spark.read.json(spark.sparkContext.wholeTextFiles("file.json").values)
Reading large files in this manner is not recommended, from the wholeTextFiles docs
Small files are preferred, large file is also allowable, but may cause bad performance.

Answer 4

I run into the same problem. I used sparkContext and sparkSql on the same configuration:
val conf = new SparkConf()
.setMaster("local[1]")
.setAppName("Simple Application")
val sc = new SparkContext(conf)
val spark = SparkSession
.builder()
.config(conf)
.getOrCreate()
Then, using the spark context I read the whole json (JSON - path to file) file:
val jsonRDD = sc.wholeTextFiles(JSON).map(x => x._2)
You can create a schema for future selects, filters...
val schema = StructType( List(
StructField("toid", StringType, nullable = true),
StructField("point", ArrayType(DoubleType), nullable = true),
StructField("index", DoubleType, nullable = true)
))
Create a DataFrame using spark sql:
var df: DataFrame = spark.read.schema(schema).json(jsonRDD).toDF()
For testing use show and printSchema:
df.show()
df.printSchema()
sbt build file:
name := "spark-single"
version := "1.0"
scalaVersion := "2.11.7"
libraryDependencies += "org.apache.spark" %% "spark-core" % "2.0.2"
libraryDependencies +="org.apache.spark" %% "spark-sql" % "2.0.2"