CODEWITHSUNDEEP
javajsonapache-sparkapache-spark-sqlspark-structured-streaming
Developer87
Developer87

Reputation: 2698

How to use from_json standard function (in select) in streaming query?

I process messages from Kafka with the following JSON structure:

{"unix_time": 1557678233, "category_id": 1000, "ip": "172.10.34.17", "type": "view"}

I want to print out what I'm receiving. Here is a code snippet of what I've done already:

JavaSparkContext sc = createJavaSparkContext();
JavaStreamingContext streamingContext =
                new JavaStreamingContext(sc, Durations.seconds(BATCH_DURATION_IN_SECONDS));

SparkSession sparkSession = SparkSession
        .builder()
        .config(new SparkConf())
        .getOrCreate();

Dataset<Row> df = sparkSession
        .readStream()
        .format("kafka")
        .option("kafka.bootstrap.servers", CommonUtils.KAFKA_HOST_PORT)
        .option("subscribe", KAFKA_TOPIC)
        .load();

StreamingQuery query = df.selectExpr("CAST(value AS STRING)")
            .select(from_json(new Column("value"), getSchema())).as("data").
                    select("data.category_id").writeStream().foreach(new ForeachWriter<Row>() {
                @Override
                public void process(Row value) {
                    System.out.println(value);
                }

                @Override
                public void close(Throwable errorOrNull) {

                }

                @Override
                public boolean open(long partitionId, long version) {
                    return true;
                }
            })
            .start();

    query.awaitTermination();

Schema method:

private static StructType getSchema() {
    return new StructType(new StructField[]{
            new StructField(UNIX_TIME, DataTypes.TimestampType, false, Metadata.empty()),
            new StructField(CATEGORY_ID, DataTypes.IntegerType, false, Metadata.empty()),
            new StructField(IP, DataTypes.StringType, false, Metadata.empty()),
            new StructField(TYPE, DataTypes.StringType, false, Metadata.empty()),
    });
}

The problem is that I'm constantly receiving error while writing from Spark:

Exception in thread "main" org.apache.spark.sql.AnalysisException: cannot resolve 'data.category_id' given input columns: [jsontostruct(value)];; 'Project ['data.category_id] +- SubqueryAlias data +- Project [jsontostruct(StructField(unix_time,TimestampType,false), StructField(category_id,IntegerType,false), StructField(ip,StringType,false), StructField(type,StringType,false), value#15) AS jsontostruct(value)#18]

How to overcome this issue? Any suggestions on that?

Upvotes: 4

Views: 2369

Answers (1)

Jacek Laskowski
Jacek Laskowski

Reputation: 74619

This part of the exception tells you exactly where to look for answers:

cannot resolve 'data.category_id' given input columns: [jsontostruct(value)]

In other words, there is no column data.category_id among the available columns which is just one jsontostruct(value).

That means that the only select in the streaming query does not work. And the reason is fairly simple (I could qualify it as a typo) -- there are too many closing brackets before as("data") that is available on Column as well as Dataset types.

In summary, replace the following part of the query:

.select(from_json(new Column("value"), getSchema())).as("data")

to the following:

.select(from_json(new Column("value"), getSchema()).as("data"))

Note that I shifted one closing bracket to the end.

Upvotes: 3

Related Questions