Kafka JSON Data with Schema is Null in PySpark Structured Streaming. Mismatched input on new schema - apache-spark

Question

I am trying to read Kafka messages in JSON in Spark Structured Streaming. Example of the messages in Kafka is as follows:
{
"_id": {
"$oid": "5eb292531c7d910b8c98dbce"
},
"Id": 37,
"Timestamp": {
"$date": 1582889068616
},
"TTNR": "R902170286",
"SNR": 91177446,
"State": 0,
"I_A1": "FALSE",
"I_B1": "FALSE",
"I1": 0.0037385,
"Mabs": -20.9814753,
"p_HD1": 31.0069236,
"pG": 27.640614,
"pT": 1.7169713,
"pXZ": 3.4712914,
"T3": 25.2174444,
"nan": 179.3099976,
"Q1": 0,
"a_01X": [
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925,
62.7839925
]
}
After reading the stream in Kafka, the value field as string looks like this:
|value |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|
{"_id":{"$oid":"5eb292531c7d910b8c98dbce"},"Id":37,"Timestamp":{"$date":1582889068616},"TTNR":"R902170286","SNR":91177446,"State":0,"I_A1":"FALSE","I_B1":"FALSE","I1":0.0037385,"Mabs":-20.9814753,"p_HD1":31.0069236,"pG":27.640614,"pT":1.7169713,"pXZ":3.4712914,"T3":25.2174444,"nan":179.3099976,"Q1":0,"a_01X":[62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925]}
|
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
A schema has been defined to select some fields as follows:
json_schema=StructType([ \
StructField("_id",StructField("$oid",StringType())), \
StructField("Id", DoubleType()), \
StructField('Timestamp', StructField("$date", LongType())), \
StructField("TTNR", StringType()), \
StructField("SNR", DoubleType()), \
StructField("State", LongType()), \
StructField("I_A1", StringType()), \
StructField("I_B1", StringType()), \
StructField("I1", DoubleType()), \
StructField("Mabs", DoubleType()), \
StructField("p_HD1", DoubleType()), \
StructField("pG", DoubleType()), \
StructField("pT", DoubleType()), \
StructField("pXZ", DoubleType()), \
StructField("T3", DoubleType()), \
StructField("nan", DoubleType()), \
StructField("Q1", LongType()), \
StructField("a_01X", ArrayType(DoubleType()))
])
(Solved with parsing error)But after trying to print to the console, I get null values:
data_stream_json = data_stream_value.select(from_json(col("value"), json_schema).alias("json_detail"))
data_stream_output = data_stream_json \
.writeStream \
.outputMode("append") \
.format("console") \
.start()
+----+----+----+----+
| Id|TTNR| SNR| Q1|
+----+----+----+----+
|null|null|null|null|
+----+----+----+----+
(New Error) After changing the schema, there is a new problem parsing the string:
pyspark.sql.utils.ParseException: u'\nmismatched input \'{\' expecting {\'SELECT\', \'FROM\', \'ADD\', \'AS\', \'ALL\', \'ANY\', \'DISTINCT\', \'WHERE\', \'GROUP\', \'BY\', \'GROUPING\', \'SETS\', \'CUBE\', \'ROLLUP\', \'ORDER\', \'HAVING\', \'LIMIT\', \'AT\', \'OR\', \'AND\', \'IN\', NOT, \'NO\', \'EXISTS\', \'BETWEEN\', \'LIKE\', RLIKE, \'IS\', \'NULL\', \'TRUE\', \'FALSE\', \'NULLS\', \'ASC\', \'DESC\', \'FOR\', \'INTERVAL\', \'CASE\', \'WHEN\', \'THEN\', \'ELSE\', \'END\', \'JOIN\', \'CROSS\', \'OUTER\', \'INNER\', \'LEFT\', \'SEMI\', \'RIGHT\', \'FULL\', \'NATURAL\', \'ON\', \'PIVOT\', \'LATERAL\', \'WINDOW\', \'OVER\', \'PARTITION\', \'RANGE\', \'ROWS\', \'UNBOUNDED\', \'PRECEDING\', \'FOLLOWING\', \'CURRENT\', \'FIRST\', \'AFTER\', \'LAST\', \'ROW\', \'WITH\', \'VALUES\', \'CREATE\', \'TABLE\', \'DIRECTORY\', \'VIEW\', \'REPLACE\', \'INSERT\', \'DELETE\', \'INTO\', \'DESCRIBE\', \'EXPLAIN\', \'FORMAT\', \'LOGICAL\', \'CODEGEN\', \'COST\', \'CAST\', \'SHOW\', \'TABLES\', \'COLUMNS\', \'COLUMN\', \'USE\', \'PARTITIONS\', \'FUNCTIONS\', \'DROP\', \'UNION\', \'EXCEPT\', \'MINUS\', \'INTERSECT\', \'TO\', \'TABLESAMPLE\', \'STRATIFY\', \'ALTER\', \'RENAME\', \'ARRAY\', \'MAP\', \'STRUCT\', \'COMMENT\', \'SET\', \'RESET\', \'DATA\', \'START\', \'TRANSACTION\', \'COMMIT\', \'ROLLBACK\', \'MACRO\', \'IGNORE\', \'BOTH\', \'LEADING\', \'TRAILING\', \'IF\', \'POSITION\', \'EXTRACT\', \'DIV\', \'PERCENT\', \'BUCKET\', \'OUT\', \'OF\', \'SORT\', \'CLUSTER\', \'DISTRIBUTE\', \'OVERWRITE\', \'TRANSFORM\', \'REDUCE\', \'SERDE\', \'SERDEPROPERTIES\', \'RECORDREADER\', \'RECORDWRITER\', \'DELIMITED\', \'FIELDS\', \'TERMINATED\', \'COLLECTION\', \'ITEMS\', \'KEYS\', \'ESCAPED\', \'LINES\', \'SEPARATED\', \'FUNCTION\', \'EXTENDED\', \'REFRESH\', \'CLEAR\', \'CACHE\', \'UNCACHE\', \'LAZY\', \'FORMATTED\', \'GLOBAL\', TEMPORARY, \'OPTIONS\', \'UNSET\', \'TBLPROPERTIES\', \'DBPROPERTIES\', \'BUCKETS\', \'SKEWED\', \'STORED\', \'DIRECTORIES\', \'LOCATION\', \'EXCHANGE\', \'ARCHIVE\', \'UNARCHIVE\', \'FILEFORMAT\', \'TOUCH\', \'COMPACT\', \'CONCATENATE\', \'CHANGE\', \'CASCADE\', \'RESTRICT\', \'CLUSTERED\', \'SORTED\', \'PURGE\', \'INPUTFORMAT\', \'OUTPUTFORMAT\', DATABASE, DATABASES, \'DFS\', \'TRUNCATE\', \'ANALYZE\', \'COMPUTE\', \'LIST\', \'STATISTICS\', \'PARTITIONED\', \'EXTERNAL\', \'DEFINED\', \'REVOKE\', \'GRANT\', \'LOCK\', \'UNLOCK\', \'MSCK\', \'REPAIR\', \'RECOVER\', \'EXPORT\', \'IMPORT\', \'LOAD\', \'ROLE\', \'ROLES\', \'COMPACTIONS\', \'PRINCIPALS\', \'TRANSACTIONS\', \'INDEX\', \'INDEXES\', \'LOCKS\', \'OPTION\', \'ANTI\', \'LOCAL\', \'INPATH\', IDENTIFIER, BACKQUOTED_IDENTIFIER}(line 1, pos 0)\n\n== SQL ==\n{"fields":[{"metadata":{},"name":"_id","nullable":true,"type":{"metadata":{},"name":"$oid","nullable":true,"type":"string"}},{"metadata":{},"name":"Id","nullable":true,"type":"double"},{"metadata":{},"name":"Timestamp","nullable":true,"type":{"metadata":{},"name":"$date","nullable":true,"type":"long"}},{"metadata":{},"name":"TTNR","nullable":true,"type":"string"},{"metadata":{},"name":"SNR","nullable":true,"type":"double"},{"metadata":{},"name":"State","nullable":true,"type":"long"},{"metadata":{},"name":"I_A1","nullable":true,"type":"string"},{"metadata":{},"name":"I_B1","nullable":true,"type":"string"},{"metadata":{},"name":"I1","nullable":true,"type":"double"},{"metadata":{},"name":"Mabs","nullable":true,"type":"double"},{"metadata":{},"name":"p_HD1","nullable":true,"type":"double"},{"metadata":{},"name":"pG","nullable":true,"type":"double"},{"metadata":{},"name":"pT","nullable":true,"type":"double"},{"metadata":{},"name":"pXZ","nullable":true,"type":"double"},{"metadata":{},"name":"T3","nullable":true,"type":"double"},{"metadata":{},"name":"nan","nullable":true,"type":"double"},{"metadata":{},"name":"Q1","nullable":true,"type":"long"},{"metadata":{},"name":"a_01X","nullable":true,"type":{"containsNull":true,"elementType":"double","type":"array"}}],"type":"struct"}\n^^^\n'
I want some help with this.

Answer 1

Note If you have complex nested json try to use this DataType.fromJson method to convert json schema to StructType schema & keep the json schema outside of code. Any change in schema just update json schema & restart your application, it will take new schema automatically.
I have converted json data to schema string, Please check below code.
scala> val jsonSchema = """{"type":"struct","fields":[{"name":"I1","type":"double","nullable":true,"metadata":{}},{"name":"I_A1","type":"string","nullable":true,"metadata":{}},{"name":"I_B1","type":"string","nullable":true,"metadata":{}},{"name":"Id","type":"long","nullable":true,"metadata":{}},{"name":"Mabs","type":"double","nullable":true,"metadata":{}},{"name":"Q1","type":"long","nullable":true,"metadata":{}},{"name":"SNR","type":"long","nullable":true,"metadata":{}},{"name":"State","type":"long","nullable":true,"metadata":{}},{"name":"T3","type":"double","nullable":true,"metadata":{}},{"name":"TTNR","type":"string","nullable":true,"metadata":{}},{"name":"Timestamp","type":{"type":"struct","fields":[{"name":"$date","type":"long","nullable":true,"metadata":{}}]},"nullable":true,"metadata":{}},{"name":"_id","type":{"type":"struct","fields":[{"name":"$oid","type":"string","nullable":true,"metadata":{}}]},"nullable":true,"metadata":{}},{"name":"a_01X","type":{"type":"array","elementType":"double","containsNull":true},"nullable":true,"metadata":{}},{"name":"nan","type":"double","nullable":true,"metadata":{}},{"name":"pG","type":"double","nullable":true,"metadata":{}},{"name":"pT","type":"double","nullable":true,"metadata":{}},{"name":"pXZ","type":"double","nullable":true,"metadata":{}},{"name":"p_HD1","type":"double","nullable":true,"metadata":{}}]}"""
jsonSchema: String = {"type":"struct","fields":[{"name":"I1","type":"double","nullable":true,"metadata":{}},{"name":"I_A1","type":"string","nullable":true,"metadata":{}},{"name":"I_B1","type":"string","nullable":true,"metadata":{}},{"name":"Id","type":"long","nullable":true,"metadata":{}},{"name":"Mabs","type":"double","nullable":true,"metadata":{}},{"name":"Q1","type":"long","nullable":true,"metadata":{}},{"name":"SNR","type":"long","nullable":true,"metadata":{}},{"name":"State","type":"long","nullable":true,"metadata":{}},{"name":"T3","type":"double","nullable":true,"metadata":{}},{"name":"TTNR","type":"string","nullable":true,"metadata":{}},{"name":"Timestamp","type":{"type":"struct","fields":[{"name":"$date","type":"long","nullable":true,"metadata":{}}]},"nullable":true,"metadata":{}},{"name":"_id","type":{"type":"struct","fields":[{"name":"$oid","type":"string","nullable":true,"metadata":{}}]},"nullable":true,"metadata":{}},{"name":"a_01X","type":{"type":"array","elementType":"double","containsNull":true},"nullable":true,"metadata":{}},{"name":"nan","type":"double","nullable":true,"metadata":{}},{"name":"pG","type":"double","nullable":true,"metadata":{}},{"name":"pT","type":"double","nullable":true,"metadata":{}},{"name":"pXZ","type":"double","nullable":true,"metadata":{}},{"name":"p_HD1","type":"double","nullable":true,"metadata":{}}]}
scala> val schema = DataType.fromJson(jsonSchema).asInstanceOf[StructType]
schema: org.apache.spark.sql.types.StructType = StructType(StructField(I1,DoubleType,true), StructField(I_A1,StringType,true), StructField(I_B1,StringType,true), StructField(Id,LongType,true), StructField(Mabs,DoubleType,true), StructField(Q1,LongType,true), StructField(SNR,LongType,true), StructField(State,LongType,true), StructField(T3,DoubleType,true), StructField(TTNR,StringType,true), StructField(Timestamp,StructType(StructField($date,LongType,true)),true), StructField(_id,StructType(StructField($oid,StringType,true)),true), StructField(a_01X,ArrayType(DoubleType,true),true), StructField(nan,DoubleType,true), StructField(pG,DoubleType,true), StructField(pT,DoubleType,true), StructField(pXZ,DoubleType,true), StructField(p_HD1,DoubleType,true))

Answer 2

I don't know your whole code, but by seeing the one you put here seems to me that you need to first convert your kafka input into String first as it initially comes in HexaDecimal and then you use your schema on that String.

Answer 3

I figured it out.
The trick was to change my Kafka serializer from AVRO to string format. Although AVRO preserves the schema, it also introduced some preceeding characters like newline (see below) that was hard to remove and parse as a json in my case.
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|value |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|
{"_id":{"$oid":"5e58f86d5afd84039c135405"},"Id":1,"Timestamp":{"$date":1582889068580},"TTNR":"R902170286","SNR":92177446,"State":0,"I_A1":"FALSE","I_B1":"FALSE","I1":0.0036622,"Mabs":-20.5236976,"p_HD1":30.985062,"pG":27.7779473,"pT":1.727958,"pXZ":3.4487671,"T3":25.2296518,"nan":215.3000031,"Q1":0,"a_01X":[62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925,62.7839925]}
|
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Getting my input as a String introduced more fields, which was easier to remove. I had to define a bigger schema, but parsing was successful.