I'm new to data processing with pyspark, pandas. I need some guidence to understand how I can process a relatively complex json coming out of puppet db.
Schema is something like below
root
|-- Hostname: string (nullable = true)
|-- facts-mountpoints: struct (nullable = true)
| |-- /: struct (nullable = true)
| | |-- available: string (nullable = true)
| | |-- available_bytes: long (nullable = true)
| | |-- capacity: string (nullable = true)
| | |-- device: string (nullable = true)
| | |-- filesystem: string (nullable = true)
| | |-- options: array (nullable = true)
| | | |-- element: string (containsNull = true)
| | |-- size: string (nullable = true)
| | |-- size_bytes: long (nullable = true)
| | |-- used: string (nullable = true)
| | |-- used_bytes: long (nullable = true)
| |-- /acfs01: struct (nullable = true)
| | |-- available: string (nullable = true)
| | |-- available_bytes: long (nullable = true)
| | |-- capacity: string (nullable = true)
| | |-- device: string (nullable = true)
| | |-- filesystem: string (nullable = true)
| | |-- options: array (nullable = true)
| | | |-- element: string (containsNull = true)
| | |-- size: string (nullable = true)
| | |-- size_bytes: long (nullable = true)
| | |-- used: string (nullable = true)
| | |-- used_bytes: long (nullable = true)
I use pyspark to create dataframe and process the data.
My problem is that each host can have extra different NFS mounts attached so facts-mountspoints is dynamic and not same across hosts to just flatten/explode and do the work.
To ease of the problem I want to filter out the filesystem="nfs" and get only mounts which are standard and are non nfs.
No matter what I tried I still could not find how to do a filter like below to build my columns.
facts-mountpoints.*.filesystem<>'nfs'
Is there a magical way to filter out on the known struct->unknown struct->field with json dataframes ?
If thats not possible maybe filter out on the mount point names (second struct)
Sample json file can be found here
https://github.com/coskan/stackof/blob/0b29f4f0645e28d3efa297a1c4e949f4a985c639/sample_data.json
I am trying to get the value of "__delta" from the following JSON schema that has been loaded to a dataframe. How do I do that in Pyspark?
root
|-- d: struct (nullable = true)
| |-- __delta: string (nullable = true)
| |-- __next: string (nullable = true)
| |-- results: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- ABRVW: string (nullable = true)
| | | |-- ADRNR: string (nullable = true)
| | | |-- ANRED: string (nullable = true)
with the struct type JSON object just select the object with the attribute you want to get.
df.select("d.__delta")
How about df.select($"d.__delta")
I have a JSON file that I am reading in spark.
The schema is getting displayed however when I am trying to read the info column or any sub-element it is always NULL. (which is not NULL)
//reading file
val df = spark.read.json("FilePath")
df.printSchema()
root
|-- data_is: boolean (nullable = true)
|-- Student: struct (nullable = true)
| |-- Id: string (nullable = true)
| |-- JoinDate: string (nullable = true)
| |-- LeaveDate: string (nullable = true)
|-- Info: struct (nullable = true)
| |-- details: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- Father_salary: double (nullable = true)
| | | |-- Mother_salary: double (nullable = true)
| | | |-- Address: String (nullable = true)
| |-- studentInfo: struct (nullable = true)
| | |-- Age: double (nullable = true)
| | |-- Name: String (nullable = true)
df.select("Student").show()
shows the filed value in Student element
even when I parse Student.Id I can get the ID
But whenever parsing the Info, I am always getting a NULL value which is not NULL in the file.
df.select("Info").show() // is showing as NULL
df.select("Info.detail").show() // is showing as NULL
even Info.Summary is NULL.
Can anybody suggest how to get the actual field value instead of NULL?
JSON File
{"Student":{"JoinDate":"20200909","LeaveDate":"20200909","id":"XA12"},"Info":{"studentInfo":{"Age":13,"Name":"Alex"},"details":[{"Father_salary":1234.00,"Mother_salary":0,"Address":""}]},"data_is":true}
I'm using spark structured streaming to read kafka topic and want to convert following complex JSON (kafka-msgs) in to dataframe having "NAME,ADDRESS,DESCRIPTION,CODE,DEPARTMENT,INFA_OP_TYPE,DTL__CAPXTIMESTAMP" columns.
{
"meta_data": [{"name":{"string":"INFA_SEQUENCE"},"value":
{"string":"2,PWX_GENERIC"},"type":null},
{"name":{"string":"INFA_TABLE_NAME"},"value":{"string":"customers"},"type":null},
{"name":{"string":"INFA_OP_TYPE"},"value":{"string":"INSERT_EVENT"},"type":null},
{"name":{"string":"DTL__CAPXRESTART1"},"value":{"string":"B+IABwAfA"},"type":null},
{"name":{"string":"DTL__CAPXRESTART2"},"value":{"string":"AAABpMwgRDk="},"type":null},
{"name":{"string":"DTL__CAPXUOW"},"value":{"string":"AAMKPgAAqaIABg=="},"type":null},
{"name":{"string":"DTL__CAPXUSER"},"value":null,"type":null},
{"name":{"string":"DTL__CAPXTIMESTAMP"},"value":{"string":"201807310934257270000000"},"type":null},
{"name":{"string":"DTL__CAPXACTION"},"value":{"string":"I"},"type":null}],
"columns":{"array":[{"name":{"string":"NAME"},"value":{"string":"ABCD"},"isPresent":{"boolean":true}},
{"name":{"string":"ADDRESS"},"value":{"string":"123,Bark street"},"isPresent":{"boolean":true}},
{"name":{"string":"DESCRIPTION"},"value":{"string":"Canadian"},"isPresent":{"boolean":true}},
{"name":{"string":"CODE"},"value":{"string":"3_1"},"isPresent":{"boolean":true}},
{"name":{"string":"DEPARTMENT"},"value":{"string":"HR"},"isPresent":{"boolean":true}}
] }
}
I'm able to extract two json object "meta_data" and "columns" but I'm unable to explode "columns.array"
newJsonObj = events.select(get_json_object(events.value,'$.meta_data').alias('meta_data'),get_json_object(events.value,'$.columns.array').alias('columns'))
And I don't know how to extract values from two json object and create dataframe having columns from both json object.
-- Schema of events dataframe --
root
|-- columns: struct (nullable = true)
| |-- array: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- isPresent: struct (nullable = true)
| | | | |-- boolean: boolean (nullable = true)
| | | |-- name: struct (nullable = true)
| | | | |-- string: string (nullable = true)
| | | |-- value: struct (nullable = true)
| | | | |-- string: string (nullable = true)
|-- meta_data: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- name: struct (nullable = true)
| | | |-- string: string (nullable = true)
| | |-- type: string (nullable = true)
| | |-- value: struct (nullable = true)
| | | |-- string: string (nullable = true)
I'm trying to get information from Jsons to create tables in Hive.
This is my Json schema:
root
|-- info: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- stations: array (nullable = true)
| | | |-- element: struct (containsNull = true)
| | | | |-- bikes: string (nullable = true)
| | | | |-- id: string (nullable = true)
| | | | |-- slots: string (nullable = true)
| | | | |-- streetName: string (nullable = true)
| | | | |-- type: string (nullable = true)
| | |-- updateTime: long (nullable = true)
|-- date: string (nullable = true)
|-- numRecords: string (nullable = true)
I'm using this query:
sqlContext.sql("SELECT info.updateTime FROM STATIONS").foreach(println)
This is what i get:
[WrappedArray(1449098169, 1449108553, 1449098468)]
But i don't know how to put this information in a table to use it after from the Hive console.
I used this:
query.write.save("/home/cloudera/Desktop/select")
And it creates something, but i don't know how to use it.
Thanks
You can do it in several ways...it depends.
First way: Have the table created in the query
sqlContext.sql("create table mytable AS SELECT info.updateTime FROM STATIONS")
// now you can query mytable
Second way: write the DataFrame with saveAsTable()
sqlContext.sql("SELECT info.updateTime FROM STATIONS").saveAsTable("othertable")