I have a sequence file containing multiple json records. I want to send every json record to a function . How can I extract one json record at a time?
Unfortunately there is no standard way to do this.
Unlike YAML which has a well-defined way to allow one file contain multiple YAML "documents", JSON does not have such standards.
One way to solve your problem is to invent your own "object separator". For example, you can use newline characters to separate adjacent JSON objects. You can tell your JSON encoder not to output any newline characters (by forcing escaping it into \ and n). As long as your JSON decoder is sure that it will not see any newline character unless it separates two JSON objects, it can read the stream one line at a time and decode each line.
It has also been suggested that you can use JSON arrays to store multiple JSON objects, but it will no longer be a "stream".
You can read content of your sequence files to RDD[String] and convert it to Spark Dataframe.
val seqFileContent = sc
.sequenceFile[LongWritable, BytesWritable](inputFilename)
.map(x => new String(x._2.getBytes))
val dataframeFromJson = sqlContext.read.json(seqFileContent)
Related
I would like to read events from eventhub using Databricks, events are in json format but they can have different schema (it's important because i find solutions in which the schema was given to from_json(jsonStr,schema) function, but i cannot use it in my use case). When i use
.withColumn('Value', col('value').cast(StringType() in dataframe returns json output with backslashes "{\"time\": 1432826855000,\"host\":...... .
I found a solution How to prevent spark sql with kafka from adding backslash to JSON string in dataframe but in Delta Live Tables framework we create streaming tables by returning a dataframe, so i cant use this solution.
Should i use non pyspark functions in etl process such as
How to remove backslash from decoded JSON string? ?
Will it be efficient during streaming from eventhub to bronze?
You shouldn't worry about that backslashes - it's just a visual representation of your string when you display data and it has " character embedded into a string. Internally, data will be stored without backslashes, like: {"time": 1432826855000,"host":.......
Cross posting from Julia Discourse in case anyone here has any leads.
I’m just looking for some insight into why the below code is returning a dataframe containing just the first line of my json file. If you’d like to try working with the file I’m working with, you can download the aminer_papers_0.zip from the Microsoft Open Academic Graph site, I’m using the first file in that group of files.
using JSON3, DataFrames, CSV
file_name = "path/aminer_papers_0.txt"
json_string = read(file_name, String)
js = JSON3.read(json_string)
df = DataFrame([js])
The resulting DataFrame has just one line, but the column titles are correct, as is the first line. To me the mystery is why the rest isn’t getting processed. I think I can rule out that read() is only reading the first JSON object, because I can index into the resulting object and see many JSON objects:
enter image description here
My first guess was maybe the newline \n was causing escape issues, and tried to use chomp to get rid of them, but couldn’t get it to work.
Anyway - any help would be greatly appreciated!
I think the problem is that the file is in JSON Lines format, and the JSON3 library only returns the first valid JSON value that it finds at the start of a string unless told otherwise.
tl;dr
Call JSON3.read with the keyword argument jsonlines=true.
Why?
By default, JSON3 interprets a string passed to its read function as a single "JSON text", defined by RFC 8259 section 1.3.2:
A JSON text is a serialized value....
(My emphasis on the use of the indefinite singular article "a.") A "JSON value" is defined in section 1.3.3:
A JSON value MUST be an object, array, number, or string, or one of the following three literal names: false, null, true.
A string with multiple JSON values in it is technically multiple "JSON texts." It is up to the parser to determine what part of the string argument you give it is a JSON text, and the authors of JSON3 chose as the default behavior to parse from the start of the string to the end of the first valid JSON value.
In order to get JSON3 to read the string as multiple JSON values, you have to give it the keyword option jsonlines=true, which is documented as:
jsonlines: A Bool indicating that the json_str contains newline delimited JSON strings, which will be read into a JSON3.Array of the JSON values. See jsonlines for reference. [default false]
Example
Take for example this simple string:
two_values = "3.14\n2.72"
Each one of these lines is a valid JSON serialization of a number. However, when passed to JSON3.read, only the first is parsed:
using JSON3
#assert JSON3.read(two_values) == 3.14
Using jsonlines=true, both values are parsed and returned as a JSON3.Array struct:
#assert JSON3.read(two_values, jsonlines=true) == [3.14, 2.72]
Other Packages
The JSON.jl library, which people might use by default given the name, does not implement parsing of JSON Lines strings at all, leaving it up to the caller to properly split the string as needed:
using JSON
JSON.parse(two_values)
# ERROR: Expected end of input
# Line: 1
# Around: ...3.14 2.72...
# ^
A simple way to implement reading multiple values is to use eachline:
#assert [JSON.parse(line) for line in eachline(IOBuffer(two_values))] == [3.14, 2.72]
Issue
I recently encountered a challenge in Azure Data Lake Analytics when I attempted to read in a Large UTF-8 JSON Array file and switched to HDInsight PySpark (v2.x, not 3) to process the file. The file is ~110G and has ~150m JSON Objects.
HDInsight PySpark does not appear to support Array of JSON file format for input, so I'm stuck. Also, I have "many" such files with different schemas in each containing hundred of columns each, so creating the schemas for those is not an option at this point.
Question
How do I use out-of-the-box functionality in PySpark 2 on HDInsight to enable these files to be read as JSON?
Thanks,
J
Things I tried
I used the approach at the bottom of this page:
from Databricks that supplied the below code snippet:
import json
df = sc.wholeTextFiles('/tmp/*.json').flatMap(lambda x: json.loads(x[1])).toDF()
display(df)
I tried the above, not understanding how "wholeTextFiles" works, and of course ran into OutOfMemory errors that killed my executors quickly.
I attempted loading to an RDD and other open methods, but PySpark appears to support only the JSONLines JSON file format, and I have the Array of JSON Objects due to ADLA's requirement for that file format.
I tried reading in as a text file, stripping Array characters, splitting on the JSON object boundaries and converting to JSON like the above, but that kept giving errors about being unable to convert unicode and/or str (ings).
I found a way through the above, and converted to a dataframe containing one column with Rows of strings that were the JSON Objects. However, I did not find a way to output only the JSON Strings from the data frame rows to an output file by themselves. The always came out as
{'dfColumnName':'{...json_string_as_value}'}
I also tried a map function that accepted the above rows, parsed as JSON, extracted the values (JSON I wanted), then parsed the values as JSON. This appeared to work, but when I would try to save, the RDD was type PipelineRDD and had no saveAsTextFile() method. I then tried the toJSON method, but kept getting errors about "found no valid JSON Object", which I did not understand admittedly, and of course other conversion errors.
I finally found a way forward. I learned that I could read json directly from an RDD, including a PipelineRDD. I found a way to remove the unicode byte order header, wrapping array square brackets, split the JSON Objects based on a fortunate delimiter, and have a distributed dataset for more efficient processing. The output dataframe now had columns named after the JSON elements, inferred the schema, and dynamically adapts for other file formats.
Here is the code - hope it helps!:
#...Spark considers arrays of Json objects to be an invalid format
# and unicode files are prefixed with a byteorder marker
#
thanksMoiraRDD = sc.textFile( '/a/valid/file/path', partitions ).map(
lambda x: x.encode('utf-8','ignore').strip(u",\r\n[]\ufeff")
)
df = sqlContext.read.json(thanksMoiraRDD)
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
I'm trying to write output from a scalding flow in json form, and reading it in Spark. This is working fine, except if the json contains strings with new lines. The output is one json object per line, and newlines in a value on the json is causing one bit of json to be fragmented across two lines. As such, when I read lines into Spark, I can't deserialize some of them. Is there a standard way to deal with this?