I have some truncated/broken json input, such as
{"#version":"1","logger_name":"foo","message":"a truncate field
I known this json have lost the valid ending, how to configure logstash deal with such broken json? My goal is processing the previous two filed correctly, and the last message field have the value "a truncate field".
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I can't send the question due to some mysterious error, so I'll share a screenshot of the question.
Can anyone help me solve this?
I have reproduced the above and got same error when the Expression checkbox in checked.
Remove the Expression checkbox check in dataflow pipeline assignment and pass it as a string. Now it won't give the error.
It will take the Dataflow parameter like this.
Also, along with the Date time string pass the format in toTimestamp() function to avoid null values.
This is my sample input data:
sample filter condition:
toTimestamp(start_date,'yyyy-MM-dd\'T\'HH:mm:ss')
Filtered Result:
I have data factory pipeline with source (DelimitedText) on SFTP with ecodage ISO-8859-13, it was working without any problems with the special characetrs, but yesterday it's blocked with many errors, with the special characters,
Have you any solution for this problem ? whitch kind of encodage you use on ADF to read special characters (like this: commerçant)
Best regards,
I try to copy data with data factory pipeline
ADF delimited text format supports below encoding types to read/write files.
Ref doc: ADF Delimited format dataset properties
I just tried with default encoding type UTF-8 for the special character you have provided (like this: commerçant) and it works fine without any issue.
I have also tried with encoding type ISO-8859-13 and didn't notice any issue for the character type you have provided.
Hence assuming some other special characters might be causing the problem. But I would recommend trying to default UTF-8 and see if that helps. In case if the issue still persists, then try using the Skip incompatible rows, which will help filter down the bad records and log them to a log file from where you can get the exact rows which are causing the pipeline to fail and based on those records you can choose the encoding type in your dataset settings.
I'm loading a table data into a dataframe and creating multiple JSON part files. The structure of the data is good, but the elements in JSON are not separated by commas.
This is the output:
{"time_stamp":"2016-12-08 01:45:00","Temperature":0.8,"Energy":111111.5,"Net_Energy":1111.3}
{"time_stamp":"2016-12-08 02:00:00","Temperature":21.9,"Energy":222222.5,"Net_Energy":222.0}
I'm supposed to get something like this:
{"time_stamp":"2016-12-08 01:45:00","Temperature":0.8,"Energy":111111.5,"Net_Energy":1111.3},
{"time_stamp":"2016-12-08 02:00:00","Temperature":21.9,"Energy":222222.5,"Net_Energy":222.0}
How do I do this?
Your output is correct JSONlines output: one JSON record per line, separated by newlines. You do not need commas between the lines. In fact, that would be invalid JSON.
If you absolutely need to turn the entire output of a Spark job into a single JSON array of objects, there are two ways to do this:
For data that fits in driver RAM, df.as[String].collect.mkString("[", ",", "]").
For data that does not fit in driver RAM... you really shouldn't do it... but if you absolutely have to, use shell operations to begin with [, add a comma to every line of output and end in ].
I'm just starting out with Hive, and I have a question about Input/Output Format. I'm using the OpenCSVSerde serde, but I don't understand why for text files the Input format is org.apache.hadoop.mapred.TextInputFormat but the output format is org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat.
I've read this question but it's still not clear to my why the Input/Output formats are different. Isn't that basically saying your going to store data added to this table differently the data that's read from the table??
Anyway, any help would be appreciated
In a TextInputFormat, Keys are the position in the file (long data type), and values are the line of text. When the program reads a file, It might use the keys for random read, where while writing the text data using HiveIgnoreKeyTextOutputFormat there is no value in maintaining position as it doesn't make sense.
Hence, using HiveIgnoreKeyTextOutputFormat passes keys as null to underlining RecordWriter. When the RecordWriter receives key as null, it ignores key and just write the value with line separator. Otherwise, RecordWriter will key, then delimiter, then value and finally a line separator.
I am new with logstash and grok filters. I am trying to parse a string from an Apache Access Log, with a grok filter in logstash, where the username is part of the access log in the following format:
name1.name2.name3.namex.id
I want to build a new field called USERNAME where it is name1.name2.name3.namex with the id stripped off. I have it working, but the problem is that the number of names are variable. Sometimes there are 3 names (lastname.firstname.middlename) and sometimes there are 4 names (lastname.firstname.middlename.suffix - SMITH.GEORGE.ALLEN.JR
%{WORD:lastname}.%{WORD:firstname}.%{WORD:middle}.%{WORD:id}
When there are 4 names or more it does not parse correctly. I was hoping someone can help me out with the right grok filter. I know I am missing something probably pretty simple.
You could use two patterns, adding another one that matches when there are 4 fields:
%{WORD:lastname}.%{WORD:firstname}.%{WORD:middle}.%{WORD:suffix}.%{WORD:id}
But in this case, you're creating fields that it sounds like you don't even want.
How about a pattern that splits off the ID, leaving everything in front of it, perhaps:
%{DATA:name}.%{INT}