Using Apache NiFi I need to filter out the records in a csv which have a set of special characters.
As an example if the set of special characters are "FFF". My csv would be
name,age,city
John,23,New York
FFF,45,London
Himsara,18,Adelaide
Then the second record must be taken out from the csv and put into another csv. Also even if "FFF" is in the city or age columns the whole record must be removed.
Please suggest me the processors that are needed for me to achieve this. Also it would be really helpful if u can list out the configurations, that are needed to be changed.
As an alternative, you can use the RouteText processor. It will split the flow file based on a condition. Lines containing FFF will route to the matched relationship, the other lines will route to the unmatched relationship.
RouteText processor setup:
Use QueryRecord processor in nifi and define Record Reader/Writer Avro schemas to read your incoming flowfile.
Then add new property to QueryRecord processor as (Apache calcite sql)
select * from FLOWFILE where name !="FFF"
Now use the newly added relationship from QueryRecord processor for further processing and NiFi will result flowfile where name is not equal to 'FFF'.
Related
When using a copy activity in Azure Data Factory to copy a typical CSV file with a header row into Parquet sink, the SINK fails with the following error due to the column names in the CSV having spaces in the header.
The column name is invalid. Column name cannot contain these
character:[,;{}()\n\t=]
The CSV is pipe delimited and displays just fine using the preview feature of the dataset with the first row marked as the header. I see no options to handle this use-case on the parquet side (sink) of the copy activity. I realize this can probably be addressed using a data flow to transform column names to remove spaces, but does that mean the native copy activity is incapable of handling this condition where a space in included in a header row?
EDIT: I should have added that dataset uses default mappings so that we can use the same dataset for any CSV to PARQUET copy. The answer provided will work for explicit mappings, but we don't see any resolution for folks who use default/dynamic mappings since we do not have access to the column names to remove spaces.
As we can note from the official Doc here
Error code: ParquetInvalidColumnName
Message: The column name is invalid. Column name cannot contain these character:[,;{}()\n\t=]
Cause: The column name contains invalid characters.
Resolution: Add or modify the column mapping to make the sink column name valid.
If you would like continue to use copy activity, there are few workarounds
1. make sure you have selected Column delimiter as Pipe(|)
2. If feasible, in mapping settings > import schema and rename the column name without spaces in destination column.
This is still an ongoing issue or request, follow here for more.
I am trying to load some CSV files into BigQuery from Google Cloud Storage and wrestling with schema generation. There is an auto-generate option but it is poorly documented. The problem is that if I choose to let BigQuery generate the schema, it does a decent job of guessing data types, but only sometimes does it recognizes the first row of the data as a header row, and sometimes it does not (treats the 1st row as data and generates column names like string_field_N). The first rows of my data are always header rows. Some of the tables have many columns (over 30), and I do not want to mess around with schema syntax because BigQuery always bombs with an uninformative error message when something (I have no idea what) is wrong with the schema.
So: How can I force it to recognize the first row as a header row? If that isn't possible, how do I get it to spit out the schema it generated in the proper syntax so that I can edit it (for appropriate column names) and use that as the schema on import?
I would recommend doing 2 things here:
Preprocess your file and store the final layout of the file sans the first row i.e. the header row
BQ load accepts an additional parameter in form of a JSON schema file, use this to explicitly define the table schema and pass this file as a parameter. This allows you the flexibility to alter schema at any point in time, if required
Allowing BQ to autodetect schema is not advised.
Schema auto detection in BigQuery should be able to detect the first row of your CSV file as column names in most cases. One of the cases for which column name detection fails is when you have similar data types all over your CSV file. For instance, BigQuery schema auto detect would not be able to detect header names for the following file since every field is a String.
headerA, headerB
row1a, row1b
row2a, row2b
row3a, row3b
The "Header rows to skip" option in the UI would not help fixing this shortcoming of schema auto detection in BigQuery.
If you are following the GCP documentation for Loading CSV Data from Google Cloud Storage you have the option to skip n number of rows:
(Optional) An integer indicating the number of header rows in the source data.
The option is called "Header rows to skip" in the Web UI, but it's also available as a CLI flag (--skip_leading_rows) and as BigQuery API property (skipLeadingRows)
Yes you can modify the existing schema (aka DDL) using bq show..
bq show --schema --format=prettyjson project_id:dataset.table > myschema.json
Note that this will result in you creating a new BQ table all together.
I have way to schema for loading csv into bigquery. You just enough edit value column, for example :
weight|total|summary
2|4|just string
2.3|89.5|just string
if use schema generator by bigquery, field weight and total will define as INT64, but when insert second rows so error or failed. So, you just enough edit first rows like this
weight|total|summary
'2'|'4'|just string
2.3|89.5|just string
You must set field weight & total as STRING, and if you want to aggregate you just use convert type data in bigquery.
cheers
If 'column name' type and 'datatype' are the same for all over the csv file, then BigQuery misunderstood that 'column name' as data. And add a self generated name for the column. I couldn't find any technical way to solve this. So I took another approach.
If the data is not sensitive, then add another column with the 'column name' in string type. And all of the values in the column in number type. Ex. Column name 'Test' and all values are 0. Upload the file to the BigQuery and use this query to drop the column name.
ALTER TABLE <table name> DROP COLUMN <Test>
Change and according to your Table.
I'm able to get Apache NiFi to generate a schema via the CSVReader, and then I can write the schema out to an attribute using ConvertRecord. However I then need to add fields using UpdateRecord, but the fields are not being added to the flow file or the to the schema attribute. I believe this is because the fields are not part of the initially inferred schema. I can't create the schema in the registry because it's being inferred from the file. So how can I add fields to a record when the schema doesn't include the fields?
Are you using InferAvroSchema to not have to worry about generating the schema(s), or because you really will not know the schema of the CSV files? If the former, then send one CSV through, then copy the inferred schema into a CSVReader, and add the fields from UpdateRecord into the write schema.
I've written up NIFI-5524 to cover the automation of adding/updating fields in the outgoing schema based on UpdateRecord properties.
Yes, that is because your writer controller service doesn't have the new fields defined in it.
If you are adding new fields then we need to define new avro schema with the additional fields included in the schema writer controller service.
Change the Schema Access Strategy to either
Use 'Schema Name' Property (or) Use 'Schema Text' Property
then Define your new schema including new fields in it so that Update Record processor will add the new fields to the output flowfile.
Please look into this article, as i have added ts_tz,current_ts..etc fields in it that doesn't exist in the input data and defined the writer controller service with the new avro schema that includes all the new/old fields in it.
I achieved the same by adding columns to the CSV using replace text processor (this will add same values for header and values in the csv), use replacement mode "Line-By-Line" and then use update record to update values only of the new columns to something meaningful.
No need to know the schema beforehand using this approach.
I want to upload csv data into BigQuery. When the data has different types (like string and int), it is capable of inferring the column names with the headers, because the headers are all strings, whereas the other lines contains integers.
BigQuery infers headers by comparing the first row of the file with
other rows in the data set. If the first line contains only strings,
and the other lines do not, BigQuery assumes that the first row is a
header row.
https://cloud.google.com/bigquery/docs/schema-detect
The problem is when your data is all strings ...
You can specify --skip_leading_rows, but BigQuery still does not use the first row as the name of your variables.
I know I can specify the column names manually, but I would prefer not doing that, as I have a lot of tables. Is there another solution ?
If your data is all in "string" type and if you have the first row of your CSV file containing the metadata, then I guess it is easy to do a quick script that would parse the first line of your CSV and generates a similar "create table" command:
bq mk --schema name:STRING,street:STRING,city:STRING... -t mydataset.myNewTable
Use that command to create a new (void) table, and then load your CSV file into that new table (using --skip_leading_rows as you mentioned)
14/02/2018: Update thanks to Felipe's comment:
Above comment can be simplified this way:
bq mk --schema `head -1 myData.csv` -t mydataset.myNewTable
It's not possible with current API. You can file a feature request in the public BigQuery tracker https://issuetracker.google.com/issues/new?component=187149&template=0.
As a workaround, you can add a single non-string value at the end of the second line in your file, and then set the allowJaggedRows option in the Load configuration. Downside is you'll get an extra column in your table. If having an extra column is not acceptable, you can use query instead of load, and select * EXCEPT the added extra column, but query is not free.
I'm going to a pick a csv file in BizTalk and after some process I wanted to update it with two or more different systems.
In order to getting the csv file, I'm using the default Flatfile Disassembler for breaking it and constructing it as XML with the help of genereted schema. I can do that successfully with some consistent data however if I use a data with comma in it (other than delimiters), BizTalk fails!
Any other way to do this without using a custom pipeline component?
Expecting a simple configuration within the flatfile disassembler component!
So, here's the deal. BizTalk is not failing. Well, it is, but that is the expected and correct behavior.
What you have in an invalid CSV file. The CSV specification disallows the comma in field data unless a wrap character is used. Either way, both are reserved characters.
To accept the comma in field data, you must choose a wrap character and set that in the Wrap Character property in the Flat File Schema.
This is valid:
1/1/01,"Smith, John", $5000
This is not:
1/1/01,Smith, John, $5000
Since your schema definition has ',' as delimiter, flat file disassembler will consider the data with comma as two fields and will fail due to mismatch in columns.
You have few options:
Either add a new field to schema if you know , in data will only be present in a particular field.
Or change the delimiter in flat file from , to |(pipe) or some other character so that data does not conflict with delimiter.
Or as you mentioned manipulate the flat file in a custom pipeline component, which should be last resort if above two are not feasible.