[ANNOUNCE] Apache NiFi 1.21.0 release.
Hello The Apache NiFi team would like to announce the release of Apache NiFi 1.21.0. Apache NiFi is an easy to use, powerful, and reliable system to process and distribute data. Apache NiFi was made for dataflow. It supports highly configurable directed graphs of data routing, transformation, and system mediation logic. More details on Apache NiFi can be found here: https://nifi.apache.org/ The release artifacts can be downloaded from here: https://nifi.apache.org/download.html Maven artifacts have been made available and mirrored as per normal ASF artifact processes. Issues closed/resolved for this list can be found here: https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12316020=12352899 Release note highlights can be found here: https://cwiki.apache.org/confluence/display/NIFI/Release+Notes#ReleaseNotes-Version1.21.0 Thank you The Apache NiFi team
Re: Handling CSVs dynamically with NiFi
Hello Bryan. Thank you for your question.
A downstream consumer requires the complete set in json. So that's part of
why I convert.
Other downstream tools require json input, but not the entire set. The data
needs to be routed based on certain features. Geographic location. Sales
data by zip codes. Etc, etc. Splitting the records out seemed to be a
reasonable option to route individual records.
I appreciate you taking the time to ask. You are far more familiar with
nifi best practices than me. If there is a better way than what I intended,
please fire away. I'd love to mArch down a better path if there is one.
Cheers,
Jim
On Fri, Apr 7, 2023 at 10:57 AM Bryan Bende wrote:
> James,
>
> I'm not sure what the end goal is, but why do you need to use
> EvaluateJsonPath and SplitJson?
>
> Generally you don't want to split a flow file of multiple records into
> 1 record per flow file, this is an anti-pattern that leads to poor
> performance in the flow.
>
> Thanks,
>
> Bryan
>
> On Fri, Apr 7, 2023 at 9:41 AM James McMahon wrote:
> >
> > Very interesting, very helpful insights. Thank you again, Mike.
> > Late last night I decided to punt on a pure NiFi solution. I knew I
> could do this easily with Groovy scripting, and I knew that was well-within
> my wheelhouse. So that's what I did: Groovy from an ExecuteScript
> processor. I'm 90% of the way there. Just a few more refinements to get
> just what I want, which I'll tackle later tonight.
> > Groovy is pretty cool. Flexible, easily tailored to just what you need.
> I like having that flexibility. And I like having options, too: your
> results have motivated me to look at using QueryRecords, etc etc.
> > Jim
> >
> > On Fri, Apr 7, 2023 at 9:32 AM Mike Sofen wrote:
> >>
> >> This is where I felt Nifi wasn’t the right tool for the job and
> Postgres was. After I imported the CSV directly into a staging table in
> the database (using Nifi), I converted the payload part of the columns into
> jsonb and stored that into the final table in a column with additional
> columns as relational data (timestamps, identifiers, etc). It was an
> object-relational data model.
> >>
> >>
> >>
> >> THEN, using the amazingly powerful Postgres jsonb functions, I was able
> to extract the unique keys in an entire dataset or across multiple datasets
> (to build a data catalog for example), perform a wide range of validations
> on individual keys, etc. I use the word amazing because they are not just
> powerful functions but they run surprisingly fast given the amount of
> string data they are traversing.
> >>
> >>
> >>
> >> Mike Sofen
> >>
> >>
> >>
> >> From: James McMahon
> >> Sent: Thursday, April 06, 2023 2:03 PM
> >> To: users@nifi.apache.org
> >> Subject: Re: Handling CSVs dynamically with NiFi
> >>
> >>
> >>
> >> Can I ask you one follow-up? I've gotten my ConvertRecord to work. I
> created a CsvReader service with Schema Access Strategy of Use String
> Fields From Header. I created a JsonRecordSetWriter service with Schema
> Write Strategy of Do Not Write Schema.
> >>
> >> When ConvertRecord is finished, my result looks like this sample:
> >>
> >> [ {
> >> "Bank Name�" : "Almena State Bank",
> >> "City�" : "Almena",
> >> "State�" : "KS",
> >> "Cert�" : "15426",
> >> "Acquiring Institution�" : "Equity Bank",
> >> "Closing Date�" : "23-Oct-20",
> >> "Fund" : "10538"
> >> }, {
> >> "Bank Name�" : "First City Bank of Florida",
> >> "City�" : "Fort Walton Beach",
> >> "State�" : "FL",
> >> "Cert�" : "16748",
> >> "Acquiring Institution�" : "United Fidelity Bank, fsb",
> >> "Closing Date�" : "16-Oct-20",
> >> "Fund" : "10537"
> >> }, {
> >> "Bank Name�" : "The First State Bank",
> >> "City�" : "Barboursville",
> >> "State�" : "WV",
> >> "Cert�" : "14361",
> >> "Acquiring Institution�" : "MVB Bank, Inc.",
> >> "Closing Date�" : "3-Apr-20",
> >> "Fund" : "10536"
> >> }]
> >>
> >>
> >>
> >> I don't really have a schema. How can I use a combination of SplitJson
> and EvaluateJsonPath to split each json object out to its own nifi
> flowfile, and to pull the json key values out to define the fields in the
> csv header? I've found a few examples through research that allude to this,
> but they all seem to have a fixed schema and they don't offer
> configurations for the SplitJson. In a case where my json keys definition
> changes depending on the lfowfile, what should JsonPathExpression be set to
> in the SplitJson configuration?
> >>
> >>
> >>
> >> On Thu, Apr 6, 2023 at 9:59 AM Mike Sofen wrote:
> >>
> >> Jim – that’s exactly what I did on that “pre” step – generate a schema
> from the CSVReader and use that to dynamically create the DDL sql needed to
> build the staging table in Postgres. In my solution, there are 2 separate
> pipelines running – this pre step and the normal file processing.
> >>
> >>
> >>
> >> I used the pre step to ensure that all incoming files were from a known
> and valid source and that they conformed to the schema
Re: Handling CSVs dynamically with NiFi
James,
I'm not sure what the end goal is, but why do you need to use
EvaluateJsonPath and SplitJson?
Generally you don't want to split a flow file of multiple records into
1 record per flow file, this is an anti-pattern that leads to poor
performance in the flow.
Thanks,
Bryan
On Fri, Apr 7, 2023 at 9:41 AM James McMahon wrote:
>
> Very interesting, very helpful insights. Thank you again, Mike.
> Late last night I decided to punt on a pure NiFi solution. I knew I could do
> this easily with Groovy scripting, and I knew that was well-within my
> wheelhouse. So that's what I did: Groovy from an ExecuteScript processor. I'm
> 90% of the way there. Just a few more refinements to get just what I want,
> which I'll tackle later tonight.
> Groovy is pretty cool. Flexible, easily tailored to just what you need. I
> like having that flexibility. And I like having options, too: your results
> have motivated me to look at using QueryRecords, etc etc.
> Jim
>
> On Fri, Apr 7, 2023 at 9:32 AM Mike Sofen wrote:
>>
>> This is where I felt Nifi wasn’t the right tool for the job and Postgres
>> was. After I imported the CSV directly into a staging table in the database
>> (using Nifi), I converted the payload part of the columns into jsonb and
>> stored that into the final table in a column with additional columns as
>> relational data (timestamps, identifiers, etc). It was an object-relational
>> data model.
>>
>>
>>
>> THEN, using the amazingly powerful Postgres jsonb functions, I was able to
>> extract the unique keys in an entire dataset or across multiple datasets (to
>> build a data catalog for example), perform a wide range of validations on
>> individual keys, etc. I use the word amazing because they are not just
>> powerful functions but they run surprisingly fast given the amount of string
>> data they are traversing.
>>
>>
>>
>> Mike Sofen
>>
>>
>>
>> From: James McMahon
>> Sent: Thursday, April 06, 2023 2:03 PM
>> To: users@nifi.apache.org
>> Subject: Re: Handling CSVs dynamically with NiFi
>>
>>
>>
>> Can I ask you one follow-up? I've gotten my ConvertRecord to work. I created
>> a CsvReader service with Schema Access Strategy of Use String Fields From
>> Header. I created a JsonRecordSetWriter service with Schema Write Strategy
>> of Do Not Write Schema.
>>
>> When ConvertRecord is finished, my result looks like this sample:
>>
>> [ {
>> "Bank Name�" : "Almena State Bank",
>> "City�" : "Almena",
>> "State�" : "KS",
>> "Cert�" : "15426",
>> "Acquiring Institution�" : "Equity Bank",
>> "Closing Date�" : "23-Oct-20",
>> "Fund" : "10538"
>> }, {
>> "Bank Name�" : "First City Bank of Florida",
>> "City�" : "Fort Walton Beach",
>> "State�" : "FL",
>> "Cert�" : "16748",
>> "Acquiring Institution�" : "United Fidelity Bank, fsb",
>> "Closing Date�" : "16-Oct-20",
>> "Fund" : "10537"
>> }, {
>> "Bank Name�" : "The First State Bank",
>> "City�" : "Barboursville",
>> "State�" : "WV",
>> "Cert�" : "14361",
>> "Acquiring Institution�" : "MVB Bank, Inc.",
>> "Closing Date�" : "3-Apr-20",
>> "Fund" : "10536"
>> }]
>>
>>
>>
>> I don't really have a schema. How can I use a combination of SplitJson and
>> EvaluateJsonPath to split each json object out to its own nifi flowfile, and
>> to pull the json key values out to define the fields in the csv header? I've
>> found a few examples through research that allude to this, but they all seem
>> to have a fixed schema and they don't offer configurations for the
>> SplitJson. In a case where my json keys definition changes depending on the
>> lfowfile, what should JsonPathExpression be set to in the SplitJson
>> configuration?
>>
>>
>>
>> On Thu, Apr 6, 2023 at 9:59 AM Mike Sofen wrote:
>>
>> Jim – that’s exactly what I did on that “pre” step – generate a schema from
>> the CSVReader and use that to dynamically create the DDL sql needed to build
>> the staging table in Postgres. In my solution, there are 2 separate
>> pipelines running – this pre step and the normal file processing.
>>
>>
>>
>> I used the pre step to ensure that all incoming files were from a known and
>> valid source and that they conformed to the schema for that source – a very
>> tidy way to ensure data quality.
>>
>>
>>
>> Mike
>>
>>
>>
>> From: James McMahon
>> Sent: Thursday, April 06, 2023 6:39 AM
>> To: users@nifi.apache.org
>> Subject: Re: Handling CSVs dynamically with NiFi
>>
>>
>>
>> Thank you both very much, Bryan and Mike. Mike, had you considered the
>> approach mentioned by Bryan - a Reader processor to infer schema - and
>> found it wasn't suitable for your use case, for some reason? For instance,
>> perhaps you were employing a version of Apache NiFi that did not afford
>> access to a CsvReader or InferAvroSchema processor?
>>
>> Jim
>>
>>
>>
>> On Thu, Apr 6, 2023 at 9:30 AM Mike Sofen wrote:
>>
>> Hi James,
>>
>>
>>
>> I don’t have time to go into details, but I had nearly the same scenario and
>>
Re: Handling CSVs dynamically with NiFi
Very interesting, very helpful insights. Thank you again, Mike.
Late last night I decided to punt on a pure NiFi solution. I knew I could
do this easily with Groovy scripting, and I knew that was well-within my
wheelhouse. So that's what I did: Groovy from an ExecuteScript processor.
I'm 90% of the way there. Just a few more refinements to get just what I
want, which I'll tackle later tonight.
Groovy is pretty cool. Flexible, easily tailored to just what you need. I
like having that flexibility. And I like having options, too: your results
have motivated me to look at using QueryRecords, etc etc.
Jim
On Fri, Apr 7, 2023 at 9:32 AM Mike Sofen wrote:
> This is where I felt Nifi wasn’t the right tool for the job and Postgres
> was. After I imported the CSV directly into a staging table in the
> database (using Nifi), I converted the payload part of the columns into
> jsonb and stored that into the final table in a column with additional
> columns as relational data (timestamps, identifiers, etc). It was an
> object-relational data model.
>
>
>
> THEN, using the amazingly powerful Postgres jsonb functions, I was able to
> extract the unique keys in an entire dataset or across multiple datasets
> (to build a data catalog for example), perform a wide range of validations
> on individual keys, etc. I use the word amazing because they are not just
> powerful functions but they run surprisingly fast given the amount of
> string data they are traversing.
>
>
>
> Mike Sofen
>
>
>
> *From:* James McMahon
> *Sent:* Thursday, April 06, 2023 2:03 PM
> *To:* users@nifi.apache.org
> *Subject:* Re: Handling CSVs dynamically with NiFi
>
>
>
> Can I ask you one follow-up? I've gotten my ConvertRecord to work. I
> created a CsvReader service with Schema Access Strategy of Use String
> Fields From Header. I created a JsonRecordSetWriter service with Schema
> Write Strategy of Do Not Write Schema.
>
> When ConvertRecord is finished, my result looks like this sample:
>
> [ {
> "Bank Name�" : "Almena State Bank",
> "City�" : "Almena",
> "State�" : "KS",
> "Cert�" : "15426",
> "Acquiring Institution�" : "Equity Bank",
> "Closing Date�" : "23-Oct-20",
> "Fund" : "10538"
> }, {
> "Bank Name�" : "First City Bank of Florida",
> "City�" : "Fort Walton Beach",
> "State�" : "FL",
> "Cert�" : "16748",
> "Acquiring Institution�" : "United Fidelity Bank, fsb",
> "Closing Date�" : "16-Oct-20",
> "Fund" : "10537"
> }, {
> "Bank Name�" : "The First State Bank",
> "City�" : "Barboursville",
> "State�" : "WV",
> "Cert�" : "14361",
> "Acquiring Institution�" : "MVB Bank, Inc.",
> "Closing Date�" : "3-Apr-20",
> "Fund" : "10536"
> }]
>
>
>
> I don't really have a schema. How can I use a combination of SplitJson and
> EvaluateJsonPath to split each json object out to its own nifi flowfile,
> and to pull the json key values out to define the fields in the csv header?
> I've found a few examples through research that allude to this, but they
> all seem to have a fixed schema and they don't offer configurations for the
> SplitJson. In a case where my json keys definition changes depending on the
> lfowfile, what should JsonPathExpression be set to in the SplitJson
> configuration?
>
>
>
> On Thu, Apr 6, 2023 at 9:59 AM Mike Sofen wrote:
>
> Jim – that’s exactly what I did on that “pre” step – generate a schema
> from the CSVReader and use that to dynamically create the DDL sql needed to
> build the staging table in Postgres. In my solution, there are 2 separate
> pipelines running – this pre step and the normal file processing.
>
>
>
> I used the pre step to ensure that all incoming files were from a known
> and valid source and that they conformed to the schema for that source – a
> very tidy way to ensure data quality.
>
>
>
> Mike
>
>
>
> *From:* James McMahon
> *Sent:* Thursday, April 06, 2023 6:39 AM
> *To:* users@nifi.apache.org
> *Subject:* Re: Handling CSVs dynamically with NiFi
>
>
>
> Thank you both very much, Bryan and Mike. Mike, had you considered the
> approach mentioned by Bryan - a Reader processor to infer schema - and
> found it wasn't suitable for your use case, for some reason? For instance,
> perhaps you were employing a version of Apache NiFi that did not afford
> access to a CsvReader or InferAvroSchema processor?
>
> Jim
>
>
>
> On Thu, Apr 6, 2023 at 9:30 AM Mike Sofen wrote:
>
> Hi James,
>
>
>
> I don’t have time to go into details, but I had nearly the same scenario
> and solved it by using Nifi as the file processing piece only, sending
> valid CSV files (valid as in CSV formatting) and leveraged Postgres to land
> the CSV data into pre-built staging tables and from there did content
> validations and packaging into jsonb for storage into a single target
> table.
>
>
>
> In my case, an external file source had to “register” a single file (to
> allow creating the matching staging table) prior to sending data. I used
> Nifi for that pre-staging step to derive the schema
RE: Handling CSVs dynamically with NiFi
This is where I felt Nifi wasn’t the right tool for the job and Postgres was.
After I imported the CSV directly into a staging table in the database (using
Nifi), I converted the payload part of the columns into jsonb and stored that
into the final table in a column with additional columns as relational data
(timestamps, identifiers, etc). It was an object-relational data model.
THEN, using the amazingly powerful Postgres jsonb functions, I was able to
extract the unique keys in an entire dataset or across multiple datasets (to
build a data catalog for example), perform a wide range of validations on
individual keys, etc. I use the word amazing because they are not just
powerful functions but they run surprisingly fast given the amount of string
data they are traversing.
Mike Sofen
From: James McMahon
Sent: Thursday, April 06, 2023 2:03 PM
To: users@nifi.apache.org
Subject: Re: Handling CSVs dynamically with NiFi
Can I ask you one follow-up? I've gotten my ConvertRecord to work. I created a
CsvReader service with Schema Access Strategy of Use String Fields From Header.
I created a JsonRecordSetWriter service with Schema Write Strategy of Do Not
Write Schema.
When ConvertRecord is finished, my result looks like this sample:
[ {
"Bank Name�" : "Almena State Bank",
"City�" : "Almena",
"State�" : "KS",
"Cert�" : "15426",
"Acquiring Institution�" : "Equity Bank",
"Closing Date�" : "23-Oct-20",
"Fund" : "10538"
}, {
"Bank Name�" : "First City Bank of Florida",
"City�" : "Fort Walton Beach",
"State�" : "FL",
"Cert�" : "16748",
"Acquiring Institution�" : "United Fidelity Bank, fsb",
"Closing Date�" : "16-Oct-20",
"Fund" : "10537"
}, {
"Bank Name�" : "The First State Bank",
"City�" : "Barboursville",
"State�" : "WV",
"Cert�" : "14361",
"Acquiring Institution�" : "MVB Bank, Inc.",
"Closing Date�" : "3-Apr-20",
"Fund" : "10536"
}]
I don't really have a schema. How can I use a combination of SplitJson and
EvaluateJsonPath to split each json object out to its own nifi flowfile, and to
pull the json key values out to define the fields in the csv header? I've found
a few examples through research that allude to this, but they all seem to have
a fixed schema and they don't offer configurations for the SplitJson. In a case
where my json keys definition changes depending on the lfowfile, what should
JsonPathExpression be set to in the SplitJson configuration?
On Thu, Apr 6, 2023 at 9:59 AM Mike Sofen mailto:mso...@runbox.com> > wrote:
Jim – that’s exactly what I did on that “pre” step – generate a schema from the
CSVReader and use that to dynamically create the DDL sql needed to build the
staging table in Postgres. In my solution, there are 2 separate pipelines
running – this pre step and the normal file processing.
I used the pre step to ensure that all incoming files were from a known and
valid source and that they conformed to the schema for that source – a very
tidy way to ensure data quality.
Mike
From: James McMahon mailto:jsmcmah...@gmail.com> >
Sent: Thursday, April 06, 2023 6:39 AM
To: users@nifi.apache.org
Subject: Re: Handling CSVs dynamically with NiFi
Thank you both very much, Bryan and Mike. Mike, had you considered the approach
mentioned by Bryan - a Reader processor to infer schema - and found it wasn't
suitable for your use case, for some reason? For instance, perhaps you were
employing a version of Apache NiFi that did not afford access to a CsvReader or
InferAvroSchema processor?
Jim
On Thu, Apr 6, 2023 at 9:30 AM Mike Sofen mailto:mso...@runbox.com> > wrote:
Hi James,
I don’t have time to go into details, but I had nearly the same scenario and
solved it by using Nifi as the file processing piece only, sending valid CSV
files (valid as in CSV formatting) and leveraged Postgres to land the CSV data
into pre-built staging tables and from there did content validations and
packaging into jsonb for storage into a single target table.
In my case, an external file source had to “register” a single file (to allow
creating the matching staging table) prior to sending data. I used Nifi for
that pre-staging step to derive the schema for the staging table for a file and
I used a complex stored procedure to handle a massive amount of logic around
the contents of a file when processing the actual files prior to storing into
the destination table.
Nifi was VERY fast and efficient in this, as was Postgres.
Mike Sofen
From: James McMahon mailto:jsmcmah...@gmail.com> >
Sent: Thursday, April 06, 2023 4:35 AM
To: users mailto:users@nifi.apache.org> >
Subject: Handling CSVs dynamically with NiFi
We have a task requiring that we transform incoming CSV files to JSON. The CSVs
vary in schema.
There are a number of interesting flow examples out there illustrating how one
can set up a flow to handle the case where the CSV schema
