I am using Apache Flink framework dataset API to extract data from various sources, transform it and load it to various sinks.
During this period, I am working to some scenarios where datasets of POJOs are loaded to CSV files. For this purpose, I am using writeAsFormattedText method if Flink framework and the respective text formatters in order to avoid mapping our data to tuples (and use writeTextAsCsv). One of my requirements is to add a header row on top of the CSV file produced. For example,
id,name,age
1,john,30
2,helen,42
How can such requirement be covered using the dataset API of the Apache Flink?
Thanks in advance
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I have to sink data to s3 using flink and orc format, and i found two libraries doing that, what the one to be used
as per the doc, they show orc library, but in my project, they are already using orc-nohive
Hi I am planning to use flink as a backend for my feature where we will show a UI to user to graphically create event patterns for eg: Multiple login failures from the same Ip address.
We will create the flink pattern programmatically using the given criteria by the user in the UI.
Is there any documentation on how to dynamically create the jar file and dynamically submit the job with it to flink cluster?
Is there any best practice for this kind of use case using apache flink?
The other way you can achieve that is that you can have one jar which contains something like an “interpreter” and you will pass to it the definition of your patterns in some format (e.g. json). After that “interpreter” translates this json to Flink’s operators. It is done in such a way in https://github.com/TouK/nussknacker/ Flink’s based execution engine. If you use such an approach you will need to handle redeployment of new definition in your own application.
One straightforward way to achieve this would be to generate a SQL script for each pattern (using MATCH_RECOGNIZE) and then use Ververica Platform's REST API to deploy and manage those scripts: https://docs.ververica.com/user_guide/application_operations/deployments/artifacts.html?highlight=sql#sql-script-artifacts
Flink doesn't provide tooling for automating the creation of JAR files, or submitting them. That's the sort of thing you might use a CI/CD pipeline to do (e.g., github actions).
Disclaimer: I work for Ververica.
As mentioned in the document:
For example a data pipeline might monitor a file system directory for new files and write their data into an event log. Another application might materialize an event stream to a database or incrementally build and refine a search index.
So, how can I follow a local file system file updating while using Flink?
Here, the document also mentioned that:
File system sources for streaming is still under development. In the future, the community will add support for common streaming use cases, i.e., partition and directory monitoring.
Does this mean I could use the API to do some special streaming? If you know how to use streaming file system source, please tell me. Thanks!
Is it possible to serialize protobuff data to Avro and write to files using Apache Flink sink?
There is currently no out-of-the-box solution for protobuf yet. It's high on the priority list.
You can use protobuf-over-kryo or parse/serialize manually in the meantime.
If I want to use a google data studio native community connector as my data source, for instance the Google Cloud Storage one, how can I add a report template to this data source? My problem is that I will need to create multiple reports (sharing a common template) based on the same data source.
Somewhat surprisingly, this is possible using custom community connectors but not via the Google native ones.
I didn't fully understand your requirement. I wanted to clear out the terminologies:
Connector: Connectors are generic pipes to any data you might have. Connectors can be native or Community Connectors. e.g. Google Sheets connector.
Data Source: When you provide necessary configuration and authentication to a connector and instantiate it, you create a data source. e.g. When you use the Google Sheets connector to connect to a specific Sheet that you own, a new data source is created with that Sheet's name. A single data source can be used across different reports.
Report: Reports are your dashboards. A report can contain multiple data sources.
Template: Any report that has 'copying' enabled, can be used as a template. Simply copy the report and replace the data sources attached to the original report.
Given this, I'm reiterating your requirements: you have a report that uses a data source based on the Google Cloud Storage native connector. Now you want to create multiple reports from the same report using the same data source.
The solution is to simply copy the report and make edits to the copied reports as needed.