- Learning Apache Flink
- Learning Apache Flink
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Customer Feedback
- Preface
- 1. Introduction to Apache Flink
- 2. Data Processing Using the DataStream API
- 3. Data Processing Using the Batch Processing API
- Data sources
- Transformations
- Map
- Flat map
- Filter
- Project
- Reduce on grouped datasets
- Reduce on grouped datasets by field position key
- Group combine
- Aggregate on a grouped tuple dataset
- MinBy on a grouped tuple dataset
- MaxBy on a grouped tuple dataset
- Reduce on full dataset
- Group reduce on a full dataset
- Aggregate on a full tuple dataset
- MinBy on a full tuple dataset
- MaxBy on a full tuple dataset
- Distinct
- Join
- Cross
- Union
- Rebalance
- Hash partition
- Range partition
- Sort partition
- First-n
- Broadcast variables
- Data sinks
- Connectors
- Iterations
- Use case - Athletes data insights using Flink batch API
- Summary
- 4. Data Processing Using the Table API
- Registering tables
- Accessing the registered table
- Operators
- The select operator
- The where operator
- The filter operator
- The as operator
- The groupBy operator
- The join operator
- The leftOuterJoin operator
- The rightOuterJoin operator
- The fullOuterJoin operator
- The union operator
- The unionAll operator
- The intersect operator
- The intersectAll operator
- The minus operator
- The minusAll operator
- The distinct operator
- The orderBy operator
- The limit operator
- Data types
- SQL
- Use case - Athletes data insights using Flink Table API
- Summary
- 5. Complex Event Processing
- 6. Machine Learning Using FlinkML
- 7. Flink Graph API - Gelly
- 8. Distributed Data Processing with Flink and Hadoop
- 9. Deploying Flink on Cloud
- 10. Best Practices
After the data transformations are done, we need to save the results somewhere. The following are some options that Flink DataSet API provides to save the results:
writeAsText(): This writes records one line at a time as strings.writeAsCsV(): This writes tuples as comma-separated value files. Row and field delimiters can also be configured.print()/printErr(): This writes records to the standard output. You can also choose to write to a standard error.write(): This supports writing data in a customFileOutputFormat.output(): This is used for datasets which are not file-based. This can be used where we want to write data to some database.
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.