Flink and Stateful Streaming

Himanshu Gupta explains some of the benefits Apache Flink offers for stateful streaming applicatons:

The 2 main types of stream processing done are:
1. Stateless: Where every event is handled completely independent from the preceding events.
2. Stateful: Where a “state” is shared between events and therefore past events can influence the way current events are processed.

Stateless stream processing is easy to scale up because events are processed independently. But Stateful stream processing is difficult to scale up because the “state” needs to be shared across the events.

Himanshu does point out alternatives, but this isn’t a comparison exercise.

Related Posts

Pivoting Spark DataFrames

Unmesha Sreeveni shows how we can pivot a DataFrame in Apache Spark using one line of code: A pivot can be thought of as translating rows into columns while applying one or more aggregations. Lets see how we can achieve the same using the above dataframe. We will pivot the data based on “Item” column. […]

Read More

Troubleshooting Spark Performance

Bikas Saha and Mridul Murlidharan explain some of the basics of performance tuning with Apache Spark: Our objective was to build a system that would provide an intuitive insight into Spark jobs that not just provides visibility but also codifies the best practices and deep experience we have gained after years of debugging and optimizing […]

Read More

Categories

March 2019
MTWTFSS
« Feb Apr »
 123
45678910
11121314151617
18192021222324
25262728293031