Unlike I expected, setting up the project with Junit proved to be really time-consuming for me. - Source: dev.to / 7 months ago

First, I chose a testing framework for my java project. JUnit is the most pupular testing framework for java. - Source: dev.to / 8 months ago

This code defines a JUnit test case for the getStrings() method of the MyClass class. Then it creates an instance of MyClass, calls the getStrings() method, and asserts that the result is not null using the assertNotNull() method. - Source: dev.to / 9 months ago

How you can link JUnit 5 tests with issues in your task tracker systems? - Source: dev.to / about 1 year ago

JUnit is a popular Java testing framework used for unit testing. It's an open-source tool that's designed to make it easy for developers to write and run automated tests. JUnit provides a set of annotations and assertions that can be used to define test cases and expected outcomes, and it can be easily integrated with other DevOps tools like Jenkins and Maven. - Source: dev.to / about 1 year ago

Restate is built as a sharded replicated state machine similar to how TiKV (https://tikv.org/), Kudu (https://kudu.apache.org/kudu.pdf) or CockroachDB (https://github.com/cockroachdb/cockroach) since it makes it possible to tune the system more easily for different deployment scenarios (on-prem, cloud, cost-effective blob storage). Moreover, it allows for some other cool things like seamlessly moving from one log... - Source: Hacker News / 14 days ago

I’ve recently started my journey with Apache Flink. As I learn certain concepts, I’d like to share them. One such "learning" is the expansion of array type columns in Flink SQL. Having used ksqlDB in a previous life, I was looking for functionality similar to the EXPLODE function to "flatten" a collection type column into a row per element of the collection. Because Flink SQL is ANSI compliant, it’s no surprise... - Source: dev.to / about 1 month ago

You should let the Apache Flink team know, they mention exactly-once processing on their home page (under "correctness guarantees") and in their list of features. [0] https://flink.apache.org/ [1] https://flink.apache.org/what-is-flink/flink-applications/#building-blocks-for-streaming-applications. - Source: Hacker News / about 2 months ago

Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / 3 months ago

Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 5 months ago