What is/are the main difference(s) between Flink and Storm?

Disclaimer: I'm an Apache Flink committer and PMC member and only familiar with Storm's high-level design, not its internals.

Apache Flink is a framework for unified stream and batch processing. Flink's runtime natively supports both domains due to pipelined data transfers between parallel tasks which includes pipelined shuffles. Records are immediately shipped from producing tasks to receiving tasks (after being collected in a buffer for network transfer). Batch jobs can be optionally executed using blocking data transfers.

Apache Spark is a framework that also supports batch and stream processing. Flink's batch API looks quite similar and addresses similar use cases as Spark but differs in the internals. For streaming, both systems follow very different approaches (mini-batches vs. streaming) which makes them suitable for different kinds of applications. I would say comparing Spark and Flink is valid and useful, however, Spark is not the most similar stream processing engine to Flink.

Coming to the original question, Apache Storm is a data stream processor without batch capabilities. In fact, Flink's pipelined engine internally looks a bit similar to Storm, i.e., the interfaces of Flink's parallel tasks are similar to Storm's bolts. Storm and Flink have in common that they aim for low latency stream processing by pipelined data transfers. However, Flink offers a more high-level API compared to Storm. Instead of implementing the functionality of a bolts with one or more readers and collectors, Flink's DataStream API provides functions such as Map, GroupBy, Window, and Join. A lot of this functionality must be manually implemented when using Storm. Another difference are processing semantics. Storm guarantees at-least-once processing while Flink provides exactly-once. The implementations which give these processing guarantees differ quite a bit. While Storm uses record-level acknowledgments, Flink uses a variant of the Chandy-Lamport algorithm. In a nutshell, data sources periodically inject markers into the data stream. Whenever an operator receives such a marker, it checkpoints its internal state. When a marker was received by all data sinks, the marker (and all records which have been processed before) are committed. In case of a failure, all sources operators are reset to their state when they saw the last committed marker and processing is continued. This marker-checkpoint approach is more lightweight than Storm's record-level acknowledgments. This slide set and the corresponding talk discuss Flink's streaming processing approach including fault tolerance, checkpointing, and state handling.

Storm also offers an exactly-once, high-level API called Trident. However, Trident is based on mini-batches and hence more similar to Spark than Flink.

Flink's adjustable latency refers to the way that Flink sends records from one task to the other. I said before, that Flink uses pipelined data transfers and forwards records as soon as they are produced. For efficiency, these records are collected in a buffer which is sent over the network once it is full or a certain time threshold is met. This threshold controls the latency of records because it specifies the maximum amount of time that a record will stay in a buffer without being sent to the next task. However, it cannot be used to give hard guarantees about the time it takes for a record from entering to leaving a program because this also depends on the processing time within tasks and the number of network transfers among other things.

Adding to the answer of Fabian Hueske:

Flink improves on Storm additionally also in the following ways:

• Backpressure: Flink's streaming runtime is well behaved when different operators run at different speeds, because downstream operators backpressure upstream operators very well though the network layer's manages buffer pools.

• User-defined state: Flink allows programs to maintain custom state in your operators. That state can actually participate in the checkpointing for fault tolerance, providing exactly-once guarantees for custom user-defined state. See this example of a user-defined state machine inside an operator, that is consistently checkpointed together with the data stream.

• Streaming Windows: Stream windowing and window aggregations are a crucial building block for analyzing of data streams. Flink comes with a quite powerful windowing system that supports many types of windows.

Disclaimer: I am an employee of Cloudera, a major supporter of Storm and (soon) Flink.

Functional

A lot of good technical points have already been presented. A very short summary of highlights:

• Both Flink and Storm can do per-event processing
• Storm does not appear to support event-time out of the box
• Storm has not lifted SQL support out of the experimental stage

Non-Functional

• Many customers found Storm (too) hard to use
• Storm adoption slowed down, and the community of Flink now appears to be more active than Storm
• Flink still has some catching up to do (e.g. documented examples), but overall it has caught up in nearly every area you might think of

Conclusion

Cloudera has recently announced the deprecation of Storm (in HDP). And simultaneously Flink was announced as its successor.

So, if you have usecases on storm, they will of course continue to work. But for new usecases I would look into Flink or other streaming engines.