SOSouthShoreShip·Sep 7, 2022
What do you mean no Akka in Flink ? https://github.com/apache/flink/blob/master/flink-rpc/flink-...
In We Are Changing the License for Akkaapache
flink
Java2016–2025
apache / flink
flink
Apache Flink
7Distinct people
7Mentions
0Explicit recommenders
26.5KGitHub stars
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What it is
Apache Flink
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
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This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 7:02:26 AM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year7 dated mentions
Observed mentions: 2016-03-09 16:33:49 UTC to 2025-07-25 02:16:11 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 1 | 1 | 0 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
2 sourcesIt took me some time to get a good grasp of the power of SQL; and it really kicked in when I learned about optimization rules. A query is a program that you rewrite, just like an optimizing compiler would.
You state what you want; then you have different ways to fetch and match and massage data; and you can search through this space to produce a physical plan. Hopefully you used knowledge to weight parts to be optimized (table statistics, like Java's JIT would detect hot spots).
I find it fascinating to peer through database code to see what is going on. Lately, there's been new advances towards streaming databases, which bring a whole new design space. For example, now you have latency of individual new rows to optimize for, as opposed to batch it whole to optimize the latency of a dataset. Batch scanning will be benefit from better use
Read the rest
of your CPU caches.
And maybe you could have a hybrid system which reads history from a log and aggregates in a batched manner, and then switches to a stream-optimized execution plan when it reaches the end of the log.
If you want to have a peek at that here are Flink's set of rules [1], generic and stream-specific ones. The names can be cryptic, but usually give a good sense of what is going on. For example: PushFilterIntoTableSourceScanRule makes the WHERE clause apply the earliest possible, to save some CPU/network bandwidth further down. PushPartitionIntoTableSourceScanRule tries to make a fan-out/shuffle happen the earliest possible, so that parallelism can be made use of.
[1] https://github.com/apache/flink/blob/5f8fb304fb5d68cdb0b3e3c...