If you want to integrate fzf with rg, fd, bat to fuzzy find files, directories or ripgrep the content of a file and preview using bat, but the fzf document only has commands for Linux shell (bash,...), and you want to achieve that on your Windows Machine using Powershell, this post may be for you. - Source: dev.to / 25 days ago

Ripgrep: A super-fast file searcher. You can install it using your system's package manager (e.g., brew install ripgrep on macOS). Fd: Another blazing-fast file finder. Installation instructions can be found here: https://github.com/sharkdp/fd. - Source: dev.to / 4 months ago

Hyperfine is such a great tool that it's one of the first I reach for when doing any sort of benchmarking. I encourage anyone who's tried hyperfine and enjoyed it to also look at sharkdp's other utilities, they're all amazing in their own right with fd[1] being the one that perhaps get the most daily use for me and has totally replaced my use of find(1). [1]: - Source: Hacker News / 5 months ago

You call it with `n` and get an interactive fuzzy search for your directories. If you do `n https://github.com/sharkdp/fd. - Source: Hacker News / 6 months ago

Many (most?) of them have been overhauled with success. For find there is fd[1]. There's batcat, exa (ls), ripgrep, fzf, atuin (history), delta (diff) and many more. Most are both backwards compatible and fresh and friendly. Your hardwon muscle memory still of good use. But there's sane flags and defaults too. It's faster, more colorful (if you wish), better integration with another (e.g. exa/eza or aware of git... - Source: Hacker News / 6 months 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 / 17 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