TGTGInsighttelegram intelligenceLIVE / telegram public index
← GitHub Trends

TGINSIGHT SIMILAR POSTS

Find similar content

Source channel @githubtrending · Post #15397 · Jan 7

#java#cdc#chunjun#dataops#datax#etl#flink#flink_streaming#java TIS is an easy enterprise data integration tool using batch (DataX) and streaming (Flink-CDC, Chunjun) with a simple interface to sync data end-to-end without complex scripts. Its v5.0.0 adds Pipeline AI Agent, letting you describe needs in natural language for auto-pipeline creation, smart plugin installs, and low-cost AI like DeepSeek. Install quickly via single-node, Docker, or K8S. This saves you time, cuts errors, simplifies ETL tasks, and boosts fun, efficient data pipelines for real-time analytics. https://github.com/datavane/tis

Results

1 similar post found

Search: #parallelism

当前筛选 #parallelism清除筛选
djangoproject

@djangoproject · Post #118 · 08/08/2016, 11:44 AM

https://docs.python.org/3/library/multiprocessing.html multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows. The #multiprocessing module also introduces #APIs which do not have analogs in the #threading#module. A prime example of this is the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data #parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using Pool,