为您找到"
Flink
"相关结果约100,000,000个
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Learn about Flink capabilities, use cases, APIs, performance, scalability, and recent blog posts.
Apache Flink is a unified framework for stream and batch processing, developed by the Apache Software Foundation. It supports dataflow programming, fault-tolerance, state management, and various data sources and sinks.
Apache Flink is an open-source, distributed engine for stateful processing over unbounded (streams) and bounded (batches) data sets. Stream processing applications are designed to run continuously, with minimal downtime, and process data as it is ingested. Apache Flink is designed for low latency processing, performing computations in-memory, for high availability, removing single point of ...
Flink has taken the same capability ahead and Flink can break all the types of Big Data problems. Apache Flink is a general-purpose cluster calculating tool, which can handle batch processing, interactive processing, Stream processing, Iterative processing, in-memory processing, graph processing.
Apache Flink is a framework that supports both batch and streaming programs with high performance and low latency. Learn how to use Flink APIs, libraries, and integration with Hadoop, YARN, and other components.
Understand Flink Apache Flink® is a distributed system and requires effective allocation and management of compute resources in order to execute streaming applications. It integrates with common cluster resource managers. Currently, you can deploy Confluent Platform for Apache Flink with Kubernetes. This section contains an overview of Flink's architecture and describes how its main ...
This web page introduces the basics of Apache Flink, a scalable streaming data processing framework. It covers concepts such as stream processing, event time, stateful stream processing, and fault tolerance.
Discover Apache Flink, explore how it differs from other tools such as Kafka and Spark, and the benefits and challenges of using it.
Apache Flink 2.0.0 introduces innovative features for cloud-native, data lake, and AI-driven workflows, such as Disaggregated State Management and Materialized Tables. Learn how Flink 2.0.0 addresses the challenges and opportunities of real-time data processing in the 2025 era.
Preparation when using Flink SQL Client To create Iceberg table in Flink, it is recommended to use Flink SQL Client as it's easier for users to understand the concepts. Download Flink from the Apache download page. Iceberg uses Scala 2.12 when compiling the Apache iceberg-flink-runtime jar, so it's recommended to use Flink 1.20 bundled with Scala 2.12.