Application Development

Overview

Flink is a unified computing framework that supports both batch processing and stream processing. It provides a stream data processing engine that supports data distribution and parallel computing. Flink features stream processing and is a top open-source stream processing engine in the industry.

Flink provides high-concurrency pipeline data processing, millisecond-level latency, and high reliability, making it suitable for low-latency data processing.

Figure 1 shows the technology stack of Flink.

Figure 1 Technology stack of Flink

The following lists the key features of Flink in the current version:

• DataStream
• Checkpoint
• Window
• Job Pipeline
• Configuration Table

Architecture

Figure 2 shows the architecture of Flink.

Figure 2 Flink architecture

As shown in Figure 2, the entire Flink system consists of three parts:

• Client

  Flink client is used to submit jobs (streaming jobs) to Flink.

• TaskManager

  TaskManager (also called worker) is a service execution node of Flink. It executes specific tasks. A Flink system could have multiple TaskManagers. These TaskManagers are equivalent to each other.

• JobManager

  JobManager (also called master) is a management node of Flink. It manages all TaskManagers and schedules tasks submitted by users to specific TaskManagers. In high-availability (HA) mode, multiple JobManagers are deployed. Among these JobManagers, one of which is selected as the leader, and the others are standby.

Flink provides the following features:

• Low latency

  Millisecond-level processing capability.

• Exactly once

  Asynchronous snapshot mechanism, ensuring that all data is processed only once.

• High availability

  Leader/Standby JobManagers, preventing single point of failure (SPOF).

• Scale out

  Manual scale out supported by TaskManagers.

Flink Development APIs

Flink DataStream API can be developed using Scala and Java languages, as shown in Table 1.

Flink Basic Concepts

• DataStream

  DataStream is the minimum unit of Flink processing and is one of core concepts of Flink. DataStreams are initially imported from external systems in formats of socket, Kafka, and files. After being processed by Flink, DataStreams are exported to external systems in formats of socket, Kafka, and files.

• Data Transformation

  A data transformation is a data processing unit that transforms one or multiple DataStreams into a new DataStream.

  Data transformation can be classified as follows:

  • One-to-one transformation, for example, map.
  • One-to-zero, one-to-one, or one-to-multiple transformation, for example, flatMap.
  • One-to-zero or one-to-one transformation, for example, filter.
  • Multiple-to-one transformation, for example, union.
  • Transformation of multiple aggregations, for example, window and keyby.
• Checkpoint

  Checkpoint is the most important Flink mechanism to ensure reliable data processing. Checkpoints ensure that all application statuses can be recovered from a checkpoint in case of failure occurs and data is processed exactly once.

• Savepoint

  Savepoints are externally stored checkpoints that you can use to stop-and-resume or update your Flink programs. After the upgrade, you can set the task status to the savepoint storage status and start the restoration, ensuring data continuity.