Lindorm Distributed Processing System (LDPS) is a high-performance, cost-effective, stable, and reliable distributed computing service that is provided based on the core features of ApsaraDB for Lindorm (Lindorm). LDPS can meet the computing requirements in scenarios that are supported by Lindorm, such as data production, interactive analytics, machine learning, and graph computing scenarios. LDPS can process large amounts of data at high concurrency. LDPS supports open source Spark computing models and programming interfaces, and integrates the features of the storage engine LindormStore. LDPS can fully use the features of underlying data storage and indexing capabilities to complete distributed computing tasks in an efficient manner.
Features
LDPS provides the following features:
Multiple access methods: You can perform interactive data analytics by using Java Database Connectivity (JDBC). You can also submit JAR files to customize distributed computing tasks.
Out-of-the-box: A unified permission system is supported for LDPS and the storage engine LindormStore, including the wide table engine LindormTable, the time series engine LindormTSDB, the search engine LindormSearch, and the file engine LindormFile. You do not need to configure complex settings for underlying components. Developers need to only understand SQL and have Spark development experience to use LDPS.
O&M-free: You do not need to focus on cluster O&M operations, such as configuration, upgrade or downgrade of configurations, and scale-out or scale-in operations. You need to focus on only job management in the console and SparkUI.
Auto scaling: LDPS supports auto scaling. If predicted fluctuations occur in your service loads, LDPS can help you reduce computing costs and wastage of idle resources, and respond to traffic peaks.
Pay-as-you-go: You are charged for the compute resources that you use.