Alibaba Log Service Integrations

Build business monitoring capabilities with real-time response based on frontend monitoring, application monitoring, and custom business monitoring capabilities. Application Real-Time Monitoring Service (ARMS) is an end-to-end Alibaba Cloud monitoring service for Application Performance Management (APM). You can quickly develop real-time business monitoring capabilities using the frontend monitoring, application monitoring, and custom monitoring features provided by ARMS. Implements APM performance and anomaly monitoring in distributed applications, based on tracing information. Reflects user webpage browsing behavior in real time using dimensions such as geological regions, ISPs, and URLs. Allows you to create real-time monitoring alarms and dashboards based on your business needs. Integrates custom monitoring, frontend monitoring, and application monitoring into a centralized alarm and report platform.

CloudMonitor collects monitor metrics of Alibaba Cloud resources and custom metrics. The service can be used to detect the availability of your service and allows you to set alarms on specific metrics. CloudMonitor enables you to view and fully understand the usage of the cloud resources, and the status and health of your business, so that you can act promptly to ensure the availability of your application when an alarm is triggered. No coding is required. You can set up CloudMonitor and alarms through the wizard in a few steps. You can set alarms based on different scenarios, and send alarms using multiple methods. A comprehensive service that monitors the basic resources, application availability, and also custom business metrics. Allows you to manage cloud resources that are used in different applications by group.

EMR is an all-in-one enterprise-ready big data platform that provides cluster, job, and data management services based on open-source ecosystems, such as Hadoop, Spark, Kafka, Flink, and Storm. Alibaba Cloud Elastic MapReduce (EMR) is a big data processing solution that runs on the Alibaba Cloud platform. EMR is built on Alibaba Cloud ECS instances and is based on open-source Apache Hadoop and Apache Spark. EMR allows you to use the Hadoop and Spark ecosystem components, such as Apache Hive, Apache Kafka, Flink, Druid, and TensorFlow, to analyze and process data. You can use EMR to process data stored on different Alibaba Cloud data storage service, such as Object Storage Service (OSS), Log Service (SLS), and Relational Database Service (RDS). You can quickly create clusters without the need to configure hardware and software. All maintenance operations are completed on its Web interface.

Tracing Analysis provides a wide range of tools to help developers identify performance bottlenecks of distributed applications. This helps developers improve the efficiency of developing and troubleshooting applications that use the microservices architecture. The provided tools can be used to map traces, offer trace topologies, analyze application dependencies, and calculate the number of requests. To use Tracing Analysis, you must activate Log Service. You do not need to pay for the Log Service resources that are consumed to offer the Tracing Analysis service. Simplifies the troubleshooting of distributed applications. You no longer need to log on to individual machines to obtain logs for troubleshooting. Allows you to use open source SDKs to specify tracking points, such as SDKs for Zipkin, Jeager, and OpenTracing. Tracing Analysis provides the pay-as-you-go billing method.

Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.

Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale. Any kind of data is produced as a stream of events. Credit card transactions, sensor measurements, machine logs, or user interactions on a website or mobile application, all of these data are generated as a stream. Apache Flink excels at processing unbounded and bounded data sets. Precise control of time and state enable Flink’s runtime to run any kind of application on unbounded streams. Bounded streams are internally processed by algorithms and data structures that are specifically designed for fixed sized data sets, yielding excellent performance. Flink is designed to work well each of the previously listed resource managers.