Open, Universal, and High-Performance: Time-series Data Storage for Log Service Empowers Comprehensive Enterprise-level Monitoring Solutions

Time-series Data Is Everywhere

  • Stock trading software provides investors with candlestick charts covering many different aspects that they can use as a reference.
  • The Apple Watch monitors the wearer’s heart rate information to help detect serious heart diseases early.
  • The State Grid analyzes the electricity consumption curve of each community and household to detect electricity leakage and theft.
  • E-commerce companies quickly detect various abnormalities by monitoring changing trends in key processes such as order placements, transactions, returns, and reviews.
  • Gaming platforms analyze user behavior patterns, such as their actions and locations, to determine whether cheating tools are being used.

What Kind of Time-series Storage Is Needed?

  1. High Performance: Time-series data usually generates a high traffic load, requires a long retention period, and must be searchable over a long time range. For these reasons, support for large-scale writes and fast queries is a prerequisite for time-series storage.
  2. Openness: Generally, multiple departments in a company perform different types of analysis and monitoring on the time-series data in different systems. Time-series storage must be open enough to support various methods of data access and downstream consumption.
  3. Low Cost: Time-series storage requires low resource and manual O&M costs. In accordance with Moore’s law, the cost per unit of resources is constantly decreasing, but personnel cost per unit is increasing every year. Controlling the labor cost of O&M for time-series storage is key to reducing overall cost.
  4. Intelligence: Static rules alone are not always sufficient to find abnormalities in monitored objects, in particular when a large number of objects are being monitored. Intelligent algorithms are required on the upper layer of time-series storage systems to improve monitoring accuracy.

Release of Time-series Data Storage for Log Service (SLS)

Features

  • Rich variety of upstream and downstream systems: SLS supports many methods of data access, including various open-source agents as well as a channel for monitoring data within Alibaba Cloud. Time-series data stored in SLS can also be connected with various stream computing and offline computing engines, making data completely open.
  • High performance: The separation of computing and storage in SLS ensures optimal use of cluster capabilities. The end-to-end speed increases significantly when a large amount of data is processed.
  • Zero O&M: Time-series storage for SLS is provided as a service. Users do not need to operate and maintain instances themselves, and three replicas of all data are stored, making it unnecessary to worry about data reliability.
  • Open-source-friendliness: Time-series storage for SLS has native support for writing and querying data in Prometheus. It supports SQL-92 analysis methods and can natively connect to visualization solutions such as Grafana.
  • **Intelligence**: SLS provides a variety of AIOps algorithms with which you can build an intelligent alerting and diagnosis platform suited to your company. These time-series algorithms include multi-period estimation, prediction, error detection, and classification.

Typical Scenarios

Application and Service Monitoring

Cloud-Native Monitoring

Access Log Analysis

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