Overview

Lindorm is a cloud-based database service that can store and analyze large amounts of data with low costs and supports multiple data models including wide tables, time series data, files, and search engines. Lindorm is capable of processing online data within milliseconds. You can use unified SQL statements to query, search, and analyze data of multiple models in Lindorm. Lindorm also allows you to perform computing in real time by using its built-in stream computing engine. Lindorm is the ideal database service for businesses in a variety of scenarios, including finance, billing, logging, Internet of Things (IoT), Internet of Vehicles (IoV), industrial Internet, monitoring, recommendation systems, risk control, and medical imaging.

Benefits

• Cost-effectiveness Lindorm can respond to tens of millions concurrent requests within milliseconds. Lindorm uses cost-effective storage media to build a storage pool that can be shared by multiple data engines and supports intelligent separation of hot and cold data and adaptive data compression.
• Scalability Lindorm utilizes an architecture in which storage and computing resources are decoupled and can be separately scaled. Lindorm also provides serverless services which you can scale and pay for based on actual usage.
• High stability Lindorm is built on a high availability architecture and provides enterprise-grade stability. This architecture has been tested by the production environment of Alibaba Group for more than 10 years and has proven reliability.
• High compatibility Lindorm is compatible with the standard APIs of multiple open source services. You can connect Lindorm with various computing engines such as Spark and Flink in IoV and industrial IoT scenarios to smoothly integrate your business into the mainstream ecosystems.

Features

Converged data models

Lindorm builds a variety of data engines based on cloud native storage and allows you to query data by using the unified multi-model query mode or open-source APIs for flexible and efficient development. Wide table engine The wide table engine is used to store large amounts of key-value data and table data. It provides global secondary indexes, multi-dimensional queries, dynamic columns, and Time to Live (TTL). It is applicable to scenarios such as the storage of metadata, orders, bills, user personas, social networking information, feeds, and logs. The wide table engine is compatible with the open standards of multiple open source software, such as Apache HBase, Apache Phoenix, and Apache Cassandra. Time series engine The time series engine is used to store and process time series data such as measurement data and device operational data in scenarios such as IoT and monitoring. It provides an HTTP API and is compatible with the OpenTSDB API. It also supports SQL queries. The time series engine provides a dedicated compression algorithm for time series data. The time series engine supports multi-dimensional queries and aggregate computing of large amounts of data and provides downsampling and pre-aggregation. Search engine The search engine provides capabilities such as full-text searches, aggregate computing, and complex multi-dimensional queries of large amounts of text and table data. It can also be seamlessly used to store the indexes of the wide table and time series engines. This accelerates the data retrieval and queries. The search engine is applicable to scenarios such as the queries of logs, bills, and user personas. It is compatible with the open standards of the open source Solr platform. File engine Lindorm is designed to store large amounts of unstructured data and can provide flexible and cost-efficient file storage capabilities that are compatible with HDFS. Lindorm provides a unified storage pool that is shared by multiple data engines and allows external systems to access the underlying files in the data engines. Lindorm is suitable for various businesses such as big data analysis and data lakes, and can be accessed by using open-source HDFS clients. Spatiotemporal engine The spatiotemporal engine is designed to store and process large amounts of spatial, spatiotemporal, and GIS data and is compatible with the standard APIs of open source tools such as GeoMesa and GeoTools. This engine features efficient multi-dimensional indexing capability for spatiotemporal data and query capability for spatial and spatiotemporal data. You can use the spatiotemporal engine with DLA to analyze and mine information from complex spatiotemporal data. The spatiotemporal engine is ideal for business based on spatiotemporal data, such as transportation, travel, logistics, IoT, navigation, aviation, and mobile base stations.

Intelligent unified storage

Different engines in Lindorm share a cost-effective unified storage pool that can provide high reliability, scalability, and hybrid capabilities. In addition, Lindorm provides standard APIs that is compatible with open-source HDFS file systems. Intelligent separation storage of hot and cold data Data in Lindorm is stored in different layers in a flexible manner based on the frequency at which the data is accessed, which reduces the storage costs of cold data and improves the access performance of hot data. Applications can directly access data stored in Lindorm without modification. Multiple storage media Lindorm supports a series of storage media that covers multiple scenarios from high performance to low costs.

Rich Query Features

Lindorm is integrated with multiple data query features such as secondary indexes, inverted indexes, and time series indexes to quickly query and process large amounts of data. Global secondary index Lindorm is built based on a global distributed structure and provides strong consistency and redundancy. Lindorm is compatible with schemaless data models and automatically use indexes on demand to accelerate queries without primary keys. Integrated storage retrieval Lindorm integrates multiple engines, including the wide table engine, time series engine, and search engine, to store and query data in a unified manner. For example, you can synchronize the data stored in wide table and time series engine to the search engine in real time. This way, you can perform hybrid operations such as storage, multi-dimensional queries, or full-text index on large amount of data in a unified manner.

Enterprise Support

Lindorm provides multiple capabilities that are required by enterprises to run their large-scale critical business, such as security, reliability, and efficiency. Therefore, Lindorm is ideal for Internet of Things (IoT) and Internet of Vehicles (IoV) businesses. Global geo-redundancy Lindorm supports global data distribution and multi-master data replication. This way, applications can access data that is geographically near. In addition, Lindorm supports automatic disaster recovery and fault tolerance, which allow you to deploy applications in multiple regions around the globe. Strong consistency and high availability Lindorm stores multiple replicas of data in different zone and supports the cross-zone deployment of clusters to achieve data recovery based on data centers and strong data consistency. You can also use the eventual consistency mode to achieve better performance and availability. Secure access Lindorm provides a variety of methods such as VPC and security groups to control access and utilize a complete process that includes authentication, permission control, encryption, and audit to ensure data security. Backup and restoration Lindorm supports on-demand regular data backup and restoration. You can quickly restore large amounts of backup data at the specified point of time without interruption. This can help you comply to the regulations of enterprises and the government. Low latency Lindorm can respond to tens of millions concurrent requests within milliseconds. Intelligent diagnostics Lindorm is integrated with LDInsight that can be used to diagnose common problems such as slow requests and hot data. LDInsight can also be used for performance diagnostics, capacity analysis, schema design, automatic troubleshooting, and suggestion recommendation.

Compatibility with Ecosystem

Lindorm can be seamlessly integrated with computing engines, such as Spark, Flink, Data Lake Analytics (DLA), and MaxCompute. Data tunnel You can use other services such as LTS and DTS to synchronize real-time data or migrate full historical data between Lindorm and common storage services, such as ApsaraDB for HBase, ApsaraDB for RDS MySQL, and Log Service. Computing and analytics Lindorm provides standard interfaces and converts data format as required. You can integrate Lindorm with open-source computing engines such as Spark, Flink, DLA, and Hive to perform real-time interactive analysis and complex analysis in batch. Data visualization Lindorm can connect to QuickBI and DataV to view and analyze data in a visualized manner.