Lindorm:Jenis Mesin

Engine Type

Compatibility

Scenario

Description

Wide table engine (LindormTable)

Compatible with SQL, the HBase API, Cassandra Query Language (CQL), and the Amazon S3 API.

Suitable for managing and analyzing metadata, orders, bills, user personas, social information, feeds, logs, trajectories.

A distributed wide-column engine designed for massive volumes of semi-structured and structured data. It supports global secondary indexes, multi-dimensional retrieval, dynamic columns, TTL, throughput of tens of millions of high-concurrency operations, and storage capacity of hundreds of petabytes. Its throughput performance is 3 to 7 times higher than that of open-source HBase. Its P99 latency is one-tenth that of open-source HBase. It supports hot and cold data separation. Its compression ratio is twice that of open-source HBase. Its overall storage cost is half that of open-source HBase. It includes built-in GanosBase spatio-temporal service, which is designed for various types of spatial or spatio-temporal data and supports large-scale historical trajectory query and analysis scenarios.

Time series engine (LindormTSDB)

Provides an HTTP API and is compatible with the OpenTSDB API.

Suitable for storing and processing time series data such as measurement data and operational data of devices in scenarios such as IoT and monitoring.

The time series engine is a distributed storage engine that is used to process large amounts of time series data. It supports SQL queries. The time series engine provides a dedicated compression algorithm for time series data. This helps improve the data compression ratio. The time series engine lets you use multiple dimensions to query and aggregate large amounts of time series data by timeline. The engine also supports downsampling and elastic scaling.

Search engine

Compatible with SQL, Apache Solr, and Elasticsearch APIs.

Suitable for querying large amounts of data, such as logs, text, and documents. For example, you can use the search engine to search for logs, bills, and user personas.

Lindorm provides a distributed search engine. The search engine uses an architecture in which storage is decoupled from computing. The search engine can be seamlessly used to store the indexes of the wide table engine and the time series engine to accelerate data retrieval. The search engine provides various capabilities, including full-text searches, aggregation, and complex multi-dimensional queries. It also supports an architecture that consists of one write replica and multiple read-only replicas and provides features such as horizontal scaling, cross-zone disaster recovery, and TTL to meet the requirements of efficient retrieval of large amounts of data.

Compute Engine

Compatible with the Apache Spark API.

Suitable for scenarios such as the production of large amounts of data, interactive analytics, computational learning, and graph computing.

The compute engine provides distributed computing services based on a cloud native architecture. It supports Community Edition computing models and programming interfaces. The compute engine also integrates the features of the Lindorm storage engine and uses the underlying data storage features and indexing capabilities to efficiently complete distributed jobs.

Streaming engine

Compatible with SQL and the Apache Kafka API.

Suitable for scenarios such as IoT data processing, application log processing, logistics aging analysis, travel data processing, and real-time trajectory processing.

The Lindorm streaming engine is used to store and process streaming data. It provides lightweight computing capabilities. You can use the stream computing engine to store streaming data to Lindorm to meet the requirements for the processing and application of streaming data. You can use the Lindorm GanosBase service provided by the wide table together with the Lindorm streaming engine to implement real-time trajectory analysis features, such as electronic geofencing and regional statistic collection.