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Lindorm:Features and functions

Last Updated:Sep 07, 2022

This topic describes the features of the Lindorm time series engine.

High read and write performance for time series data

The Lindorm time series engine can efficiently handle concurrent read and write requests. The Lindorm time series engine allows you to read millions of data points per second and write tens of millions of data points per second.

  • Write data

    You can use one of the following methods to write data to the Lindorm time series engine:

    • Use the INSERT statement of SQL. We recommend that you use Java Database Connectivity (JDBC) if you choose this method.

    • Use a compatible device to write data over Message Queuing Telemetry Transport (MQTT).

    • Use InfluxDB line protocol to write data.

    • Use the OpenTSDB API for writing data.

    • Use Prometheus to write monitoring data to the remote storage.

  • Query data

    You can execute SQL statements to query data or use the OpenTSDB API for querying data. You can also use the data query feature in the Lindorm console to visualize the queried data based on data groups, downsampling, and spatial aggregation.

Data management

  • Database management

    The databases that store time series data are isolated from each other. You can execute SQL statements to specify the interval between time-based partitions, cold/hot data boundary, time-to-live (TTL) values of data for these databases.

    For more information, see Database management.

  • User and permission management

    You can verify the identities of the users that want to access your time series data and check the permissions that the users have on the storage that stores the time series data. You can execute SQL statements or use the Lindorm console to manage the users of the Lindorm time series engine and the permissions of these users.

    For more information, see User and permission management.

Efficient time series data storage technology

  • Time series data compression

    The Lindorm time series engine uses efficient compression technology to reduce the average storage space occupied by each data point to 1 to 2 bytes. This reduces the overall storage usage by 90% and increases the speed of data inserts.

  • Cold data archiving

    The Lindorm time series engine allows you to archive cold data for long-term business and store the archives in cost-effective storage. This can reduce the storage costs by more than 70%.

Computing capabilities for time series data

The Lindorm time series engine provides various computing functions to allow you to process time series data based on downsampling, data interpolation, and spatial aggregation. This can meet the requirements for handling complex queries.

Monitoring & O&M

The Lindorm time series engine provides an O&M system. You can use the O&M system to maintain your Lindorm clusters. You can view the status, performance metrics, and storage usage of your clusters in real time. You can also configure alert rules and alert notification methods. This way, you can identify resource bottlenecks at the earliest opportunity.

Data security and cluster security

The Lindorm time series engine provides the following solutions to ensure the security of your data and Lindorm clusters:

  • The Lindorm time series engine allows you to access your clusters over virtual private clouds (VPCs) to ensure the security of the clusters.

  • The Lindorm time series engine allows you to configure whitelists. You can configure a whitelist for a cluster and add the IP addresses of the clients that you want to allow to access the cluster to the whitelist. This further secures your clusters and data. If a client and a Lindorm cluster are deployed in the same VPC, but the IP address of the client is not included in the whitelist of the cluster, the client cannot access the cluster.

  • The Lindorm time series engine allows you to manage user permissions at the database level. You can use whitelists together with permission control to manage data access in a more fine-grained manner.

  • By default, the Lindorm time series engine creates multiple replicas to ensure data availability.