Abstract: A method for storing time series data is provided, the method includes: caching time series data containing at least one record to a memory in a row-based storage manner; when the time series data needs to be written to a disk, determining a sum of number of records according to the to-be-written-to-disk time series data and time series data in a LAST file of the disk; if the sum is less than a pre-set number N, writing the to-be-written-to-disk time series data to the LAST file of the disk; if the sum is greater than or equal to the pre-set number N, merging the to-be-written-to-disk time series data and the time series data in the LAST file of the disk, and writing merged time series data to a DATA file for permanently saving data of the disk in a column-based storage manner.

Abstract: The present invention discloses an efficient storage method for time-series data, comprising: storing time-series data collected by each data sampler and static attribute data of its data sampler separately; when each time series is stored, based on a unique ID of each time series, storing corresponding time-series data; and when the static attribute data is stored, the static attribute data are stored in another database independently, wherein each data sampler saves one corresponding record in a database storing the static attribute data. According to the present invention, the storage space can be saved, the processing efficiency and the processing speed can be improved, and the query processing can be flexibly carried out.

Abstract: The invention discloses an aggregation optimized processing method for time-series data, characterized by comprising the following steps: writing a time-series data record into a database, forming a time-series database file, wherein the time-series database file comprises a data file and an index file, the data file comprises multiple data blocks, the index file comprises index blocks, and each index block correspond to one data block; by scanning an index file according to a start time period and a stop time period, extracting all index blocks of the time series that need to be aggregated that meet the time period conditions, and then sorting the index blocks according to the data block offset recorded in the index block; and by scanning the data file according to a data block offset order recorded in sorted index blocks, performing specified reading and calculating on each data block, and aggregating calculation results.

Abstract: The invention discloses a processing method for process changing time-series database table structure, wherein comprising following steps: an application side generates a data table containing a table name ID, a schema version, and a column ID; when a data column is increased, the application side modifies a schema of the data table, increases the schema version, assigns a new column ID number to a newly increased column incrementally, and assigns a default value to the newly increased column; according to a data insertion request of the application side, a data node receives data of a schema version carrying a data table from the application side and writes the received data, wherein writing the received data comprises: storing the schema version of the data table carried by data from the application side by the data node, and writing the received data, and writing the received data; when the data node receives new data from the application side, comparing the schema version of the data table carried by the

Abstract: The invention discloses an aggregation optimized processing method for time-series data, characterized by comprising the following steps: writing a time-series data record into a database, forming a time-series database file, wherein the time-series database file comprises a data file and an index file, the data file comprises multiple data blocks, the index file comprises index blocks, and each index block correspond to one data block; by scanning an index file according to a start time period and a stop time period, extracting all index blocks of the time series that need to be aggregated that meet the time period conditions, and then sorting the index blocks according to the data block offset recorded in the index block; and by scanning the data file according to a data block offset order recorded in sorted index blocks, performing specified reading and calculating on each data block, and aggregating calculation results.

Abstract: The present invention discloses an efficient storage method for time-series data, comprising: storing time-series data collected by each data sampler and static attribute data of its data sampler separately; when each time series is stored, based on a unique ID of each time series, storing corresponding time-series data; and when the static attribute data is stored, the static attribute data are stored in another database independently, wherein each data sampler saves one corresponding record in a database storing the static attribute data,. According to the present invention, the storage space can be saved, the processing efficiency and the processing speed can be improved, and the query processing can be flexibly carried out.

Abstract: The invention discloses a processing method for process changing time-series database table structure, wherein comprising following steps: an application side generates a data table containing a table name ID, a schema version, and a column ID; when a data column is increased, the application side modifies a schema of the data table, increases the schema version, assigns a new column ID number to a newly increased column incrementally, and assigns a default value to the newly increased column; according to a data insertion request of the application side, a data node receives data of a schema version carrying a data table from the application side and writes the received data, wherein writing the received data comprises: storing the schema version of the data table carried by data from the application side by the data node, and writing the received data, and writing the received data; when the data node receives new data from the application side, comparing the schema version of the data table carried by the