Method and device for compressing time sequence data and method and device for querying time sequence data

Abstract

The invention provides a method for compressing time sequence data. The method comprises the steps of writing obtained time sequence data into a temporary table; determining to-be-compressed time sequence data from the temporary table according to a query time range; compressing the to-be-compressed time sequence data, then writing the compressed time sequence data into a final table, judging whether a current query time range can be updated or not, if the current query time range can be updated, turning to the step of determining the to-be-compressed time sequence data from the temporary table after the query time range is updated; and if the current query time range cannot be updated, compressing a compression result of the time sequence data written into the final table. The invention also provides a method for querying the time sequence data. The method comprises the steps of obtaining a generation time range of to-be-queried time sequence data; comparing marking time in a query time range employed when first compression is carried out with the generation time range, and determining a storage position of the to-be-queried time sequence data according to a comparison result, wherein the storage position comprises the temporary table and / or the final table; and obtaining the to-be-queried time sequence data according to the determined storage position.

Description

【Technical field】 [0001] The invention relates to the field of cloud computing, in particular to a method and device for compressing time series data and querying time series data. 【Background technique】 [0002] Time-series data can be used to analyze the past, monitor the present, and predict the future. For enterprises, time-series data is of inestimable value. Therefore, it is necessary to store time series data. However, the time-series data in existing scenarios is characterized by a large amount of data, and the resulting problem is that the cost of storing time-series data is high. In order to reduce the storage cost of time series data, it is generally realized by compressing the time series data. However, when the prior art compresses the time series data, there are the following disadvantages: the compression rate is not high, for example, when using OpenTsdb to compress, only the data files generated by the time series database are compressed, so the high compr...

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Problems solved by technology

However, when the prior art compresses the time series data, there are the following disadvantages: the compression rate is not high, for example, when using OpenTsdb to compress, only the data files generated by the time series database are compressed, so the high compression rate of the time series data cannot be obtained; The time-series data imported has many restrictions. For example, Gorilla of Facebook, although using this algorithm can obtain a high compression rate for time-series data, but it requires that the time-series data to be written must be within a certain range of the current timestamp, and the time-series data cannot be compressed. Compression of longer historical data

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