Query time prediction method for time sequence database
A query time and prediction method technology, applied in the computer field, can solve the problem of slow query time prediction speed, etc., and achieve the effect of considerable response speed
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specific Embodiment approach 1
[0028] Specific implementation mode one: refer to image 3 Specifically illustrate this embodiment, a kind of time-series database-oriented query time prediction method described in this embodiment includes the following steps:
[0029] Step 1: Read time series data;
[0030] Step 2: Write the time series data into CnosDB, and CnosDB uses CnoSQL query statements to query and retrieve the time series data, and record the query time;
[0031] Step 3: Encode the query statement into vectorized data;
[0032] Step 4: Extract data distribution features from the vectorized data;
[0033] Step 5: Use PCA to reduce the dimensionality of the data distribution characteristics;
[0034] Step 6: Use vectorized data and dimensionally reduced data distribution features as input, query time as output, and train the gradient boosting regression tree model;
[0035] Step 7: Use the trained gradient boosting regression tree model to predict query time.
specific Embodiment approach 2
[0036] Embodiment 2: This embodiment is a further description of Embodiment 1. The difference between this embodiment and Embodiment 1 is that the following steps are included before the third step: rewriting CnoSQL into standard SQL.
specific Embodiment approach 3
[0037] Embodiment 3: This embodiment is a further description of Embodiment 2. The difference between this embodiment and Embodiment 2 is that the encoding in Step 3 includes join graph encoding and column information encoding, join graph encoding and column information The result of the encoding is concatenated as the encoding of the entire query.
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