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Community Blog How to Optimize Data Queries for Time Series Database

How to Optimize Data Queries for Time Series Database

PostgreSQL can optimize data queries in a good way, and here you can get some query optimization tips on how to optimize queries.

Data merging and data cleaning are required in many scenarios. We can use window query for this kind of operation, but how can we make it faster and quickly retrieve batch data?

Here is a quick summary of the common methods of optimizing time sequence data querying:

  1. Recursion is used when there are few unique values and an unknown range.
  2. Use subquery when the number of unique values is relatively small and you know the specific range of the unique values.
  3. Window query is more appropriate than the above method when there are many unique values.
  4. However, stream computing is even better in the same scenarios.

Efficiency Comparison Table

Data volume Number of Unique Values Window Query (ms) Subquery (ms) Recursive Query (ms)
5 million 1 million 6,446 2,892 6,706
5 million 1,000 6,176 7 9

PostgreSQL is the best choice in open-source databases as it provides several solutions to the same problems. It leaves you free to choose the most appropriate solution for you and your individual needs.

  1. Use recursion when the number of unique values is relatively small and the range of the unique values is unknown.
  2. Use subquery when the number of unique values is relatively small and the range of the unique values is determined. For example, if the total range is 1 million pieces of data, but only 500,000 pieces of data are included in this batch, then the performance is optimal if you have the IDs for these 500,000 entries. Otherwise you need to scan 1 million pieces of data. Another example is that there are a total of 100 million users, but an interval includes only tens of thousands of active users.
  3. Window query is more appropriate if the number of unique values is relatively large.
  4. Steaming computing is better than method 3 if the number of unique values is relatively large.

For the detailed comparison information for recursion, subquery and window queries, please see Optimizing Time Series Querying on Alibaba Cloud RDS for PostgreSQL.

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